Of Anchors & Sails

Personality-Ability Trait Constellations

by
Kevin C. Stanek & Deniz S. Ones

University of Minnesota Libraries Publishing

Minneapolis

Copyright © 2023 Kevin C. Stanek and Deniz S. Ones

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Published by the University of Minnesota Libraries Publishing
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ISBN: 978-1-946135-98-8
DOI: 10.24926/9781946135988

Preface

This is a book about personality and intelligence. Personality is what a person typically does, thinks, and feels. Intelligence is what a person is cognitively capable of. How are personality and intelligence related, and why? By shining a light on the intricate networks of traits and abilities, we aim to not only deepen our knowledge but also challenge prevailing conceptions of human individuality.

This book presents a landmark study of relations between personality traits and cognitive abilities. It is the most inclusive and in-depth empirical coverage of the topic. Our study synthesizes data from more than 1,300 studies conducted over the last century, involving over two million participants from 50 countries and a diverse array of cultures and demographic groups. It embraces the power of meta-analysis—a methodological tool that combines results from multiple studies into a harmonized chorus of scientific evidence. Meta-analysis not only provides a more reliable panorama of the relations between personality traits and cognitive abilities, but also paves the way for the synthesis of novel theories based on robust cumulative findings. By distilling the essence of many studies into a coherent narrative, we reveal patterns that might otherwise remain obscured in individual studies. This research is a testament to the transformative potential of a bird’s eye view on a mountain of data.

The chapters of this book do not need to be read in order. Rather, we recommend that readers orient themselves by reading the introduction provided in Chapter 1, then proceed according to their knowledge, aims, and interests. Chapter 2 provides necessary background for readers unfamiliar with contemporary hierarchical models of personality and cognitive abilities. Chapter 3 offers information about our methodology. Chapter 4 frames the results for researchers with greater interest in or knowledge of cognitive abilities. Chapter 5 frames the results for researchers with greater interest in or knowledge of personality. Chapters 6 and 7 are best read together, with the former presenting our theory and the latter providing a theoretical interpretation of the findings. Chapters 8 and 9 allow the reader to take stock of key patterns in the results and opportunities for future research. Chapter 10 aims to stimulate expansive thought and profound contemplation by offering an elucidation of what the results mean for science, individuals, and humanity as a whole.

This research represents the most monumental and meticulous work either one of us has ever undertaken. Beyond being just a research project, this work consumed our lives and recast our worldviews. We were stretched intellectually, physically, and psychologically in ways we never imagined. Through our journey of writing this book, we developed and changed. We gained a deeper understanding of ourselves, deciphered foundational intricacies of human psychology, and, we believe, unveiled fundamental phenomena about how the universe operates.

Our journey started almost 14 years ago, with an ambition to conduct an unparalleled study of two of the most defining domains of individuality: personality traits and cognitive abilities. After examining the available large-scale studies and meta-analyses, we realized that a sweeping set of meta-analyses was needed to examine the full spectrum of personality-ability connections. On the personality side, many aspects and facets of well-known personality traits, such as the Big Five, as well as higher order and compound traits, had never been previously meta-analyzed. The same was true for many overlooked primary and specific abilities. After unearthing dozens, then hundreds, then thousands of primary studies, the project grew into Kevin’s dissertation under Deniz’ mentorship. Yet even a 1,000+ page dissertation (Stanek, 2014) was just a stepping stone to understanding the grander pattern of connections.

Our next step was to revisit how we were grouping traits and measures. Doing this accurately and reliably was critical to avoiding the jingle jangle jungle (Kelley, 1927) that has splintered so much psychological research on individual differences. We therefore spent nearly four years getting the taxonomic organization of personality traits and abilities right, reflecting contemporary consensus about these domains’ hierarchical structures. As importantly, we compiled a compendium of measures for each domain, indicating which traits or abilities are assessed by each measure (Stanek & Ones, 2018; see also Chapter 3 and Appendices A–E of this book). Although we knew that our personality compendium converged well with existing large-scale, multi-inventory analyses and previous personality taxonomies and compendia (e.g., Barrick & Mount, 1991; Credé et al., 2010; Hough & Ones, 2002), further evidence was needed to support our abilities compendium. Fortunately, Kostal (2019) devoted a large portion of his dissertation to validating our compendium, finding good convergent and divergent validity for our ability clusters. In this volume, we examine these well-understood and well-documented 79 personality traits and 97 cognitive abilities that range from the broad, such as general intelligence and extraversion, to the specific, such as reading speed and assertiveness.

Next, we updated our 2014 analyses, which clearly indicated that there were personality aspects and facets beyond openness that had meaningful relations with various non-invested and invested abilities. Accordingly, we prepared a summary of our findings for publication, circulating it to our immediate intellectual family. Their useful and insightful comments helped us further hone the content. However, one comment raised a pivotal question: Can you explain why or how these personality-ability relations emerge? Without such an explanation, our work would be incomplete.

Our initial instinct was to search for existing theories that could adequately explain the findings. Existing theories indeed offered some explanations for the personality-ability connections we observed. For instance, the theory of self-regulation suggests that certain personality traits, like conscientiousness, may enhance cognitive performance by improving focus and effort regulation. Similarly, skill acquisition theories propose that traits like openness to experience can facilitate learning and thereby enhance cognitive abilities. But each theory only explained part of the picture. We spent almost two years examining theories, eventually poking holes in all of them. Each theory seemed too limited to one domain (e.g., knowledge acquisition) or field (e.g., psychology).

A more comprehensive, elemental theory that spoke to fields beyond psychology, while explaining the patterns of relations revealed in our expansive meta-analyses, was needed. We synthesized Cybernetic Trait Complexes Theory (CTCT; see Chapter 6) as an elegant theoretical account that explains our findings as well as other related observations from fields as diverse as neuroscience, economics, biology, military science, and genetics. Chapter 7 illustrates how thousands of our findings can be accounted for by this single theory, which forms the foundation of our ultimate conclusions about why personality and ability are related.

After writing up our detailed results and theoretical account, the resulting manuscript was too large to fit in any refereed journal. We also needed to reach readers across disciplines. Therefore, we opted to publish a summary of our meta-analytic findings in the Proceedings of National Academy of Sciences (Stanek & Ones, 2023), with this book as a companion. Here we describe the full results, lay out our theory, and interpret the findings through that lens. The rich details and descriptions provided in this book offer the reader a deeper understanding of personality-ability relations and why they exist. This volume’s significance extends beyond the scientific discoveries described, encompassing the intricate, meaningful patterns that have emerged from our findings.

In the enterprise of scientific exploration, bold propositions are likely to raise both eyebrows and objections. However, any work that hopes to be groundbreaking must disrupt accepted wisdoms. We anticipate that some may question the ambitious scope of our meta-analytic approach, the personality traits and abilities we studied, or even some fundamental assumptions that underpin our research. We do not claim infallibility, and we firmly believe that diligent empirical inquiry, voluminous and comprehensive data, and rigorous analysis, as well as open debate, will deepen understanding of the complex interconnections of personality and ability. Our study is but a milestone in this ongoing journey.

We hope our contribution will inspire, provoke thought, and fuel further inquiry into the grand tapestry of human nature. Above all, we want this book to serve as a foundation for applications that maximize and enhance human potential. Could it help career advisors to tailor advice with greater precision? Educators to design programs that resonate with each unique personality-ability profile? Clinicians to pinpoint personalized therapies? Organizations and individuals to find their perfect fit? We believe it can, and that such practical applications will enhance people’s lives in meaningful ways.

Deciphering the architecture of personality-intelligence relations not only unlocks scientific insights and practical applications, but also reveals deep-rooted, profound patterns in human diversity. With this book, we set the stage for understanding and utilizing the fascinating interplay between who we are and why we are. Just as ecosystems are built from a diversity of species, each playing their part in the symphony of life, so too does this work seek to embrace the complexity and diversity of human nature. We invite you to journey with us into this rich landscape, to challenge the assumptions of the known, and to explore the vast uncharted terrain of human individuality.

References

Barrick, M. R., & Mount, M. K. (1991). The Big Five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44(1), 1–26.

Credé, M., Roch, S. G., & Kieszczynka, U. M. (2010). Class Attendance in College A Meta-Analytic Review of the Relationship of Class Attendance With Grades and Student Characteristics. Review of Educational Research, 80(2), 272–295.

Hough, L. M., & Ones, D. S. (2002). The structure, measurement, validity, and use of personality variables in industrial, work, and organizational psychology. In Handbook of industrial, work, and organizational psychology (Vol. 1, pp. 233–277). Sage.

Kelley, T. L. (1927). Interpretation of educational measurements. World Book Co. http://psycnet.apa.org/psycinfo/1928-00533-000

Kostal, J. W. (2019). Human cognitive abilities: The structure and predictive power of group factors. [Unpublished doctoral dissertation]. University of Minnesota-Twin Cities.

Stanek, K. C. (2014). Meta-Analyses of Personality and Cognitive Ability [Doctoral dissertation, University of Minnesota-Twin Cities]. https://hdl.handle.net/11299/201107

Stanek, K. C., & Ones, D. S. (2018). Taxonomies and compendia of cognitive ability and personality constructs and measures relevant to industrial, work and organizational psychology. In D. S. Ones, C. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work & organizational psychology: Personnel psychology and employee performance (pp. 366–407). Sage.

Stanek, K. C., & Ones, D. S. (2023). Meta-analytic relations between personality and cognitive ability. Proceedings of the National Academy of Sciences, 123(23). https://doi.org/10.1073/pnas.2212794120

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Chapter 1

Why Personality-Intelligence Relations Matter

CHAPTER SUMMARY

Personality and intelligence are defining domains of human individuality that drive important behaviors and outcomes.

These domains are commonly believed to be unrelated.

However, if personality and intelligence are related, then assuming they are independent could bias research, theories, and applications.

Some existing theories suggest there could be ties between personality and intelligence.

We posit that:

There are significant connections between personality and intelligence.

There is an overarching structure and purpose to these trait constellations, shaped by environmental and evolutionary forces.

Looking beyond the Big Five personality traits and common cognitive abilities provides important insights about the purpose and nature of differences between people.

Our study uses modern taxonomies of personality traits and cognitive abilities to provide a higher resolution view of these domains than previous investigations.

Our research is broad and deep. We examine major personality traits and cognitive abilities with more data than ever used before, and 93% of the personality-intelligence relations discussed here have not been previously described in detail using meta-analytic methods.

“Who are you?”

This is perhaps the most commonly asked question among humans. Despite the diverse array of answers—from names to religious beliefs, from character descriptions to life histories—a deeper question that is never asked is “Why are you?” This book builds on a century of science describing who people are to see the broader pattern in why they are.

Personality and cognitive ability are two of the most consequential sets of differences between individuals (Roberts et al., 2007). This book presents the most comprehensive examination of their relations. Cognitive abilities describe what an individual is cognitively capable of. Personality traits describe what an individual typically does. These domains encompass some of the most potent predictors of important behaviors and outcomes in educational, occupational, and personal life domains (Gottfredson, 1997; Judge et al., 1999). They influence the trajectories of individuals, including what activities they prefer (Webb et al., 2007), what environments they gravitate toward (Furnham, 1981), who they are drawn to associate with (Buss, 1985), and how well they perform their work (Dilchert, 2018; Le et al., 2007; Ones et al., 2016; Salgado et al., 2003; Schmidt & Hunter, 1998). They are also key determinants of physical, mental, and even financial health (Hatch et al., 2007; Kuncel et al., 2010). Although considerable research has separately examined the correlates and consequences of cognitive abilities and personality traits (Ones et al., 2007; Wilmot et al., 2019; Wilmot & Ones, 2019, 2022), much less is known about connections between the two domains.

In fact, cognitive abilities and personality traits are generally considered to be unrelated (e.g., Mammadov, 2022; Sackett & Ellingson, 1997). Yet, individual lives unfold as both domains of psychological differences co-manifest across environments. Springing from the confluence of genetic and environmental influences, unique constellations of personality traits, cognitive abilities, and other characteristics jointly define individuality. Our meta-analyses establish significant overlap between human cognitive abilities and personality traits by cumulating quantitative findings from the past 100 years of research.

Focusing narrowly on either cognitive abilities or personality traits hampers research, understanding, and application. If cognitive abilities and personality traits substantially covary, then studies omitting constructs from one of these domains would yield biased results, referred to as the omitted variables problem (Linn & Werts, 1971; Sackett et al., 2003). Incorrectly assuming that personality and ability are independent also results in deficient and misleading scientific theories, research studies, and behavior interventions.1 Meaningful relations between the domains would also raise the etiological question of whether some personality attributes and cognitive abilities are causally related.

Conceptual reasoning suggests that personality and cognitive ability are more closely related than commonly acknowledged. General theories linking personality traits and cognitive abilities include Cattell’s Investment Theory (1987); Ackerman and Heggestad’s Emergence of Trait Complexes Theory (1997); von Stumm and Ackerman’s Theory of Intellectual Engagement (2013); DeYoung’s Cybernetic Big Five Theory (CB5T; 2015); and Ortony, Norman, and Revelle’s Affects, Behaviors, Cognitions, and Desires (ABCD) Theory (2005). Bonds between personality and ability domains would also be consistent with other prominent individual differences frameworks (e.g., behavioral activation and behavioral inhibition [Gray, 1981, 1982], and getting along and getting ahead [Hogan & Holland, 2003]). Evolutionary theory also offers an informative frame:

(1) “evolutionary processes have sculpted not merely the body, but also the brain, the psychological mechanisms it houses, and the behavior it produces; (2) many of those mechanisms are best conceptualized as psychological adaptations designed to solve problems that historically contributed to survival and reproduction, broadly conceived; (3) psychological adaptations, along with byproducts of those adaptations, are activated in modern environments that differ in some important ways from ancestral environments; (4) critically, the notion that psychological mechanisms have adaptive functions is a necessary, not an optional, ingredient for a comprehensive psychological science.” (Buss, 2009)

We view both cognitive ability and personality as evolved psychological solutions to the difficulties and delights of life. The current research builds on previous efforts by quantifying the strength of more links than previously considered, identifying cross-domain trait constellations, and articulating an explanation of the pattern of these relations.

Previous meta-analytic examinations report personality-cognitive ability relations (Ackerman & Heggestad, 1997; Anglim et al., 2022; Schilling et al., 2021; von Stumm & Ackerman, 2013; Wolf & Ackerman, 2005). However, these efforts were limited in the scope of their investigations (e.g., focused on one factor of the Big Five or a few abilities), the sources of their data (e.g., mostly relying on published research), and the taxonomies they used to map different measures to constructs. Consequently, the findings and conclusions were also limited. Nevertheless, two main conclusions are by now well-known in the literature. First, cognitive abilities are moderately associated with a variety of openness-related traits (Ackerman & Heggestad, 1997; Anglim et al., 2022; von Stumm & Ackerman, 2013) but appear to be weakly related to other personality characteristics such as extraversion (Anglim et al., 2022; Wolf & Ackerman, 2005). Second, investment of abilities into acquired knowledge (i.e., invested abilities) requires a hungry mind (i.e., openness traits) and interest in the domain of knowledge to be acquired (Ackerman & Heggestad, 1997).

The research presented in this volume is a 24-fold expansion2 on these previous meta-analyses, enabling us to propose a more expansive theoretical framework to account for personality-ability relations. We had three goals. First, to provide a comprehensive and definitive reporting of the degree of overlap/independence between personality traits and cognitive abilities based on well-supported taxonomies of each domain. Second, to direct the attention of researchers to co-variation among previously unrecognized personality-ability pairings that may share etiology. Finally, to interpret the findings using a theoretical framework that recognizes constellations of traits and abilities that support organismal homeostasis, change, and fitness via cybernetic control. Given the pervasiveness of personality and cognitive ability and the unprecedented depth and breadth of research presented here, this book is relevant to researchers in all fields involving people, from organizational psychology to counseling to education to political science to design to epidemiology to medicine.

Our Research

People’s psychological characteristics do not come in tidy, discrete categories. Rather, individuals and their accompanying attributes are the product of evolutionary pressures that have biological and, eventually, psychological consequences. Across generations and within lifetimes, fitness in varying environments calls for adaptive strategies that result from conscious and unconscious mechanisms. Traits and abilities serve functions, and we reason that if traits and abilities serve similar functions, they will be co-related. We find empirical support for significant connections between personality and intelligence beyond openness-related personality traits, which are explained in a theoretical framework described in Chapter 6 of this book. We posit that these relations reveal an overarching structure and purpose to trait constellations, which is rooted in the evolutionary interplay between genes and environment. Finally, looking beyond the Big Five personality traits and common cognitive abilities reveals important insights about the purpose and nature of differences between individuals.

Previously, individual studies have estimated the relations between personality traits and cognitive abilities (e.g., Busato et al., 2000; Chamorro-Premuzic & Furnham, 2008; Conn & Ricke, 1994), and quantitative summaries have identified a few relations, particularly between cognitive abilities and the broad openness trait (Ackerman & Heggestad, 1997; Anglim et al., 2022; von Stumm & Ackerman, 2013). Our meta-analyses significantly build on these efforts by estimating relations of 97 cognitive abilities, 84 of which were not included in previous meta-analyses. Developments in personality models and taxonomies during the past decade and a half also afforded our research a more fine-grained, hierarchical model of the personality domain (Stanek & Ones, 2018) that extends above and below the Big Five personality factors of neuroticism, agreeableness, conscientiousness, openness, and extraversion. As a result, we were able to quantitatively estimate relations of 79 personality constructs, 28 of which were not included in previous meta-analytic investigations. Our results thus offer insights across the spectrum of personality traits and cognitive abilities (e.g., facets vs. broader factors; Cronbach, 1960; Cronbach & Gleser, 1965; Ones & Viswesvaran, 1996; Shannon & Weaver, 1949; Wittmann & Süß, 1999).

In sum, 93% of the 3,543 ability-personality relations we meta-analyzed had not been previously meta-analytically estimated. Each of these new meta-analyses contributes to basic and applied disciplines that involve personality and cognitive ability constructs and measures in their theories, research, and applications. Results reveal considerable links.

The Organization of This Book

This book is organized to communicate the thinking, approaches, findings, and implications related to our meta-analytic investigations of personality-intelligence relations. In Chapter 2, we provide a brief overview of the personality and cognitive ability domains. Chapter 3 summarizes our meta-analytic methodology, with more comprehensive details provided in Appendix E. Chapters 4 and 5 present the results of our meta-analyses from the perspective of cognitive ability and personality, respectively. Chapter 6 lays out a theory of individual differences that accounts for our meta-analytic results, Chapter 7 presents the results of our meta-analyses in the light of our theory, and Chapter 8 discusses trends that cut across the results. Chapter 9 notes the boundaries of our current understanding of personality-ability relations as well as opportunities for future exploration. Chapter 10 concludes the book by discussing the meaning and implications of our findings. Appendices A and C provide definitions of all the cognitive ability and personality constructs we refer to throughout the book, while Appendices B and D provide information about which measures we used as indicators of each construct.

Only a pair of fools would try to distill 100 years of research on two of the broadest topics in psychology and thousands of meta-analyses into 225 pages of unorthodox conclusions. We proceed.

References

Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin, 121, 219–245.

Anglim, J., Dunlop, P. D., Wee, S., Horwood, S., Wood, J. K., & Marty, A. (2022). Personality and intelligence: A meta-analysis. Psychological Bulletin, 148(5–6), 301.

Busato, V. V., Prins, F. J., Elshout, J. J., & Hamaker, C. (2000). Intellectual ability, learning style, personality, achievement motivation and academic success of psychology students in higher education. Personality and Individual Differences, 29(6), 1057–1068.

Buss, D. M. (1985). Human mate selection: Opposites are sometimes said to attract, but in fact we are likely to marry someone who is similar to us in almost every variable. American Scientist, 73(1), 47–51.

Buss, D. M. (2009). How can evolutionary psychology successfully explain personality and individual differences? Perspectives on Psychological Science, 4(4), 359–366.

Cattell, R. B. (1987). Intelligence: Its structure, growth and action. Elsevier.

Chamorro-Premuzic, T., & Furnham, A. (2008). Personality, intelligence and approaches to learning as predictors of academic performance. Personality and Individual Differences, 44(7), 1596–1603.

Conn, S. R., & Ricke, M. L. (1994). The 16 PF Fifth Edition Technical Manual. Institute for Personality and Ability Testing, Inc.

Cronbach, L. J. (1960). Essentials of psychological testing (2nd ed.). Harper.

Cronbach, L. J., & Gleser, G. C. (1965). Psychological tests and personnel decisions. University of Illinois Press.

DeYoung, C. G. (2015). Cybernetic big five theory. Journal of Research in Personality, 56, 33–58.

Dilchert, S. (2018). Cognitive ability. In D. S. Ones, N. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work & organizational psychology (2nd ed., Vol. 3, pp. 248–276). Sage.

Furnham, A. (1981). Personality and activity preference. British Journal of Social Psychology, 20(1), 57–68.

Gottfredson, L. S. (1997). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79–132.

Gray, J. A. (1981). A critique of Eysenck’s theory of personality. In H. J. Eysenck (Ed.), A model for personality (pp. 246–276). Springer.

Gray, J. A. (1982). The neuropsychology of anxiety: An equiry of the septo-hippocampal system. Oxford University Press.

Hatch, S. L., Jones, P. B., Kuh, D., Hardy, R., Wadsworth, M. E., & Richards, M. (2007). Childhood cognitive ability and adult mental health in the British 1946 birth cohort. Social Science & Medicine, 64(11), 2285–2296.

Hogan, J., & Holland, B. (2003). Using theory to evaluate personality and job-performance relations: A socioanalytic perspective. Journal of Applied Psychology, 88(1), 100.

Judge, T. A., Higgins, C. A., Thoresen, C. J., & Barrick, M. R. (1999). The big five personality traits, general mental ability, and career success across the life span. Personnel Psychology, 52(3), 621–652.

Kuncel, N. R., Ones, D. S., & Sackett, P. R. (2010). Individual differences as predictors of work, educational, and broad life outcomes. Personality and Individual Differences, 49(4), 331–336.

Le, H., Oh, I. S., Shaffer, J., & Schmidt, F. (2007). Implications of methodological advances for the practice of personnel selection: How practitioners benefit from meta-analysis. Academy of Management Perspectives, 21(3), 6–15.

Linn, R. L., & Werts, C. E. (1971). Considerations for studies of test bias. Journal of Educational Measurement, 8(1), 1–4.

Mammadov, S. (2022). Big Five personality traits and academic performance: A meta-analysis. Journal of Personality, 90(2), 222–255.

Ones, D. S., Dilchert, S., Viswesvaran, C., & Judge, T. A. (2007). In support of personality assessment in organizational settings. Personnel Psychology, 60(4), 995–1027.

Ones, D. S., Dilchert, S., Viswesvaran, C., & Salgado, J. F. (2016). Cognitive abilities: Measurement and validity for employee selection. In J. L. Farr & N. T. Tippins (Eds.), Handbook of employee selection. Routledge.

Ones, D. S., & Viswesvaran, C. (1996). Bandwidth–fidelity dilemma in personality measurement for personnel selection. Journal of Organizational Behavior, 17(6), 609–626.

Ortony, A., Norman, D. A., & Revelle, W. (2005). Effective functioning: A three level model of affect, motivation, cognition, and behavior. In J. Fellous & M. Arbib (Eds.), Who needs emotions? The brain meets the machine (pp. 173–202). Oxford University Press.

Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science, 2(4), 313.

Sackett, P. R., & Ellingson, J. E. (1997). The effects of forming multi-predictor composites on group differences and adverse impact. Personnel Psychology, 50(3), 707–721.

Sackett, P. R., Laczo, R. M., & Lippe, Z. P. (2003). Differential prediction and the use of multiple predictors: The omitted variables problem. Journal of Applied Psychology, 88(6), 1046.

Salgado, J. F., Anderson, N., Moscoso, S., Bertua, C., De Fruyt, F., & Rolland, J. P. (2003). A meta-analytic study of general mental ability validity for different occupations in the European community. Journal of Applied Psychology, 88(6), 1068.

Schilling, M., Becker, N., Grabenhorst, M. M., & König, C. J. (2021). The relationship between cognitive ability and personality scores in selection situations: A meta-analysis. International Journal of Selection and Assessment, 29(1), 1–18.

Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124(2), 262.

Shannon, C. E., & Weaver, W. (1949). The Mathematical Theory of Communication. University of Illinois Press.

Stanek, K. C., & Ones, D. S. (2018). Taxonomies and compendia of cognitive ability and personality constructs and measures relevant to industrial, work and organizational psychology. In D. S. Ones, C. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work & organizational psychology: Personnel psychology and employee performance (pp. 366–407). Sage.

von Stumm, S., & Ackerman, P. L. (2013). Investment and intellect: A review and meta-analysis. Psychological Bulletin, 139(4), 841–869.

Webb, R. M., Lubinski, D., & Benbow, C. P. (2007). Spatial ability: A neglected dimension in talent searches for intellectually precocious youth. Journal of Educational Psychology, 99(2), 397.

Wilmot, M. P., & Ones, D. S. (2019). A century of research on conscientiousness at work. Proceedings of the National Academy of Sciences, 116(46), 23004–23010.

Wilmot, M. P., & Ones, D. S. (2022). Agreeableness and Its Consequences: A Quantitative Review of Meta-Analytic Findings. Personality and Social Psychology Review, 10888683211073008.

Wilmot, M. P., Wanberg, C. R., Kammeyer-Mueller, J. D., & Ones, D. S. (2019). Extraversion advantages at work: A quantitative review and synthesis of the meta-analytic evidence. Journal of Applied Psychology, 104(12), 1447.

Wittmann, W. W., & Süß, H.-M. (1999). Investigating the paths between working memory, intelligence, knowledge, and complex problem-solving performances via Brunswik symmetry. In P. L. Ackerman, P. C. Kyllonen, & R. D. Roberts (Eds.), Learning and individual differences: Process, trait, and content determinants (pp. 77–108). American Psychological Association. https://doi.org/10.1037/10315-004

Wolf, M. B., & Ackerman, P. L. (2005). Extraversion and intelligence: A meta-analytic investigation. Personality and Individual Differences, 39(3), 531–542.

Endnotes

1 Examples abound in research on education, job performance, longevity, and numerous other areas.

2 Compared to previous publication with largest number of contributing effect sizes (Anglim et al., 2022).

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Chapter 2

Cognitive Ability and Personality Domains

CHAPTER SUMMARY

Cognitive ability

Intelligence is the ability to grasp information and handle complexity. The many specific manifestations of intelligence can be organized into a hierarchy of cognitive ability dimensions.

General mental ability is one of, if not the most, potent predictors of diverse, important life outcomes (e.g., longevity, work accomplishments, wealth creation, mental health).

We leverage an expanded taxonomy of cognitive abilities to organize high-resolution meta-analyses according to ability constructs, rather than just scale/measure names.

Personality

Personality summarizes the typical ways individuals act, feel, desire, and think. These tendencies can be organized into a hierarchy of traits.

Personality is a pervasive predictor of important life outcomes (e.g., longevity, job performance, athletic achievement, educational attainment, and creative output).

We leverage an expanded taxonomy of personality traits to organize high-resolution meta-analyses according to trait constructs rather than personality scale names.

The personality taxonomy used to organize results encapsulates the Big Five, meta-traits, aspects, facets, and compound traits.

To fully understand the results, theory, and implications of the present volume, it is critical to have a basic understanding of intelligence and personality. We provide very brief overviews of each domain and its structure below. Those seeking a fuller understanding should consult Stanek and Ones (2018) as well as the works referenced therein.

What is Intelligence?

In the pantheon of psychological individual differences, cognitive ability1 occupies an elevated position (Viswesvaran & Ones, 2002). Cognitive ability does not simply refer to “book smarts” or “school learning.” Instead, it refers to a diverse set of hierarchically arranged abilities that index the capacity of an individual’s brain decipher information to navigate various forms of complexity effectively, whether that complexity is recalling information from short-term memory, processing a visual image, reproducing a learned fact, extrapolating a pattern, reasoning inductively or deductively, perceiving similarities and differences, or any other complexities. General mental ability (g) is a very broad construct that “involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience.” (Gottfredson, 1997b, p. 13). In this book, the term “cognitive ability” refers to the hierarchy of interrelated abilities with a general mental ability factor (g) at the apex (Carroll, 1993; Schneider & McGrew, 2012; Spearman, 1904). We view general mental ability as the manifestation of causal connections between specific abilities (e.g., high fluid abilities generating larger volumes of acquired knowledge) as well as common factors underlying brain development (e.g., neural connectivity).

In the last 100 years, scientific research has established cognitive ability as a key factor in educational success (e.g., Jensen, 1993; Kuncel et al., 2004; Kuncel & Hezlett, 2007), occupational success (e.g., Dilchert, 2018; Ones et al., 2016), career success (e.g., Judge et al., 1999; Schmidt & Hunter, 2004), socio-economic success (e.g., Kuncel et al., 2010; Strenze, 2007), exceptional attainments (e.g., Lubinski et al., 2001), mental health (e.g., Hatch et al., 2007), and physical health (e.g., Gottfredson & Deary, 2004), among other important behaviors and outcomes. In characterizing general mental ability, Gottfredson noted, “no specific ability, personality trait, social advantage, or fund of experience has been identified that can compensate for mental powers too weak to lift a task’s cognitive load” (Gottfredson, 2016, p. 214). Indeed, the measurement of cognitive ability has been hailed “as the most practical contribution made to humanity by all of psychology” (R. D. Roberts et al., 2004, p. 333). From our theoretical perspective (see Chapter 6), intelligence involves using information to know what to preserve and what to evolve to maximize the probability of achieving goals in given environments.

A Contemporary Taxonomy of Cognitive Abilities

A major purpose of the research presented in this volume is to describe, more fully and comprehensively than ever before, cognitive abilities’ relations with personality traits, expanding knowledge of their nomological networks, and thereby contributing to understanding of the mechanisms that determine individual success/failure in many life domains.

To do this systematically and as comprehensively as possible, we leveraged a contemporary taxonomy of cognitive abilities (as well as a contemporary taxonomy of personality traits as noted below). Indeed, for over 100 years, numerous cognitive abilities have been recognized,2 and their structure and organization have been a persistent focus of research (e.g., Carroll, 1993; Ekstrom et al., 1976). One of the most important discoveries in the study of psychological individual differences was the recognition that all cognitive abilities are correlated, revealing a general cognitive ability factor (i.e., general mental ability, or g) at the apex of the hierarchy of more specific cognitive abilities (Carroll, 1993; Spearman, 1904).

Cattell (1957, 1963) identified an important distinction between the dimensions below general mental ability — fluid ability and crystallized ability — and he put forward the investment theory of intelligence to explain their differences. Fluid abilities are non-invested abilities, whereas crystallized abilities are invested abilities. All acquired knowledge constructs, from the most general verbal abilities to the most narrow domain-specific abilities, are included in the latter category.

The next significant contribution to the structure of cognitive abilities was the result of Carroll’s (1993) intensive study of more than 460 cognitive ability correlation matrices. His resulting three-stratum model included 69 narrow (i.e., specific) abilities (e.g., closure flexibility) in the first stratum, eight group dimensions (i.e., primary abilities such as broad visual perception) in the second stratum, and a general factor of cognitive ability, g, in the third stratum at the apex of the hierarchy. Carroll’s Three-Stratum Model of cognitive abilities integrated and built on Cattell and Horn’s earlier conceptualizations. The combined model is referred to as the Cattell-Horn-Carroll (CHC) model of cognitive abilities (McGrew, 1997, 2005, 2009). In the past two decades, refinements to the CHC model have resulted in additional abilities being added (Schneider & McGrew, 2012) and other abilities being distinguished from each other (e.g., learning efficiency and retrieval fluency3 as two major sub-dimensions of long term storage & retrieval [Jewsbury et al., 2016]).

In a recent publication, we updated the CHC model (McGrew, 2009). Our modifications to Schneider and McGrew’s (2012) CHC model were primarily in hierarchical representations of the primary ability constructs, recognizing that there are currently at least five levels of cognitive constructs that can be reflected in the hierarchy (Stanek & Ones, 2018). While these levels of distinction are meaningful, we simultaneously acknowledge that general mental ability accounts for about 50% percent of the common variance among broad cognitive ability test batteries (Lubinski, 2004), and that all cognitive abilities tap into this general factor in varying degrees. We refer to this updated CHC conceptualization as the Unified CHC cognitive abilities taxonomy (Stanek & Ones, 2018).

As seen in Figure 1, specific abilities are hierarchically organized below their superordinate constructs. That is, superordinate primary abilities provide general boundaries that are further refined in subordinate specific abilities. The lateral organization of the figure is from fluid ability on the left to acquired/crystallized abilities on the right. The figure also indicates primary ability dimensions, organized according to brain function (grouped within shaded boxes) and conceptual relations (grouped within dashed-line boxes). Memory, speed, and invested abilities (i.e., acquired knowledge) constitute three distinct areas of organization, according to brain function. Multiple primary and specific abilities are included in each of these. For example, the speed cluster includes abilities related to processing speed as well as reaction and decision speed primary abilities. Primary abilities can also be grouped conceptually into domain independent and domain specific clusters. Domain independent abilities are general capacities such as memory constructs and fluid abilities. Whereas domain specific abilities are more related to specific sensory-motor systems (e.g., visual processing and auditory processing).4 Descriptions of each cognitive ability construct in the Unified CHC, reproduced from Stanek and Ones (2018), are provided in Appendix A.

The Unified CHC taxonomy offers a clear point of view for understanding the structure and organization of cognitive abilities, but the Stanek and Ones (2018) compendium, linking thousands of measures to these constructs, is perhaps even more consequential. Taxonomies provide meaningful schemes for grouping psychological constructs, but researchers often interpret variables differently, which leads to disparate measures being used to assess the “same” trait. Our compendia reduce this risk and allow users to easily identify measures of each trait. The most recent version of this compendium is provided at http://stanek.workpsy.ch/cognitive-ability-map/cognitive-ability-test-compendium/, and the version used for this book is provided in Appendix B of the online supplementary materials of this book.

In an empirical test of the Unified CHC taxonomy and its associated compendium, Kostal (2018) conducted a comprehensive meta-analysis of cognitive ability intercorrelations based on data from over 1,000 studies. His results provided support for the Stanek and Ones compendium’s classification of cognitive ability tests into cognitive ability constructs as well as for the basic distinctions among major Unified CHC taxonomy ability dimensions.

In studying cognitive ability-personality relations, the research in this volume focuses on 97 cognitive ability constructs that were measured in studies contributing to our meta-analyses (i.e., personality correlates were reported for 97 abilities in empirical research). These abilities are part of the Unified CHC cognitive ability hierarchy described above.

Figure 1. Unified CHC cognitive abilities taxonomy.

Note. See Appendix A for construct definitions. Shaded boxes group abilities invoking similar brain functions. Dashed lines group abilities that are conceptually related.

What is Personality?

Personality is a set of enduring tendencies to act, feel, desire, and think in certain ways across situations (e.g., to be extraverted, compassionate, tidy, anxious, rebellious [Allport, 1937; Emmons, 1989; Johnson, 1997; Revelle, 2008; Winter et al., 1998]). Personality traits have wide-ranging and substantial impacts on behaviors and outcomes across a variety of domains, from education and career path choices (e.g., Connelly & Ones, 2010; Judge et al., 1999; Poropat, 2009), to art preferences (e.g., Furnham & Chamorro-Premuzic, 2004), to inventions and creations (e.g., Barron & Harrington, 1981; Furnham & Bachtiar, 2008), to job performance (e.g., Ones et al., 2007), and even to longevity (e.g., Bogg & Roberts, 2004; Kuncel et al., 2010). The research literature incorporating personality constructs is vast: tens of thousands of research studies in diverse fields include a plethora of personality constructs.

A Contemporary Taxonomy of Personality Traits

One of the primary purposes of the research presented is to describe personality traits’ relations with cognitive abilities. To do this as systematically and comprehensively as possible, we leveraged a contemporary taxonomy of personality constructs.

The Five Factor Model (FFM) of personality emerged in the second half of the 20th century as the most parsimonious, cross-culturally replicable, empirically supported taxonomy of personality traits (Allport & Odbert, 1936; Cattell, 1943; Goldberg, 1977, 1992). The FFM identifies five families of personality traits, commonly known as the Big Five: neuroticism,5 extraversion, openness/intellect, agreeableness, and conscientiousness. These factors have been obtained and refined based on multiple methodologies over the past century, including lexical studies of phenotypic personality traits, joint factor analyses of personality instruments built to assess the Big Five, and factor analyses of personality instruments based on other theoretical perspectives. Their biological (e.g., genetic, neurological) etiologies also continue to be elucidated (DeYoung, 2010a, 2010b, 2015; DeYoung & Gray, 2009; Pincombe et al., 2007). All in all, the FFM is a framework that provides useful organizational structure to the personality construct space. Lower level traits associated with each of the Big Five have also been examined and reported on (Connelly et al., 2014; Davies, 2012; B. W. Roberts et al., 2005; Wilmot & Ones, 2019, 2022; Woo et al., 2014). Furthermore, the Big Five are not orthogonal, giving rise to higher order personality factors (Davies et al., 2015; DeYoung, 2006; Digman, 1997).

Synthesizing these empirical studies and extant literature leads to the following conclusions. Personality traits can be hierarchically organized: there are both higher and lower order factors of the Big Five. At the lowest level are responses to individual items. At a higher level are clusters of items that indicate specific personality facets, which can vary in specificity.6 Facets that share psychological meaning, and likely also similar etiologies, are further grouped into personality aspects (DeYoung et al., 2007), which combine to define the Big Five personality factors.

For example, agreeableness is a broad factor that combines the aspects of compassion and politeness, which themselves encompass various facets, such as cooperation, trust, and modesty (Davies, 2012; Davies et al., 2009; Wilmot & Ones, 2022). Items on individual scales uniquely assess different agreeableness facets to varying degrees. For example, “I prefer to work with others to accomplish goals” is an item that primarily indicates cooperation, whereas “Most people are honest and well intentioned” is an item that indicates trust. The global factor of agreeableness is defined by the common variance that is shared across such facets and items. Personality facets reflect unique genetic and environmental influences (Jang et al., 1998, 2002) and capture variance beyond the global personality factors.

There are three higher order personality traits above the Big Five factors. The first is the construct of stability (DeYoung, 2006; DeYoung et al., 2002), also referred to as socialization, which involves psycho-social maturity and abiding by society’s rules, norms, and conventions. Conscientiousness, agreeableness, and neuroticism define this higher order factor, which Digman referred to as “factor alpha.”

The second higher order factor above the Big Five arises from the shared variance between extraversion and openness/intellect. This factor of “personal growth” is termed “factor beta,” and has more recently referred to as “plasticity” (DeYoung et al., 2002). Plasticity is defined by exploratory tendencies related to both extraversion and openness — extraversion contributing to behavioral exploration, and openness representing intellectual exploration. Meta-analyses have provided support for the existence of both factor alpha and factor beta (Davies et al., 2015; Markon et al., 2005).

Third, there is evidence for a general factor of personality at the highest level of the personality hierarchy, at least in self-report measures (Davies et al., 2015; Irwing et al., 2012; van der Linden et al., 2017). Such a general factor in self-reports partially represents a substantive global self-evaluation trait (i.e., self-regard), rather than simple social desirability, untethered from reality.

We previously integrated the lower and higher level constructs of personality into a framework that we refer to as the Pan-Hierarchical Five Factor Model (Pan-Hierarchical FFM; Stanek & Ones, 2018), depicted in Figure 2.

Figure 2. Stanek & Ones (2018) Pan-Hierarchical Five Factor Model of Personality.

Note. Construct names in italics indicate those with consistent loadings on other Big Five factors. See Table 1, below, for compound traits and their Big Five linkages. See Appendix C for definitions of personality traits.

The facet level traits of the Pan-Hierarchical FFM are based on nomological-web clustering (Hough et al., 2015). The defining feature of this approach is an examination of the full nomological network of a proposed personality construct’s convergent and divergent relations, as well as relations with external variables, including criteria. Constructs that exhibit similar relations with other variables, and strong relations with each other, can be grouped together. By examining items, factor analyses, scale descriptions, and relations with other variables, nomological-web clustering improves on more common methods that rely on superficial measure/scale descriptions. Although it can be tempting to ascribe constructs to measures based on the scale name or a primary study author’s description, such methods suffer from significant flaws, including the jingle-jangle fallacy (Kelley, 1927), disparate usage of common terms (e.g., extraversion), and researchers misunderstanding what constructs are actually captured by the measures they employ. Empirical validation (Credé et al., 2016) has provided support for a predecessor of the Pan-Hierarchical FFM (Hough & Ones, 2001). Our updates to Hough and Ones (2001) relied heavily on empirical studies that have appeared since (e.g., Connelly et al., 2014; Jackson et al., 2010; B. W. Roberts et al., 2004, 2005; Woo et al., 2014).

Finally, many personality attributes do not fit neatly within a single superordinate trait family, such as one Big Five factor. Such personality traits are referred to as compound traits (Ones, 2005), as they represent variance from more than one Big Five trait. Table 1 reproduces a compilation of compound personality constructs from Stanek and Ones (2018). For each trait, the Big Five-related personality constructs that contribute variance to that construct are indicated. Definitions of all personality traits studied in the present research are provided in Appendix C.

Table 1. Compound personality traits and their sources of variance.

N

E

O

A

C

Other

Emotional Intelligence

−

+

+

+

+

Borderline Personality Disorder

+

+

+

−

Interpersonal Sensitivity

−

+

+

+

Achievement via Independence

−

+

+

+

Generalized Self Efficacy

−

+

+

+

Present Focused

−

+

+

−

Intellectual and Confident

−

+

Intellect+

Innovation

(−)

+

+

Type A

+

+

−

+

Hysteria

Somatic

Complaints+

+

Lack of

Aggression-

Managerial Potential

−

+

+

Passive Aggressive

+

−

−

−

Self Monitoring—Other Directedness

+

(−)

−

−

Social Suave

−

Sociability+

+

Narcissism

(Volatility+, Withdrawal−)

+

−

Psychopathy—Primary

−

(+)

−

Self Esteem

−

+

+

Locus of Control (Internal)

−

(Positive Emotionality+)

+

Optimism

−

+

Self Esteem—Accepting of One’s Weaknesses

−

+

−

Rumination

Perseveration+

Positive

Emotionality−

Routine Seeking

Variety

Seeking−

Order+

Resourcefulness

−

+

+

Pragmatic

−

Intellect-

+

Superstitiousness

+

+

Schizophrenia

+

Experiencing+

Machiavellianism

+

−

−

(Factor Alpha-)

Self Monitoring—Global

(−)

−

(−)

Factor Beta+

Family Problems

+

(−)

(−)

Customer Service

−

+

+

Integrity

−

+

+

Stress Tolerance

−

+

+

Unforgiving

+

Compassion−

Volatile Aggression

Volatility+

Lack of Aggression−

Trust

−

+

Secure from Social Pressures

−

−

Micromanaging

+

+

Self Control

−

+

Self-Transcendence

+

+

+

Risk Taking

+

+

−

Mischievous

Sensation

Seeking+

(Experiencing+)

−

Quiet Achiever

−

(+)

+

Openness to Emotions

+

+

Hedonism

Enthusiasm+

Experiencing+

Input Seeking

Dominance-

+

Cautiousness+

Warmth

+

+

Compassionate and Sociable

Sociability+

Compassion+

Grandiosity and Intimidation

+

−

Ambition

+

+

Sales Potential

+

Industriousness+, Cautiousness−

Ambitious Risk Taking

Assertiveness+

Achievement+, Cautiousness−

Acting Without Thinking

Sensation

Seeking+

Cautiousness−

Restrained Expression

−

+

Rigid Introversion

Sociability−

Order+

Psychopathy—Secondary

(+)

Factor Alpha−

Data Rational

Intellect+

−

(+)

Tolerance

+

+

Independent of Conventions and Others

+

−

Creative Personality

Non

Traditional+

Industriousness+,

Orderliness−, Cautiousness−

Factor Beta+

Exploration for Perfection

Intellect+

+

Industrious Curiosity

Intellect+

Industriousness+

Judging-Perceiving

−

+

Cold Efficiency

−

+

Rugged Individualism

Industriousness+

Factor Beta+

Proving Self

Ambition+,

Self Esteem−

Note: Parentheses indicate suspected or inconsistently observed contribution.

If a facet or aspect specifically contributed to a compound trait, it was listed in place of the more general “+”/“-” for a Big Five factor.

Achievement via independence and generalized self efficacy appear to share the same sources of Big Five variance, but achievement via independence involves more openness than generalized self efficacy, which involves more neuroticism and less conscientiousness.

Customer service, integrity, and stress tolerance also appear to share the same formulation, but customer service is more influenced by agreeableness, integrity is more influenced by conscientiousness, and stress tolerance is more influenced by (low) neuroticism.

Similar to our treatment of the cognitive abilities domain, we have compiled a vast compendium of measures that assess each personality construct in the Pan-Hierarchical Five Factor Model of personality. The most recent version of this compendium is provided at http://stanek.workpsy.ch/personality-map/personality-measure-compendium/, and the version used for this book — the 2022 version of the Stanek of Ones (2018) compendium — is provided in Appendix D of the online supplementary materials of this book. The Pan-Hierarchical FFM and its compendium were used to classify and categorize personality measures in the current study. Its cumulative, evidence-based approach is a major strength vis-à-vis individual investigations relying on one or even a few datasets to sort out the multilevel, multifactorial structure of personality constructs. In studying personality-cognitive ability relations, we examine 79 personality traits that were measured with cognitive abilities in studies contributing to our meta-analyses.

In sum, the use of the up-to-date, large-scale, evidence-based taxonomies for both the cognitive ability and personality domains is a major strength of the work presented. Using such taxonomies highlighted similarities as well as differences among specific traits/abilities as well as trait-ability clusters. Furthermore, the clarity and high resolution of these taxonomies enables users interested in other taxonomic structures to re-organize the constructs and examine the resulting patterns of effects. In addition, these taxonomies afford clear definitions of each construct that cut through the jingle-jangle fallacy (Kelley, 1927) pervasive in these two domains. This clarity not only increases consistency in the construct categorization process but also precision in the discussion of findings. Finally, the use of transparent, open-access, well-validated compendia for both the personality domain and the cognitive ability domain supports the replicability and future extension of the analyses presented.

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Endnotes

1 Sometimes simply referred to as intelligence.

2 Already by the 1970s, over 60 primary abilities had been identified.

3 Note that retrieval fluency is more closely related to acquired knowledge and processing speed than learning efficiency.

4 Artificial intelligence capabilities and sub-fields appear to follow a similar structure (e.g., memory, computer vision, natural language processing) as well as the more general coordination amongst these.

5 Also referred to as emotional stability when describing its positive pole.

6 Homogeneous item clusters can be thought of as narrow facets.

<span data-node-type="text" data-node-uuid="59c75dd29e8de406be03689f1d61f408e90c0559" data-text-digest="1862b2b4c8d19f6150fe8993ffbbde328bff2355">03_Chapter_3</span>

Chapter 3

Our Methodology

CHAPTER SUMMARY

In this research, 3,543 meta-analyses cumulated the empirical evidence of relations between 79 personality traits with 97 cognitive abilities.

Through nine search strategies, our research team identified 1,325 primary studies that contributed to these meta-analyses.

Contributing studies were conducted in more than 50 countries and represent millions of participants across demographic groups.

Measures in each study were mapped to personality and cognitive ability constructs in modern personality and cognitive ability taxonomies and compendia (Stanek & Ones, 2018) to avoid the idiosyncrasies of specific measures and ensure consistent construct definitions.

Most effect sizes came from unpublished sources, reducing the risk of publication bias.

The resulting meta-analytic database has resolution that is orders of magnitude greater than previous investigations’. We are making this database freely available as a resource for other scholars.

Psychometric meta-analysis was used to aggregate the effect sizes, estimate the true relations, and quantify the degree of true variability across studies.

This chapter begins with rationale for conducting sweeping meta-analyses. We then describe our process for gathering relevant data, including a description of the studies included and resulting database, and provide key information on how measures were grouped for personality and ability constructs as well as how the data were quantitatively cumulated. Finally, we offer interpretive guidance.

Rationale for Sweeping Meta-Analyses

Our goal in this volume was to quantify and understand the grander pattern of relations between two of the largest domains of psychological individual differences: personality and cognitive abilities. In this endeavor, we could have conducted a large scale study. However, any individual empirical study, no matter how well-designed and -conducted, has major limitations. Sample sizes are limited due to feasibility considerations, since large sample sizes require substantial effort, time, and, often, financial resources to gather. Representation of a wide swath of cultures and countries is another persistent challenge. Even when large samples from multiple cultures are recruited, the time needed and the willingness of participants limit the number of measures that can be administered to the same sample. Covering the entire personality and cognitive ability construct space is not feasible, as such an effort would require the completion of hundreds of personality scales and cognitive ability tests. Additionally, psychological measurement fads, fashions, and falderol influence scale and test choices at any given point in time. Consequently, the use of different psychological measures has waxed and waned over the years, leaving gaps in the measurement record for personality traits and cognitive abilities. And relations between psychological constructs may vary over time periods (e.g., the strengthening relation between extraversion and political leadership in the past century is a case in point [Rubenzer et al., 2000]). In essence, findings from any study represent a limited set of individuals, from a specific period of time, through the lens of idiosyncratic measures, and therefore may not yield enduring truths.

The only approach that currently addresses all these concerns is sweeping meta-analyses. Sweeping meta-analyses pool data from numerous samples, multiple measures, many cultures, and extensive time periods. A pre-requisite for such meta-analyses is that many primary studies must have estimated the focal relations. Fortunately, personality-intelligence relations have been reported for a century with many variations in samples, cultures, disciplines, settings, and measures. Taking a meta-analytic approach stimulates discovery of universals, while also enabling investigations of particulars.

In sum, our goal was to construct a full and detailed panorama of personality-intelligence relations using meta-analytic techniques. In this endeavor, we attempted to amass all relevant existing quantitative relations, accurately map them to psychological constructs, carefully organize those constructs in models that incorporate the latest nomological evidence, and meta-analytically cumulate the effect sizes. The result is the quantification of the relations of 3,543 cognitive ability-personality trait pairs. Each step is described below.

Gathering Relevant Data

We sought to identify all empirical research that provided an estimate of personality trait-cognitive ability relations. In pursuit of this aim, we made significant efforts to include unpublished studies. For example, we did not limit our compilation to just reported effect sizes, but rather, we also located and included studies where personality-intelligence relations could have been reported but were not. In some of these cases, authors provided the relations upon request. In other cases, we obtained raw data from the study investigators, and computed the relevant relations ourselves. Study identification, inclusion/exclusion, and coding details of our research are described at length in the “Detailed Methods” of Appendix E. The diagram below highlights the magnitude of our enterprise, and Table 2 provides a comparison to previous meta-analyses.

Figure 3. Study inclusion flow diagram.

Note. Detailed search procedures and reasons for inclusion/exclusion are provided in Appendix E.

Description of Studies Included

The search strategies and inclusion/exclusion criteria, detailed in Appendix E, yielded 1,325 studies contributing independent data to the meta-analyses.1 We were as exhaustive as possible in our efforts. In striving to be comprehensive, we aimed to minimize the impact of publication bias and to reflect data from non- Western, educated, industrialized, rich, and democratic societies as well as several ethnicities, age groups, and other demographics. We also were careful to avoid the biases inherent in the paradigm of any one field or research perspective (Henrich et al., 2010; Simmons et al., 2011). Consistent with our aim of inclusion and generalization, we included data from across the past century. Despite these efforts and obtaining data from millions of participants, however, the current study is still unlikely to be fully exhaustive, and there are almost certainly studies that we did not locate. Nonetheless, we are confident that our search methods yielded a broadly representative majority of the relevant, available, quantitative relations between personality traits and cognitive abilities.

Comparison of the number of studies included in this set of meta-analyses with prior meta-analyses of personality-cognitive ability relations indicates that we created the largest and most comprehensive database to date on this topic, as well as one of the largest meta-analytic databases on any topic (cf. only larger N/K set of meta-analyses we are aware of [Polderman et al., 2015], though ours examined a larger number of relations). We have made this meta-analytic database publicly available in Appendix F of the online supplementary materials as well as on Open Science Framework, along with a full list of studies contributing effect sizes to the present meta-analyses (Appendix K).

Table 2. Current study compared to previous meta-analyses.

Personality Constructs

Cognitive Ability Constructs

Studies

Effect Sizes

Individuals

Current Study

79

97

1,976

60,690

2,010,980

Ackerman & Heggestad (1997)

19

10

135

2,033

64,592

Wolf & Ackerman (2005)*

3

10

100

1,018

56,016

von Stumm & Ackerman (2013)**

30

4

112

234

60,097

Schilling et a. (2021)***

5

n/a

66

115

46,265

Anglim et al. (2022)****

45

3

272

2,508

162,636

Note: As a result of differing exclusion criteria (e.g., ipsative measures, participants younger than 12 years old, course grades as measures of cognitive ability) and areas of focus, the present meta-analyses include most but not all of the materials included in previous meta-analyses; see "Data availability" section in Appendix E for additional details. Conversely, however, our meta-analyses include over 1,000 studies that were not included in any of the previous meta-analyses.
* Focused on relations between extraversion-related traits and cognitive abilities.
** Focused on relations between openness-related traits and cognitive abilities.
*** Organized cognitive tests by content rather than construct, resulting in a heterogeneous mix of constructs in each meta-analysis and therefore, results that cannot be directly compared with ours.
**** Reported 45 personality constructs included in our meta-analyses.

Database Description

The final database contained 60,690 personality-ability correlation coefficients and their associated characteristics. Each meta-analysis only contained independent effect sizes. Table 3 details the sources of the effect sizes in the database. The two largest contributing source types were published or publicly available raw datasets (41%; mostly from grant-funded studies) and peer-reviewed journal articles (29%).

Table 4 presents the proportions of participant types. Community samples formed the largest part of our database. Almost another quarter of the effect sizes were from occupational samples (i.e., employees, military members, and job applicants).

The average age of participants ranged from 12.0 to 100.3 (see Figure 4 for average age distribution), and 28% of the effect sizes came from samples with average ages between 30 and 50. Across the effect sizes that reported sex proportions, the average sample was 54.1% male.

Table 3. Sources of effect sizes.

Publication Type

Number of Effect Sizes

% of Effect Sizes Contributed

Publicly Available Raw Data

24,981

41%

Journal Article

17,550

29%

Dissertation

8,158

13%

Technical Manual

2,588

4%

Organizational Report

2,198

4%

Book

2,151

4%

Master’s Thesis

1,388

2%

Conference Material

1,214

2%

Undergraduate Thesis

280

<1%

Manuscript / Working Paper

182

<1%

Total

60,690

Note: For publicly available raw data, we computed relevant effect sizes. These newly computed effect sizes are now available to the research community as part of our meta-analytic database.

Table 4. Participants in contributing research.

Sample Type

Count of Effect Sizes

% of Effect Sizes

Community

22,103

36%

Student

18,333

30%

Applicant

5,541

9%

Employee

4,754

8%

Military

3,888

6%

Patient

58

<1%

Combination

1,386

2%

(Not Reported)

4,627

8%

Total

60,690

Figure 4. Number of effect sizes by samples’ age groups.

Samples were drawn from more than 50 countries as diverse as Australia, Chile, China, Estonia, Iran, Morocco, Nigeria, and Turkey (see Table 5). The majority of effect sizes contributing to our meta-analyses were from the United States of America (USA), which is consistent with where most psychological research has been conducted (Bauserman, 1997), though less than ideal for global representation. Nevertheless, our meta-analytic database did include studies from all peopled continents except Antarctica. Representation from Asia was modest but included several of the most populous countries (e.g., China, India, South Korea, Japan, Taiwan, Hong Kong, Singapore, Philippines); representation from South America (Brazil, Chile) and Africa (South Africa, Nigeria, Ethiopia) was weak.

Table 5. Effect sizes by country.

Country

Count of Effect Sizes

% of Effect Sizes

Country

Count of Effect Sizes

% of Effect Sizes

United States of America (USA)

40,297

66%

Denmark

35

<1%

United Kingdom (UK)

5,228

9%

British Isles

32

<1%

Germany

3,365

6%

Croatia

28

<1%

Canada

2,306

4%

Portugal

27

<1%

Australia / New Zealand

1,408

2%

Cyprus

25

<1%

Dominica

432

<1%

Russia

15

<1%

South Africa

426

<1%

Hong Kong

14

<1%

Sweden

418

<1%

Puerto Rico

13

<1%

Spain

366

<1%

Morocco

11

<1%

Netherlands

343

<1%

Luxembourg

10

<1%

China

322

<1%

Belgium

5

<1%

Finland

202

<1%

Chile

5

<1%

Poland

174

<1%

Nigeria

5

<1%

Estonia

171

<1%

Hungary

3

<1%

India

152

<1%

Greece

2

<1%

Japan

125

<1%

Iran

2

<1%

Norway

119

<1%

Philippines

1

<1%

Taiwan

110

<1%

Ethiopia

1

<1%

Israel

102

<1%

.....

combination (Cyprus & Greece)

60

<1%

Switzerland

100

<1%

combination (Estonia & Russia)

5

<1%

Italy

95

<1%

combination (Europe & East Asia)

1

<1%

Turkey

72

<1%

combination (Europe (15 countries))

5

<1%

South Korea

72

<1%

combination (Europe)

277

<1%

Austria

67

<1%

combination (France & Pakistan)

5

<1%

France

59

<1%

combination (global (mostly USA & UK))

63

<1%

Mexico

58

<1%

combination (global)

332

<1%

Bosnia and Herzegovina

55

<1%

combination (Netherlands & Belgium)

15

<1%

Romania

53

<1%

combination (Southeast Asia)

5

<1%

Slovenia

51

<1%

combination (UK & USA)

60

<1%

Brazil

48

<1%

combination (USA & Canada)

9

<1%

Singapore

39

<1%

(Not Reported)

2,784

5%

Total

60,690

Mapping Measures to Personality and Ability Taxonomies

As noted above, we wished our analyses, results, findings, and conclusions to reflect enduring constructs, not be constrained by particular measures. Specific measures have idiosyncrasies that undermine generalizability and limit scientific knowledge. Multiple measurements of the same construct increase the likelihood that inferences focus on the construct, rather than refer to a particular measure. Having a common vocabulary across measures and even models facilitates science, just as in other areas there are conversions between metric and standard. Other major meta-analyses (e.g., Barrick & Mount, 1991; Salgado, 1998) have also focused on constructs rather than specific measures. The two authors of this volume coded and classified the personality and cognitive ability measures into constructs based on the Stanek and Ones (2018)2 personality and cognitive ability taxonomies and compendia. Additional details about the categorization of personality and ability constructs are provided in the “Construct categorization” section of the Detailed Methods in Appendix E.

Across all levels of the personality hierarchy, 79 personality constructs are included in the present meta-analyses. In Chapter 2, Figure 2 depicts the structure of major personality constructs utilized as an organizing framework in this research. Definitions of each construct and a compendium of scales assessing each may be found in Stanek and Ones (2018) as well as at http://stanek.workpsy.ch/personality-map/personality-taxonomy/.

Figure 5. Number of meta-analyses involving each personality construct.

Note. More saturated colors indicate higher levels of the personality hierarchy, except for compound traits, where coloring indicates which Big Five trait contributes most to the construct.

Ninety-seven abilities were studied, including 70 specific abilities (e.g., number facility and quantitative reasoning) representing 10 dimensions of primary abilities (e.g., processing speed and fluid abilities), which in turn can be grouped into four clusters: domain independent general capacities (i.e., fluid abilities, memory), sensory-motor domain specific abilities (i.e., visual processing, auditory processing), speed capacities (i.e., processing speed, reaction and decision speed), and invested abilities (i.e., acquired knowledge).3 In Chapter 2, Figure 1 depicts the structure of major ability constructs utilized as an organizing framework in this research. Definitions of each construct and a compendium of scales assessing each can be found in Stanek and Ones (2018) and at http://stanek.workpsy.ch/cognitive-ability-map/cognitive-ability-taxonomy/.

Figure 6. Number of meta-analyses involving each cognitive ability construct.

Note. Darker shades indicate higher levels of the construct hierarchy.

Quantitatively Cumulating the Evidence Through Meta-Analyses

Psychometric meta-analysis (Schmidt & Hunter, 2014) was used to combine effect sizes across studies and improve statistical power, reduce error variance of the estimated effect sizes, and estimate the degree to which results might generalize to populations and situations beyond those investigated in individual primary studies. In psychometric meta-analysis, the mean observed correlation (r̄ ) is corrected for unreliability as well as other applicable statistical artifacts (e.g., dichotomization) to obtain the estimated mean corrected correlation p̂ (i.e., estimated true-score correlation). In our meta-analyses, correlations were corrected for sampling error and unreliability in both the cognitive ability and personality measures (see Appendix E’s Tables S1 and S2 in the online supplementary materials for the reliability distributions of each personality trait and cognitive ability).4 Such corrections result in estimates of the construct-level relations free of measurement error. SDr indicates the average variation of observed correlations. Observed standard deviations reflect the fact that unreliability and other statistical artifacts inflate variation in effects observed across studies. Therefore, corrections are necessary to control for the differential reliabilities of various scales contributing to each meta-analysis in order to estimate true variability. The estimated standard deviation of corrected correlations (SDp̂) indicates the degree of true (i.e., non-artifactual) variability associated with the mean corrected correlation and indexes true heterogeneity. We also computed 80% credibility values for each meta-analytic result. The credibility value range indicates the range in which most individual true-score correlations would be expected to fall (e.g., when new studies are conducted) (Schmidt & Hunter, 2014). Reporting credibility value ranges reveals whether the examined relations are expected to generalize (Ones et al., 2017; Wiernik et al., 2017). We also computed 90% confidence intervals for each meta-analytic result, illustrating the precision with which each meta-analytic mean correlation is estimated. Details of analyses (e.g., reliability artifact distributions) are reported in Appendix E.

Interpreting Results

We consistently use specific terms to describe the meta-analytic estimates. These terms are defined here to ensure clarity and facilitate understanding. Descriptors of effect size magnitude correspond to the behavioral science benchmarks provided by Funder and Ozer (Funder & Ozer, 2019), and are as follows:

an effect-size r of .05 is very small for the explanation of single events but potentially consequential in the not-very-long run, an effect-size r of .10…is still small…but potentially more ultimately consequential, an effect-size r of .20 indicates a medium effect that is of some explanatory and practical use even in the short run and therefore even more important, and an effect-size r of .30 indicates a large effect that is potentially powerful in both the short and the long run.

We use the term “homogenous” to refer to meta-analytic estimates that have small standard deviations (i.e., SDp̂ close to 0). In contrast, when the contributing effect sizes were more dispersed (i.e., SDp̂ farther from 0), we use the term “variable” to describe the meta-analytic estimate. We use the term “generalizable” to refer to meta-analytic estimates whose credibility value range did not include zero (i.e., if another primary study were conducted it would be likely that its effect size would fall within the reported credibility value range). “Inconsistent” is used to denote cases where a construct in one domain (e.g., fluid ability) displayed surprisingly different relations with multiple, similar constructs in another domain (e.g., neuroticism and negative affect). “Uniform” is used to describe relations where a construct in one domain displayed similar relations to multiple constructs in another domain.

Since Project Talent was the largest contributing source for several meta-analyses, we provide versions of the results tables with and without Project Talent data (for the latter see Supplementary Tables 100–196 and 276–354 in Appendices H and J as well as the “Impact of extremely large studies” section in Chapter 9). In this context, it is worth noting that when the number of contributing effect sizes is small (e.g., fewer than 10) SDp̂ will be biased. Therefore, in analyses where K is small, we cannot tell whether any potential changes in the estimated correlation are due to second-order sampling error or to the influence of a potential outlier.

Distillation of Our Methodology

Thousands of primary studies have investigated the relations between personality traits and cognitive abilities (Busato et al., 2000; Chamorro-Premuzic & Furnham, 2008; Conn & Ricke, 1994), and a few meta-analyses have summarized relations at a broad level (Ackerman & Heggestad, 1997; Anglim et al., 2022; von Stumm & Ackerman, 2013; Wolf & Ackerman, 2005).5 The research presented in this volume meta-analytically examined 97 cognitive abilities, more than 85% of which were not included in previous meta-analyses. Similarly, developments in personality models during the past decade and a half have led to a more-fine-grained, hierarchical taxonomy of the personality domain (Stanek & Ones, 2018). Our meta-analyses examine 79 personality constructs, more than a third of which were not included in previous meta-analytic investigations. The present set of analyses thus considers co-variation across the broad and deep hierarchies of personality constructs and cognitive abilities (e.g., facets vs. broader factors) (Cronbach, 1960; Cronbach & Gleser, 1965; Ones & Viswesvaran, 1996; Shannon & Weaver, 1949; Wittmann & Süß, 1999).

We investigated 3,543 relations in all, most (93%) of which have not previously been meta-analytically examined. Each of these new and unique meta-analyses contributes to the scientific literature as well as to basic and applied disciplines that utilize personality and cognitive ability constructs and measures in their theories, research, and applications.

Complete and detailed quantitative results for this study’s meta-analyses, including point estimates of magnitudes and their associated credibility intervals are reported in Supplementary Tables 3–354. Figures 7–17 in Chapters 4 and 5 visualize these results. Readers interested in relations between specific cognitive ability-personality pairs are directed to these detailed materials and to the interactive webtool for a summary visualization (stanek.workpsy.ch/interactivewebtool). The online supplementary materials of this book (Appendices A, B, C, and D) also contain definitions of each cognitive ability and personality trait as well as their respective measures.

The following chapters focus on the most notable findings that highlight cross-domain constellations of traits based on statistically robust results (i.e., at least 1,000 individuals or 10 effect sizes). In a few cases, we also review corroborative findings supporting a noteworthy trend.

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Endnotes

1 Our search strategies originated in the first author’s doctoral dissertation with the second author as advisor. The core database used in the current volume has been expanded and updated beyond what was presented in the dissertation.

2 The compendia provided with the current volume have been further expanded and are the most up-to-date, as of October 2022, for substantive and normal-range personality traits and cognitive abilities.

3 Compound ability measures that included variance from two or three primary abilities were also included.

4 We were unable to make corrections for range restriction due to limited reporting in primary studies. Most samples were not directly selected based on cognitive ability or personality. The relations we report would only be larger if corrected.

5 Schilling et al. (2021) also meta-analytically examined personality-ability relations with a focus on situational influences. These meta-analyses did not distinguish cognitive ability constructs, but instead focused on item content, such as written/verbal measures, so their estimates could not be included or compared here.

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Chapter 4

How Cognitive Abilities Relate to Personality Traits

CHAPTER SUMMARY

Organizing the results according to cognitive ability dimensions offers a useful perspective.

General mental ability:

Is most closely, positively, and generalizably associated with openness and compound personality traits involving emotional stability, extraversion, and conscientiousness.

May be a hallmark of general cybernetic effectiveness (i.e., general fitness) along with self esteem and general factor of personality. These characteristics are foundational individual differences for agile and effective surviving and thriving.

Is lower among individuals who are depressed, anxious, uneven tempered, or suspicious.

Correlates negatively and non-negligibly with agreeableness’ modesty facet.

Fluid abilities:

Induction and quantitative reasoning have sizable, positive correlations with personality constructs associated with behavioral activation and proactivity.

Are negatively related to the orderliness aspect of conscientiousness.

Have differential patterns of relations with agreeableness and extraversion constructs, depending on the specific ability.

Induction and quantitative reasoning specific abilities are both negatively related to depression and associated neuroticism facets.

Show notably positive relations with openness, especially its intellect aspect, facets most related to intellect (i.e., need for cognition, ideas, and curiosity), and compound personality traits involving intellect (e.g., creative personality).

Short term memory abilities:

Show consistent, small, positive relations with agreeableness and extraversion traits.

Are negatively correlated with depression-related traits.

Are related to openness traits, but relations vary by facet.

Memory span and working memory capacity specific abilities have differing patterns of relations with conscientiousness constructs.

Have modest, positive relations with compound personality traits reflecting variance from combinations of emotional stability, extraversion, and conscientiousness.

Long term storage and retrieval abilities:

Learning efficiency abilities are generally unrelated to personality traits.

Retrieval fluency abilities are positively related to global extraversion and its aspects as well as to its dominance facet.

Retrieval fluency abilities are negatively correlated with neuroticism facets depression, anxiety, and uneven tempered.

Retrieval fluency abilities are positively related to self esteem.

Several retrieval fluency abilities have negative, albeit weak, associations with conscientiousness’ cautiousness facet.

Retrieval fluency abilities have relatively uniform, positive correlations with openness, as well as with its aspects and facets.

Visual processing abilities:

Are consistent, positive correlates of the compound personality trait of rugged individualism.

Show some modest, positive relations with compound personality traits that involve behavioral activation, drive, and proactivity.

Are less related to neuroticism and its aspects and facets than are other major ability constructs, including fluid abilities.

The specific ability visualization is a mild, negative correlate of the modesty facet of agreeableness.

Are more closely related to the intellect aspect and associated facets of openness than its experiencing aspect and associated facets.

Processing speed abilities:

Display positive relations with industriousness as well as with the orderliness aspect of conscientiousness.

Are negatively related to uneven tempered, suspiciousness, depression, and anxiety facets of neuroticism.

Tend to correlate positively with the agreeableness facet cooperation.

Are related to the factor alpha meta-trait and stress tolerance compound trait.

Are positively related to extraversion facets sociability and activity.

Specific processing speed abilities are consistently, homogenously, and positively related to self esteem.

Specific processing speed abilities also positively relate to ambition.

May correlate positively with openness’ experiencing aspect at a similar or stronger magnitude than its intellect aspect.

Acquired knowledge:

Across dimensions of acquired knowledge, activity was a substantial positive correlate.

Ambition was a positive correlate of quantitative and verbal acquired abilities.

Quantitative abilities appear to have negative associations with all Big Five personality factors, except openness.

Mathematics achievement correlates negligibly with the Big Five personality factors at the global level, though it correlates positively with proactive tendencies (e.g., self esteem, internal locus of control, and ambition).

Verbal abilities are negatively and substantially related to the uneven tempered and suspiciousness facets of neuroticism.

Verbal abilities generally relate positively and substantially to conscientiousness’ industriousness aspect and order facet.

Verbal abilities are positively related to extraversion’s sociability.

Comprehension knowledge verbal abilities correlate more strongly with the facets aligned with the intellect aspect of openness than with the facets aligned with its experiencing aspect.

Domain specific knowledge is positively related to openness, but magnitudes of relations are inconsistent.

Domain specific knowledge has small to negligible associations with global neuroticism.

The relations between cognitive abilities and personality traits are not limited to general mental ability and openness. Instead, several other cognitive abilities show relations with personality traits that are of even greater magnitude. These relations are likely the result of neuro-biological networks that drive mechanical models of information processing. The meta-analyses in this book focus on the manifestations of these models as measured by psychometric cognitive ability tests and personality assessments.

This chapter describes our meta-analytic findings for how cognitive abilities relate to personality traits. We organize findings around the Stanek and Ones (2018) Unified CHC cognitive abilities taxonomy. Definitions of each cognitive ability construct and personality trait may be found in Appendices A and C. By reviewing findings from the perspective of cognitive abilities, we hope that intelligence researchers will come to recognize abilities that are linked to personality and understand the importance of ability-personality clusters in psychological research and applications.

We first present non-invested abilities, including domain-independent general capacities: fluid abilities, short term memory, long term storage abilities, and long term retrieval abilities. Next, we report sensory-motor domain specific abilities of visual processing and auditory processing abilities. Finally, empirical relations with the speed abilities of reaction and decision speed as well as processing speed are discussed. For each of these ability clusters, we present and describe results for specific abilities that constitute the higher order primary abilities (e.g., induction, general sequential reasoning, and quantitative reasoning for fluid abilities).

We then turn to invested abilities involving acquired knowledge: verbal abilities, quantitative abilities, and domain specific knowledge. For verbal abilities, we distinguish between reading and writing abilities and comprehension knowledge abilities. For quantitative abilities, we distinguish between mathematics knowledge and mathematics achievement. For domain specific knowledge, we delineate sub-dimensions by content (e.g., social science knowledge vs. mechanical knowledge). For each major acquired knowledge ability, we also report and discuss findings for its specific abilities (e.g., reading comprehension within reading and writing, lexical knowledge within comprehension knowledge).

We close by describing findings for general mental ability, and in tables we also report findings for compound ability measures that combine multiple specific abilities (e.g., fluid ability and visual processing).

Tables reporting full meta-analytic results for cognitive abilities are presented in Supplementary Tables 3–99 (see Appendix G in the online supplementary materials). These tables provide uncorrected correlations as well as those corrected for unreliability. None of the meta-analyses presented have applied range restriction corrections. Each table is devoted to a distinct cognitive ability (e.g., induction) and its associations with the full set of personality variables examined. The indentation of each construct name within the table reflects the altitude of each trait (e.g., factor, aspect, facet).

This chapter and its associated tables and figures describe 3,543 meta-analytic relations of 97 cognitive abilities with 79 personality constructs.

Overviews of results are visualized in Figures 7 through 11. In these visualizations, p̂ estimates (i.e., meta-analytic correlations) are reported in black type if the number of independent effect sizes contributing the specific meta-analysis was greater than or equal to 10 or if the effect was based on at least 1,000 participants. Otherwise, the estimates are in grey type. Grey-filled cells had no usable data in our meta-analytic database. Green-filled cells indicate positive effect sizes, and red-filled cells indicate negative effect sizes. Fill saturation indicates effect size magnitude.

In our description and discussion of findings, we highlight the main findings and trends rather than noting every single meta-analytic relation examined. We encourage readers who are interested in certain abilities, primary and specific, to examine the detailed results tables.

When interpreting findings, the reader should keep in mind that relations around .10 indicate effect sizes that are

small…but potentially more ultimately consequential, an effect-size r of .20 indicates a medium effect that is of some explanatory and practical use even in the short run and therefore even more important, and an effect-size r of .30 indicates a large effect that is potentially powerful in both the short and the long run. (Funder & Ozer, 2019)

Non-Invested Abilities and Personality

Domain Independent General Capacities

Fluid Abilities

Fluid abilities involve solving unfamiliar problems that demand abstract reasoning rather than prior knowledge. Specific fluid abilities include induction, general sequential reasoning, and quantitative reasoning. Induction focuses on identifying underlying rules/patterns, while general sequential reasoning emphasizes applying known rules to draw conclusions (Stanek & Ones, 2018). As a set, these abilities are likely involved in multiple adaptive psycho-social, cognitive, and behavioral strategies that contribute to general fitness in modern environments. Specific fluid abilities can be involved in both ensuring stability and promoting change, potentially highlighting functional and/or developmental pathways.

Figure 7 summarizes results for fluid abilities. The indentation of each construct name in this and subsequent figures reflects the altitude of each trait (e.g., factor, aspect, facet). Complete, detailed results are presented in Supplementary Tables 4–7 (see Appendix G).

Figure 7. Domain independent general capacities correlate with personality traits.

Note.“cd.” denotes compound traits. Meta-analytic correlations (i.e., p̂) are only reported in black type when the number of independent effect sizes contributing the specific meta-analysis was ≥ 10 or the sample was ≥ 1,000 participants. Grey-filled cells had no usable data. Green-filled cells indicate positive effects, and red-filled cells indicate negative effects. Saturation indicates effect magnitude. Indentation of construct labels reflects the altitude of each trait in its respective construct hierarchy.

Induction and quantitative reasoning have sizable, positive correlations with the Big Five factors, facets, and compound personality constructs associated with behavioral activation and proactivity. Among fluid abilities, induction had the largest positive relations with personality attributes of behavioral activation. Such traits include internal locus of control1 (p̂ = .20, N = 14,820, K = 24), self esteem2 (p̂ = .13, N = 420,227, K = 133), creative personality3 (p̂ = .21, N = 3,264, K = 11), and achievement via independence4 (p̂ = .24, N = 4,867, K = 22).5 The latter two correlates of induction also reflect the intellect aspect of openness in addition to extraversion and conscientiousness (primarily the achievement facet), but the relation with creative personality was not generalizable (lower CV = -.07). Induction was also substantially related to compound personality traits that had elements of high extraversion and conscientiousness alongside low neuroticism. For example, induction correlated .13 with the industriousness aspect of conscientiousness and .14 with the activity facet of extraversion (N = 450,608 and 388,190, K = 73 and 87, respectively). Overall, individuals who have strong induction capabilities tend to be behaviorally and emotionally positively activated. Specifically, they are active and industrious. They also have higher self esteem and internal locus of control.6 Finally, they tend to achieve most in unstructured situations that allow for individual initiative and freedom rather than situations requiring that set procedures be followed. These clusters of traits correspond to a “high-energy” setting in individuals that is likely best suited to resource-rich environments.

While general sequential reasoning was much less related to these personality constructs than induction, there were two entirely different personality traits that related positively and generalizably to general sequential reasoning. The non manipulative facet of agreeableness and compound trait of cold efficiency7 correlated .13 and .14 with general sequential reasoning, respectively (N = 2,254 and 7,630, K = 18 and 25). Individuals who score high on general sequential reasoning appear to be principled, logical, and rational as well as not typically deceitful or deceptive. Knowing that general sequential reasoning measures deductive abilities provides insight into the cool, direct, and methodical problem-solving function of this cluster of traits.

Quantitative reasoning’s relations mirrored those of induction rather than general sequential reasoning. The enthusiasm aspect and activity facet of extraversion, as well as some of the same high extraversion, high conscientiousness, and low neuroticism compound traits (e.g., self esteem,8 internal locus of control,9 and optimism10), correlated positively and generalizably with quantitative reasoning (mean11 p̂ = .17, range = .10 to .21, N range = 16,902 to 52,212, K range = 16 to 34). In addition, quantitative reasoning’s positive association with rugged individualism12 was also indicative of the same personality trait amalgam (p̂ = .15, N = 10,822, K = 16). Finally, quantitative reasoning’s positive associations with both factor beta13 (p̂ = .18, N = 2,085, K = 11) and factor alpha14 (p̂ = .12, N = 55,414, K = 17) likely capture the same core, proactive variance shared across the personality constructs of extraversion, conscientiousness, and (low) neuroticism.

Overall, these meta-analytic findings substantiate a set of personality traits associated with induction and quantitative reasoning that drive behavioral activation and pursuit of development and growth to support the capacity of individuals for change and self-evolution. Put differently, those who are more likely to take action and have the ability to identify and apply patterns tend to be able to change themselves more to fit changing environments. In contrast, those higher on deductive general sequential reasoning may achieve the same goals with cool and earnest methodicalness.

Fluid abilities are negatively related to the orderliness aspect of conscientiousness. Orderliness encompasses organization, caution, discipline, and precision, whereas order focuses more narrowly on planning and organization (Stanek & Ones, 2018). All specific fluid abilities were similarly related to orderliness. Induction correlated -.12 with orderliness and -.21 with routine seeking15 (N = 199,564 and 5,030, K = 113 and 16, respectively). General sequential reasoning correlated -.12 with order and -.14 with cautiousness (N = 8,685 and 12,720, K = 36 and 38, respectively). Quantitative reasoning correlated -.17 with orderliness and -.16 with judging-perceiving,16 a compound attribute that can be described as a desire for definiteness or a preference for structure versus flexibility (N = 77,752 and 6,012, K = 21 and 9, respectively). A few exceptions notwithstanding, the consistent pattern and magnitude of these relations indicates that individuals who are high on fluid abilities might be better equipped to handle disorder well, particularly lack of routine, lack of plans/protocols, and disorganization. That is, as proposed by the compensation hypothesis (Moutafi et al., 2004), individuals with higher fluid ability may not need the structure and scaffolding produced by orderliness-related traits to self-regulate toward goal states. Another interpretation of orderliness-related traits’ negative relations with fluid abilities involves their differential functional foci: fluid abilities may best support change-focused strategies whereas orderliness (aspect) and order (facet) may best support stability-focused strategies. Their negative association points to a trait complex that suggests a compensatory mechanism built upon the trade-off between change and stability.

Different fluid abilities relate to different agreeableness and extraversion constructs. For induction and quantitative reasoning, relations with agreeableness and extraversion were mildly negative. Induction correlated -.12 with the politeness aspect and -.11 with the tender mindedness facet of agreeableness (N = 73,945 and 28,968, K = 11 and 62, respectively). Quantitative reasoning displayed a negative relation with the agreeableness-related compound trait warmth17 as well as the sociability facet of extraversion (p̂ = -.12 and -.13, N = 82,733 and 84,335, K = 24 and 44, respectively). In contrast, general sequential reasoning was mostly unrelated to agreeableness and extraversion constructs. As noted above, an exception was a positive correlation with the non manipulative facet of agreeableness (p̂ = .13, N = 2,254, K = 18), which may be useful for maintaining self in social environments. This relation is consistent with general sequential reasoning’s broader role in following/applying rules to aid self-preservation.

Specific fluid abilities induction and quantitative reasoning are both negatively related to depression and associated neuroticism facets. Induction correlated -.13 with neuroticism constructs depression, negative affect, and withdrawal (see Supplementary Tables 4–7 in Appendix G). Quantitative reasoning correlated -.25 with the depression facet of neuroticism (N = 51,201, K = 24, CV = -.12 to -.38). All of these relations were generalizable. Data were sparse for general sequential reasoning, but they also suggested the same negative relations. It may be that depression detracts from these specific fluid reasoning abilities (e.g., when psychological stability cannot be maintained and emotional dysregulation interferes with effective reasoning) or that individuals who are lower on these fluid abilities have greater proclivity to be depressed (e.g., when lower fluid abilities are not sufficient to regulate emotions). Evolution offers a less pathological explanation. It may be that when survival is the goal and the environment is resource-scarce or unsupportive, traits that correspond to low energy usage (e.g., depression, lower fluid reasoning) may actually be adaptive.

Like most abilities, fluid abilities show notable, positive relations with openness, especially its intellect aspect and associated facets (i.e., need for cognition, ideas, and curiosity) as well as compound personality traits involving it. Induction had a mean meta-analytic correlation of .17 with the intellect aspect and aligned facets of openness. The corresponding mean meta-analytic correlations for general sequential reasoning and quantitative reasoning were .21 and .19, respectively. In contrast, a few other traits that are more closely aligned with openness’ experiencing aspect rather than its intellect aspect showed small, negative relations (e.g., aesthetics with quantitative reasoning [p̂ = -.15, N = 73,152, K = 8], tolerance18 with general sequential reasoning [p̂ = -.09, N = 7,491, K = 29], and experiencing with quantitative reasoning [p̂ = -.07, N = 3,402, K = 11]). This constellation of traits may energize the individual to change by identifying that change is needed and activating mechanisms to reason out what actions to take.

Short Term Memory Abilities

Short term memory involves encoding, maintaining, and manipulating information in immediate awareness (McGrew et al., 2014). Sub-dimensions include memory span, working memory capacity, attentional executive control, and meaningful memory. Short term memory abilities’ relations with personality variables have not been previously meta-analyzed, and the results presented here indicate positive associations with interpersonal traits from the agreeableness and especially extraversion domains as well as negative associations with depression-related traits.

Figure 7 summarizes results for short term memory constructs. Complete, detailed results are presented in Supplementary Tables 24–27 (see Appendix G).

Short term memory abilities show consistent, small, positive relations with agreeableness and extraversion traits. Both agreeableness and extraversion are interpersonal traits. Agreeableness and its facets tender mindedness and non manipulative correlated in the .10 to .11 range with memory span. Relations with some extraversion traits were somewhat stronger. Memory span correlated .17 with extraversion’s enthusiasm aspect (N = 43,618, K = 32), .13 with its activity facet (N = 379,981, K = 19), .12 with its positive emotionality facet (N = 5,805, K = 20), and .12 with its sociability facet (N = 364,141, K = 23).

Working memory capacity echoed the positive associations between memory span and both extraversion and agreeableness. Its correlations were positive and homogenous with compound personality traits involving extraversion, agreeableness, or both, such as optimism19 (p̂ = .13, N = 10,828, K = 7, SD .00), trust20 (p̂ = .17, N = 941, K = 11, SD .00), and openness to emotions21 (p̂ = .10, N = 1,109, K = 12, SD .00). The consistency of these relations is remarkable. Working memory capacity also correlated .14 with enthusiasm (N = 63,416, K = 23). One puzzling null finding, however, involved warmth,22 which only correlated .05 (N = 8,840, K = 18) with working memory capacity.

There may be several explanations for the generally modest, but positive and consistent, associations between short term memory constructs and compound personality constructs involving extraversion and agreeableness. For example, having a greater memory span may facilitate the demonstration of extraverted and agreeable behaviors and tendencies by allowing individuals to actively engage with others, remember names of group members, or echo someone else’s emotional frame of mind. Alternatively, extraverts and those high on agreeableness may invest more time and effort into developing their memory span to facilitate engagement with other people. Either way, the function of this trait complex is likely to maintain the individual in social environments.

Short term memory abilities are negatively correlated with depressive personality traits. Most abilities’ relations with neuroticism, especially its withdrawal aspect and depression facet, tended to be negative. This was also true for short term memory. Memory span correlated -.19 (N = 46,779, K = 36) with the depression facet and -.12 (N = 12,475, K = 8) with the withdrawal aspect of neuroticism. Memory span’s relation with the uneven tempered facet of neuroticism was also negative and sizable (p̂ = -.17, N = 371,911, K = 32). Memory span correlated -.27 with the suspiciousness facet of neuroticism, though only seven samples were available to examine this correlation (N = 2,300). Remarkably, neither memory span nor working memory capacity related strongly to the anxiety facet of neuroticism (p̂ = -.05 and -.09, N = 8,434 and 8,552, K = 31 and 25, respectively). Paralleling results for memory span, the depression facet and withdrawal aspect of neuroticism were also notable, negative correlates of working memory capacity (p̂ = -.19 and -.14, N = 52,223 and 11,494, K = 18 and 11, respectively). In contrast, the uneven tempered facet was not (p̂ = -.05, N = 2,930, K = 17). This constellation of short term memory abilities and depressive personality traits suggests potentially pathological psychological instability involving withdrawal of cognitive resources from short term memory storage and retrieval alongside psychological disengagement. Such a trait constellation can be an adaptive low-energy strategy for coping with unsupportive or resource-poor environments.

Short term memory is related to openness traits, but relations vary by facet. Memory span and working memory capacity both correlated positively with global openness (p̂ = .19 and .12, N = 37,229 and 15,245, K = 61 and 36, respectively). Their relations with the ideas facet were also positive and of similar magnitude (p̂ = .19 and .25, N = 2,471 and 1,626, K = 19 and 15, respectively). Memory span’s relation with the experiencing aspect of openness was also of similar magnitude (p̂ = .22, N = 365,232, K = 12). Yet relations were negligible or much smaller with the facets associated with the experiencing aspect of openness: .00 with fantasy, .07 with aesthetics, -.04 with non traditional, and .10 with variety seeking (see Supplementary Tables 24–27 in Appendix G). A similar, but not identical, pattern of results emerged for working memory capacity. Working memory capacity’s relations were .12 with global openness, .25 with the ideas facet, and .16 with need for cognition, the latter two being indicators of the intellect aspect of openness (see Supplementary Tables 24–27 in Appendix G). Working memory capacity’s relations were somewhat weaker for indicators of the experiencing aspect of openness: .15 with fantasy, and .11 with aesthetics (N = 1,090 and 1,079, K = 12 and 12, respectively). Relations with non traditional and variety seeking facets tended toward positive but negligible (p̂ = .08 for both, N = 3,528 and 2,923, K = 15 and 19, respectively).

Short term memory abilities of memory span and working memory capacity have differing patterns of relations with conscientiousness constructs. Memory span’s relations with conscientiousness were positive and varied by conscientiousness facet. Its relations with the industriousness aspect, as well as dependability and order facets, were in the .15 to .21 range, whereas its relations with other conscientiousness facets (e.g., achievement, persistence, cautiousness) were much smaller, in the -.04 to .09 range. Surprisingly, working memory capacity’s relations with conscientiousness constructs were negligible to small in magnitude (p̂ = -.01 to .08). Short term memory abilities’ positive relations with some conscientiousness constructs may signal their co-functioning for maintenance of self in the environment (i.e., ability to maintain homeostasis despite changing circumstances).

Short term memory has modest, positive relations with compound personality traits reflecting variance from emotional stability, extraversion, and conscientiousness trait combinations. Such a finding was unsurprising since emotional stability, extraversion, and conscientiousness also display small but persistent positive relations with short term memory constructs. Memory span related to both self esteem23 and internal locus of control24 (p̂ = .15 and .13, N = 390,216 and 84,015, K = 40 and 42, respectively). Working memory capacity correlated .07 and .11 with self esteem and internal locus of control (N = 8,496 and 61,986, K = 16 and 19, respectively). Lastly, the meta-trait factor alpha25 also related positively though modestly to both memory span and working memory capacity (p̂ = .11 and .13, N = 21,609 and 7,370, K = 18 and 11, respectively). Considering the meaning of its constituent traits and abilities, this constellation of associated individual differences reflects adaptability—a general fitness for both self-preservation and change.

Long Term Storage & Retrieval Abilities

Long term memory involves two clusters of abilities: learning efficiency and retrieval fluency. Learning efficiency refers to the processing and storage of information beyond immediate awareness/working memory. Learning efficiency sub-dimensions include associative memory, meaningful memory, episodic memory, free recall memory, and long term visual memory. Retrieval fluency refers to the ability to access information stored in long term memory. Retrieval fluency sub-dimensions include abilities related to the production of ideas (e.g., expressional fluency), word retrieval (e.g., word fluency), and figural fluency (i.e., figural fluency). In a comprehensive meta-analysis, Kostal (2019) found evidence that these two ability clusters are distinct, supporting the call of Schneider and McGrew (2018) and Stanek and Ones (2018) for their distinction as primary abilities.

The current meta-analytic results also help clarify questions regarding the taxonomic structure of cognitive abilities. For example, Schneider and McGrew (2012) classified free recall memory as part of the learning efficiency cluster within long term storage and retrieval but, the pattern of relations observed in the current meta-analyses indicates that this ability may nomologically be part of the retrieval fluency cluster.

Figure 7 summarizes results for long term storage and retrieval constructs. Complete, detailed results are presented in Supplementary Tables 9–23 (see Appendix G).

Relations of learning efficiency and retrieval fluency with personality constructs were markedly different from one another. Notwithstanding the few exceptions noted below, learning efficiency abilities were mostly unrelated to personality constructs. In contrast, retrieval fluency abilities had substantial, positive links with personality traits.

Individuals higher on retrieval fluency abilities tended to be higher on extraversion’s dominance facet, assertiveness and enthusiasm aspects, self esteem,26 and openness traits as well as lower on conscientiousness’ cautiousness facet and neuroticism’s depression, anxiety, and uneven tempered facets. These findings repeat a pattern observed across many abilities: positive relations between cognitive abilities and personality traits associated with initiating adaptation to the environment as well as growth in individual complexity. Put differently, individuals who can come up with many ideas, are confident in themselves, and are socially driven tend to see many possibilities, which fuels their inner growth and their ability to fit with their environment.

Learning efficiency abilities are generally unrelated to personality traits. This was true for overall measures of learning efficiency as well as associative memory, meaningful memory, and long term visual memory. While relations hovered between -.06 and .06 for many personality constructs examined with these abilities, two sets of exceptions stood out.27 First, associative memory, meaningful memory, and episodic memory were all positively and notably correlated with openness (p̂ = .22, .23, and .11, respectively; Supplementary Tables 9–23 in Appendix G). Second, episodic memory displayed mild, positive relations with agreeableness and conscientiousness (p̂ = .11 and .12, N = 3,674 and 3,671, K = 6 and 6, respectively). Associative memory had inconsistent relations with conscientiousness-related constructs: it correlated -.11 with conscientiousness but .13 with the internal locus of control compound trait28 (N = 3,085 and 17,892, K = 5 and 3, respectively). Given the small number of primary studies contributing to these meta-analytic estimates, we refrain from drawing firm conclusions from these exceptions to the pattern of generally negligible relations between learning efficiency and personality.

Retrieval fluency abilities are positively related to global extraversion as well as its aspects and dominance facet. This constellation of associated traits and abilities reflects activation for change and ideation of potential next steps for action. Retrieval fluency and its components ideational fluency, associational fluency, expressional fluency, originality and creativity, naming facility and speed of lexical access, and word fluency all had positive, though modest, relations with global extraversion (p̂ = .13, .16, .10, .08, .12, .08, and .07, respectively; see Supplementary Tables 9–23 in Appendix G). However, for some of these relations, associated SDp̂s were large enough to result in wide credibility value ranges (see Supplementary Tables 9–23 in Appendix G). A more fine-grained examination of extraversion’s links to retrieval fluency measures was thus needed to help clarify the nature of these relations.

Results indicated that the enthusiasm aspect of extraversion was a consistent, positive correlate of several retrieval fluency abilities centered on verbal retrieval. These abilities included associational fluency, naming facility and speed of lexical access, and word fluency (p̂ = .18, .18, and .15, respectively; see Supplementary Tables 9–23 in Appendix G). Data were more sparse for the assertiveness aspect but indicated a similar pattern (mean p̂ = .13). In addition, extraversion’s dominance facet consistently displayed relatively homogenous, positive relations with retrieval fluency abilities: .16 with ideational fluency, .14 with expressional fluency, .14 with word fluency, .09 with associational fluency, and .09 with originality and creativity (see Supplementary Tables 9–23 in Appendix G). For both ideational fluency as well as originality and creativity, relations with most extraversion facets were positive and modest to sizeable (e.g., p̂ = .25 and .20 for relations with activity, N = 3,788 and 361,957, K = 6 and 9, respectively). One clear conclusion is that retrieval fluency abilities are not independent of extraversion constructs. These relations were not identified in previous meta-analyses, which were based on smaller amounts of data and less precise construct taxonomies.

Retrieval fluency abilities are negatively correlated with neuroticism facets depression, anxiety, and uneven tempered. The covariance of depression, anxiety, and uneven tempered with retrieval fluency abilities was evident. Where sufficient data were available, robust and negative relations were detected. Retrieval fluency abilities of naming facility and speed of lexical access, free recall memory, ideational fluency, and word fluency correlated negatively with neuroticism’s depression facet (p̂ = -.22, -.17, -.13, and -.10, respectively; see Supplementary Tables 9–23 in Appendix G). Anxiety correlated -.10 and -.11 with ideational fluency and word fluency (N = 5,507 and 1,477, K = 12 and 12, respectively). In addition, originality and creativity ability had a substantial, negative correlation with the uneven tempered facet of neuroticism (p̂ = -.25, N = 363,640, K = 12). Withdrawal had substantial relations similar to these facets (mean p̂ = -.13). In other words, individuals who are higher on retrieval fluency appear also to be less depressed, withdrawn, anxious, and uneven tempered. The negative relations between retrieval fluency abilities and these neuroticism traits reflects a fitness function for achieving psychological stability and internal homeostasis. In other words, when stability is the adaptive goal, the ability to generate many original ideas is less useful and tendencies to worry or withdraw are more useful.

Retrieval fluency abilities are positively related to self esteem. As we previously noted, self esteem is a compound personality trait that incorporates variance from both emotional stability and extraversion, along with conscientiousness. Notable relations of retrieval fluency abilities with extraversion and emotional stability were noted above. It was therefore not surprising that meta-analytic results showed positive relations between self esteem and several retrieval fluency abilities. Specifically, originality and creativity, expressional fluency, ideational fluency, word fluency, and associational fluency all had positive correlations with self esteem (p̂ = .22, .18, .12, .10, and .09, respectively; see Supplementary Tables 9–23 in Appendix G). In all these cases, there was little variation in the size of these relations for each set of effect sizes. That is, self esteem’s positive association with retrieval fluency abilities appeared to be quite homogenous for each meta-analysis.

Several retrieval fluency abilities have negative, albeit weak, associations with conscientiousness’ cautiousness facet. Although conscientiousness constructs were mostly independent of retrieval fluency abilities, cautiousness stood out as a negative, albeit weak, correlate. Ideational fluency, originality and creativity, as well as expressional fluency correlated -.09 to -.12 with cautiousness. Such associations may reflect several potential developmental possibilities: the lower inhibitions associated with low cautiousness may assist in the development of retrieval abilities, higher cautiousness may inhibit development or utilization of retrieval abilities, high retrieval abilities may reduce cautiousness, and low retrieval abilities may encourage cautiousness. More than one of these causal explanations could even act at the same time, all avenues future research could pursue.

Retrieval fluency abilities have relatively uniform, positive correlations with openness, as well as its aspects and facets. This constellation of associated traits and abilities encapsulates characteristics for behavioral activation for change. The mean meta-analytic correlation between retrieval fluency and the openness factor, aspects, and facets (regardless of N or K) was .19. Using the same method for the broader set of openness-related traits (see Figures 25 and 26 in Chapter 8 for lists),29 similar mean meta-analytic correlations were found for specific abilities: free recall memory (p̂ = .19), ideational fluency (p̂ = .16), associational fluency (p̂ = .16), word fluency (p̂ = .16), expressional fluency (p̂ = .15), and originality and creativity (p̂ = .14). There did not appear to be particularly notable differences between how openness’ intellect and experiencing aspects and their associated facets related to retrieval fluency abilities, as credibility value ranges overlapped. Since judging/perceiving is a compound personality attribute that includes variance from both low openness and high conscientiousness, it was not surprising that it correlated negatively with several retrieval fluency abilities (p̂ = -.17 for expressional fluency, -.12 for associational fluency, and -.13 for ideational fluency but only -.07 for originality and creativity; see Supplementary Tables 9–23 in Appendix G).

Sensory-Motor Domain Specific Abilities

Visual Processing Abilities

Visual processing abilities revolve around the ability to simulate mental imagery to solve problems. This cluster of abilities has also been referred to as spatial ability in the literature. It includes specific abilities such as closure speed and spatial scanning. Visualization scales (e.g., shape rotation) appear to be the best markers for visual processing abilities (Carroll, 1993; Kostal, 2019).

Figure 8 summarizes results for visual processing constructs. Complete, detailed results of results are presented in Supplementary Tables 28–34 (see Appendix G).

Figure 8. Sensory-motor domain specific abilities correlate with personality traits.

Note. “cd.” denotes compound traits. Meta-analytic correlations (i.e., p̂) are only reported in black type when number of independent effect sizes contributing the specific meta-analysis was ≥ 10 or the sample was ≥ 1,000 participants. Grey-filled cells had no usable data. Green-filled cells indicate positive effects, and red-filled cells indicate negative effects. Saturation indicates effect magnitude. Indentation of construct labels reflects the altitude of each trait in its respective construct hierarchy.

Visual processing abilities are consistent, positive correlates of the compound personality trait of rugged individualism. Rugged individualism30 has traditionally been referred to as masculinity in the personality literature, because it refers to traits stereotypically associated with males, such as adventurousness, competitiveness, and preferences for exploration and independence. Across specific visual processing abilities, regardless of N or K, the mean meta-analytic correlation with rugged individualism was .12, which has not previously been noted. Visualization specifically correlated .14 with rugged individualism (N = 7,148, K = 19). It is well known that, among all cognitive abilities, visual processing abilities show the largest sex differences (Kell & Lubinski, 2013; Lubinski & Benbow, 1992; Reilly & Neumann, 2013). Their positive relations with rugged individualism provide evidence of a complex of sex-related personality attributes and visual processing abilities, further suggesting sex-based developmental trajectories.

Visual processing abilities display some modest, positive relations with compound personality traits that involve behavioral activation, drive, and proactivity. As previously discussed, tendencies toward behavioral activation, drive, and proactivity involve low neuroticism, high extraversion, and conscientiousness. Although data were sparse, there were several relations supportive of a positive link between visual processing abilities and compounds of these traits. Visualization correlated with self esteem31 .12 (N = 397,889, K = 42). Spatial scanning correlated with locus of control32 .16 (N = 21,594, K = 11). The mean meta-analytic correlation between all visual processing abilities and achievement via independence33 was .22. While neither extraversion nor conscientiousness by themselves were notable correlates of visual processing abilities, both (low) neuroticism and (high) openness were (see below).

Visual processing abilities are more weakly related to neuroticism and its aspects and facets than other major ability constructs, including fluid abilities. All relations with neuroticism traits were negative but weaker than those found for other abilities. Therefore, visual processing does not appear to play a central role in conferring psychological stability. Neuroticism facets depression, uneven tempered, and suspiciousness all correlated -.11 to -.12 with visualization (see Supplementary Tables 28–34 in Appendix G). These relations were quite homogeneous, with associated SDp̂s between .03 and .07. Relations between other visual processing abilities and neuroticism constructs were of similar magnitudes, but small numbers of effect sizes and/or sample sizes constrained definitive conclusions. The mean meta-analytic correlation between all visual processing abilities and the neuroticism factor, aspects, and facets was -.08 (regardless of N or K), similar to some other primary ability clusters (e.g., -.14 with fluid abilities, -.12 with processing speed abilities).34

Visualization has a mild, negative correlation with the modesty facet of agreeableness. Visual processing abilities in general, and visualization in particular, were mostly uncorrelated with agreeableness, its aspects, and its facets. Relations were nil, negligibly small, or heterogeneous enough to produce credibility value ranges that contained 0. However, one agreeableness facet stood out as a homogenous, if humble, negative correlate of visualization: modesty (p̂ = -.12, N = 970, K = 11). A similar correlation was also observed for the general visual processing construct (p̂ = -.11, N = 1,225, K = 10).

Visual processing abilities are more closely related to the intellect aspect and associated facets of openness than its experiencing aspect and associated facets. As noted above, visual processing abilities are less related to openness traits compared to other major abilities. Nonetheless, differential relations with openness constructs were found. Visual processing abilities and intellect aspect traits’ relations reflect a functional cluster for cognitive exploration to identify and initiate adaptations to the environment. Visualization correlated .20 with intellect as well as .24 with the ideas and .21 with curiosity facets (see Supplementary Tables 28–34 in Appendix G). In contrast, visualization correlated just .02 with experiencing (N = 368,387, K = 28), .00 with introspection (N = 73,135, K = 10), .09 with fantasy (N = 2,599, K = 22), -.09 with aesthetics (N = 74,525, K = 24), .10 with non traditional (N = 16,742, K = 41), and .04 with variety seeking (N = 2,539, K = 17). The positive pairing of visualization with ideas and curiosity highlights an externally oriented trait complex, since both are directed at environmental cues. The same pattern held for closure speed and flexibility of closure, even though the number of contributing effect sizes and the sample sizes were much smaller. For example, the mean correlation across all meta-analytic estimates for intellect and associated facets with closure speed was .15, whereas the corresponding value for the experiencing aspect and aligned facets35 was .04.

Auditory Processing Abilities, Psychomotor Abilities, and Psychomotor Speed Abilities

Auditory processing encapsulates abilities related to hearing and deciphering auditory signals. Our meta-analytic database for auditory processing was very sparse. Auditory processing abilities, psychomotor abilities, and psychomotor speed abilities36 were only included in the meta-analyses reported here in an exploratory manner if they were encountered during searches for materials with other cognitive abilities. Therefore, we are not able to reach generalizable conclusions about the nature of relations between auditory processing abilities and personality traits. Based on the available information, negative relations with neuroticism facets and positive relations with openness-related traits were suggested.

Figures 8 and 9 summarize results. Complete, detailed results are presented in Supplementary Tables 35, 81–84, and 85–86, respectively (see Appendix G).

Speed Abilities

Processing Speed Abilities

Processing speed abilities involve the ability to perform relatively simple, mostly perception-based, repetitive tasks quickly and accurately. Specific abilities that are part of this primary ability are perceptual speed (including complex perceptual speed, scanning, and pattern recognition), reading speed, and number facility. Processing speed abilities present a nomological network with personality traits that is quite distinct from other primary abilities. Processing speed abilities had positive relations with conscientiousness, emotional stability, and agreeableness, highlighting a complex of traits focused more on preserving homeostasis than pursuing change.

Figure 9 summarizes results for processing speed abilities. Complete, detailed results are presented in Supplementary Tables 36–40 (see Appendix G).

Figure 9. Speed abilities correlate with personality traits.

Note. “cd.” denotes compound traits. Meta-analytic correlations (i.e., p̂) are only reported in black type when number of independent effect sizes contributing the specific meta-analysis was ≥ 10 or the sample was ≥ 1,000 participants. Grey-filled cells had no usable data. Green-filled cells indicate positive effects, and red-filled cells indicate negative effects. Saturation indicates effect magnitude. Indentation of construct labels reflects the altitude of each trait in its respective construct hierarchy.

Processing speed abilities relate similarly and positively to the industriousness aspect as well as the order facet of conscientiousness. Perceptual speed correlated with industriousness (p̂ = .17, N = 367,829, K = 34, SDp̂ = .03, CV = .14 to .21), persistence (p̂ = .15, N = 2,262, K = 17, SDp̂ = .00, CV = .15 to .15), and order (p̂ = .20, N = 362,327, K = 22, SDp̂ = .00, CV = .20 to .20). Scanning correlated .15 with industriousness (N = 434,549, K = 8, SDp̂ = .00, CV = .15 to .15). Number facility correlated .13 with orderliness (N = 2,642, K = 9, SDp̂ = .00, CV = .13 to .13). For all these relations, the associated variability was extremely small, and relations were generalizable. Number facility also had a generalizable, positive correlation of .25 with global conscientiousness (N = 34,605, K = 21, CV = .02 to .47). These findings indicate a stronger link between processing abilities and conscientiousness-related traits, especially for its industriousness aspect and order facet, than most other non-invested/non-crystallized abilities. This constellation of associated traits and abilities may contribute to individuals’ general subsistence by helping them accomplish goals through adherence to principles, paying attention to details, and by following set procedures and routines.

Processing speed abilities are negatively related to uneven tempered, suspiciousness, depression, and anxiety facets of neuroticism. This constellation of traits confers psychological stability. Processing abilities had negligible to weak, negative correlations with global neuroticism but somewhat stronger relations with the trait’s facets. For example, global neuroticism was a mild but consistent negative correlate of scanning, pattern recognition, and number facility (mean p̂ = -.11, p̂ range = -.11 to -.10, N range = 7,884 to 100,600, K range = 6 to 45). Relations with some neuroticism facets were somewhat stronger. For example, the suspiciousness facet of neuroticism consistently related to processing speed abilities (mean p̂ = -.16, range = -.22 to -.07 regardless of N or K). Processing abilities for which sufficient data were available also correlated negatively and similarly with the uneven tempered facet of neuroticism (mean p̂ = -.17, p̂ range = -.18 to -.15, N range = 362,127 to 434,318, K range = 12 to 42). Number facility correlated -.17 with the depression facet of neuroticism (N = 4,953, K = 8), but depression correlated only -.04, -.09, and -.06 with other processing speed abilities of perceptual speed, scanning, and pattern recognition, respectively (N = 6,530, 10,410, and 8,052, K = 21, 10, and 2). Scanning and number facility abilities correlated -.10 and -.13 with anxiety, respectively (N = 72,705 and 695, K = 8 and 7), yet perceptual speed was unrelated to anxiety (p̂ = .00, N = 5,912, K = 28). The dearth of relations between processing speed abilities and neuroticism facets precludes robust identification of generalizable links between these abilities and this part of the personality domain. Nonetheless, the conclusion that individuals who score higher on processing speed abilities tend to be slightly more even tempered, less suspicious, less depressed, and less neurotic is indicated by the available data.

Processing speed abilities tend to correlate positively with the agreeableness facets cooperation and lack of aggression. This constellation of traits is functional for maintaining self in the social environment. Perceiving stimuli (e.g., faces) quickly and accurately can enhance cooperation and reduce aggression. Lower aggression and higher levels of cooperation can free up cognitive resources for performing processing tasks well. Either way, the cooperation and lack of aggression facets of agreeableness showed some positive correlations with processing speed abilities. Specifically, perceptual speed correlated .10 with lack of aggression (N = 2,305, K = 15), and number facility correlated .20 with cooperation (N = 21,025, K = 8). However, the negligible correlation between perceptual speed and cooperation (p̂ = -.03, N = 4,304, K = 12) called into question the consistency of relations across specific processing speed abilities. In addition, relations with most other agreeableness aspects and facets were tenuous. Further research should examine the links between processing speed abilities and the promising agreeableness facets pointed to by the meta-analyses and conceptual reasoning presented here.

Processing speed abilities are related to the factor alpha meta-trait and stress tolerance compound trait. Given the meta-analytic evidence for the positive relations with conscientiousness and agreeableness alongside negative relations with neuroticism, it was expected that processing abilities would relate positively to the factor alpha meta-trait, which represents shared variance among these three traits. Factor alpha is a higher order trait, referred to as stability by some researchers (DeYoung, 2006) and interpreted as socialization by others (Davies et al., 2015; Digman, 1997). It is a prime component of homeostasis and helps maintain and preserve the individual. We expected processing abilities to correlate sizably with factor alpha. This expectation was borne out for two of the three processing speed abilities for which there were sufficient data to estimate relations with factor alpha. Number facility and perceptual speed were both positively and generalizably related to factor alpha (p̂ = .17 and .08, N = 32,591 and 15,333, K = 14 and 20, respectively). Similarly, the compound trait of stress tolerance captures variance from the same Big Five factors as factor alpha, with a higher proportion from (low) neuroticism. Stress tolerance correlated .16 and .20 with scanning and number facility, respectively (N = 74,168 and 21,321, K = 5 and 8, respectively). Oddly, perceptual speed appeared to be unrelated to stress tolerance, but this was based on a smaller number of independent effect sizes, and therefore could be due to second-order sampling error (p̂ = .04, N = 2,156, K = 3). Taken together, these findings indicate that individuals who perform well on processing speed tests appear to be well-adjusted and socialized individuals. In general, processing abilities are the strongest positive correlates of the personality domain’s meta-trait factor alpha (stability/socialization), which is also in line with their positive relations with conscientiousness, agreeableness, and emotional stability—reflecting a complex of traits and abilities that confers stability to individuals, especially when conditions are difficult and/or the environment is unsupportive.

Processing speed abilities are positively related to extraversion facets sociability and activity. This constellation of associated traits and abilities indicates adaptation to the environment. The activity facet of extraversion was consistently, homogenously, and positively related to processing speed abilities. The three processing abilities for which data were available yielded similar correlations: .15 for perceptual speed (N = 372,882, K = 27), .13 for scanning (N = 370,501, K = 6), and .16 for number facility (N = 359,806, K = 3). The sociability facet of extraversion related positively and more substantially to perceptual speed, scanning, and number facility (p̂ = .18, .14, and .17, respectively; see Supplementary Tables 36–40 in Appendix G). Overall, it appears that individuals who score high on processing speed are also more active and sociable. These results suggest that adapting to social environments relies on personality facets of sociability and activity from the extraversion domain but also on processing speed abilities, the latter perhaps instrumental in perceiving details pertaining to social situations.

Specific processing speed abilities are consistently, homogenously, and positively related to self esteem. Recall from earlier sections that self esteem is a compound personality construct that captures variance from three Big Five factors (i.e., low neuroticism, high extraversion, and high conscientiousness), which were all correlated with processing speed. As expected, self esteem correlated positively with perceptual speed (p̂ = .15, N = 377,279, K = 36), scanning (p̂ = .14, N = 388,494, K = 13), and number facility (p̂ = .16, N = 359,882, K = 5). Individuals who are self-confident and self-assured tended to score higher on processing speed abilities. Such a trait and ability pairing may confer general fitness—that is, successfully using both self-preservation and self-evolution to adapt to the environment as conditions demand. Self esteem contributes to psychological resilience to accomplish goals, and processing speed facilitates recognition of the need to adapt.

Specific processing speed abilities positively relate to ambition. This constellation of traits corresponds to behavioral activation, especially for initiating adaptation. Ambition is a compound of extraversion and conscientiousness traits. Similar to the industriousness aspect of conscientiousness and the enthusiasm aspect of extraversion, ambition showed positive relations with scanning and number facility (p̂ = .66 and .25, N = 8,779 and 8,880, K = 3 and 5, respectively). Fewer data points were available for the relation with perceptual speed, but the estimated relation was .15, which was also similar to industriousness at .17 (N = 899 and 367,829, K = 5 and 34, respectively).

Processing speed abilities may correlate positively with openness’ experiencing aspect at a similar or stronger magnitude than its intellect aspect. This constellation of associated traits and abilities reflects activation for adapting to the environment. Openness constructs, especially its intellect aspect and related facets, typically constitute the strongest positive personality correlates of cognitive ability constructs. However, this may not be the case for processing abilities. Perceptual speed had a mean meta-analytic correlation of .10 across the openness factor, aspects, and facets (excluding openness compounds), and correlated .10 with the general openness factor. The correlations with openness’ intellect aspect and the aligned facet need for cognition were .13 (N = 947 and 3,342, K = 6 and 8, respectively). Its relation with the ideas facet was somewhat stronger, at .19 (N = 2,308, K = 18). The experiencing aspect also correlated .19 with perceptual speed (N = 366,026, K = 24). However, relations with experiencing-aligned facets fantasy and aesthetics were weak and non-generalizable (p̂ = .01 and .06, N = 1,909 and 1,916, K = 19 and 19, CV = -.08 to .11 and -.04 to .16, respectively). There were positive and generalizable relations with openness’ experiencing aspect and scanning as well as number facility (p̂ = .15 and .17, N = 362,578 and 363,819, K = 11 and 9, CV = .15 to .15 and .14 to .20, respectively).

Reaction & Decision Speed Abilities

Reaction and decision speed refers to the speed and accuracy of a decision/behavior after perceiving and/or judging a stimulus. Sub-dimensions include simple reaction time, choice reaction time, semantic processing speed, mental comparison speed, and inspection time.

Figure 9 summarizes results for reaction and decision speed abilities. Complete, detailed results are presented in Supplementary Tables 41–48 (see Appendix G).

Our database was relatively sparse for reaction and decision speed. However, small, negative relations with both aspects of neuroticism (mean p̂ = -.12 for withdrawal and -.12 for volatility regardless of N or K) as well as negligible relations with agreeableness- and conscientiousness-related traits (excluding compounds) were observed (mean across all meta-analytic estimates were p̂ = .01 and -.03, respectively regardless of N or K). The enthusiasm aspect of extraversion displayed positive relations with simple reaction time (p̂ = .21, N = 329, K = 4), choice decision time (p̂ = .15, N = 5,187, K = 5), and choice movement time (p̂ = .16, N = 5,187, K = 5), which was very similar to the pattern observed for the compound trait of optimism.37 Most other traits did not have enough data to draw robust conclusions, but the current results suggest that openness may be less related to reaction and decision speed abilities than other major cognitive abilities.

Invested Abilities: Acquired Knowledge

Acquired knowledge encompasses a large set of invested abilities that can be grouped into three clusters that appear to have distinct nomological networks with personality constructs: quantitative abilities, verbal abilities, and domain specific abilities. These clusters are all concerned with the acquisition, recollection, and utilization of knowledge. Dimensions of knowledge in the quantitative domain showed differential relations with personality traits compared with dimensions of knowledge in the verbal domain. Key findings for each cluster are highlighted below.

Acquired Knowledge: Quantitative Ability

Quantitative ability describes abilities invested in the mathematical area. These abilities are distinct from quantitative reasoning ability and number facility. Quantitative reasoning ability is a fluid ability that involves using induction or deduction in reasoning with quantitative concepts. Number facility is a perceptual ability that involves speeded performance of basic arithmetic operations. In contrast, acquired quantitative ability is a knowledge-based (i.e., invested) ability focused on information about mathematics, including symbols and operations. Quantitative ability has two major components: mathematics knowledge and mathematics achievement. Mathematics knowledge is the declarative and procedural knowledge of mathematics (e.g., knowing how to compute the volume of a sphere). Mathematics achievement is typically indicated by performance on standardized mathematics achievement tests (e.g., tests used in educational settings to assess invested ability to solve quantitative problems using mathematical knowledge).

Figure 10 summarizes results for quantitative ability constructs. Complete, detailed results are presented in Supplementary Tables 50–52 (see Appendix G).

Figure 10. Invested abilities correlate with personality traits.

Note. “cd.” denotes compound traits. Meta-analytic correlations (i.e., p̂) are only reported in black type when number of independent effect sizes contributing the specific meta-analysis was ≥ 10 or the sample was ≥ 1,000 participants. Grey-filled cells had no usable data. Green-filled cells indicate positive effects, and red-filled cells indicate negative effects. Saturation indicates effect magnitude. Indentation of construct labels reflects the altitude of each trait in its respective construct hierarchy.

Quantitative abilities appear to have negative associations with all Big Five personality constructs, except openness. Available data for specific quantitative abilities revealed robust, negative relations with neuroticism, agreeableness, conscientiousness, and extraversion.

Broad quantitative ability displayed negative relations with global neuroticism, its anxiety and uneven tempered facets, as well as test anxiety (p̂ = -.18, -.28, -.27, and -.20, respectively; see Supplementary Tables 50–52 in Appendix G). It also had a slight, negative relation with extraversion (p̂ = -.09, N = 4,875, K = 12), indicating that introverted individuals score slightly higher on quantitative ability.

Stronger relations were found for mathematics knowledge, which related negatively to global agreeableness (p̂ = -.15), conscientiousness (p̂ = -.17), and extraversion (p̂ = -.17; see Supplementary Tables 50–52 in Appendix G). It is worth noting that the dominance facet of extraversion had a positive relation with mathematics knowledge, though it was weaker (p̂ = .11, N = 444,593, K = 6). These results generally indicate that individuals who have invested abilities in the mathematics knowledge domain appear to be lower in agreeableness, conscientiousness, and extraversion. As stipulated by the investment theory of intelligence (Cattell, 1987), quantitative abilities are developed over time, and therefore these personality traits may play a role in creating the quantitative tilt in individuals’ cognitive ability profiles. An alternative explanation based on our Cybernetic Trait Complexes Theory (see Chapter 6) may be that the preceding personality traits, which are mostly associated with maintenance of homeostasis, are starved for psychological energy at the expense of energy-intensive pursuits like the acquisition of quantitative abilities.

Lastly, we note that openness correlated positively but more weakly with quantitative ability and mathematics knowledge than other domains of cognitive abilities (e.g., .11 with quantitative ability and .09 with mathematics knowledge; see Supplementary Tables 50–52 in Appendix G).

Mathematics achievement correlates negligibly with the Big Five personality factors at the global level, though it correlates positively with proactive tendencies (e.g., self esteem, internal locus of control, and ambition). Very few studies were found that reported relations between mathematics achievement and personality. Each meta-analysis with mathematics achievement was based on fewer than 10 effect sizes. Second-order sampling error notwithstanding, the global Big Five personality traits were negligibly related to mathematics achievement. More sizable relations were found for some specific traits, which appear to be masked at the global Big Five level. Five compound personality traits were correlated substantially and positively with mathematics achievement: self esteem,38 internal locus of control,39 and achievement via independence40 (p̂ = .23, .46, and .41, respectively; Supplementary Tables 50–52 in Appendix G) as well as ambition41 (p̂ = .19) and rugged individualism42 (p̂ = .26). What these traits have in common is that they bring together variance from low neuroticism, high extraversion, and high conscientiousness (for self esteem, locus of control, and achievement via independence) and high extraversion and conscientiousness (for ambition and rugged individualism). Therefore, the proactive behavioral tendencies (i.e., behavioral activation tendencies) associated with the joint effects of extraversion and conscientiousness might play a role in mathematics achievement.

Acquired Knowledge: Verbal Ability

Verbal ability describes abilities invested in language acquisition, development, and utilization, encompassing reading and writing abilities as well as comprehension knowledge abilities. Reading and writing abilities are defined as, “knowledge and skills related to written language” (McGrew et al., 2014). More specific sub-abilities include reading comprehension, reading decoding, reading speed, native language usage, writing ability, and spelling ability. Comprehension knowledge abilities are described as verbal “knowledge and skills that are valued by one’s culture” (McGrew et al., 2014). They include specific abilities such as general verbal information, language development, lexical knowledge, communication ability, and listening ability. Of the 545 verbal ability-personality relations examined, most verbal abilities related similarly to personality constructs. Differential relations for specific verbal abilities were exceedingly rare.

Figure 10 summarizes results for verbal ability constructs. Complete, detailed results of results are presented in Supplementary Tables 53–65 (see Appendix G).

Verbal abilities are negatively and quite substantially related to the uneven tempered and suspiciousness facets of neuroticism. Similar to other abilities, verbal abilities had negative relations with neuroticism traits. Depression and anxiety facets displayed relations generally in the -.11 to -.23 range. Stronger relations were found for some verbal ability and neuroticism construct pairs (e.g., native language usage ability correlated -.31 with neuroticism’s test anxiety facet, N = 1,381, K = 6). Relations for uneven tempered and suspiciousness were as large, if not larger, than those for other abilities. Uneven tempered correlated -.33 with general verbal information, -.30 with verbal ability broadly defined, -.30 with reading comprehension, -.28 with native language usage, -.26 with lexical knowledge, -.26 with language development, -.26 with comprehension knowledge, and -.25 with spelling ability (see Supplementary Tables 53–65 in Appendix G). While data were sparser for the suspiciousness facet of neuroticism, similar, albeit somewhat weaker, relations were apparent. Correlations were -.28 with comprehension knowledge, -.23 with lexical knowledge, -.22 with general verbal information, -.21 with reading comprehension, and -.15 with verbal ability broadly defined (see Supplementary Tables 53–65 in Appendix G). These sizable, negative relations indicate that individuals who have uneven tempers or are suspicious do not tend to invest their abilities in the verbal domain, or that individuals who score higher on verbal abilities are better able to manage their neurotic tendencies, especially when it comes to displaying uneven temper or suspiciousness. In either case, higher verbal abilities, even temper, and lower levels of suspiciousness form a trait constellation that may support the psychological and social stability of individuals (e.g., by allowing them to verbally process through events with a positive mindset).

Verbal abilities generally relate positively and substantially to conscientiousness’ industriousness aspect and order facet. Verbal abilities correlated positively with many conscientiousness traits. Focusing on relations where robust conclusions could be drawn based on sample size and number of contributing effect sizes, the industriousness aspect and order facet stood out. Industriousness relations were .34 with general verbal information, .33 with verbal ability broadly defined, .31 with native language usage, .31 with reading comprehension, .31 with spelling ability, .29 with language development, .27 with comprehension knowledge, and .26 with lexical knowledge (see Supplementary Tables 53–65 in Appendix G). These findings are in line with verbal abilities’ positive relations with the achievement facet of conscientiousness (e.g., broad verbal ability p̂ = .12 [N = 2,467, K = 18] and reading comprehension p̂ = .20 [N = 1,174, K = 5], though achievement’s relations with comprehension knowledge abilities were negligible). In addition, compound trait ambition, which incorporates variance from conscientiousness but also extraversion, appeared to be positively related to some verbal abilities (p̂ = .13 for broad verbal ability, .30 for reading and writing, and .14 for lexical knowledge; N = 4,506, 2,703, and 7,057, K = 18, 2, and 18, respectively), though these relations may not all be generalizable (CV = -.08 to .35, .26 to .34, and -.03 to .32, respectively). Achievement via independence is another compound trait that incorporates variance from high conscientiousness (especially industriousness) as well as extraversion, openness, and low neuroticism. It correlated .42 (N = 2,884, K = 12) with broad verbal abilities and .38 (N = 5,238, K = 11) with lexical knowledge. Other compound personality traits that have variance from conscientiousness, but also include variance from extraversion and low neuroticism, correlated positively with verbal abilities as well (i.e., self esteem,43 locus of control,44 and rugged individualism45). These relations were as large as .45 (e.g., for internal locus of control and reading comprehension) but as small as .02 (e.g., for rugged individualism and language development).

Second, conscientiousness’ order facet displayed positive, sizable relations with verbal abilities. Relations were .25 with verbal ability broadly defined, .21 with reading comprehension, .25 with comprehension knowledge, .20 with general verbal information, .30 with spelling ability, .26 with native language usage, .25 with language development, and .19 with lexical knowledge (see Supplementary Tables 53–65 in Appendix G). The orderliness aspect of conscientiousness, which includes organization, scheduling, and perfectionistic strivings above and beyond the concepts of neatness and non-messiness encapsulated in the order facet, had unremarkable relations with verbal abilities. There are two potential explanations for this finding. First, it may be that the spatial and conceptual aspects of being organized are positively related to verbal abilities, whereas the temporal aspects of organization are not. This explanation is consistent with findings from other relations observed between verbal ability and conscientiousness-related traits discussed below. Second, outliers may have clouded the relations between orderliness and verbal abilities (see Supplementary Tables 150–162 in Appendix H as well as the “Impact of extremely large studies” section in Chapter 9).

Finally, the conscientiousness-related compound personality trait of routine seeking46 appears to be negatively related to some verbal abilities. Relations were -.21 with broad verbal ability and -.23 with lexical knowledge (N = 2,751 and 5,634, K = 8 and 9, respectively). Similarly, the cautiousness facet of conscientiousness correlated negatively with verbal abilities, though relations were modest (e.g., -.14 with general verbal information, -.13 with language development, -.12 with lexical knowledge, -.10 with reading comprehension, and -.08 with broad verbal ability) (see Supplementary Tables 53–65 in Appendix G).

At face value, this pattern of positive, null, and negative results highlights the complex role that conscientiousness-related traits may have in the development of verbal abilities and acquisition of verbal knowledge; conceptual and spatial order as well as industriousness may be helpful, whereas cautiousness and routine seeking may detract. Future studies are needed to further delineate these relations.

Verbal abilities are positively related to the activity and sociability facets of extraversion. Although global extraversion was not consistently related to verbal abilities, meta-analytic results supported verbal abilities’ positive relations with several extraversion traits. Chief among them was the activity facet. Relations with verbal abilities were in the .20s (p̂ = .28 with general verbal information, .24 with lexical knowledge, .23 with reading comprehension, .22 with broad verbal ability, .20 with native language usage, .19 with comprehension knowledge, and .18 with spelling ability; see Supplementary Tables 53–65 in Appendix G). Relations with sociability and dominance were still positive but weaker. Relations with sociability were in the .14 to .22 range, except for lexical knowledge (p̂ = .08). Relations with dominance were weaker, in the .09 to .14 range, except for comprehension knowledge and lexical knowledge (p̂ = .07 and .08, respectively). Dominance thus showed modest but consistently positive relations with verbal abilities. For the remaining extraversion aspects and facets, either there were limited data (e.g., for positive emotionality) or relations across verbal abilities were not consistent (e.g., for sensation seeking). In summary, extraversion facets’ positive relations with verbal abilities varied in strength. The strongest relations were for verbal abilities with the activity facet of extraversion, which was also a substantial correlate of quantitative abilities and domain specific knowledge (mean p̂ = .20 and .21, respectively).

Comprehension knowledge verbal abilities correlate more strongly with facets of openness aligned with the intellect aspect than with facets aligned with the experiencing aspect. This constellation of individual differences is associated with initiating adaptation to the environment. Relations were typically in the .24 to .30 range. Comprehension knowledge correlated .36 with the ideas facet, whereas relations were .14 with fantasy, .15 with aesthetics, .24 with non traditional, and .28 with experiencing (see Supplementary Tables 53–65 in Appendix G). Lexical knowledge is an excellent marker for comprehension knowledge. It correlated .40 with the ideas facet, .25 with the curiosity facet, and .24 with the need for cognition facet of openness (see Supplementary Tables 53–65 in Appendix G). A relation of .10 with the intellect aspect was an exception to these sizable correlations (N = 9,211, K = 16).

In contrast, lexical knowledge’s relations with facets linked to the experiencing aspect were variable and generally weaker (variety seeking p̂ = .04, aesthetics p̂ = .07, fantasy p̂ = 11, introspection p̂ = .13, non traditional p̂ = .25; see Supplementary Tables 53–65 in Appendix G). Surprisingly, lexical knowledge correlated .25 with the experiencing aspect of openness. A similar pattern was evident for other comprehension knowledge abilities such as general verbal information where relations with the more intellectual need for cognition and ideas facets were .26 and .38, respectively (N = 619 and 2,375, K = 5 and 11, respectively). In contrast, relations with more experiencing-related introspection, fantasy, aesthetics, and non traditional were .09, -.01, .11, and .13, respectively (see Supplementary Tables 53–65 in Appendix G).

Acquired Knowledge: Domain Specific Knowledge

Domain specific knowledge describes abilities invested in specific knowledge areas such as foreign language proficiency, arts and humanities, behavioral content knowledge, business knowledge, occupation-related knowledge, realistic knowledge (e.g., knowledge of machinery), general science knowledge, life sciences knowledge, mechanical knowledge, natural sciences knowledge, physical sciences knowledge, and social studies knowledge. Our investigations of domain specific knowledge areas were tied to the availability of data, rather than a systematic representation of all possible specific knowledge domains. We note that domain specific knowledge can range from quite broad (e.g., natural sciences knowledge) to extremely specific (e.g., knowledge of oak trees). Here we highlight findings for a few general domains where sufficiently large samples supported robust conclusions.

Figure 10 summarizes results for domain specific knowledge constructs. Complete, detailed results are presented in Supplementary Tables 66–80 in Appendix G.

Domain specific knowledge is positively related to openness, but magnitudes of relations are inconsistent. Openness correlated .38 with behavioral and psychological knowledge, .24 with general science knowledge (though its experiencing aspect correlated -.10), .21 with social studies knowledge (.17 with experiencing aspect), and .06 with physical sciences knowledge (see Supplementary Tables 66–80 in Appendix G). These results may suggest that knowledge domains that are closer to quantitative abilities or that require quantitative knowledge for their acquisition covary less with openness and may covary negatively with openness’ experiencing aspect. In contrast, knowledge domains that are more closely aligned with the use of verbal abilities in their acquisition (e.g., behavioral knowledge) may positively covary with openness, including its experiencing aspect.

Domain specific knowledge has negligible to weak associations with global neuroticism. Analyses revealed particularly weak relations between domain specific knowledge and neuroticism as well as most of its facets. These generally weak relations contrast with neuroticism’s closer association with verbal knowledge. Only three neuroticism-specific traits had relations greater than .10 in magnitude: neuroticism (with foreign language proficiency, mechanical knowledge, and physical sciences knowledge), test anxiety (with mechanical knowledge and social studies knowledge), and uneven tempered (with domain specific knowledge, arts and humanities, business knowledge, realistic knowledge, life sciences knowledge, mechanical knowledge, physical sciences knowledge, and social studies knowledge) (p̂ = -.12, -.13, -.13, -.15, -.31, -.31, -.32, -.31, -.19, -.29, -.18, -.16, and -.26, respectively; see Supplementary Tables 66–80 in Appendix G). The generally weak relations indicate that most neuroticism-related traits may have little impact on the acquisition of domain specific knowledge or that knowledge acquisition in a domain may even be driven by external structures (e.g., job training) that leverage neuroticism-related traits (e.g., sensitivity to punishment) to fuel learning.

While relations with conscientiousness- and extraversion-related traits appeared to differ by specific knowledge domain, the current numbers of effect sizes do not conclusively indicate whether these differences are genuine or due to second-order sampling error. Nevertheless, it is noteworthy that knowledge in verbally oriented domains tended to have relations with personality similar to verbal abilities, and knowledge in STEM domains tended to have similar personality correlates to quantitative abilities.

General Mental Ability

General fitness represents individuals’ agility in effectively moving between a focus on surviving and a focus on thriving as the environment demands or affords. General mental ability indexes the shared variance amongst cognitive ability constructs; and represents the broad capacity to reason, plan, problem-solve, learn, comprehend, and think abstractly. General mental ability is the cognitive capability to perceive, organize, retain, understand, and utilize information for cybernetic effectiveness (DeYoung & Krueger, 2018). It is the central fitness-conferring ability, and is associated with both self-evolution and growth as well as self-maintenance and stability. Multiple sets of results highlight trait constellations that incorporate this role of general mental ability.

General mental ability is most closely and generalizably associated with openness and compound personality traits that involve a combination of emotional stability, extraversion, and conscientiousness. Results for general mental ability, depicted in Figure 11 and presented in Supplementary Table 3 in Appendix G, highlight the trait complexes in which general mental ability is involved and that support self-evolution and growth as well as general fitness.

Figure 11. General mental ability and compound abilities correlate with personality traits.

Note. “cd.” denotes compound traits. Meta-analytic correlations (i.e., p̂) are only reported in black type when number of independent effect sizes contributing the specific meta-analysis was ≥ 10 or the sample was ≥ 1,000 participants. Grey-filled cells had no usable data. Green-filled cells indicate positive effects, and red-filled cells indicate negative effects. Saturation indicates effect magnitude. Indentation of construct labels reflects the altitude of each trait in its respective construct hierarchy.

Consistent with the past literature on general mental ability-openness relations (von Stumm & Ackerman, 2013), openness constructs, especially the intellect aspect and associated facets need for cognition, ideas, and curiosity, correlated in the .17 to .40 range with general mental ability.

Ideas was the openness facet that correlated most strongly with general mental ability (p̂ = .40, N = 27,990, K = 83). Global openness correlated .26 (N = 280,446, K = 368), and all these relations were generalizable (i.e., the lower credibility values were positive). Compound personality attributes that included openness constructs also tended to be positively and just as strongly related to general mental ability (e.g., creative personality;47 p̂ = .25, N = 16,860, K = 16, independent of conventions and others;48 p̂ = .13, N = 15,450, K = 21).

The more noteworthy findings for general mental ability, however, were relations with compound personality attributes defined by high levels of extraversion, high levels of conscientiousness (especially its industriousness aspect), and low levels of neuroticism. These associated traits and abilities function together as a fitness complex, balancing both homeostasis and change to determine what individuals direct their energy toward, in what amount/with what intensity, and for how long. The personality constructs in this complex include self esteem49 and internal locus of control,50 which were substantially correlated with general mental ability, .25 and .23, respectively (N = 434,320 and 56,951, K = 127 and 55).

Components of these compound personality scales were also correlated with general mental ability (e.g., extraversion facet activity p̂ = .23 [N = 381,809, K = 65], conscientiousness aspect industriousness p̂ = .32 [N = 383,960, K = 89], and neuroticism facets depression and uneven tempered, p̂ = -.18 and -.29 [N = 21,439 and 396,668, K = 49 and 100, respectively]). The same three personality constructs are also represented in interpersonal sensitivity51 and achievement via independence,52 which were themselves remarkably correlated with general mental ability, p̂ = .20 and .37, respectively (N = 9,855 and 14,096, K = 21 and 44). However, openness is also a core element of these two compound traits. Individuals who achieve most in unstructured situations, are sensitive to the emotions of others, and are generally more open-minded tend to be more intelligent. The generalizability of these relations, as well as the pervasiveness of personality traits involving extraversion, conscientiousness (especially industriousness), and emotional stability among general mental ability correlates, indicates a tilt toward behavioral activation/proactivity as an important part of general mental ability’s function in general fitness.

Behavioral activation represents pursuit of goals, reward sensitivity, and feelings of positive emotions as one approaches or attains a reward (Carver & White, 1994). Put differently, individuals who are high on general mental ability appear to have a greater tendency toward proactivity. General mental ability and these personality traits likely co-influence each other. For example, those who are smart might achieve more, which might in turn boost their self esteem. Additionally, the relations may reflect the effects of third variables (e.g., supportive environments may foster both higher ability and higher self esteem). The broader results indicate that general mental ability, self esteem, and the general factor of personality may be hallmarks of general cybernetic effectiveness (i.e., general fitness). Ultimately, these appear to be key characteristics for effectively surviving and thriving.

General mental ability is lower among individuals who are depressed, anxious, uneven tempered, or suspicious. Relations with multiple neuroticism facets, not just test anxiety, were negative, generalizable, and relatively homogenous. This complex of associated traits and abilities reflects a lowered ability to deal with complexity that corresponds with a heightened vigilance to and avoidance of threats, implying a high sensitivity to peril that corresponds with instability and decreased ability to sift signal from noise, which leads to greater uncertainty about the (ostensibly short) future. Conversely, higher general mental ability paired with higher emotional stability/lower neuroticism highlights a complex that is functional for the maintenance of self.

In terms of specific traits, general mental ability correlated -.18 with depression, -.17 with suspiciousness, and -.29 with uneven tempered (see Supplementary Table 3 in Appendix G). Although the relation with negative affect was also of similar magnitude and negative (p̂ = -.16, N = 6,453, K = 18), that relation was not generalizable (CV between .02 and -.35). Finally, although test anxiety correlated p̂ = -.20 with general mental ability, it was also the most variable (SDp̂ = .22, N = 9,293, K = 42), indicating heterogeneity across true effect sizes.

General mental ability correlates negatively and non-negligibly with agreeableness facet modesty. General mental ability appears to be somewhat lower among individuals who are humble and deferent in interactions with others. The correlation with modesty was -.13 for (N = 18,561, K = 21). One potential interpretation is that modesty may be a compensatory social strategy for individuals with low general mental ability, facilitating maintenance of self in social environments, though the effects are weak.

Distillation of Intelligence’s Relations with Personality

In this chapter, we detailed meta-analytic evidence highlighting notable relations between many cognitive abilities and personality constructs. These relations are not limited to openness-related traits.

Several cognitive abilities were found to be correlated with Big Five factors, facets, and compound personality constructs associated with behavioral activation and proactivity (i.e., those bringing together personality trait variance from emotional stability, extraversion, industriousness aspect of conscientiousness, and intellect aspect of openness). Associated abilities include induction and quantitative reasoning, short term memory abilities, specific mathematics achievement abilities, and, more modestly, visual processing abilities. Relations between retrieval fluency abilities and self esteem53 as well as visual processing and rugged individualism54 may also be linked to this trait complex.

In addition, most cognitive abilities correlated substantially and positively with openness traits. However, there was a striking difference in how some, but not all, cognitive abilities related more with the intellect aspect than the experiencing aspect. Fluid abilities and visual processing abilities showed notable, positive relations with openness, especially its intellect aspect, facets most related to intellect (i.e., need for cognition, ideas, and curiosity), and compound personality traits involving intellect. Comprehension knowledge verbal abilities also correlated more strongly with openness facets aligned to the intellect aspect compared to those aligned with the experiencing aspect. In contrast, processing abilities correlated positively with openness’ experiencing aspect at a similar or stronger magnitude than its intellect aspect. Similarly, retrieval fluency abilities had relatively uniform, positive correlations with openness, as well as its aspects and facets. Differential relations with intellect- and experiencing-related traits may shed light on distinctive functional and developmental processes that bring about such differentiations.

Although most cognitive abilities were negatively correlated with neuroticism, particularly its depression-related facets, these negative relations were especially notable for induction and quantitative reasoning, short term memory abilities, retrieval fluency abilities, and processing speed abilities. Both processing abilities and verbal abilities were also negatively linked with the uneven tempered and suspiciousness facets of neuroticism. In contrast, visual processing abilities were less related to neuroticism and its aspects and facets than other major abilities.

Several cognitive abilities displayed differential relations with conscientiousness traits. Processing speed abilities related positively and similarly to both industriousness and orderliness aspects of conscientiousness. Likewise, verbal abilities related positively and substantially to conscientiousness’ industriousness aspect and order facet. Fluid abilities, however, negatively related to the orderliness aspect of conscientiousness. Several retrieval fluency abilities had negative, albeit weak, associations with conscientiousness’ cautiousness facet.

Cognitive abilities’ relations with extraversion and agreeableness traits appeared to be sporadic and localized to specific abilities. Fluid abilities showed consistent, small, positive relations with agreeableness and extraversion traits. Retrieval fluency abilities were positively related to global extraversion as well as to its aspects and dominance facet. Both processing speed and verbal abilities were positively related to extraversion facets sociability and activity. General sequential reasoning related positively to the non manipulative facet of agreeableness. Visualization had a mild, negative correlation with the modesty facet of agreeableness. Finally, processing speed abilities correlated positively with the agreeableness facets cooperation and lack of aggression.

Results for general mental ability confirmed its close association with openness traits but also with behavioral activation and proactivity traits. Its relations with neuroticism traits revealed sizable, negative relations with depression, anxiety, uneven tempered, and suspicious. Unexpectedly, it related negatively and non-negligibly with agreeableness’ modesty facet.

The findings from the present meta-analyses reflect a rich network of relations between cognitive abilities and personality traits. We implore cognitive ability researchers to consider the potential impacts of personality co-variation on their research. Developmental, functional, and consequential explanations that focus solely on cognitive abilities cannot offer complete accounts without considering associated personality traits.

References

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Endnotes

1 Note that the extraversion component of locus of control draws variance from the positive emotionality facet, rather than global extraversion.

2 Self esteem combines low neuroticism, high extraversion, and high conscientiousness.

3 Creative personality combines the factor beta meta-trait, especially openness’ nontraditional facet, and conscientiousness’ industriousness as well as low orderliness and low cautiousness.

4 Achievement via independence combines low neuroticism, high extraversion, high openness, and high conscientiousness.

5 The definitions of all compound personality traits in this book can be found in Appendix C, and Appendix D lists measures of these traits.

6 Internal locus of control combines low neuroticism, high extraversion (in particular, the positive emotionality facet), and high conscientiousness.

7 Cold efficiency combines low agreeableness and high conscientiousness.

8 Self esteem combines low neuroticism, high extraversion, and high conscientiousness.

9 Internal locus of control combines low neuroticism, high extraversion (in particular the positive emotionality facet), and high conscientiousness.

10 Optimism combines low neuroticism and high extraversion.

11 Throughout this book, the mean provides the average of meta-analytic correlations across a set of meta-analytic findings described in the same sentence (e.g., quantitative reasoning’s relations with a set of personality traits in this example). This metric characterizes trends in the meta-analyses and avoids giving undue weight to heavily-studied constructs.

12 Rugged individualism combines high factor beta and high industriousness (aspect of conscientiousness).

13 Factor beta combines high extraversion and high openness.

14 Factor alpha combines low neuroticism, high agreeableness, and high conscientiousness.

15 Routine seeking combines low variety seeking (facet of openness) and high order (facet of conscientiousness).

16 Judging-perceiving combines low openness and high conscientiousness.

17 Warmth combines high extraversion and high agreeableness.

18 Tolerance combines high openness and high agreeableness.

19 Optimism combines low neuroticism and high extraversion.

20 Trust combines low neuroticism and high agreeableness.

21 Openness to emotions combines high extraversion and high openness.

22 Warmth combines high extraversion and high agreeableness.

23 Self esteem combines low neuroticism, high extraversion, and high conscientiousness.

24 Internal locus of control combines low neuroticism, high extraversion (in particular the positive emotionality facet), and high conscientiousness.

25 Factor alpha combines low neuroticism, high agreeableness, and high conscientiousness.

26 Self esteem combines low neuroticism, high extraversion, and high conscientiousness.

27 Free recall memory is also an exception to the pattern of null relations for learning efficiency abilities. However, the broader set of current meta-analytic results suggests that free recall ability may nomologically fit within retrieval fluency rather than learning efficiency.

28 Internal locus of control combines low neuroticism, high extraversion (in particular the positive emotionality facet), and high conscientiousness.

29 Note that some compound traits involving openness needed to be reversed (e.g., routine seeking) for these analyses.

30 Rugged individualism combines high factor beta and high industriousness (aspect of conscientiousness).

31 Self esteem combines low neuroticism, high extraversion, and high conscientiousness.

32 Internal locus of control combines low neuroticism, high extraversion (in particular the positive emotionality facet), and high conscientiousness.

33 Achievement via independence combines low neuroticism, high extraversion, high openness, and high conscientiousness.

34 These mean meta-analytic correlations only include robust meta-analytic estimates and do not include neuroticism-related compound traits, which were scored in the direction of emotional stability (i.e., reverse of neuroticism).

35 Includes introspection, non traditional, and variety seeking here even though they are less strongly aligned facets, due to the influence of other Big Five factors on them. Result is the same if only experiencing, aesthetics, and fantasy are included.

36 Psychomotor speed abilities are part of the speed abilities group but are mentioned with auditory processing abilities and psychomotor abilities because the sparsity of data warrant the same explanation.

37 Optimism combines low neuroticism and high extraversion.

38 Self esteem combines low neuroticism, high extraversion, and high conscientiousness.

39 Internal locus of control combines low neuroticism, high extraversion (in particular the positive emotionality facet), and high conscientiousness.

40 Achievement via independence combines low neuroticism, high extraversion, high openness, and high conscientiousness.

41 Ambition combines high extraversion and high conscientiousness.

42 Rugged individualism combines high factor beta and high industriousness (aspect of conscientiousness).

43 Self esteem combines low neuroticism, high extraversion, and high conscientiousness.

44 Internal locus of control combines low neuroticism, high extraversion (in particular the positive emotionality facet), and high conscientiousness.

45 Rugged individualism combines high factor beta and high industriousness (aspect of conscientiousness).

46 Routine seeking combines low variety seeking (facet of openness) and high order (facet of conscientiousness).

47 Creative personality combines high openness and factor beta more broadly, as well as the industriousness aspect of conscientiousness.

48 Independent of others combines high openness and low agreeableness.

49 Self esteem combines low neuroticism, high extraversion, and high conscientiousness.

50 Internal locus of control combines low neuroticism, high extraversion (in particular the positive emotionality facet), and high conscientiousness.

51 Interpersonal sensitivity combines low neuroticism, high extraversion, high openness, and high agreeableness.

52 Achievement via independence combines low neuroticism, high extraversion, high openness, and high conscientiousness.

53 Self esteem combines low neuroticism, high extraversion, and high conscientiousness.

54 Rugged individualism combines high factor beta and high industriousness (aspect of conscientiousness).

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Chapter 5

How Personality Traits Relate to Cognitive Abilities

CHAPTER SUMMARY

Organizing the results according to personality factors offers a useful perspective.

Neuroticism traits’ relations with cognitive abilities:

Are generally negative.

Uneven tempered facet displays substantial, negative correlations.

Anxiety facet relates negatively.

Depression facet has sizable, negative relations.

Agreeableness traits’ relations with cognitive abilities:

Are inconsistent.

Compassion and politeness aspects have opposite patterns of relations.

Compassion generally relates positively, while politeness has smaller, negative relations.

Conscientiousness traits’ relations with cognitive abilities:

Are mostly positive.

Global conscientiousness has the weakest relations amongst conscientiousness constructs.

Industriousness aspect is a noteworthy, positive correlate.

Some facets more closely aligned with industriousness have notable, positive relations.

Cautiousness has mostly weak, negative relations.

Extraversion traits’ relations with cognitive abilities:

Are mostly negligible at the global factor level.

Activity facet has sizable, positive relations, especially with long term storage and retrieval, visual processing, and processing speed abilities.

Activity facet is also a robust, positive correlate of acquired knowledge abilities.

Enthusiasm aspect and associated positive emotionality and sociability facets have modest, inconsistent relations.

Sociability facet is positively related to verbal abilities.

Sensation seeking facet has mostly negligible relations.

Assertiveness aspect and dominance facet have modest, positive correlations with long term storage and retrieval constructs related to retrieval fluency.

Openness traits’ relations with cognitive abilities:

Are stronger than other Big Five globalfactors.

Intellect aspect and its associated need for cognition and ideas facets are strong, positive correlates.

Relations with verbal abilities are stronger than quantitative abilities.

Non traditional facet is a notably positive, though inconsistent, correlate.

Experiencing aspect and its associated fantasy and aesthetics facets display mostly negligible relations, with notable exceptions being experiencing’s positive relations with acquired quantitative and verbal abilities.

Higher order traits’ relations with cognitive abilities:

Factor alpha generally has much smaller relations than those observed for several of the neuroticism- and conscientiousness-related personality constructs.

Factor beta generally has positive relations, but the true standard deviations are relatively large in most instances.

General factor of personality is sizably correlated with general mental ability, indicating a cross-domain, general fitness cluster associated with cybernetic effectiveness and the agility to move effectively between a focus on surviving and a focus on thriving, as the environment demands.

Results indicate that, contrary to common belief, the links between personality traits and cognitive abilities are not limited to the Big Five factor of openness and its components. Instead, several other personality traits show relations with cognitive abilities that are of even greater magnitude.

This chapter describes our meta-analytic findings for how personality traits relate to cognitive abilities. We organize findings according to the Pan-Hierarchical Five Factor Model (Stanek & Ones, 2018). Definitions of personality traits and of each cognitive ability construct may be found in Appendices A and C and compendia of measures for each can be found in Appendices B and D. By reviewing findings from the perspective of personality traits, we hope that personality researchers will come to recognize traits that are linked to cognitive abilities, and the importance of personality-ability clusters in psychological research and applications.

Findings for each of the Big Five personality factors and their associated aspects and facets are presented first. Next come findings for compound personality traits—traits that include substantial variance from more than one Big Five factor. Finally, empirical relations between higher order personality traits (e.g., factor beta/plasticity) and cognitive abilities are described.

Full meta-analytic results for personality traits are presented in Supplementary Tables 197–275 (see Appendix I). These tables provide uncorrected correlations as well as correlations corrected for unreliability. None of the meta-analyses presented have applied range restriction corrections. Each table is devoted to a distinct personality trait (e.g., industriousness), and its associations with the full set of cognitive abilities examined. The indentation of each construct name within the table reflects the altitude of each ability (e.g., dimension, sub-dimension).

This chapter and its associated tables and figures describe 3,543 meta-analytic relations between 79 personality traits and 97 cognitive abilities.

Overviews of results are visualized in Figures 12 through 17. In these visualizations, p̂ estimates (i.e., meta-analytic correlations) are only reported in black type if the number of independent effect sizes contributing the specific meta-analysis was greater than or equal to 10 or if the effect was based on at least 1,000 participants. Grey-filled cells had no usable data in our meta-analytic database. Green-filled cells indicate positive effect sizes, and red-filled cells indicate negative effect sizes. Fill saturation indicates effect size magnitude.

In our description and discussion of results, we highlight the main findings and trends rather than noting every single meta-analytic relation examined. We encourage readers who are interested in certain personality traits to examine in-depth the full set of tables presented.

As stated in Chapter 4, when interpreting findings, the reader should keep in mind that relations around .10 indicate effect sizes that are

small…but potentially more ultimately consequential, an effect-size r of .20 indicates a medium effect that is of some explanatory and practical use even in the short run and therefore even more important, and an effect-size r of .30 indicates a large effect that is potentially powerful in both the short and the long run. (Funder & Ozer, 2019).

Big Five Personality Traits and Cognitive Abilities

Neuroticism and Its Family of Personality Constructs

Neuroticism and its sub-traits generally involve experiencing negative emotions. Neuroticism’s aspects are volatility and withdrawal, and its major facets are anxiety, depression, negative affect, suspiciousness, and uneven tempered. The positive pole of neuroticism—emotional stability—is a core personality trait in self-preservation. Specifically, it promotes internal psychological stability, regulates negative emotions, and therefore reinforces homeostasis. Neuroticism’s relations with cognitive abilities may be understood as trait complexes that promote internal stability (e.g., increased ability to deal with complexity may be associated with calm mental states, which together insulate the internal self).

Figure 12 summarizes results for neuroticism constructs. The indentation of each construct name in this and subsequent figures reflects the altitude of each trait (e.g., factor, aspect, facet). Complete, detailed results are presented in Supplementary Tables 197–208 (see Appendix I).

Figure 12. Neuroticism-related traits correlate with cognitive abilities.

Note. “cd.” denotes compound traits. Meta-analytic correlations (i.e., p̂) are only reported in black type when the number of independent effect sizes contributing the specific meta-analysis was ≥ 10 or the sample was ≥ 1,000 participants. Grey-filled cells had no usable data. Green-filled cells indicate positive effects, and red-filled cells indicate negative effects. Saturation indicates effect magnitude. Indentation of construct labels reflects the altitude of each trait in its respective construct hierarchy. Y-axis dendrogram further reinforces level of each ability in the abilities hierarchy.

The overall pattern of neuroticism constructs’ relations with cognitive abilities is negative. Relations between global neuroticism and cognitive ability constructs were mostly negative, albeit modest. Specifically, global neuroticism’s relations were negative and most sizable with the spatial scanning sub-dimension of visual processing, processing speed, and psychomotor ability finger dexterity. In addition, it related negatively to several acquired knowledge abilities including quantitative ability, general verbal information, comprehension knowledge, foreign language proficiency, reading comprehension, mechanical knowledge, and physical sciences knowledge (p̂ range = -.12 to -.18).

Negative affect is one of the best markers for global neuroticism (Watson & Clark, 1984), but these measures were analyzed separately to examine areas of convergent and divergent relations. Overall, the relations between negative affect and cognitive ability constructs were comparable to global neuroticism in both direction and magnitude. However, general mental ability, induction, quantitative reasoning, and lexical knowledge displayed some of the largest negative relations with negative affect (p̂ = -.16, -.14, -.12, and -.27, respectively). Relations were typically larger than their counterparts for global neuroticism, but some notable variability was associated with each effect size (see Supplementary Tables 197–208 in Appendix I), leading to overlapping credibility intervals for negative affect and global neuroticism’s relations with abilities.

The pattern of negative relations held for the two aspects of neuroticism: withdrawal and volatility. For withdrawal, several true-score correlations were -.10 to -.17 in magnitude (i.e., general mental ability, fluid abilities, retrieval fluency abilities, short term memory constructs, some reaction and decision speed abilities, and lexical knowledge). The volatility aspect generally correlated less with cognitive abilities. Two exceptions were notably stronger correlations with fluid ability (p̂ = -.23) and general sequential reasoning (p̂ = -.30), but these results may be impacted by second-order sampling error (i.e., for both, N = 1,107, K = 2).

Neuroticism facets aligned with the volatility aspect (e.g., uneven tempered and suspiciousness) related more strongly and differentially to cognitive abilities, though relations with the suspiciousness facet were weaker than those with the uneven tempered facet. Suspiciousness’ negative relations, however, spanned a wide range: general mental ability (p̂ = -.17), fluid abilities (induction [p̂ = -.10] and quantitative reasoning [p̂ = -.27]), a few short and long term memory constructs (i.e., free recall memory, retrieval fluency, memory span, and working memory capacity; p̂ range = -.19 to -.27]), visualization (p̂ = -.11), perceptual speed (p̂ = -.14), and several verbal abilities (e.g., reading comprehension [p̂ = -.21] and comprehension knowledge abilities [p̂ range = -.22 to -.28]).

Uneven tempered facet of neuroticism displays considerable, negative correlations with cognitive abilities. The uneven tempered facet displayed the strongest negative relations with comprehension knowledge and some of its sub-dimensions (mean1 p̂ = -.28, p̂ range = -.26 to -.33). Uneven tempered’s relations with other acquired knowledge abilities were also sizable: reading comprehension (p̂ = -.30), mathematics achievement (p̂ = -.29), native language usage (p̂ = -.28), and spelling ability (p̂ = -.25). Furthermore, it correlated negatively and substantially with several domain specific knowledge sub-dimensions (e.g., arts and humanities knowledge [p̂ = -.32], life sciences knowledge [p̂ = -.29]) (see Supplementary Tables 197–208 in Appendix I). Its relations with domain independent abilities (e.g., induction) and processing speed were notably weaker (p̂ = -.11 and -.17 with induction and perceptual speed, respectively). Accordingly, the findings are consistent with uneven temper directly or indirectly hindering acquisition of knowledge.

Anxiety is a facet of neuroticism that is negatively related to cognitive abilities. Anxiety’s relations were weaker than for the uneven tempered and depression facets of neuroticism (discussed above and below, respectively). Anxiety correlated -.07 with general mental ability (N = 55,681, K = 128) and similarly with multiple fluid abilities. Test anxiety is a common form of anxiety in response to being assessed. Test anxiety was moderately, negatively related to most cognitive abilities (mean p̂ = -.21), which was similar to some other neuroticism facets (e.g., -.17 for depression with invested abilities, -.18 for suspiciousness with memory abilities). We also note sizeable variability across the correlations pooled for some test anxiety meta-analyses (e.g., general mental ability, SDp̂ = .22, p̂ = -.20, N = 9,293, K = 42), indicative of heterogeneous effects. Finally, the fact that positive relations are still observed for many other traits (e.g., industriousness, sociability) indicates that being anxious during a cognitive ability test does not overshadow the effects of these traits. The present dataset did not allow us to examine if test anxiety mediates these relations or exerts independent effects.

Depression is a facet of neuroticism that has sizable, negative relations with cognitive abilities. Overall, relations between the depression facet and cognitive ability constructs were negative and markedly larger in magnitude than global neuroticism. The strongest cognitive correlates were the fluid ability sub-dimension quantitative reasoning (p̂ = -.25) and the dimension of visual processing (p̂ = -.32, N = 51,201 and 1,057, K = 24 and 6, respectively). In general, notable relations were observed with fluid abilities, retrieval fluency (except for originality and creativity), short term memory, and visual processing, as well as with verbal ability sub-dimensions (p̂ range = -.09 to -.32).

What can explain these neuroticism-ability relations? Could the association between lower neuroticism and abilities reflect an adaptive advantage? When survival is the goal and the environment is resource-scarce or unsupportive (even threatening), trait complexes tuned to low energy usage may be the most adaptive life strategy. In these situations, higher depression and withdrawal accompanied by lower cognitive functioning may represent a “low-energy survival mode.” Our meta-analyses of depression support this negative, sizable link with cognitive abilities. The psychically enervating effects of depression diminish cognitive performance and invested cognitive abilities.2 It is also possible that low cognitive ability may spur a predisposition to feel anxious, cautious, and negative. Another possibility is that environmental signals (e.g., pre-natal malnutrition) trigger adjustments in the individual toward a strategy of low energy usage that is adaptive for unsupportive (i.e., resource-poor and/or threat-rich) environments. This low energy usage may manifest in low cognitive ability, including acquired knowledge, as well as an inclination toward homeostasis rather than change. In short, when the environment seems unsupportive, the brain may adjust cognitive capabilities and behavioral tendencies to optimize preservation of limited resources rather than taking risks with novelty or change.

Agreeableness and Its Family of Personality Constructs

Agreeableness comprises a family of traits that all relate to getting along with others. Its aspects are compassion and politeness, and its major facets are cooperation, lack of aggression, modesty, non manipulative, nurturance, and tendermindedness. Agreeableness traits help maintain the individual in social environments (e.g., by predisposing them toward harmony, helping others, and getting along rather than aggressiveness, selfishness, and disregard for others).

Figure 13 summarizes results for agreeableness constructs. Complete, detailed results are presented in Supplementary Tables 209–223 (see Appendix I).

Figure 13. Agreeableness-related traits correlate with cognitive abilities.

Note. “cd.” denotes compound traits. Meta-analytic correlations (i.e., p̂) are only reported in black type when the number of independent effect sizes contributing the specific meta-analysis was ≥ 10 or the sample was ≥ 1,000 participants. Grey-filled cells had no usable data. Green-filled cells indicate positive effects, and red-filled cells indicate negative effects. Saturation indicates effect magnitude. Indentation of construct labels reflects the altitude of each trait in its respective construct hierarchy. Y-axis dendrogram further reinforces level of each ability in the abilities hierarchy.

The overall pattern of agreeableness constructs’ relations with cognitive abilities is inconsistent. Of all personality constructs, agreeableness was the Big Five trait with the fewest and smallest connections to cognitive abilities. Global agreeableness and cognitive ability constructs mostly displayed both positive and negative, minute relations. Small, positive relations with global agreeableness were limited to some sub-dimensions of memory (i.e., episodic memory, free recall memory, and memory span). Larger, positive relations were found for the acquired knowledge constructs of reading decoding and life sciences knowledge (p̂ = .18 and .21, N = 2,092 and 4,281, K = 4 and 5, respectively). In contrast, a sub-dimension of processing speed and some quantitatively oriented ability dimensions displayed small, negative relations: pattern recognition (p̂ = -.11), mathematics knowledge (p̂ = -.15), and general science knowledge (p̂ = -.19; see Supplementary Tables 209–223 in Appendix I). The differential relations for agreeableness’ aspects described below may have resulted in conflated, small-to-negligible relations at the global agreeableness factor level.

Compassion and politeness aspects of agreeableness have opposite patterns of relations with cognitive abilities. Compassion tended to be positively correlated with cognitive abilities, and politeness was negatively correlated.

Compassion correlated .26 with general mental ability (N = 357,568, K = 17). Compassion’s relation with induction was .13 (N = 385,883, K = 40). Correlations for the long term storage and retrieval sub-dimension originality and creativity and the short term memory sub-dimension memory span were .19 and .21 (N = 360,973 and 369,506, K = 4 and 4, respectively).

In addition, compassion correlated positively with processing speed sub-dimensions of perceptual speed, scanning, and number facility (p̂ = .19, .15, and .17, respectively; see Supplementary Tables 209–223 in Appendix I). Perception of others’ needs and desires may be an application of perceptual processing in the social domain, perhaps contributing to reasoning about other individuals’ mental states. More direct measures of social perception abilities may reveal stronger relations than the general perceptual abilities studied here.

Lastly, compassion’s most sizable relations were with acquired knowledge dimensions and sub-dimensions—in particular, quantitative ability, mathematics knowledge, mathematics achievement, verbal ability, reading comprehension, native language usage, spelling ability, comprehension knowledge, general verbal information, language development, lexical knowledge, domain specific knowledge, arts and humanities, business knowledge, life sciences knowledge, and social studies knowledge (mean p̂ = .27) (see Supplementary Tables 209–223 in Appendix I for the full set of findings).

In stark contrast, the politeness aspect’s relations with ability constructs were mostly negative and smaller in magnitude. Politeness correlated -.12 and -.16 with induction and quantitative reasoning sub-dimensions of fluid ability (N = 73,945 and 72,068, K = 11 and 3, respectively). For acquired knowledge meta-analyses with robust numbers of participants and/or effect sizes, the relations were again negative and of similar magnitude: -.16 with mathematics knowledge, -.13 with lexical knowledge, -.22 with general science knowledge, and -.13 with physical sciences knowledge (see Supplementary Tables 209–223 in Appendix I).

Agreeableness facets generally followed the same pattern of relations with cognitive abilities as the agreeableness aspects they most closely align with, though the relations tended to be much weaker. Few true-score correlations based on robust data reached a magnitude of .10. Exceptions are reported next.

Similar to findings for agreeableness’ compassion aspect, the cooperation facet correlated .20 with processing speed and the number facility sub-dimension of processing speed (N = 22,155 and 21,025, K = 6 and 8, respectively). Lack of aggression correlated positively with perceptual speed, verbal ability, and reading comprehension (p̂ = .10, .12, and .11, respectively; see Supplementary Tables 209–223 in Appendix I). Interestingly, lack of aggression displayed very small, positive correlations with several cognitive ability constructs, hinting at the possibility that aggression may either partially be a response to a lack of cognitive capacity compared to the environmental demands or be a fitness substitute for cognitive ability.

The non manipulative facet of agreeableness had positive relations with cognitive abilities, except for ideational fluency (p̂ = -.15, N = 1,157, K = 5). That is, manipulativeness appears to be positively correlated with ideational fluency. Specific abilities correlating positively and non-negligibly with being non manipulative were general sequential reasoning, memory span, general verbal information, and lexical knowledge (p̂ = .13, .11, .13, and .11, respectively; see Supplementary Tables 209–223 in Appendix I).

Following the pattern of findings for the politeness aspect, the tender mindedness facet showed small, negative relations (e.g., induction p̂ = -.11, N = 28,968, K = 65). These results suggest that interpersonal relationship traits may garner social support to help maintain individuals in their environments, despite lower general ability to deal with complexity. In the other direction, the relation of tender mindedness with memory span was positive (p̂ = .10, N = 1,240, K = 12), suggesting memory might play a minor assistive role in kind interactions with others.

Relations with agreeableness’ modesty facet tended to be consistently negative. Specifically, general mental ability, ideational fluency, working memory capacity, visual processing, visualization, and lexical knowledge all had negative relations with modesty (p̂ = -.13, -.17, -.13, -.11, -.12, and -.11, respectively; see Supplementary Tables 209–223 in Appendix I).

Finally, agreeableness’ nurturance facet showed mixed relations. Considering robust relations based on sizable samples or numbers of effect sizes, it correlated -.14 with quantitative reasoning, -.16 with mathematics knowledge, -.21 with general science knowledge, and -.12 with physical sciences knowledge (see Supplementary Tables 209–223 in Appendix I). However, it had a small, positive relation with comprehension knowledge (p̂ = .14, N = 1,501, K = 6).

Interpreting the entire pattern of findings for agreeableness requires an understanding of the role that agreeableness plays in maintaining the individual in social environments. Agreeableness traits enable attainment of goals via genuine helpfulness and empathy. Compassion and politeness are two aspects of agreeableness. Compassion is willingness to expend energy on non-kin, which contributes to a social group and thereby weaves a social safety net for the individual. In contrast, politeness involves following socially prescribed rules for conduct, facilitating transactional interactions. Even though previous meta-analyses have reported null effects for agreeableness-cognitive abilities relations, we expected politeness-related traits that engage inhibitory cybernetic mechanisms to avoid rude, manipulative, and belligerent behavior (e.g., politeness, kindness, and modesty) would reduce cognitive resources available for goal pursuit, and therefore be negatively correlated with most cognitive abilities. An alternative hypothesis leading to a similar prediction is that higher-ability individuals may not need the socialized tendencies offered by politeness-related traits in the pursuit of goals. In contrast, compassion-related traits may be less of a net resource loss since they also build social credit that adds resources to the individual’s social and psychological capital, which helps support the individual in surviving and thriving.

Several meta-analyses provided evidence for these ideas. Specifically, we found politeness-related traits to be negatively correlated with induction and quantitative reasoning sub-dimensions of fluid ability as well as with several invested abilities (i.e., mathematics knowledge, lexical knowledge, general science knowledge, physical sciences knowledge). Such findings could come about if higher levels of psychological resources are directed to or invested in cognitive domains at the expense of investment in social and/or behavioral expressions such as politeness, which is the “reasoned (or at least cognitively influenced) consideration of and respect for others’ needs and desires” (DeYoung, 2013). Put differently, psychological energy is a finite resource, and its allocation to certain cognitive pursuits may result in the sacrifice of other areas (e.g., politeness).

Conscientiousness and Its Family of Personality Constructs

Conscientiousness designates a family of traits related to self-discipline and organization. Its aspects are industriousness and orderliness. Its major facets are achievement, cautiousness, dependability, order, persistence, and procrastination avoidance. Different aspects and facets of conscientiousness are associated with homeostasis and change. Orderliness and cautiousness can be adaptive for maintenance of homeostasis, whereas industriousness can contribute to change as well as general success.

Figure 14 summarizes results for conscientiousness constructs. Complete, detailed results are presented in Supplementary Tables 224–239 (see Appendix I).

Figure 14. Conscientiousness-related traits correlate with cognitive abilities.

Note. “cd.” denotes compound traits. Meta-analytic correlations (i.e., p̂) are only reported in black type when the number of independent effect sizes contributing the specific meta-analysis was ≥ 10 or the sample was ≥ 1,000 participants. Grey-filled cells had no usable data. Green-filled cells indicate positive effects, and red-filled cells indicate negative effects. Saturation indicates effect magnitude. Indentation of construct labels reflects the altitude of each trait in its respective construct hierarchy. Y-axis dendrogram further reinforces level of each ability in the abilities hierarchy.

The overall pattern of conscientiousness constructs’ relations with cognitive abilities is mostly positive. Conscientiousness constructs tended to correlate positively with cognitive abilities. The strongest relations were for cognitive ability constructs involving cognitive retrieval, specifically retrieval fluency (e.g., mean = .13 between retrieval fluency and conscientiousness’ facets) and knowledge retrieval related to invested verbal ability broadly defined (mean p̂ = .13 with conscientiousness, its aspects, and its facets). We interpret this complex of associated traits and abilities as indicative of overall adaptability.

Among conscientiousness constructs, global conscientiousness has the weakest relations with cognitive abilities. The relations of global conscientiousness with cognitive abilities were generally weaker and less consistent than those of conscientiousness aspects and facets. Most relations were positive but smaller than .06 in magnitude. Some notable exceptions are described below.

Global conscientiousness displayed some sizable, negative correlations with several acquired knowledge abilities in the science and mathematics domains. These included general science knowledge, mathematics knowledge, and natural sciences knowledge (p̂ = -.22, -.17, and -.11, respectively; see Supplementary Tables 224–239 in Appendix I) among others. High levels of conscientiousness being associated with a quantitative disadvantage may be partially explained by its uniformly negative, albeit mild, correlations with fluid abilities. Fluid ability correlated -.14 with conscientiousness, though fluid ability sub-dimensions’ relations were weaker, at -.05, -.06, and -.05 (for induction, general sequential reasoning, and quantitative reasoning, respectively; see Supplementary Tables 224–239 in Appendix I). Such relations suggest that less-conscientious individuals have slightly higher fluid abilities, and this dynamic may be amplified in invested science and mathematics abilities.

These negative relations notwithstanding, there were a few areas where global conscientiousness tended to be positively associated with abilities, especially those more associated with structure, procedures, and practice. First, for processing speed ability as well as its sub-dimension number facility, relations were positive and sizable in magnitude (p̂ = .17 and .25, N = 21,138 and 34,605, K = 7 and 21, respectively). Second, for two areas of domain specific knowledge, true-score correlations were positive and sizable as well (i.e., foreign language proficiency p̂ = .37 and behavioral and psychological knowledge p̂ = .20, N = 1,150 and 1,531, K = 2 and 5, respectively).

The disparate negative and positive relations for global conscientiousness are likely related to the differential effects of conscientiousness’ aspects.

Orderliness and industriousness aspects of conscientiousness have differential patterns of relations with cognitive abilities. The orderliness aspect represents general tendencies toward precision, organization, neatness, and perfectionistic strivings (DeYoung, 2013; Stanek & Ones, 2018; Tian, 2021). Orderliness’ relations mostly followed the pattern of global conscientiousness with abilities. Several negative relations with acquired science, technology, engineering, and mathematics (STEM) related abilities were found (p̂ = -.21 for mathematics knowledge, -.28 for general science knowledge, and -.09 for physical sciences knowledge; see Supplementary Tables 224–239 in Appendix I) as were negative relations with fluid ability sub-dimensions (p̂ = -.12 for induction, -.09 for general sequential reasoning, and -.17 for quantitative reasoning; see Supplementary Tables 224–239 in Appendix I). We also note the uniformly negative, even if small, relations between orderliness and visual processing abilities (e.g., p̂ = -.07 for visualization and -.10 for spatial scanning, N = 76,647 and 1,205, K = 26 and 5, respectively). Similar to global conscientiousness, orderliness was also found to have slight, positive relations with processing speed ability sub-dimensions (i.e., p̂ = .06 for perceptual speed, .08 for scanning, and .13 for number facility; see Supplementary Tables 224–239 in Appendix I).

There were a few differences though. Unlike global conscientiousness, orderliness had consistent, positive but small associations with long term memory’s retrieval fluency sub-dimensions (p̂ = .02 to .13). In addition, it had positive associations with spelling ability (p̂ = .22), native language usage (p̂ =.12), arts and humanities knowledge (p̂ = .09), and social studies knowledge (p̂ = .16). These may reflect the developmental role of orderliness in translating small processing speed and retrieval fluency advantages into acquired knowledge in these areas (see Supplementary Tables 224–239 in Appendix I). However, orderliness’ negative relation with lexical knowledge stood in contrast to this proposition (p̂ = -.17, N = 78,579, K = 30).

What are some possible explanations for these patterns of findings? There may be positive relations between conscientiousness-related traits and cognitive abilities, especially for abilities that require goal prioritization and effortful control over extended periods of time such as during knowledge acquisition (i.e., mathematical knowledge, verbal knowledge, and knowledge specific to various domains).3 However, the orderliness aspect of conscientiousness in particular may be less important to, or may even detract from, goal pursuit. That is, imposing structure on one’s environment may not always be optimal for all goals, and it may represent a failure to use self-regulation to focus on the highest-priority goals. For example, compulsion may drive a person to clean their desk before working on a more important task. An alternative explanation suggested by the compensation hypothesis (Moutafi et al., 2004; cf. Murray et al., 2014) is that higher-ability individuals may not need the structure added by orderliness-related traits to self-regulate toward goal states. Consistent with these predictions, meta-analyses for orderliness and order generally displayed negative relations with cognitive abilities. The industriousness aspect of conscientiousness reflects individual differences in being hard-working, resourceful, and competent (DeYoung et al., 2007; Stanek & Ones, 2018). Industriousness’ positive relations with cognitive abilities were supportive of the goal prioritization and effortful control explanations.

Industriousness is a noteworthy, positive correlate of cognitive abilities. The industriousness aspect of conscientiousness was one of the strongest personality correlates of general mental ability across its 89 meta-analyses (p̂ = .32). This positive relation was robust, with a lower 80% credibility value of .23. Positive but weaker correlations with specific abilities suggest that the stronger relation with general mental ability may be the cumulative consequence of industriousness’ numerous links to many specific abilities. Notably, positive relations were found for retrieval fluency sub-dimensions: associational fluency, originality and creativity, and word fluency (p̂ = .14, .26, and .09, respectively; see Supplementary Tables 224–239 in Appendix I). Short term memory and its sub-dimension memory span; visual processing and its sub-dimension visualization;4 and processing speed sub-dimensions perceptual speed, scanning, and number facility also had positive associations with industriousness (p̂ = .13, .21, .13, .11, .17, .15, and .21, respectively; see Supplementary Tables 224–239 in Appendix I). Given these results, it is not surprising that industriousness emerged as a more potent, persistent, and positive correlate of acquired knowledge abilities than global conscientiousness and its orderliness aspect. In fact, most industriousness-knowledge relations were in excess of .23, including quantitative ability (p̂ = .33), mathematics knowledge (p̂ = .23), mathematics achievement (p̂ = .34), verbal ability (p̂ = .33), reading comprehension (p̂ = .31), native language usage (p̂ = .31), spelling ability (p̂ =.31), comprehension knowledge (p̂ = .27), general verbal information (p̂ = .34), language development (p̂ = .29), lexical knowledge (p̂ = .26), domain specific knowledge (p̂ = .31), arts and humanities knowledge (p̂ = .34), business knowledge (p̂ = .33), life sciences knowledge (p̂= .29), and social studies knowledge (p̂ = .28) (see Supplementary Tables 224–239 in Appendix I). Other positive relations with acquired knowledge abilities were in the .16 to .19 range, including realistic knowledge, mechanical knowledge, natural sciences knowledge, and physical sciences knowledge. These results offer evidence that the industriousness aspect of conscientiousness may be a key element of cybernetic motivation and play an important role in the acquisition of knowledge. More specifically, industriousness likely guides the investment of short term memory, long term retrieval fluency, visual processing, and processing speed abilities into tasks of knowledge acquisition, which require persistent prioritization of goals and sustained effort over time.5

Conscientiousness facets displayed mostly positive but weak relations with cognitive abilities, but there were some notable exceptions, discussed below.

Some facets of conscientiousness more closely aligned with the industriousness aspect have notable, positive correlations with cognitive abilities, whereas some facets aligned with orderliness have less consistent relations. Achievement’s relations with cognitive abilities were generally positive though small. Of its more sizable relations, it correlated .23 with long term storage and retrieval (broadly defined) as well as with free recall memory (p̂ = .21), .15 with retrieval fluency, and .20 with reading comprehension (see Supplementary Tables 224–239 in Appendix I). Persistence’s relations with cognitive abilities were also positive but somewhat weaker than achievement’s, and few exceeded .10 in magnitude. Its more sizable relations were with ideational fluency and closure speed as well as perceptual speed (p̂ = .13, .14, and .15, respectively; see Supplementary Tables 224–239 in Appendix I).

The dependability facet of conscientiousness represents tendencies to be responsible and dutiful (Stanek & Ones, 2018). Its pattern of relations with cognitive abilities was somewhat like those of facets associated with the industriousness aspect. Dependability’s relations with acquired knowledge abilities were generally positive, though typically weaker than the industriousness aspect of conscientiousness, and varied across abilities (p̂ range = .06 to .39). Moderate, positive relations were found for dependability with general mental ability (p̂ = .16), memory constructs (p̂ = .17 for free recall memory, .21 for retrieval fluency, and .15 for memory span), visual processing (p̂ = .15), and some acquired knowledge abilities (p̂ = .16 for mathematics knowledge, .23 for mathematics achievement, .24 for verbal ability and comprehension knowledge, .27 for natural sciences knowledge, and .39 for social studies knowledge) (see Supplementary Tables 224–239 in Appendix I). Interestingly, dependability’s associations with the above-noted memory constructs suggest that dependability of character may also indicate dependability of knowledge acquisition and retrieval from long term memory.

Orderliness aspect’s order facet had more varied relations: negative with fluid abilities and positive with acquired knowledge abilities. Specifically, it related negatively to fluid ability (p̂ = -.32, N = 43,299, K = 50) and deductive capabilities (i.e., p̂ = -.12 with general sequential reasoning, N = 8,685, K = 36). Yet order displayed several positive, sizable relations with several important acquired knowledge abilities: quantitative ability (p̂ = .13), mathematics knowledge (p̂ = .12), mathematics achievement (p̂ = .14), verbal ability (p̂ = .25), reading comprehension (p̂ = .21), native language usage (p̂ = .26), spelling ability (p̂ = .30), comprehension knowledge (p̂ = .25), general verbal information (p̂ = .20), language development (p̂ = .25), lexical knowledge (p̂ = .19), domain specific knowledge (p̂ = .20), arts and humanities knowledge (p̂ = .26), business knowledge (p̂ = .20), life sciences knowledge (p̂ = .18), and social studies knowledge (p̂ = .12) (see Supplementary Tables 224–239 in Appendix I). Almost all these acquired knowledge findings, however, were driven by Project Talent. Although Project Talent was a representative and large sample study, it is difficult to discern if the same relations will be observed in a broader population of studies. Appendix E’s “Impact of Outlier Samples” section provides more information about Project Talent and its influence on some findings. The interested reader is also directed to Supplementary Tables 100–196 and 276–354 in Appendices H and J for results without Project Talent data, where some relations are still positive but smaller in magnitude.

Finally, order’s relations with other non-invested abilities were generally much weaker. This was true for long term storage and retrieval as well as visual processing abilities. One exception was order’s relations with specific processing speed abilities, which were uniformly positive and notable (i.e., perceptual speed, scanning, and number facility: p̂ = .20, .18., and .19, respectively; see Supplementary Tables 224–239 in Appendix I). This pattern of findings indicates that the order facet of conscientiousness might offer behavioral advantages (e.g., organizing one’s materials) and processing speed advantages that enable more acquisition of knowledge. These results appear to be distinct from those reported above for the orderliness aspect of conscientiousness, underscoring the previously noted distinction between order and orderliness.

Cautiousness has weak, negative relations with many abilities. Among conscientiousness facets, cautiousness’ relations with cognitive abilities stood out: most relations were negative, though small. General mental ability correlated -.08 with cautiousness (N = 380,929, K = 72), and fluid ability and general sequential reasoning were also negatively related to cautiousness (p̂ = -.07 and -.14, N = 2,772 and 12,720, K = 12 and 38, respectively). Relations with visual processing and processing speed abilities were in the negligible range and not variable. Acquired knowledge abilities evidenced small, near-uniform, negative correlations, including quantitative ability (p̂ = -.06), mathematics knowledge (p̂ = -.07), mathematics achievement (p̂ = -.07), verbal ability (p̂ = -.08), reading comprehension (p̂ = -.10), native language usage (p̂ = -.04), spelling ability (p̂ = -.06), general verbal information (p̂ = -.14), language development (p̂ = -.13), lexical knowledge (p̂ = -.12), domain specific knowledge (p̂ = -.14), arts and humanities (p̂ = -.18), business knowledge (p̂ = -.16), realistic knowledge (p̂ = -.10), life sciences knowledge (p̂ = -.10), mechanical knowledge (p̂ = -.07), physical sciences knowledge (p̂ = -.07), and social studies knowledge (p̂ = -.13) (see Supplementary Tables 224–239 in Appendix I). The pervasiveness of these small, negative correlations indicates that individual differences in being cautious are associated with slightly lower levels of knowledge acquired in most domains. In other words, taking chances—expanding beyond one’s comfort zone—may be associated with the experience of learning. Alternatively, individuals may exhibit higher levels of cautiousness to compensate for lower levels of inductive abilities and crystallized knowledge. That is, cautiousness may be a coping mechanism for lower inductive abilities in processing information into knowledge. The negative associations of cautiousness with long term storage and retrieval abilities provide some support for the former explanation.

Extraversion and Its Family of Personality Constructs

Extraversion designates a family of traits that reflect behavioral engagement with the external world and sensitivity to rewards. Its aspects are assertiveness and enthusiasm, and its major facets are activity, dominance, positive emotionality, sensation seeking, and sociability. Extraversion is closely aligned with arousal and behavioral activation rather than behavioral inhibition. Extraversion is at the root of behavioral activation for change, reflecting sensitivity and response to environmental cues that signal the need for behavioral changes. Unsurprisingly, traits in this cluster relate to the accomplishment of ambitions and status, as well as leading and influencing (Wilmot et al., 2019). Given these roles, extraversion traits of assertiveness, dominance, and activity likely work in tandem with cognitive abilities, resulting in positive associations of cognitive abilities and these extraversion traits. In contrast, we expected extraversion traits with more hedonistic utility, such as sociability and sensation seeking, to be negligibly or negatively related to cognitive abilities, except in the processing abilities arena. Previous meta-analyses reported mostly null effects for extraversion-cognitive abilities relations (Ackerman & Heggestad, 1997; Wolf & Ackerman, 2005). More recently, Anglim et al. (2022) reported small, negative relations for sociability and sensation seeking with invested ability. We found that the more nuanced view of extraversion-ability relations afforded by considering the full spectrum of extraversion facets and cognitive abilities and the higher resolution offered by the extensive database we compiled revealed previously unacknowledged relations.

Figure 15 summarizes results for extraversion constructs. Complete, detailed results are presented in Supplementary Tables 240–255 (see Appendix I).

Figure 15. Extraversion-related traits correlate with cognitive abilities.

Note. “cd.” denotes compound traits. Meta-analytic correlations (i.e., p̂) are only reported in black type when the number of independent effect sizes contributing the specific meta-analysis was ≥ 10 or the sample was ≥ 1,000 participants. Grey-filled cells had no usable data. Green-filled cells indicate positive effects, and red-filled cells indicate negative effects. Saturation indicates effect magnitude. Indentation of construct labels reflects the altitude of each trait in its respective construct hierarchy. Y-axis dendrogram further reinforces level of each ability in the abilities hierarchy.

In general, global extraversion is negligibly related to cognitive abilities. Echoing previous meta-analyses of abilities-extraversion relations, extraversion correlated in the .01 to -.06 range with general mental ability and fluid abilities. There were two exceptions to the pattern of negligible relations. First, retrieval fluency abilities were positively related to extraversion, though the magnitudes of the relations were modest (e.g., retrieval fluency [p̂ = .13], associational fluency [p̂ = .10], ideational fluency [p̂ = .16], originality and creativity [p̂ = .12], expressional fluency [p̂ = .08], naming facility and speed of lexical access [p̂ = .08], and word fluency [p̂ = .07]; see Supplementary Tables 240–255 in Appendix I). Second, some acquired knowledge abilities were positively related to extraversion (e.g., native language usage [p̂ = .14], spelling ability [p̂ = .11], and arts and humanities knowledge [p̂ = .12]), whereas some of the more quantitatively-oriented acquired knowledge abilities were negatively related (e.g., quantitative ability [p̂= -.09], mathematics knowledge [p̂ = -.17], and general science knowledge [p̂ = -.19]; see Supplementary Tables 240–255 in Appendix I).

Extraversion’s activity facet displays sizable, positive relations with cognitive abilities, especially with several long term storage and retrieval, visual processing, and processing speed abilities. In contrast to the global extraversion factor, the activity facet of extraversion correlated .23 with general mental ability (N = 381,809, K = 65). Furthermore, it correlated positively with induction (p̂ = .14), quantitative reasoning (p̂ = .10), and general sequential reasoning (p̂ = .08) (see Supplementary Tables 240–255 in Appendix I). Activity’s relations with the retrieval fluency sub-dimension of long term storage and retrieval were robust and positive (e.g., ideational fluency [p̂ = .25], originality and creativity [p̂ = .20], as well as naming facility and speed of lexical access [p̂ = .10]). Short term memory span was also positively correlated with activity (p̂ = .13, N = 379,981, K = 19). Similarly, visual processing abilities correlated in the .08 to .11 range, and processing speed abilities had a mean meta-analytic correlation of .14 with activity.

The activity facet of extraversion is also a robust, positive correlate of acquired knowledge abilities. It displayed uniform, positive correlations with all acquired knowledge constructs examined. Relations were sizable, in the .17 to .28 range, across abilities where analyses had sufficient N and K for reliable results to emerge. It appears that individuals who are energetic and active score higher on knowledge measures, regardless of the knowledge domain. Given the preceding results, it seems as though activity is related to retrieval of knowledge from long-term memory, the speed with which stimuli are processed, and the ability to process visual information. This high activity, processing ability, and long term memory trait complex corresponds to a high-performance/energy setting for individuals, which would be advantageous in resource-rich environments. Whether there is unique co-variation between activity and acquired knowledge abilities, above and beyond activity’s relations with the long term storage and retrieval, visual processing, and processing speed abilities noted above, is unanswerable with the present data and should be explored in future research.

Extraversion’s assertiveness aspect and dominance facet have modest, positive correlations with long term storage and retrieval constructs related to retrieval fluency. Assertiveness correlated with free recall memory, associational fluency, and word fluency (p̂ = .12, .16, and .15, respectively; see Supplementary Tables 240–255 in Appendix I). Dominance correlated .16 with ideational fluency, .14 with expressional fluency, and .14 with word fluency (see Supplementary Tables 240–255 in Appendix I). Its relations with short term memory abilities appeared to be weaker. Finally, findings for dominance indicated positive relations with general mental ability (p̂ = .11, N = 418,630, K = 159), as well as several acquired knowledge abilities, most notably quantitative abilities (p̂ range = .11 to .15), several verbal abilities (e.g., language development, general verbal information, spelling ability, and reading comprehension), as well as some domain specific knowledge categories (e.g., arts and humanities knowledge and business knowledge; p̂ = .14, .12, .12, .10, .16, and .12, respectively; see Supplementary Tables 240–255 in Appendix I).

Extraversion’s enthusiasm aspect and associated positive emotionality and sociability facets have modest, inconsistent relations with cognitive abilities. Short term memory abilities and long term storage and retrieval abilities appeared to have positive relations with enthusiasm (e.g., free recall memory, retrieval fluency, associational fluency, naming facility and speed of lexical access, word fluency, memory span, and working memory capacity; p̂ = .19, .11, .18, .18, .15, .17, and .14, respectively; see Supplementary Tables 240–255 in Appendix I). Other cognitive ability constructs (e.g., visual processing, processing speed, and acquired knowledge) mostly displayed similarly positive relations but supporting data were relatively sparse. Acquired knowledge abilities such as general verbal information, comprehension knowledge, lexical knowledge, and life sciences knowledge correlated positively with enthusiasm (p̂ = .20, .14, .14, and .17, respectively; see Supplementary Tables 240–255 in Appendix I).

For the most part, sociability and positive emotionality’s patterns of relations mirrored those of enthusiasm, with some exceptions. Positive emotionality was related to several memory-related constructs (e.g., free recall memory, ideational fluency, and memory span; p̂ = .15, .25, and .12, respectively; see Supplementary Tables 240–255 in Appendix I). Non-negligible, positive relations were also observed for visual processing (p̂ = .16, N = 1,213, K = 7) and for psychomotor abilities (e.g., finger dexterity, manual dexterity, and psychomotor speed), though for the latter category the numbers of effect sizes and sample sizes were small. Lastly, the positive emotionality facet was not notably correlated with acquired knowledge abilities.

Sociability’s relations were small and positive with some ability constructs and negative with others. In terms of negative relations, the most conspicuous were quantitative reasoning and general science knowledge (p̂ = -.13 and -.22, N = 84,335 and 72,873, K = 44 and 5, respectively). In terms of positive relations, sociability appeared to be correlated with components of retrieval fluency (e.g., ideational fluency; p̂ = .18, N = 2,603, K = 1; see Supplementary Tables 240–255 in Appendix I) as well as memory span and several verbal abilities.

Sociability is positively related to verbal abilities. Correlations were .18 for verbal ability, .14 for reading comprehension, .18 for native language usage, .20 for spelling ability, .18 for comprehension knowledge, .17 for general verbal information, and .22 for language development (see Supplementary Tables 240–255 in Appendix I). The communication advantages of sociable individuals also appeared to extend to the acquisition of knowledge in arts and humanities and business (p̂ = .21 and .16, N = 361,935 and 357,837, though K = 1 for both).

Sensation seeking is a facet of extraversion that generally displays negligible relations with cognitive abilities. General mental ability and fluid ability relations were negligible .06 and .07 (N = 24,581 and 31,100, K = 79 and 23, respectively). Likewise, both short and long term storage and retrieval constructs did not correlate highly with sensation seeking. One notable exception was a small, positive relation with ideational fluency (p̂ = .13, N = 4,363, K = 11). More negligible relations were found for visual processing and processing speed abilities. Very few acquired knowledge abilities emerged as correlates of sensation seeking: arts and humanities knowledge (p̂ = .30, N = 1,450, K = 2), more general verbal abilities of comprehension knowledge (p̂ = .16, N = 7,768, K = 24), and general verbal information (p̂ = .15, N = 4,993, K = 21).6

Openness and Its Family of Personality Constructs

Openness designates a set of traits related to cognitive exploration and stimulation. Its aspects are experiencing and intellect, and its major facets are aesthetics, curiosity, fantasy, ideas, introspection, need for cognition, non traditional, and variety seeking.

Figure 16 summarizes results for openness constructs. Complete, detailed results are presented in Supplementary Tables 256–272 (see Appendix I).

Figure 16. Openness-related traits correlate with cognitive abilities.

Note. “cd.” denotes compound traits. Meta-analytic correlations (i.e., p̂) are only reported in black type when the number of independent effect sizes contributing the specific meta-analysis was ≥ 10 or the sample was ≥ 1,000 participants. Grey-filled cells had no usable data. Green-filled cells indicate positive effects, and red-filled cells indicate negative effects. Saturation indicates effect magnitude. Indentation of construct labels reflects the altitude of each trait in its respective construct hierarchy. Y-axis dendrogram further reinforces level of each ability in the abilities hierarchy.

Global openness is the Big Five factor most strongly associated with cognitive abilities. It has long been known that the openness family’s personality constructs are among the most closely related to cognitive abilities (Ackerman & Heggestad, 1997; Digman, 1990; Gough, 1953). The current study’s meta-analytic investigations replicated this finding and, overall, the relations between global openness and cognitive ability constructs were larger than relations for most other personality constructs. Openness and general mental ability correlated .26 (N = 280,446, K = 368). The associations between fluid abilities and openness were similarly positive and sizable: .20 with induction, .31 with general sequential reasoning, and .16 with quantitative reasoning (see Supplementary Tables 256–272 in Appendix I). Most long term storage and retrieval constructs for which there were sufficient numbers of effect sizes and large enough sample sizes correlated in the .20 to .25 range with global openness. Short term memory abilities were more weakly correlated (p̂ range = .10 to .19), while relations with visual processing abilities were weaker still (p̂ range = .07 to .14). Relations with processing speed abilities varied by specific ability (e.g., p̂ = .01 for scanning but p̂ = .19 for pattern recognition and p̂ =.10 for perceptual speed; see Supplementary Tables 256–272 in Appendix I).

While invested abilities’ relations with global openness were positive and sizable, verbal abilities’ relations were much stronger than those of quantitative abilities. The mean meta-analytic correlation with acquired verbal abilities (i.e., verbal ability, reading comprehension, reading decoding, spelling ability, comprehension knowledge, general verbal information, lexical knowledge, and listening ability) was .29, whereas the mean meta-analytic correlation with acquired quantitative abilities (i.e., quantitative ability, mathematics knowledge, mathematics achievement) was just .07. The adjacent domain specific ability of physical sciences knowledge also had a weak relation with global openness (p̂ = .06, N = 72,491, K = 5). However, other verbally-oriented domain specific knowledge constructs correlated more strongly: foreign language proficiency, behavioral content knowledge, general science knowledge, social studies knowledge, and arts and humanities knowledge (p̂ = .27, .38, .24, .21, and .15, respectively; see Supplementary Tables 256–272 in Appendix I).

The intellect aspect of openness and its associated need for cognition and ideas facets are strong, positive correlates of cognitive abilities. Relations for intellect, need for cognition, and ideas mostly tracked each other within limits of sampling error. Correlations with general mental ability were .26, .28, and .40, respectively (see Supplementary Tables 256–272 in Appendix I).

Among fluid abilities, induction correlated less with these three openness constructs, .15, .14, and .18, respectively (see Supplementary Tables 256–272 in Appendix I). The mean meta-analytic correlations with general sequential reasoning and quantitative reasoning were .21 and .19, respectively. Relations with long term storage and retrieval, as well as short term memory abilities, were of similar magnitude (mean p̂ across all meta-analyses = .18 and .20, respectively). Relations with visual processing abilities averaged to .21. In general, memory constructs displayed somewhat larger correlations with the ideas facet than with the need for cognition or curiosity facets.

Following this trend, visual processing abilities’ relations were stronger for openness’ ideas facet (p̂ = .31 for general visual processing ability, p̂ = .24 for visualization, p̂ = .22 for closure speed, p̂ = .26 for flexibility of closure) than for the curiosity facet (p̂ = -.06 for general visual processing ability, p̂ = .21 for visualization, p̂ = .01 for closure speed, p̂ = .21 for flexibility of closure) or the need for cognition facet (p̂ = .07 for general visual processing ability, p̂ = .23 for visualization, p̂ = .20 for closure speed; no data were available for flexibility of closure) (see Supplementary Tables 256–272 in Appendix I). Processing speed abilities were modestly, but still positively, correlated with need for cognition, ideas, and curiosity facets (mean p̂ = .13).

Overall, the relations between the intellect aspect and cognitive ability constructs were similar to previous meta-analytic estimates (Ackerman & Heggestad, 1997; Anglim et al., 2022; von Stumm & Ackerman, 2013), with many tending toward moderate to moderately large, positive relations (e.g., general mental ability). Notably, relations between need for cognition and cognitive ability constructs were not dramatically higher than other openness-related traits. Relations of need for cognition with most cognitive abilities were in the same, uniformly moderate to large range (p̂ range = .20 to .36).

The ideas facet was the personality trait that was most strongly and consistently related to cognitive ability constructs (mean p̂= .28) with the magnitudes of the relations especially large for acquired knowledge constructs (p̂ range = .30 to .50). For the most part, these findings mirrored those observed for the intellect aspect but with greater strength.

The intellect aspect of openness and its associated need for cognition and ideas facets are stronger, positive correlates of verbal abilities than quantitative abilities. Mirroring findings for global openness, the mean meta-analytic correlation of intellect with all verbal abilities was .23, whereas the mean meta-analytic correlation with all acquired quantitative abilities was .14. There were also a few large correlations between some domain specific acquired knowledge abilities and the ideas facet (i.e., p̂ = .41 for natural sciences knowledge and p̂ = .50 for social studies knowledge, though second-order sampling error could explain these larger than typical relations given N = 1,614 and 1,676, K = 1 and 2, respectively).

Much less information was available for the curiosity facet, which is also associated with the intellect aspect of openness. Despite the paucity of data, the patterns of relations between curiosity and cognitive abilities largely followed the patterns for the intellect aspect and its related facets described above. These patterns suggest that verbally oriented knowledge acquisition may be more attainable via behaviors (e.g., an interest in ideas) than quantitatively oriented knowledge.

The non traditional facet of openness is a notable, though inconsistent, correlate of cognitive abilities. The non traditional facet describes a predisposition to question long-established beliefs and practices. Its mean meta-analytic correlation with fluid abilities was .19. The corresponding value for all long term storage and retrieval abilities was .13. Although this facet’s relations with visual processing, processing speed, and acquired quantitative abilities were either highly variable, based on sparse data, or negligible, there were sizable relations (p̂ = .24 to .31) with several verbal abilities (e.g., comprehension knowledge, lexical knowledge). It is worth noting that non traditional’s meta-analytic relations often contained zero within the 80% credibility value ranges, indicating lack of homogeneity across effect sizes and therefore, uncertain generalizability.

Openness’ introspection and variety seeking facets have positive but weak relations with cognitive abilities. Their mean meta-analytic correlations with major ability constructs did not exceed .10 (mean p̂ = .05 for fluid abilities, .10 memory abilities, .07 for visual processing abilities, and .04 for processing speed abilities). Relations with acquired quantitative abilities and verbal abilities were also meager (mean for all quantitative abilities p̂ = .14 and for all verbal abilities .07). Stronger relations with domain specific knowledge were based on small numbers of effect sizes, and were likely affected by second-order sampling error: introspection was strongly correlated with natural sciences knowledge (p̂ = .45, N = 1,614, K = 1) and social studies knowledge (p̂ = .49, N = 1,792, K = 2).

The experiencing aspect of openness and its associated fantasy and aesthetics facets are negligibly related to most cognitive abilities. Fluid abilities were uncorrelated with these openness constructs (mean p̂ = .01). Similarly, the mean meta-analytic correlation with visual processing abilities was .00. Relations with all processing speed and memory abilities were somewhat larger (mean p̂ = .08 for processing speed abilities and .09 for memory constructs). Cognitive ability constructs involving creativity, including associational fluency, originality and creativity, and word fluency, demonstrated consistently modest, positive relations (mean p̂ = .12, .17, and .14, respectively; see Supplementary Tables 256–272 in Appendix I).

The experiencing aspect has notable, positive relations with acquired knowledge. In particular, arts and humanities knowledge had the largest association with the experiencing aspect (p̂ = .38, N = 363,385, K = 3). Verbal abilities also demonstrated a sizable relation (mean p̂ = .28). Meta-analytic relations with acquired quantitative abilities were positive and in the .17 to .18 range.7

Across the results for all openness traits there is a distinctive pattern of ability relations for the intellect versus experiencing aspect, which is not surprising given previous work (DeYoung et al., 2005). The positive and substantial relations between intellect-related traits and general mental ability, inductive abilities, and even visuo-spatial abilities might indicate a trait complex associated with initiating adaptation to the external environment through curiosity and engagement with complexities encountered. Indeed, the intellect aspect and its associated facets need for cognition, ideas, and curiosity were among the strongest correlates of general mental ability (e.g., p̂ = .40 for ideas facet). Fluid abilities also evidenced strong and consistent relations with intellect-related traits, though induction displayed weaker correlations compared to more deductive fluid abilities. The same traits had quite strong relations with visuo-spatial abilities as well as with long term memory (i.e., storage and retrieval) and short term memory abilities. Taken together, these results indicate that tendencies to seek cognitively engaging stimuli as well as to interpret stimuli and situations through reasoning and deductive logic appear to be positively and sizably associated with fluid, visuo-spatial, and memory abilities.

As expected, experiencing-related traits from the openness domain (e.g., fantasy, aesthetics) had notably weaker relations with these abilities than those of intellect-related traits (DeYoung et al., 2014). This is because experiencing traits are internally and perceptually oriented, increasing their likelihood to positively cluster with perceptual abilities (e.g., processing speed) as well as creative abilities (e.g., originality) more strongly than intellect-related traits. Such a complex of perceptual abilities and openness’ experiencing constructs reflects interpretation of stimuli and situations through perception. Similarly, experiencing traits’ positive relations with originality and creativity ability provided evidence for unique personality traits and cognitive abilities leading to creativity—a finding even more remarkable given the weaker relations between experiencing and general mental ability.

Overall, openness’ experiencing-related traits were more strongly correlated with verbal knowledge than quantitative knowledge, representing the need to verbalize a variety of perceptual experiences while also contemplating them intellectually.

Compound Personality Traits and Cognitive Abilities

Compound personality traits incorporate substantial variance from more than one Big Five factor. Such traits have been utilized in research and practice for as long as primary personality factors (Bernreuter, 1935), and they are some of the most commonly studied and potent predictors of important outcomes (Ones et al., 1993; Ones & Viswesvaran, 2001). Many of these traits have been defined and their components identified via previous factor analyses, content examinations, and nomological network investigations (Hough & Ones, 2002; Stanek & Ones, 2018).8 Many compound traits displayed non-trivial relations with abilities. In a few cases, the mixtures diluted effects. For example, the compound personality trait of openness to emotions combines variance from high extraversion and high openness, which were related to general mental ability -.02 and .26, and the resulting compound’s relation was .06. In other cases, non-trivial effects of multiple components combined to generate larger relations. For example, extraversion related -.01 and agreeableness related .02 with spatial scanning, while the compound trait of warmth, which incorporates variance from both, related -.15 with the same ability. We summarize the findings for compound personality constructs below and in Figures 12–16. Complete, detailed results are presented in Supplementary Tables 206–208, 218–223, 233–239, 248–255, and 267–272 in Appendix I.

Neuroticism-Affiliated Compound Personality Traits

The compound personality traits most influenced by neuroticism include self esteem,9 stress tolerance,10 and locus of control.11 Self esteem represents self-confidence, self-assurance, and self-worth. Stress tolerance is the tendency to handle pressure well and without anxiety, even in stressful conditions. Internal locus of control is a tendency to believe that one has control over what happens to oneself. Self esteem had a mean meta-analytic correlation of .15 across abilities, and was especially correlated with acquired knowledge constructs (mean p̂ = .21). Stress tolerance had a mean meta-analytic correlation of .14 across abilities, and was especially correlated with processing speed abilities, verbal abilities, and domain specific knowledge (mean p̂ = .15, .16, and .19, respectively). Finally, internal locus of control had a mean meta-analytic correlation of .15 across abilities, and was especially correlated with acquired knowledge constructs but also fluid abilities (mean p̂ = .29 and .18, respectively).

Agreeableness-Affiliated Compound Personality Traits

The compound personality traits most influenced by agreeableness include warmth,12 interpersonal sensitivity,13 customer service,14 trust,15 Machiavellianism,16 and self monitoring.17 Warmth is the tendency to be friendly and affiliative. Interpersonal sensitivity is the tendency to be tactful in social situations and sensitive to others’ moods. Customer service combines personality traits associated with high performance in customer-facing roles, especially those that demand high agreeableness. Trust is the tendency to believe in the honesty of others and not question their motives. Machiavellianism is the tendency to disregard social norms and use deceit to achieve personal gains. Self monitoring is the tendency to control one’s public self-presentations.

These traits had negligible relations with cognitive abilities in most cases, but there were a few noteworthy trends. Warmth was negatively correlated with quantitative abilities and with science-related knowledge (mean p̂ = -.12 and -.11, respectively). Interpersonal sensitivity generally had insufficient data to draw conclusions but displayed an intriguing correlation with general mental ability (p̂ = .20, N = 9,855, K = 21) as well as a non-robust but suggestive, positive relation with behavioral content knowledge (p̂ = .24, N = 247, K = 1). Customer service meaningfully correlated with a few cognitive abilities, including general mental ability and the fluid abilities of induction and quantitative reasoning (p̂ = .10, .14, and .13, respectively). Trust displayed small, positive relations with fluid abilities, short term memory abilities, and comprehension knowledge abilities (mean p̂ = .10, .14, and .10, respectively). Machiavellianism correlated positively with fluid abilities, visual processing abilities, mathematics knowledge, and science-related knowledge (mean p̂ = .18, .12, .23, and .22, respectively). Data were very sparse for self monitoring but a small, positive relation was detected with general mental ability (p̂ = .12, N = 3,501, K = 6).

Conscientiousness-Affiliated Compound Personality Traits

The compound personality traits most influenced by conscientiousness include routine seeking,18 cold efficiency,19 judging-perceiving,20 self control,21 type A,22 achievement via independence,23 and rugged individualism.24 Routine seeking is the tendency to desire predictable and orderly daily experiences. Cold efficiency is the tendency to rigidly focus on principles despite costs. Judging-perceiving is the tendency to desire structure and definiteness. Self control is the tendency to restrain behaviors and reactions. Type A is the tendency to be competitive, impatient, and ambitious. Achievement via independence is the tendency to be driven to achieve goals and objectives, particularly in unstructured and unregulated situations. Rugged individualism is the tendency to be bold, adventurous, and put effort toward pursuing goals.

The meta-analytic results indicated that routine seeking was a sizable, negative correlate of cognitive abilities across the spectrum, especially acquired knowledge (mean p̂ = -.20 and -.28, respectively). Cold efficiency showed a small, positive relation with general sequential reasoning (p̂ = .14, N = 7,630, K = 25) and a small, negative relation with originality and creativity (p̂ = -.11, N = 1,188, K = 6). Judging-perceiving was a negative correlate of several cognitive abilities (mean p̂ = -.09), especially fluid abilities and retrieval fluency abilities (mean p̂ = -.14 and -.12, respectively). Self control was not associated with any cognitive abilities, except small relations with physical sciences knowledge (positive), general verbal information (negative), and psychomotor speed (p̂ = .10, -.10, and -.13, respectively). Type A was not a meaningful correlate of any cognitive abilities (mean p̂ = .03). Achievement via independence was a strong, positive correlate of several cognitive abilities (mean p̂ = .31), especially general mental ability, fluid abilities, visual processing abilities, processing speed abilities, and acquired knowledge (p̂ = .37, .17, .26, .13, and .37, respectively). Rugged individualism was positively correlated with several cognitive abilities, in particular acquired knowledge (mean p̂ = .16).

Extraversion-Affiliated Compound Personality Traits

The compound personality traits most influenced by extraversion include optimism,25 ambition,26 ambitious risk taking,27 risk taking,28 managerial potential,29 grandiosity and intimidation,30 narcissism,31 and restrained expression.32 Optimism is the tendency to ascribe positive attributions to events and people. Ambition is the tendency toward initiative-taking, competitiveness, and strong will in pursuit of goals. Ambitious risk taking is the tendency to pursue dangerous activities to achieve power, status, or other rewards without regard for consequences. Risk taking is the tendency to downplay hazards and engage with uncertain ventures. Managerial potential is the tendency to provide fair, stable leadership. Grandiosity and intimidation is the tendency to believe that one is superior to others and deserves special treatment. Narcissism is the tendency to be self-centered, exploitative, and desirous of admiration. Restrained expression is the tendency to be serious, responsible, and in control of one’s impulses.

Optimism was positively correlated with several cognitive abilities, in particular general mental ability, quantitative reasoning, retrieval fluency abilities, short term memory abilities, choice reaction time abilities, acquired quantitative ability, and life sciences knowledge (p̂ = .13, .21, .16, .14, .11, .25, and .22, respectively). Some of ambition’s relations suggest even stronger positive connections, though several were based on small sample sizes and numbers of effect sizes (e.g., mean p̂ = .30 with retrieval fluency constructs across four meta-analyses, but none had more than 1,000 participants or 10 effect sizes). Its strongest robust relations were with acquired knowledge constructs (mean p̂ = .14) as well as processing speed scanning and number facility (p̂ = .66 and .25, respectively). Very few meta-analyses involving ambitious risk taking or risk taking had sufficient data. Managerial potential also had sparse data, but positive relations were observed with general mental ability and lexical knowledge (p̂ = .27 and .10, respectively). Grandiosity and intimidation also had sparse data, but small relations were observed with general mental ability and general science knowledge (p̂ = -.11 and .10, respectively). Narcissism also had sparse data, but the few robust relations were negligible (mean p̂ = -.02). Restrained expression relations were small and positive for quantitative reasoning and verbal ability, but more sizable for general science knowledge (p̂ = .12, .20, and .30, respectively).

Openness-Affiliated Compound Personality Traits

The compound personality traits most influenced by openness include openness to emotions,33 tolerance,34 innovation,35 creative personality,36 independent of conventions and others,37 and resourcefulness.38 Openness to emotions is the tendency to be in touch with one’s feelings and emotional experiences. Tolerance is the tendency to appreciate differences, especially between people. Innovation is the tendency to seek and engage with novelty, often resulting in novel ideas. Creative personality is the proclivity toward ingenuity. Independent of conventions and others is the tendency to be self-sufficient, individualistic, and autonomous. Resourcefulness is the tendency to solve problems with persistence, planning, and novel thinking.

Openness to emotions correlated positively with retrieval fluency abilities (mean p̂ = .23) and perhaps even long term storage and retrieval abilities more broadly (not considering N and K thresholds, the mean p̂ .23). Short term memory abilities, visual processing abilities, perceptual speed, and lexical knowledge appeared to be positive correlates, though weaker (mean p̂ = .11, .11, .12, and .16, respectively). Tolerance displayed sizable, positive relations with several cognitive abilities (mean p̂ = .20), especially acquired knowledge constructs (mean .37). Innovation was positively related to visual processing abilities as well as lexical knowledge and general science knowledge (mean p̂ = .14, .13, and .10, respectively). Creative personality was a sizable, positive correlate of several cognitive abilities (mean p̂ = .24), especially verbal abilities and fluid abilities (mean p̂ = .32 and .18, respectively). Independent of conventions and others displayed small, positive relations with general mental ability and acquired knowledge constructs (p̂ = .13 and mean p̂ = .14, respectively). Resourcefulness was a sizable, positive correlate of several abilities (mean p̂ = .21), ranging from induction to free recall memory to visualization to perceptual speed to lexical knowledge (mean p̂ = .25, .23, .10, .27, and .35, respectively).

Higher Order Factors of the Big Five

The findings above cover the Big Five personality factors, their aspects, facets, and even compounds, but there are also meaningful personality constructs at higher levels of the personality hierarchy (see Chapter 2). Findings for higher order personality constructs are summarized below and in Figure 17. Complete, detailed results are presented in Supplementary Tables 273–275 in Appendix I.

Meta-Trait Factor Alpha: Stability/Socialization

Factor alpha, which is also referred to as stability or socialization, is a higher order meta-trait that captures the variance shared between emotional stability, agreeableness, and conscientiousness. Individuals with high levels of factor alpha tend to focus on doing a lot of little things right to avoid large calamities. They also tend to function well in human society as safe, harmonious, and dependable people with low levels of antisocial tendencies (Stanek & Ones, 2018).

Results for factor alpha are summarized in Figure 17. Complete, detailed results are presented in Supplementary Table 274 in Appendix I.

The relations between the higher order trait of factor alpha/stability and cognitive ability constructs were much smaller in magnitude than what was observed for several of the neuroticism- and conscientiousness-related personality constructs. The general trend was toward negligible to very small, though positive, relations. Nevertheless, some modest, positive relations were found for fluid quantitative reasoning (p̂ = .12, N = 55,414, K = 17), memory span (p̂ = .11, N = 21,609, K = 18), working memory capacity (p̂ = .13, N = 7,370, K = 11), the long term storage and retrieval constructs free recall memory and word fluency (p̂ = .13 for both, N = 6,751 and 6,196, K = 5 and 14, respectively), and processing speed abilities (mean p̂ = .13). Much more sizable relations were found for acquired quantitative abilities (mean p̂ = .26, SDp̂ = .06 to .23) and reading comprehension (p̂= .40, SDp̂ = .00) (see Supplementary Table 274 in Appendix I). In general, factor alpha was not a notable, consistent correlate of most cognitive abilities, likely because of the divergent influences of its contributors’ relations with cognitive abilities.

Meta-Trait Factor Beta: Plasticity

Factor beta, which is also referred to as plasticity, is a higher order meta-trait that captures the shared variance between extraversion and openness. Individuals with high levels of factor beta tend to place bigger, riskier behavioral bets rather than consistently toiling for more sure, small gains. They see many interesting opportunities that could lead to success and are more willing to explore and pivot. Results for factor beta are summarized in Figure 17 and described below. Complete, detailed results are presented in Supplementary Table 275 in Appendix I.

Factor beta tended to be positively related to cognitive ability constructs, but its associated true standard deviations were relatively large in most instances. Its relations with cognitive abilities were generally smaller than those found for the intellect aspect and related facets of openness. General mental ability correlated .20 with factor beta (N = 22,759, K = 66). Relations with specific fluid abilities varied in magnitude (p̂ = .09 for induction and .18 for quantitative reasoning, N = 38,189 and 2,085, K = 78 and 11, respectively). The memory constructs ideational fluency and memory span both displayed small to moderate, positive, and, in the case of ideational fluency, variable relations. However, relatively small numbers of studies contributed to these meta-analyses, raising the specter of second-order sampling error (p̂ =.14 for both, SDp̂ = .16 and .04, N = 1,850 and 5,340, K = 7 and 4, respectively). Several acquired knowledge constructs displayed more positive and more consistent relations: mathematics achievement (p̂ = .32, SDp̂ = .10, N = 1,708, K = 2), verbal ability (p̂ = .28, SDp̂ = .11, N = 3,678, K = 16), native language usage (p̂ = .26, SDp̂ = .00, N = 1,356, K = 2), reading comprehension (p̂ = .23, SDp̂ = .09, N = 1,362, K = 2), lexical knowledge (p̂ = .21, SDp̂ = .18, N = 6,637, K = 16), mechanical knowledge (p̂ = .25, SDp̂ = .00, N = 1,777, K = 2), natural sciences knowledge (p̂ = .32, SDp̂ = n/a, N = 1,614, K = 1), and social studies knowledge (p̂ = .43, SDp̂ = .07, N = 1,934, K = 2). Plasticity appears to be a relevant personality trait for knowledge acquisition, though whether this is above and beyond the effects of extraversion and openness is not answerable with the present data.

General Factor of Personality

The general factor of personality sits at the apex of the Pan-Hierarchical Five Factor Model and represents individual differences in general self-evaluations. It may also indicate inter- and intra-personal integration (Davies et al., 2015; Stanek & Ones, 2018). Results for the general factor of personality are summarized in Figure 17 and described below. Complete, detailed results are presented in Supplementary Table 273 in Appendix I.

The general factor of personality correlated .36 with general mental ability. This relation reflects a cluster of general fitness traits associated with cybernetic effectiveness and the agility to move effectively between a focus on surviving and a focus on thriving, as the environment demands. Relations with most other abilities were based on sparse data and could not be used to derive robust conclusions. The few acquired knowledge abilities for which data were sufficient had negligible relations in the -.03 to .04 range. Research should examine the general factor of personality’s relations with other specific ability clusters (e.g., short term memory, retrieval fluency, and visual processing ability, in particular) since the existing, small amount of meta-analytic data was suggestive of modest-to-sizable relations.

Figure 17. Higher order personality traits correlate with cognitive abilities.

Note. “cd.” denotes compound traits. Meta-analytic correlations (i.e., p̂ ) are only reported in black type when the number of independent effect sizes contributing the specific meta-analysis was ≥ 10 or the sample was ≥ 1,000 participants. Grey-filled cells had no usable data. Green-filled cells indicate positive effects, and red-filled cells indicate negative effects. Saturation indicates effect magnitude. Indentation of construct labels reflects the altitude of each trait in its respective construct hierarchy. Y-axis dendrogram further reinforces level of each ability in the abilities hierarchy.

Distillation of Personality’s Relations with Intelligence

In this chapter, we detailed evidence that personality’s relations with cognitive abilities extend beyond openness constructs. Nevertheless, openness is a sizable correlate of cognitive abilities, and its intellect aspect and associated facets (e.g., ideas) are even more strongly related. Among invested abilities, verbal abilities are more strongly related to openness’ intellect aspect than quantitative abilities. The experiencing aspect of openness is mostly unrelated to cognitive abilities, except for processing speed abilities and acquired verbal abilities as well as some domain specific knowledge constructs.

Neuroticism’s relations with cognitive abilities were somewhat unexpected. While past research has documented test anxiety as a negative correlate of cognitive abilities, we found broader enervating effects associated with neuroticism traits, including anxiety, uneven tempered, suspiciousness, and depression.

Previous meta-analytic research had reported unremarkable relations between extraversion traits and cognitive abilities (Anglim et al., 2022; Wolf & Ackerman, 2005). Our results were similar for overall extraversion, but the enhanced resolution of our database led to a more nuanced set of conclusions for extraversion’s lower-level traits and compounds that highlighted important relations. For example, the sociability facet is positively related to verbal abilities. Extraversion’s assertiveness aspect and associated dominance facet are positively related to long term memory’s retrieval constructs involving retrieval fluency. Most notably, extraversion’s activity facet is positively related to several retrieval fluency abilities, visual processing, processing speed abilities, and numerous acquired knowledge dimensions.

Previous meta-analytic research had reported similarly negligible-to-small relations between conscientiousness and cognitive abilities (Ackerman & Heggestad, 1997; Anglim et al., 2022), but again, our results revealed some important relations. In particular, conscientiousness traits assessing industriousness and associated facets are positively, and sometimes substantially, related to cognitive abilities. In contrast, orderliness and the compound trait of routine seeking are negatively related.

Agreeableness traits are the weakest personality correlates of cognitive abilities, with mostly negligible relations, though the compassion and politeness aspects display opposite associations: compassion positive and politeness negative.

Taking a bird’s eye view of these findings, we are struck by the constellation of intellect, activity, industriousness, and low neuroticism forming a consistent set of cognitive ability correlates. These traits represent behavioral activation and proactivity, which are also resource-intensive tendencies. The strong relations for compound traits directly assessing the amalgam of these traits further reinforce this conclusion. Finally, we would be remiss if we did not note the intriguingly strong relation between general mental ability and general factor of personality.

References

Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin, 121, 219–245.

Anglim, J., Dunlop, P. D., Wee, S., Horwood, S., Wood, J. K., & Marty, A. (2022). Personality and intelligence: A meta-analysis. Psychological Bulletin, 148(5–6), 301.

Bandura, A., & Walters, R. H. (1977). Social Learning Theory (Vol. 1). Prentice Hall.

Bernreuter, R. G. (1935). Manual for the personality inventory. Stanford University Press.

Davies, S. E., Connelly, B. S., Ones, D. S., & Birkland, A. S. (2015). The general factor of personality: The “Big One,” a self-evaluative trait, or a methodological gnat that won’t go away? Personality and Individual Differences, 81, 13–22.

DeYoung, C. G. (2013). A psychobiological framework for personality neuroscience. http://deyoung.psych.umn.edu/research

DeYoung, C. G., Peterson, J. B., & Higgins, D. M. (2005). Sources of openness/intellect: Cognitive and neuropsychological correlates of the fifth factor of personality. Journal of Personality, 73(4), 825–858.

DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology, 93(5), 880.

DeYoung, C. G., Quilty, L. C., Peterson, J. B., & Gray, J. R. (2014). Openness to experience, intellect, and cognitive ability. Journal of Personality Assessment, 96(1), 46–52.

Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual Review of Psychology, 41(1), 417–440.

Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2(2), 156–168.

Gough, H. G. (1953). A nonintellectual intelligence test. Journal of Consulting Psychology, 17(4), 242.

Hough, L. M., & Ones, D. S. (2002). The structure, measurement, validity, and use of personality variables in industrial, work, and organizational psychology. In Handbook of industrial, work, and organizational psychology (Vol. 1, pp. 233–277). Sage.

Moutafi, J., Furnham, A., & Paltiel, L. (2004). Why is conscientiousness negatively correlated with intelligence? Personality and Individual Differences, 37(5), 1013–1022.

Murray, A. L., Johnson, W., McGue, M., & Iacono, W. G. (2014). How are conscientiousness and cognitive ability related to one another? A re-examination of the intelligence compensation hypothesis. Personality and Individual Differences, 70, 17–22.

Ones, D. S., & Viswesvaran, C. (2001). Integrity Tests and Other Criterion-Focused Occupational Personality Scales (COPS) Used in Personnel Selection. International Journal of Selection and Assessment, 9(1–2), 31–39.

Ones, D. S., Viswesvaran, C., & Schmidt, F. L. (1993). Comprehensive meta-analysis of integrity test validities: Findings and implications for personnel selection and theories of job performance. Journal of Applied Psychology, 78(4), 679.

Stanek, K. C., & Ones, D. S. (2018). Taxonomies and compendia of cognitive ability and personality constructs and measures relevant to industrial, work and organizational psychology. In D. S. Ones, C. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work & organizational psychology: Personnel psychology and employee performance (pp. 366–407). Sage.

Tian, J. (2021). Comprehensive meta-analysis of perfectionism and a synthesis for Work Psychology [Doctor of Philosophy]. University of Minnesota-Twin Cities.

von Stumm, S., & Ackerman, P. L. (2013). Investment and intellect: A review and meta-analysis. Psychological Bulletin, 139(4), 841–869.

Watson, D., & Clark, L. A. (1984). Negative affectivity: The disposition to experience aversive emotional states. Psychological Bulletin, 96(3), 46.

Wilmot, M. P., Wanberg, C. R., Kammeyer-Mueller, J. D., & Ones, D. S. (2019). Extraversion advantages at work: A quantitative review and synthesis of the meta-analytic evidence. Journal of Applied Psychology, 104(12), 1447.

Wolf, M. B., & Ackerman, P. L. (2005). Extraversion and intelligence: A meta-analytic investigation. Personality and Individual Differences, 39(3), 531–542.

Endnotes

1 Throughout this book, the mean p̂ provides the average of meta-analytic correlations across a set of meta-analytic findings described in the same sentence (e.g., uneven tempered’s relations with comprehension knowledge abilities in this example). This metric characterizes trends in the meta-analyses and avoids giving undue weight to heavily studied constructs.

2 The lower level of invested abilities is likely due to the cumulative drag of depression on the process of knowledge acquisition over time.

3 Relations between conscientiousness and general mental ability might also represent a bundle of individual differences that enables vicarious learning in line with Social Learning Theory (Bandura & Walters, 1977).

4 Though across all robustly estimated visual processing abilities the average meta-analytic relation was just .07.

5 An alternative interpretation is that people with high levels of cognitive ability may feel motivated by challenges as something to be overcome by achievement (eustress). In contrast, those with lower levels of ability may have less positive experiences with challenges and view them as a source of distress.

6 One potential explanation of these generally null findings is that it may be important to distinguish individuals’ motivations for seeking sensations (e.g., physical rush vs. rationally valuable gains).

7 However, excluding effect sizes from a large N, influential sample (Project Talent), only one meta-analytic estimate had sufficient N or K to warrant interpretation: mathematics achievement, which had a negative relation with the experiencing aspect (p̂ = -.23, N = 2,547, K = 4).

8 Similarly, compound abilities incorporate substantial variance from more than one ability dimension, though the variance is not diverse enough to index general mental ability.

9 Self esteem combines low neuroticism, high extraversion, and high conscientiousness.

10 Stress tolerance combines low neuroticism, high agreeableness, and high conscientiousness. This compound draws more variance from neuroticism compared to other compounds with the same formulation (e.g., customer service).

11 Internal locus of control combines low neuroticism and high conscientiousness as well as a lesser degree of high positive emotionality (facet of extraversion).

12 Warmth combines high extraversion and high agreeableness.

13 Interpersonal sensitivity combines low neuroticism, high extraversion, high openness, and high agreeableness.

14 Customer service combines low neuroticism, high agreeableness, and high conscientiousness. This compound draws more variance from agreeableness than other compounds with the same formulation (e.g., stress tolerance).

15 Trust combines low neuroticism and high agreeableness.

16 Machiavellianism is primarily composed of low agreeableness but also high neuroticism and low conscientiousness (indicating low meta-trait factor alpha).

17 Self monitoring combines low agreeableness and high meta-trait factor beta as well as lesser degrees of low neuroticism and low conscientiousness.

18 Routing seeking combines low variety seeking (facet of openness) and high order (facet of conscientiousness).

19 Cold efficiency combines low agreeableness and high conscientiousness.

20 Judging-perceiving combines low openness and high conscientiousness.

21 Self control combines low neuroticism and high conscientiousness.

22 Type A combines high neuroticism, high extraversion, low agreeableness, and high conscientiousness.

23 Achievement via independence combines low neuroticism, high extraversion, high openness, and high conscientiousness. This compound involves more openness than compounds with the same components (e.g., generalized self efficacy).

24 Rugged individualism combines high industriousness (aspect of conscientiousness) and high meta-trait factor beta.

25 Optimism combines low neuroticism and high extraversion.

26 Ambition combines high extraversion and high conscientiousness.

27 Ambitious risk taking combines high assertiveness (aspect of extraversion), high achievement (facet of conscientiousness), and low cautiousness (facet of conscientiousness).

28 Risk taking combines high extraversion, high openness, and low conscientiousness.

29 Managerial potential combines low neuroticism, high extraversion, and high conscientiousness.

30 Grandiosity and intimidation combines high extraversion and low agreeableness.

31 Narcissism combines high extraversion and low agreeableness as well as lesser degrees of high volatility (aspect of neuroticism) and low withdrawal (aspect of neuroticism).

32 Restrained expression combines low extraversion and high conscientiousness.

33 Openness to emotions combines high extraversion and high openness.

34 Tolerance combines high openness and high agreeableness.

35 Innovation combines high extraversion, high openness, and to a lesser degree low neuroticism.

36 Creative personality combines high non traditional (facet of openness), high industriousness (aspect of conscientiousness), low orderliness (aspect of conscientiousness), low cautiousness (facet of conscientiousness), and high meta-trait factor beta.

37 Independent of conventions and others combines high openness and low agreeableness.

38 Resourcefulness combines low neuroticism, high openness, and high conscientiousness.

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Chapter 6

Cybernetic Trait Complexes Theory

CHAPTER SUMMARY

Our data provide novel insights that depict a new theory of personality-intelligence relations, building on existing work.

Individuals are cybernetic systems trying to leverage resources to achieve goals (e.g., survival, reproduction, getting promoted, running a marathon). They adjust their behavior and goals based on feedback loops.

Cybernetic loops consist of three stages (i.e., current state, transition state, and goal state), which each involve three steps (i.e., sense, evaluate, and decide).

Cybernetic loops involve goals, strategies, and behaviors that can be mapped along two fundamental dimensions: homeostasis vs. change and internal vs. external.

Personality and intelligence jointly impact how individuals balance homeostasis and change internally and externally in order to pursue self-preservation and self-evolution.

Individuals’ personalities and abilities can only be fully understood by considering both their genes/biology and their environments.

Personality represents typical strategies for dealing with internal and external stimuli in pursuit of self-preservation and self-evolution. Intelligence indexes how effectively information is leveraged to set goals and employ strategies that maximize the probability of goal achievement. These traits work in concert to maximize odds of surviving and thriving by orchestrating growth and homeostasis in environmentally sensitive ways.

Individuals have multiple goals at any given time, and personality and intelligence co-function to prioritize, balance, and pursue these goals effectively.

Cybernetic Trait Complexes Theory (CTCT) identifies constellations of personality traits and cognitive abilities optimized to support goals and strategies associated with internal and external homeostasis and change.

Examining over a century of quantitative research on personality and intelligence, the vast meta-analyses summarized and discussed in this volume reveal patterns of relations that form the basis of a theory for thinking about how personality and intelligence are connected and interrelated.

These meta-analyses were exhaustive, yet not driven by strong initial hypotheses. We were open to the emergence of new, previously unknown relations. We thus sought to meta-analytically construct a high-resolution picture using all personality-cognitive ability relations from the past 100 years.

We looked across the meta-analytic results to discern patterns, inductively identifying general trends (see Woo et al., 2017). The lack of previous inductive research in examining personality-cognitive ability relations has had a choking effect in the study of individual differences, limiting researchers to previously discovered or hypothesized relations (e.g., openness-cognitive ability [von Stumm & Ackerman, 2013], and extraversion-cognitive ability [Wolf & Ackerman, 2005], respectively). Limiting research only to questions that have theoretical or previous empirical support discourages discovery and advancement of knowledge (Spector, 2017).

In our work, we followed meta-analytic induction to abduct1 a new theory. That is, using meta-analytic findings and inductive reasoning, we derived the most plausible explanations of the observed personality-cognitive ability relations. The Cybernetic Trait Complexes Theory (CTCT) we articulate below aims to explain and provide a basis for future research concerning why personality-ability relations exist.

CTCT builds on existing theories (e.g., Cattell’s Investment Theory [1987]; Ackerman & Heggestad’s Emergence of Trait Complexes [1997]; Ortony et al.’s Affects, Behaviors, Cognitions, and Desires (ABCD) Theory [2005]; von Stumm & Ackerman’s Theory of Intellectual Engagement [2013]; and DeYoung’s Cybernetic Big Five Theory (CB5T) [2015]). It is also consistent with other prominent individual differences frameworks (e.g., behavioral activation and behavioral inhibition [Gray, 1981, 1982], getting along and getting ahead [Hogan & Holland, 2003], and promotion and prevention focus [Lanaj et al., 2012]).

In sum, CTCT has been built by applying inductive and abductive reasoning to this volume’s meta-analytic findings. Yet we also acknowledge that it incorporates major tenets from existing theories, where they were consistent with the empirical findings. CTCT can thus also be viewed as an evidence-based synthesis of these theoretical accounts.

Cybernetic Beings: Individuals as Cybernetic Systems

The term “cybernetic” denotes goal-directed systems that self-regulate via feedback (DeYoung, 2015). Individuals are cybernetic systems that must prioritize finite resources among competing goals and strategies.2 Individuals set goals, detect their current state and movement toward goal states, consider their options, can take action to try to advance toward their goal state, and iteratively adjust their behaviors and goals based on feedback loops. This process can be summarized by three stages, each with a sub-loop of three steps (see Figure 18).

Figure 18. Stages and steps of cybernetic self-regulation.

*Includes behaviors, affects, desires, and cognitions.
Note. The cybernetic process may be halted at any stage/step (e.g., if a goal is abandoned).

Three Stages of Being: Current State, Transition, and Goal State.

The current state describes the present self and its environment, including the cumulative impact of historical events as well as forecasts of the future. The transition stage signifies movement between the current state and goal state, which involves identifying behavioral options that best reduce the discrepancy between current state and goal state while satisfying other criteria (e.g., alignment with personal values, availability of resources). The goal state represents the desired self and/or its environment.

The “behavioral” options noted for the transition stage can involve behaviors and actions as well as affects, desires, and cognitions associated with transition. That is, reducing discrepancies between current and goal states may be achieved by altering actions, changing cognitions, adjusting affect, and/or transforming desires. Current state vs. goal state discrepancies can also be reduced or increased by changes in the individual’s environment.

Three Steps of Action: Sense, Evaluate, and Decide

Three operational steps occur within each stage. Sensing involves perception of current self and environment in the context of one’s goals, awareness of behavioral options available to move toward goal states, and vision of desired self and/or environment. Evaluating involves prioritizing a portfolio of goals based on factors like alignment with identity and importance to future, weighing the value of behavioral options, and considering the desirability and likelihood of achieving the goal state compared to alternative outcomes. Deciding involves ongoing decisions to continue pursuing a goal; what level of energy to commit as well as what actions to take; and whether or not the goal has been achieved, is in-progress, or should be altered.

These stages and steps propel the feedback loops that help individuals set, progress toward, and achieve or alter their goals. An excellent explication of cybernetic self-regulation as it relates to psychological individual differences may be found in DeYoung (2015).

Homeostasis vs. Change

At a very broad level, the cycles depicted in Figure 18 result in goals, strategies, and behaviors that vary along two fundamental life strategy axes: homeostasis vs. change, and self/internal focus vs. environment/external focus (see Figure 19). These axes define the balances that characterize life. Such a structure is not meant to imply that the system is hyper-rational; many of these processes occur outside of awareness or are influenced by irrational or subconscious factors.3 Instead, the axes characterize an individual’s strategy for leveraging resources in service of self-preservation and self-evolution, with personality traits and cognitive abilities jointly directing resources. In the string of goals known as life, success hinges on effectively leveraging information to know when to preserve and when to evolve.4 Feedback loops inform resource allocation and help balance homeostasis and change, as well as fit between the individual and environment, rendering the cybernetic system inherently adaptive. Fundamental characteristics of environments vary along two major axes of threats and resources, each varying from none to plentiful.5 Different environments pose differing types of challenges and opportunities based on threat and resource levels.

In summary, personality traits and cognitive abilities jointly support psychological stability and maintain the individual in varying environments, and they also fuel change by energizing adaptation, development, and growth. Indeed, such a self-perpetuating capacity for the coordinated use of energy to achieve balance between homeostasis and change is the essence of life.

Figure 19. Cognitive ability-personality trait fitness complexes.

Note. Each quadrant identifies main individual difference complexes for fitness. Each row represents a fundamental fitness strategy: homeostasis (preservation-focused trait complexes), and change (evolution-focused trait complexes). Each column highlights the focal arena of trait complexes: self/internal (internally-targeted trait complexes), and environmental/external (trait complexes targeting preservation or adaptation to the environment).

Fixed vs. Flexible Selves: Personality and Intelligence in Cybernetics

Individuals have a given range of flexibility around their general tendencies and limits to their capabilities (e.g., Fleeson, 2001; Lykken & Tellegen, 1996). Genetic endowments and environments provide resources and impart constraints, favoring different life strategies. The interplay between these forces results in co-evolution of certain individual differences characteristics. Evolution pressures certain trait complexes to come together as viable strategies for utilizing finite individual and environmental resources to accomplish the cybernetic steps and stages and, therefore, goals. Covariation between personality traits and cognitive abilities reflects these forces.

In general, personality traits represent different cybernetic strategies for sensing, evaluating, and behaving to help individuals effectively balance homeostasis and change. Personality traits represent typical strategies for managing both internal (e.g., emotional) and external (e.g., environmental) stimuli. Cognitive abilities are the tools (e.g., perception, reasoning, and knowledge) to discern, select, and coordinate complex and effective responses to environments. Cognitive abilities index how efficiently, proficiently, and successfully goals are set, and pursued in complex environments.6 That is, cognitive abilities represent how effectively information is leveraged to set goals and employ strategies that maximize the probability of goal achievement.

In a functioning human cybernetic system, both cognitive abilities and personality traits orchestrate homeostasis and growth in environmentally sensitive ways to increase the odds of surviving and thriving. Together, these co-influential, and potentially co-dependent, domains energize action toward goal achievement and help generate new goals, interpretations, and strategies as goals are attained or obstructed or as behaviors are perceived to be productive/unproductive. For example, negative emotions can depress cognitive processing and reduce individuals’ abilities to clearly identify their goals, accurately sense their current state, or productively forecast which actions will advance them toward their goals. Conversely, higher intelligence may help individuals more clearly see how to apply their personality tendencies and channel their predispositions to make progress toward goals. Indeed, cognitive abilities are responsible for perceiving, processing, manipulating, and applying information to drive decisions and actions. They also help to adjust personality expression and behavior based on the environment.7 Although cognitive ability is an evolutionary individual difference tuning parameter, it is one where higher levels have tended to be more adaptive and worth the additional resource costs (e.g., caloric demands).

Complexity arises for individuals, in part, from the multiple competing goal states within and across individuals, intra-individual variance in personality and cognitive abilities, the variety of actions available, the diversity of environments encountered, and the uncertain consequences forecasted for each action. Personality traits have utility in identifying which goals are attractive and prioritized (i.e., considered worthwhile), which actions are preferred, and how uncertainty is weighed. Cognitive abilities involve the effective utilization of information to achieve goals. They involve knowing when to strive for change to maximize the probability of achieving goals in a given environment and when to protect homeostasis. Therefore, cognitive abilities are a key determinant in the effectiveness with which goals are set, pursued, and accomplished.

Cybernetic trait complexes theory (CTCT) proposes that self-regulatory mechanisms for homeostasis and change are responsible for predictable linkages between individuals’ personality tendencies and cognitive capacities. Covariation of ability and personality phenotypes can be thought of as trait constellations or complexes that provide the scaffolding to human psychological systems’ self-regulating mechanisms for setting, progressing toward, and achieving or revising goals via the coordinated use of resources. These trait complexes serve the functions of (1) maintaining psychological homeostasis (i.e., sustaining stability) and (2) enabling change (i.e., supporting adaptive development and growth). These functions can each be bifurcated according to whether they focus internally on the individual or externally on the environment. In concert with our environments,8 cognitive abilities and personality traits are co-influential, and potentially co-dependent, in ensuring adaptive fitness. That is, they serve the evolutionary purpose of helping us achieve goals and the broader functions of self-preservation and self-evolution.

Chapter 7 presents the results of our meta-analyses and interprets them from the perspective of CTCT. These overarching cybernetic patterns of relations have been hitherto overlooked in much of psychological research.

References

Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin, 121, 219–245.

Cattell, R. B. (1987). Intelligence: Its structure, growth and action. Elsevier.

DeYoung, C. G. (2015). Cybernetic big five theory. Journal of Research in Personality, 56, 33–58.

Fleeson, W. (2001). Toward a structure-and process-integrated view of personality: Traits as density distributions of states. Journal of Personality and Social Psychology, 80(6), 1011.

Gray, J. A. (1981). A critique of Eysenck’s theory of personality. In H. J. Eysenck (Ed.), A model for personality (pp. 246–276). Springer.

Gray, J. A. (1982). The neuropsychology of anxiety: An equiry of the septo-hippocampal system. Oxford University Press.

Hogan, J., & Holland, B. (2003). Using theory to evaluate personality and job-performance relations: A socioanalytic perspective. Journal of Applied Psychology, 88(1), 100.

Lanaj, K., Chang, C.-H., & Johnson, R. E. (2012). Regulatory focus and work-related outcomes: A review and meta-analysis. Psychological Bulletin, 138(5), 998.

Lykken, D., & Tellegen, A. (1996). Happiness is a stochastic phenomenon. Psychological Science, 7(3), 186–189.

Ortony, A., Norman, D. A., & Revelle, W. (2005). Effective functioning: A three level model of affect, motivation, cognition, and behavior. In J. Fellous & M. Arbib (Eds.), Who needs emotions? The brain meets the machine (pp. 173–202). Oxford University Press.

Spector, P. E. (2017). The lost art of discovery: The case for inductive methods in occupational health science and the broader organizational sciences. Occupational Health Science, 1(1), 11–27.

Stern, W. (1914). The psychological methods of testing intelligence. Warwick & York.

von Stumm, S., & Ackerman, P. L. (2013). Investment and intellect: A review and meta-analysis. Psychological Bulletin, 139(4), 841–869.

Wolf, M. B., & Ackerman, P. L. (2005). Extraversion and intelligence: A meta-analytic investigation. Personality and Individual Differences, 39(3), 531–542.

Woo, S. E., O’Boyle, E. H., & Spector, P. E. (2017). Best practices in developing, conducting, and evaluating inductive research. Human Resource Management Review, 27(2), 255–264.

Endnotes

1 Abductive reasoning logically analyzes an incomplete set of observations to identify the simplest and most likely conclusion.

2 We define a “strategy” as a pattern, designed by humans or nature, with a grander purpose.

3 Indeed, for many of us change and homeostasis are largely driven by hopes and fears.

4 An early researcher of individual differences provided a definition of intelligence that aligns with this perspective, “Intelligence is a general capacity of an individual to consciously adjust his thinking to new requirements: it is general mental adaptability to new problems and conditions of life.” (Stern, 1914).

5 Perhaps the most essential challenge for civilizations is how to allocate resources to address threats.

6 More complex environments have higher information processing needs and demand higher, and often more differentiated, cognitive abilities.

7 It is critical to note that individuals are not simply subject to their environments. Instead, they have choice about which environments to spend time in, how to interpret those environments, and they can also actively influence their environments.

8 The most salient characteristics of environments are resource and threat levels, which may be the counterforces to change and homeostasis.

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Chapter 7

A Theoretical Account of Our Results

CHAPTER SUMMARY

Covarying abilities and personality traits suggest trait complexes that reflect strategies for setting, progressing toward, and achieving or revising goals via the coordinated use of resources. Overarching patterns in personality-cognitive ability relations indicate that trait constellations serve to (1) maintain homeostasis and (2) facilitate change. Trait complexes can be further distinguished according to whether they focus internally on the individual or externally on the environment.

Trait complexes for homeostasis support internal stability and maintain the individual in ever-changing environments. These trait complexes likely evolved to cope with resource-scarce environments.

Internally-focused homeostasis constellations are evidenced by neuroticism and its facets’ associations with lower levels of general intelligence, fluid abilities, retrieval fluency abilities, processing speed, and acquired knowledge, especially verbal abilities.

Externally-focused homeostasis constellations involve attributes such as uneven tempered and aggression that tend to socially alienate the individual and/or reduce their status or ability to function in social groups. Personality characteristics such as agreeableness subcomponents compassion and cooperation, conscientiousness-related traits, and abilities such as processing speed and verbal abilities form constellations that facilitate maintenance of the individual in social environments.

Trait complexes for change energize internal development and adaptation to changing environmental circumstances. These trait complexes likely evolved to take advantage of opportunity-rich environments with higher reward/risk ratios.

Generally, traits and abilities in these constellations contained fluid abilities paired with personality traits involving behavioral activation and change, as well as acquired knowledge abilities paired with plasticity traits (i.e., openness and extraversion traits), industriousness, and activity. Prominent compound personality traits associated with initiative-taking, behavioral activation, and proactivity include self esteem, locus of control, and achievement via independence fit into these complexes.

Internally-focused change constellations were intellect aspect of openness and related personality facets and non-invested abilities such as fluid abilities, memory dimensions, and processing abilities. These traits and abilities tended to correlate positively and sizably. Relations were even larger for acquired knowledge, especially verbal knowledge.

Externally-focused change constellations involve traits associated with exploration of and responsiveness to physical and social environmental stimuli, especially openness, as well as extraversion-related traits (e.g., activity) with non-invested and invested abilities such as processing speed and verbal abilities.

Constellations of traits for general fitness represent individuals’ agility in effectively moving between a focus on surviving and a focus on thriving as the environment demands/provides.

These trait complexes involve joint functioning of emotional stability associated with homeostasis, extraversion associated with change, and industriousness associated with the motivation to move between those strategies. This amalgam of personality traits is best captured by compound personality traits, including self esteem and internal locus of control. These traits display pervasive relations with a host of cognitive abilities, especially general mental ability, memory, and processing speed. The general factor of personality is also positively associated.

Chapters 4 and 5 detailed the results of our meta-analyses from the perspectives of cognitive ability and personality trait frameworks, respectively. The results led to three major conclusions. First, personality and intelligence are not independent domains of individual differences. Second, relations for more specific traits and abilities may differ from their parent Big Five factors and primary abilities. Third, there are ability-trait constellations that indicate differential cybernetic adaptations for homeostasis, change, and general fitness.

The first two conclusions were discussed previously, and this chapter is devoted to the third conclusion and to parsimoniously explaining why certain personality traits displayed sizable positive relations with cognitive abilities, others displayed sizable negative relations, and yet others displayed negligible relations. Our theory accounting for these patterns, Cybernetic Trait Complexes Theory (CTCT), was introduced in Chapter 6, and is used here as an organizing framework for describing the meta-analytic results.

CTCT outlines how personality and intelligence jointly form constellations that describe how individuals balance homeostasis and change internally and externally to pursue self-preservation and self-evolution.

Trait Constellations for Psychological Fitness: Self-Preservation and Self-Evolution Pathways

Covariation of ability and personality can be thought of as trait complexes that reflect psychological systems’ self-regulating mechanisms for setting, progressing toward, and achieving or revising goals via the coordinated use of resources. These trait complexes serve to (1) maintain psychological homeostasis to sustain stability and (2) enable change to support adaptive development and growth. These two functions can each be further bifurcated according to whether they focus internally on the individual or externally on the environment. The following subsections describe findings for each quadrant of this 2x2 matrix (see Figure 19 in Chapter 6).

“Life’s roughest storms prove the strength of our anchors.”

Homeostasis Constellations: Trait Complexes for Self-Preservation

Cognitive abilities and personality traits for psychological homeostasis serve two purposes (a) conferring internal stability and (b) maintaining the individual in changing environments (see Figure 19 in Chapter 6). Constellations for homeostasis may be of special fitness value in resource-scarce and/or threatening (e.g., high-conflict) environments. These personality traits are thrifty from the individual’s point of view. That is, these traits are best for protecting scant resources while foregoing less certain opportunities to gain more resources. Traits that confer internal psychological stability include low anxiety, low suspiciousness, low depression, as well as high self-esteem and internal locus of control. Traits that maintain the individual in the ever-changing external environment include high dependability and cautiousness, which facilitate goal prioritization. These trait complexes include abilities from both non-invested and invested abilities domains. Notable relations are depicted in Figure 20a as well as Chapter 8’s Figure 24a for neuroticism-related traits and Figure 20b as well as Chapter 8’s Figures 24c and 27a for conscientiousness-related traits. Corresponding detailed sets of results are presented in Supplementary Tables 197, 198, and 200–205, respectively in Appendix I.

The central role of low neuroticism in the homeostasis constellation is evident. The overall pattern of the neuroticism constructs’ relations with cognitive abilities is negative (e.g., see Chapter 8’s Figure 24a for non-invested abilities and Supplementary Tables 197–208 in Appendix I for detailed results). Relations between global neuroticism and acquired cognitive abilities were mostly negative and modest, though as large as -.18 (with quantitative ability as well as comprehension knowledge). Robust relations based on at least 1,000 individuals or 10 effect sizes are depicted in Figure 20a. Acquired knowledge dimensions were negatively and typically more strongly related to neuroticism’s facets than non-invested abilities, though the most consistent findings were for verbal abilities. These sizable, negative relations indicate that individuals who have uneven tempers or are suspicious do not tend to invest their abilities in the verbal domain.1 Alternatively, these results could indicate that individuals who score higher on verbal abilities are better able to manage their neurotic tendencies, especially when it comes to displaying suspiciousness or being uneven tempered, which could be a particularly useful characteristic adaption in higher-threat environments.

Figure 20. Personality-ability relations for homeostasis. 20a. Neuroticism-related traits’ relations with invested abilities. 20b. Conscientiousness-related traits’ relations with processing speed abilities. 20c. Agreeableness-related traits’ relations with processing speed abilities and verbal abilities. Y-axes indicate p̂ values.

Homeostasis Constellations for Internal Stability: Internal Self-Maintenance

Internal stability is fundamental to homeostasis, and neuroticism is the trait that best indexes internal instability of affect. Neuroticism encompasses both the experience of discrete emotions and more enduring moods. Neuroticism traits are mostly involved in trait complexes associated with lower capacity to deal with complexity, indicating that heightened vigilance to and avoidance of threats corresponds to reduced mental resources. Figures 20a and 26a depict findings for neuroticism traits (see Supplementary Tables 197, 198, and 200–205 in Appendix I for complete technical results). As shown in Figure 24a, general mental ability appears to be lower among individuals who are uneven tempered (p̂ = -.29), depressed (p̂ = -.18), suspicious (p̂ = -.17), and, unsurprisingly, test anxious (p̂ = -.20).

Neuroticism’s negative associations are not confined to general mental ability. Neuroticism-related traits display widespread, negative relations with multiple specific, non-acquired ability dimensions. For example, the fluid abilities induction and quantitative reasoning are both negatively related to several neuroticism facets. Retrieval fluency abilities are also negatively correlated with neuroticism facets uneven tempered and depression. Originality and creativity ability has a substantial, negative correlation with the uneven tempered facet of neuroticism (p̂ = -.25). Naming facility and speed of lexical access, free recall memory, ideational fluency, and word fluency correlate negatively with neuroticism’s depression facet (p̂ = -.22, -.17, -.13, and -.10, respectively). Finally, processing speed abilities are also negatively related to the uneven tempered, suspiciousness, depression, and anxiety facets of neuroticism.

When the environment is unsupportive (e.g., resource-scarce), trait complexes that correspond to low energy usage may be adaptive. Within neuroticism traits, depression represents extreme energy conservation.2 As shown in Figure 24a, its strongest relations are with energy-intensive cognitive abilities like the fluid ability sub-dimension quantitative reasoning (p̂ = -.25) and visual processing ability (p̂ = -.32). See Supplementary Tables 197 and 201–205 in Appendix I for complete technical results.

Invested abilities showed even stronger negative relations with neuroticism facets. The most consistent findings are for acquired verbal abilities. For example, correlations of neuroticism’s uneven tempered facet with verbal abilities ranged between -.25 and -.33 (e.g., general verbal information, reading comprehension, lexical knowledge). Similarly, suspiciousness’ relations with specific verbal abilities ranged between -.21 and -.28. See Figure 20a as well as Supplementary Tables S197, 204, and 205 in Appendix I for complete technical results.

Homeostasis Constellations for External Stability: Self-Preservation in the Environment

Humans are a social species, and the social world constitutes a core environment for individuals. Prosocial personality traits and abilities help to maintain individuals in social environments (see Figures 20a and 20c for relevant personality-ability trait complexes; the corresponding set of detailed results may be found in Supplementary Tables 197, 198, and 200–205 as well as 210, 214, and 215, respectively, in Appendix I). Attributes such as aggression tend to detract from maintenance of self in one’s social groups. In contrast, personality characteristics such as agreeableness subcomponents compassion and cooperation, conscientiousness-related traits, and abilities such as processing speed and verbal abilities facilitate maintenance of the individual in the social environment.

For example, agreeableness’ compassion aspect entails willingness to expend energy on others, including non-kin. Such investments of energy are helpful in maintaining external homeostasis because they demonstrate contribution to a group and weave a social safety net for the individual. In contrast, agreeableness’ politeness aspect is more focused on facilitating transactional interactions using socially prescribed rules for conduct. Compassion demonstrates sizable, positive relations with a range of abilities (e.g., .26 with general mental ability, .21 with memory span, and numerous acquired knowledge dimensions; see Figures 24c, 27b, and 20c). Relations with perceptual speed abilities are also sizable (e.g., .19 and .17 with perceptual speed and number facility, respectively, as depicted in Figure 20c). In addition, compassion also relates positively with verbal abilities (e.g., .32 with spelling ability and .31 with language development). In stark contrast, politeness’ relations with ability constructs are mostly negative or smaller in magnitude (see Chapter 8’s Figure 27b for examples and Supplementary Tables 210 and 211 in Appendix I for detailed set of results).

Conscientiousness aspects and facets generally relate positively and substantially to processing speed. Figure 20b illustrates positive relations with processing speed abilities (e.g., perceptual speed correlated with industriousness [p̂ = .17], persistence [p̂ = .15], and order [p̂ = .20]; scanning with industriousness [p̂ = .15] and order [p̂ = .18]; and number facility with orderliness [p̂ = .13] and order [p̂ = .19] as well as global conscientiousness [p̂ = .25]). A proclivity toward structure, self-discipline, and tenacity appears to assist in the development of processing abilities. Alternatively, processing speed abilities may provide individuals with the structured mental adaptations for self-discipline and persistence.3

“Hoist your sail when the wind is fair.”

Change Constellations: Trait Complexes for Self-Evolution

Traits and abilities for change enable the individual to thrive by energizing (a) adaptation to changing environmental circumstances, or (b) advancement toward greater levels of inner development, and potentially, diversification (see Figure 19 in Chapter 6). Constellations for change may have greater fitness value in opportunity-rich environments where the reward/risk ratio is higher. These trait complexes are indicative of high-energy settings that require more resources (e.g., calories) and are advantageous in environments where such expenditures of energy are more likely to yield rewards. From the abilities domain, fluid abilities and visual processing support the initiation of adaptations as well as development and growth. Acquired knowledge’s role in constellations for change can be interpreted as an intellectual outcome of change as well as a cognitive input for further development.

From the personality domain, constellations for change include personality traits involving behavioral activation and change, including industriousness as well as activity and other plasticity traits. Prominent compound personality traits associated with behavioral activation, initiative-taking, and proactivity include self esteem, locus of control, and achievement via independence. They each draw variance from low neuroticism, high conscientiousness, and high extraversion (as well as high openness, for the trait of achievement via independence) (Stanek & Ones, 2018).

Figure 21a depicts the main non-invested ability correlates of these personality traits, especially positive relations with fluid abilities and visual processing abilities. See Supplementary Tables 206, 208, and 238 in Appendix I for complete results. Fluid abilities, particularly induction and quantitative reasoning, have notable, positive correlations. For example, relations with induction were .24 for achievement via independence, .20 for locus of control, and .13 for self esteem. The same cluster of personality traits displayed positive relations with visual processing abilities (e.g., all visual processing abilities and achievement via independence [mean4 p̂ = .22 regardless of N or K], spatial scanning with locus of control [p̂ = .16], and visualization with self esteem [p̂ = .12]).

Other prominent personality traits associated with behavioral activation and proactivity include plasticity, industriousness, and activity. Although these traits display small relations with fluid and visual abilities, they are more strongly associated with knowledge acquisition (see Figure 21b; detailed set of results are presented in Supplementary Tables 225, 244, and 275 in Appendix I). For example, the higher order personality trait of plasticity, representing the shared variance between extraversion and openness, is associated with a variety of invested abilities (e.g., mathematics achievement [p̂ = .32], verbal ability [p̂ = .28], lexical knowledge [p̂ = .21], mechanical knowledge [p̂ = .25], natural sciences knowledge [p̂ = .32], and social studies knowledge [p̂ = .43]). Industriousness is also positively associated with acquired knowledge abilities. For example, it correlated with quantitative abilities (p̂ = .23 to .34), verbal abilities (p̂ = .26 to .34), and domain specific knowledge abilities (p̂ = .16 to .34).5 These results underscore that effortful control and goal prioritization are necessary for knowledge acquisition.

Further energizing goal prioritization and active engagement in self-evolution, the activity facet of extraversion also displays uniform, positive correlations (p̂ = .17 to .28) with acquired knowledge constructs (see Supplementary Table 244 in Appendix I). Sizable, positive relations with several long term memory retrieval fluency abilities indicative of ideation and creativity (e.g., ideational fluency [p̂ = .25], originality and creativity [p̂ = .20]) further attest to activity’s diverse roles in self-evolution.

Figure 21. Personality-ability relations for change. 21a. Behavioral activation traits and non-invested abilities. 21b. Plasticity, industriousness, and activity with invested abilities. Y-axes indicate p̂ values.

Change Constellations for Internal Development and Growth: Internal Self-Evolution

Internally-driven growth is largely the result of intellectual exploration and rational reflection. Openness’ intellect aspect is the tendency to seek and engage with abstract concepts, and it is the cardinal personality trait associated with development and growth. Therefore, it is not surprising that openness’ intellect aspect correlates with both non-invested abilities and invested abilities (i.e., acquired knowledge). Personality-ability trait complexes for individual development and growth are depicted in Figure 22a (see Supplementary Tables 258–261 in Appendix I for complete technical results).

Figure 22a summarizes how intellect and related personality facets correlate positively and sizably with non-invested abilities such as fluid abilities, memory dimensions, and both spatial and perceptual processing abilities. Specifically, induction has a mean meta-analytic correlation of .17 with openness’ intellect aspect and its aligned facets. The corresponding mean meta-analytic correlations for general sequential reasoning and quantitative reasoning are .21 and .19, respectively. See Supplementary Tables 258–261 in Appendix I for complete technical results.

Relations with acquired knowledge abilities are of even larger magnitude. For example, openness’ ideas facet displayed relations between .30 to .50. The typical effect for intellect-related traits is more pronounced for verbal abilities than for quantitative abilities. For example, the mean meta-analytic correlation of intellect with acquired verbal abilities is .21, whereas the mean meta-analytic correlation with acquired quantitative abilities is .16. These results suggest that, compared to quantitative abilities, verbal abilities are more strongly linked with the rational self-reflection and intellectual investment that drive personal growth.

Change Constellations for External Development and Growth: External Self-Evolution

Personality-ability trait complexes for individual development and growth are depicted in Figure 22. Complete, detailed results are provided in Supplementary Tables 241, 243, 244, 246, 249, and 257–264 in Appendix I.

Externally-driven adaptation generally involves exploration of and responsiveness to physical and social environmental stimuli. Associated trait constellations draw from cognitive engagement, social navigation, and behavioral exploration. Specifically, the intellect and experiencing aspects of openness, as well as extraversion, have notable roles in initiating adaptation to ever-changing environments and thus spurring development and growth.

Figure 22. Personality-ability relations for individual development and growth. 22a. Intellect-related traits’ relations with non-invested and invested abilities. 22b. Experiencing-related traits’ relations with non-invested and invested abilities. 22c. Extraversion-related traits’ relations with non-invested and invested abilities. Asterisk indicates that meta-analytic correlation was larger in magnitude but truncated for visualisation purposes. Precise results shown in supplementary tables in Appendix I. Y-axes indicate p̂ values.

From the openness domain, intellect-related traits’ relations with fluid and visuo-spatial abilities as well as long term storage and retrieval and short term memory abilities are sizable (see Figure 22a). Personality tendencies to seek cognitively engaging stimuli as well as to interpret stimuli and situations through reasoning and deductive logic appear to be positively and meaningfully associated with fluid (e.g., inductive), visuo-spatial, and memory abilities as well as general mental ability. Through the prism of CTCT, positive and substantial relations between intellect-related traits and these abilities reflect a trait complex that energizes the organism to change by detecting external signals that change is needed and activating mechanisms to prospect for what actions to take. The complex of intellect-related traits and fluid, visuo-spatial, and memory abilities may further be viewed as a “cerebral constellation” that approaches the world as a puzzle to be studied, patterns to be recognized, and problems to be solved in the mind. See Supplementary Tables 241, 243–244, 246, 249, and 257–264 in Appendix I for complete technical results.

In contrast, experiencing and its related facets have more muted relations than intellect-related traits (see Figure 22b). This is true for most non-invested abilities such as fluid, memory, and visual processing abilities. For example, in the visuo-spatial abilities domain, visualization correlates .20 with intellect but only .02 with experiencing, .24 with the ideas facet but .00 with introspection, and .21 with curiosity but -.09 with aesthetics. The same pattern is also evident for closure speed and flexibility of closure. With invested abilities, the same pattern arises. For example, lexical knowledge correlates positively and sizably with intellect facets ideas, curiosity, need for cognition (p̂ = .40, .25, and .24, respectively) but at substantially lower levels with experiencing facets of introspection, fantasy, and aesthetics (p̂ = .13, .11, and .07, respectively).

Extraversion traits also have marked associations with processing speed, retrieval fluency, and acquired verbal abilities (see Figure 22c). The relations represent trait complexes for engagement with the external, especially social, environment.

Accordingly, sociability and activity are positively linked to processing speed abilities. Sociability relates positively and substantially to perceptual speed (p̂ = .18), scanning (p̂ = .14), and number facility (p̂ = .17). Similar relations are observed with the extraversion trait of activity (.15 for perceptual speed, .13 for scanning, and .16 for number facility). The compound trait of ambition, which also involves extraversion, correlates .66 with scanning and .25 with number facility.

Relatedly, retrieval fluency abilities (from the non-invested abilities domain) are positively related to global extraversion, particularly its aspects and dominance facet. Retrieval fluency and its components all have positive though modest relations with extraversion, enthusiasm, assertiveness, and dominance (mean p̂ = .11, .14, .13, and .12, respectively). The psychological meaning of this complex of associated traits and abilities is activation for change and ideation of behavioral options to develop and progress toward goals (e.g., identifying that a goal pursuit strategy is not working and fluently ideating alternatives).

Among invested abilities (i.e., acquired knowledge constructs), verbal abilities are positively related to the activity and sociability facets of extraversion. Relations with activity were mostly in the .20s (e.g., .28 with general verbal information, .24 with lexical knowledge, .23 with reading comprehension, .20 with native language usage). Relations with sociability were in the .14 to .22 range, except for lexical knowledge (p̂ = .08). Psychologically, this complex of associated traits and abilities is interpreted as sensitivity to environmental cues that signal the need for behavioral change (e.g., recognizing previously learned patterns in group communication to know that a strategy for pursuing one’s goals is becoming more/less appropriate).

General Fitness Constellations: Trait Complexes for Balancing Self-Preservation and Self-Evolution

According to Darwin’s Origin of Species, it is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is able best to adapt and adjust to the changing environment in which it finds itself. (Megginson, 1963, p.91)

General fitness represents individuals’ agility in effectively moving between a focus on surviving and a focus on thriving as the environment demands/provides. From a cybernetic perspective, fitness involves directing resources to self-preservation or self-evolution based on the ecology of the internal and external environment. Multiple trait complexes have equifinality for general fitness. These trait constellations reflect joint functioning of emotional stability associated with homeostasis, extraversion associated with change, and industriousness associated with the motivation to prioritize goals and move between homeostasis/change strategies.

This amalgam of personality traits is best captured by compound personality traits (Hough & Ones, 2001) like self esteem and internal locus of control. Such traits display pervasive relations with a host of cognitive abilities, especially general mental ability but also dimensions of memory and processing speed. Figure 23 depicts personality-ability relations for general fitness. See Supplementary Tables 206, 208, 259–261, and 273 in Appendix I for complete results. For example, self esteem correlates with non-invested abilities of originality and creativity (p̂ = .22), visual processing (p̂ = .18), perceptual speed (p̂ = .15), and quantitative reasoning (p̂ = .14) as well as invested verbal ability (p̂ = .25). Similarly, internal locus of control correlates with non-invested abilities of fluid (p̂ = .24), associational fluency (p̂ = .14), and perceptual speed (p̂ = .12) as well as invested quantitative ability (p̂ = .35).

Conspicuously, general mental ability is the central ability for general fitness. This role fits with Stern’s (1914) assertion that general mental ability reflects “mental adaptability to new problems and conditions of life.” Associations with self esteem and internal locus of control (p̂ = .25 and .23, respectively)6 also fit well in this frame. The agility to effectively move between self-preservation and self-evolution according to the context relies on both general mental ability as well as the self-regard and certitude of self-esteem and internal locus of control.

General mental ability’s relations with openness’ intellect aspect and associated facets (i.e., need for cognition, ideas, and curiosity) also denote a general fitness trait cluster (e.g., correlations in the .17 to .40 range). These relations signify the importance of intellectual openness in shifting strategies between homeostasis and change. In addition, general mental ability correlates .32 with industriousness, highlighting the essential role of motivation in pursuing effective fitness strategies.

Finally, general fitness is epitomized by the relation between general mental ability and the general factor of personality (p̂ = .36). Individuals who evaluate themselves positively and have high self-regard (i.e., score high on the general factor of personality) also have higher general mental ability and vice versa. The broader results indicate that general mental ability and traits of self esteem, internal locus of control, intellect, and openness, as well as general factor of personality, may be hallmarks of general cybernetic effectiveness (i.e., individual differences for effectively surviving and thriving).

Distillation of Our Theoretical Account of the Quantitative Results

In this chapter, we presented findings from our extensive meta-analyses of personality-ability relations using CTCT as an organizing scheme. CTCT recognizes that many personality traits and cognitive abilities covary. The meta-analytic findings make this abundantly clear (see Figures 25 and 26 in Chapter 8). Depending on their self-preservation, self-evolution, and general fitness functions, personality-ability relations can vary at different levels of the ability and personality trait hierarchies.

Homeostasis constellations support internal stability and maintain the individual in the environment. Neuroticism and its facets’ negative associations with general intelligence, fluid abilities, retrieval fluency abilities, processing speed, and acquired knowledge, especially verbal abilities constitute internally-focused homeostasis constellations.

Social environments are some of the most important to human life. Maintaining individuals in social environments relies on other clusters of interrelated traits and abilities. These externally-focused homeostasis constellations include agreeableness subcomponents compassion and cooperation, conscientiousness-related traits, and abilities such as processing speed and verbal abilities.

Trait clusters energizing change, development, and adaptation involve fluid abilities paired with plasticity and behavioral activation traits. One internally-focused change trait complex involves openness’ intellect aspect and fluid abilities, memory dimensions, and processing abilities. One externally-focused change constellation involves extraversion’s activity facet and non-invested processing speed and retrieval fluency abilities.

General fitness constellations indicate individuals’ agility in effectively moving between a focus on surviving and a focus on thriving as the environment demands/provides. Unsurprisingly, the relations of general mental ability and high self-regard (e.g., high self esteem, general factor of personality) provide prime examples of this trait complex.

Ultimately, personality and ability relations are not random and haphazard, but instead are tailored to different cybernetic strategies and goals.

Figure 23. Personality-ability relations for general fitness. Y-axis indicates p̂ values.

Note. Not all sizable relations with acquired knowledge constructs are shown due to space limitations.

References

Hough, L. M., & Ones, D. S. (2001). The structure, measurement, validity, and use of personality variables in industrial, work, and organizational psychology. In Handbook of industrial, work, and organizational psychology (Vol. 1, pp. 233–277). Sage.

Megginson, L. C. (1963). Lessons from Europe for American Business [Presidential address]. Southwestern Social Science Association, San Antonio, Texas, United States of America.

Moutafi, J., Furnham, A., & Paltiel, L. (2004). Why is Conscientiousness negatively correlated with intelligence? Personality and Individual Differences, 37(5), 1013–1022. https://doi.org/10.1016/j.paid.2003.11.010

Murray, A. L., Johnson, W., McGue, M., & Iacono, W. G. (2014). How are conscientiousness and cognitive ability related to one another? A re-examination of the intelligence compensation hypothesis. Personality and Individual Differences, 70, 17–22.

Stanek, K. C., & Ones, D. S. (2018). Taxonomies and compendia of cognitive ability and personality constructs and measures relevant to industrial, work and organizational psychology. In D. S. Ones, C. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work & organizational psychology: Personnel psychology and employee performance (pp. 366–407). Sage.

Stern, W. (1914). The psychological methods of testing intelligence. Warwick & York.

von Stumm, S., & Ackerman, P. L. (2013). Investment and intellect: A review and meta-analysis. Psychological Bulletin, 139(4), 841–869.

Endnotes

1 Perhaps because non-invested abilities are instead invested in maintaining behaviors that regulate suspicion and uneven temper.

2 Symptoms of depression often include low interest in activities, lack of energy, slowed thinking, and reduced appetite.

3 One subset of conscientiousness traits concerning orderliness appears to function differently, potentially detracting from intellectual engagement. Negative relations with fluid cognitive abilities (e.g., specific ability relations ranged from -.09 to -.17 for orderliness) suggest a compensatory mechanism to maintain homeostasis. Imposing structure on one’s environment may detract from intellectual engagement and be interpreted as a failure to use self-regulation to focus on higher-priority goal pursuit (e.g., compulsion may drive a person to clean their desk before working on a more important task). However, this may serve an adaptive purpose of psychological self-preservation by gaining control over some part of the environment. Order and orderliness’ unsurprising, positive correlation with other non-invested abilities involving structured processing (e.g., specific processing speed abilities with the more concentrated order facet ranged from .18 to .20; see Figure 20b), also suggest a homeostatic psychological function for orderliness and similar traits. Alternatively, higher-ability individuals may not need the structure added by orderliness-related traits to see patterns and self-maintain, as suggested by the compensation hypothesis (Moutafi et al., 2004, cf. Murray et al., 2014). See tables S220–S227 for complete technical results.

4 Throughout this book, the mean p̂ metric provides the average of meta-analytic correlations across a set of meta-analytic findings described in the same sentence (e.g., achievement via independence’s relations with visual processing abilities in this example). Such unit-weighted means characterize trends in the meta-analyses and avoid giving undue weight to heavily-studied constructs.

5 One exception to this pattern was general science knowledge (p̂ = -.17).

6 It is worth stressing that neither of these compound personality traits significantly involve openness.

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Chapter 8

Cross-Cutting Trends in Our Results

CHAPTER SUMMARY

Our expansive database and deeply-grounded taxonomies of personality and intelligence generated over 3,500 meta-analyses of the links between these domains. Most links have never been meta-analyzed previously and most of those that have shifted considerably.

Distinguishing constructs by level (e.g., aspect, facet, dimension, sub-dimension) matters since nomological networks are not always isomorphic.

Personality and cognitive ability are not independent domains, and they are likely connected by several neurobiological, chemical, and developmental pathways shaped by evolution and immediate environments.

Personality traits and cognitive abilities form fitness-based, functional trait complexes vital to human life. These complexes are defining features of the self-sustaining systems for setting, progressing toward, and achieving or revising goals via the coordinated use of resources.

Both personality traits and cognitive abilities contribute to strategies for surviving and thriving in varying environments.

As noted in Chapter 7, the results of our meta-analyses highlight three conclusions that cut across traits and abilities: (1) personality and cognitive ability are typically considered independent domains, but they are not; (2) distinguishing constructs by level (e.g., aspect, facet, dimension, sub-dimension) matters since nomological networks are not always isomorphic; and (3) personality traits and cognitive abilities form fitness-based, functional trait complexes vital to human life: having self-sustaining systems for setting, progressing toward, and achieving or revising goals via the coordinated use of resources. This chapter summarizes key evidence supporting each of these cross-cutting conclusions.

Co-Variation: Much More Than Openness, and Stronger Than Negligible

Personality and cognitive ability are not independent domains, although they are typically considered as such. The prevailing view is that only traits relating to intellectual openness (e.g., need for cognition, curiosity) are related to cognitive abilities (von Stumm & Ackerman, 2013). Indeed, sizable openness-abilities relations were also evident in our findings (see Chapter 5’s Figure 16 and Supplementary Tables 256–272 in Appendix I of the online supplementary materials for detailed results). However, there were many other traits with relations as sizable.1 In fact, 347 relations with non-openness traits displayed correlations of .20 or larger with cognitive abilities, which are medium-to-large relations in psychological research (Funder & Ozer, 2019). Three prime contributors to this pattern are noted below. Complete technical results for these traits are in Supplementary Tables 201, 204, 210, 225, and 244 in Appendix I (see also Figure 24 for a depiction of major examples).

First, individuals higher on neuroticism-related traits tend to score lower on most cognitive abilities. Figure 24a depicts many neuroticism-related traits’ (e.g., uneven tempered, depression, withdrawal, test anxiety, suspiciousness) sizable, negative relations with many non-invested cognitive abilities. These findings suggest that neuroticism-related traits are enervating. For example, neuroticism’s depression facet is a notable, negative correlate of visual processing and fluid ability (p̂ = -.32 and -.17, respectively). Self-regulating and -sustaining processes, including cognitive performance on tests or in life, may be disrupted among those high on depression. Alternatively, these findings may reflect the neuroticism-inducing effects of low cognitive ability (e.g., impaired cognitive strategies for regulating emotions). In the context of Cybernetic Trait Complexes Theory (CTCT), the high depression, high psychological withdrawal, and lower cognitive ability trait complex corresponds to the organism’s energy-saving setting, which would confer a survival advantage for the individual in resource-scarce environments. These findings are discussed at greater length in Chapter 7 when we describe trait complexes that contribute to homeostasis and, more specifically, promote internal stability.

Second, the activity facet of extraversion represents a contrasting example that has sizable, positive relations with several non-invested primary abilities, specifically long term memory storage and retrieval, visual processing, and processing abilities (see Figure 24b). Individuals who are active and energetic also tend to have higher levels of these abilities. Given these associations, it is not surprising that activity is also positively and robustly related to acquired knowledge abilities. In the context of CTCT, the high activity, processing ability, and long term memory trait complex corresponds to the organism’s high-performance/energy setting, which would be advantageous for the individual in resource-rich environments.

Third, there are many personality traits that show sizable correlations with acquired cognitive abilities (see Figure 24c and Supplementary Tables 49–80 in Appendix G for complete technical results). Three specific traits’ relations are depicted for illustration: conscientiousness’ industriousness aspect and agreeableness’ compassion aspect are appreciably related to several sub-dimensions of knowledge acquisition. A similar example is order’s relevance to the acquisition of verbally-based knowledge. These sizable acquired knowledge -industriousness, -order, and -compassion clusters may reflect multifaceted adaptations corresponding to persistent, systematic, and humanitarian knowledge acquisition pathways, respectively.

Figure 24. Examples of non-openness personality traits with sizable relations with cognitive abilities. 24a. Neuroticism-related traits’ relations with non-invested abilities. 24b. Activity facet of extraversion’s relations with non-invested abilities. 24c. Compassion aspect of agreeableness and conscientiousness-related traits’ relations with invested abilities. Y-axes indicate p̂ values.

In sum, even though openness has notable associations with many cognitive abilities (see Supplementary Tables 256–272 in Appendix I), consistent with previous research (Anglim et al., 2022; von Stumm & Ackerman, 2013), the findings depicted in Figure 24 illustrate some of the many other personality traits that display significant relations. Theoretically, these complexes can be interpreted as trait-ability bundles that confer unique psychological fitness advantages and drawbacks.

Complete relations between cognitive ability and personality are visualized in Figure 25 and can be further explored in Figure 26 as well as with the interactive webtool. These figures further illustrate that most personality traits show sizable relations with cognitive abilities. Within the abilities domain, relations are typically stronger for acquired knowledge abilities, highlighting specific pathways of knowledge acquisition. For the personality domain, in addition to openness, many traits drawing variance from neuroticism, conscientiousness, and extraversion factors have noteworthy relations to both non-invested and acquired cognitive abilities.

Figure 25. Cognitive ability-personality relations.

Note. Cognitive abilities are depicted in the left hemisphere and personality traits are shown in the right. Label formatting and color shading differentiates traits at different levels of the Stanek and Ones (2018) personality and ability hierarchies. Links between traits depict meta-analytically estimated effect sizes corrected for unreliability. Link widths reflect absolute magnitude of effect size. Links are only shown for robustly estimated relations that had N ≥ 1,000 or K ≥ 10. An interactive version of this figure can be viewed at http://stanek.workpsy.ch/interactivewebtool/

Figure 26. Cognitive ability-compound personality trait relations.

Note. Cognitive ability constructs are depicted in the left hemisphere and compound personality traits are shown in the right. Label formatting and color shading differentiates constructs at different levels of the Stanek and Ones (2018)’s Unified Cattell-Horn-Carroll abilities hierarchy and Pan-Hierarchical Five Factor Model. Links between traits depict meta-analytically estimated effect sizes corrected for unreliability. Link widths reflect absolute magnitude of effect size. Links are only shown for robustly estimated relations that had N ≥ 1,000 or K ≥ 10. An interactive version of this figure can be viewed at http://stanek.workpsy.chinteractivewebtool-compoundpersonalitytraits

Differential Relations by Construct Level

Previous research has suggested that sub-traits within each major Big Five personality factor are rooted in disparate biological mechanisms (DeYoung et al., 2007, 2009, 2012). Distinguishing personality trait hierarchy levels (e.g., factors, aspects, and facets) when examining relations with cognitive abilities helped identify differential relations with cognitive abilities. For example, conscientiousness’ industriousness aspect was more strongly related to cognitive abilities than the global conscientiousness factor. Openness and agreeableness’ aspects each displayed similarly unique relations, as described in Chapter 5.

Distinguishing cognitive ability hierarchy levels was useful as well. An illustrative example is neuroticism’s depression facet. Depression displays stronger, negative relations with many cognitive ability constructs compared with the withdrawal aspect it is part of (see Figure 24a above). Conscientiousness-related traits evidence similar trends. The global conscientiousness factor has the weakest relations with cognitive abilities (mean2 p̂ = .01). However, its industriousness aspect is a more potent, positive correlate of cognitive abilities, including all acquired knowledge dimensions (mean p̂ = .25). See Figure 27a for a visualization and Supplementary Tables 224–226 in Appendix I for complete, detailed results.

Figure 27. Personality aspects’ differential relations with cognitive abilities. 27a. Conscientiousness aspects’ relations with non-invested and invested abilities. 27b. Agreeableness aspects’ relations with non-invested and invested abilities. 27c. Openness aspects’ relations with non-invested and invested abilities. Y-axes indicate p̂ values.

Similarly, some meso-level aspects within personality factors have differing, and sometimes even opposing, relations with some major cognitive ability domains. Agreeableness’ compassion and politeness aspects, conscientiousness’ industriousness and orderliness aspects, and openness’ intellect and experiencing aspects all differ meaningfully in their relations with cognitive abilities. See Figure 27 for major aspect divergences and Supplementary Tables 210– 211, 225–226, and 257–258 in Appendix I for complete, detailed results. The disparate nomological networks of these aspects of the Big Five personality factors were foreshadowed by the work of DeYoung and colleagues (2007, 2009, 2012), who posited that aspect distinctions may be rooted in biology, especially for openness’ intellect and experiencing aspects. For example, from a functional perspective, the overarching openness factor reflects a predisposition for acquiring and using information (e.g., via data and/or experience [Connelly, Ones, & Chernyshenko, 2014]). Its intellect aspect may build capacity for adaptation to the environment through development and growth achieved by collecting and evaluating data rationally and logically, whereas its experiencing aspect may be adaptive for self-enrichment through collecting and evaluating information using one’s senses.

Openness’ intellect aspect and its aligned facets, such as need for cognition and ideas, are positively related to retrieval fluency abilities, visual processing abilities, and fluid abilities, including the deductive components (i.e., general sequential reasoning and quantitative reasoning; mean p̂ = .16; see Supplementary Tables 258–260 in Appendix I for complete, detailed results). Individuals with greater non-invested mental abilities may find cognitive engagement more rewarding or at least less aversive. Unsurprisingly, the intellect aspect and its aligned facets are also strong, positive correlates of acquired knowledge constructs (mean p̂ = .28). However, relations are stronger for verbal abilities than quantitative abilities (mean p̂ = .30 and .22, respectively). Intellect and related facets may function as an activating force for pursuing and acquiring verbal abilities, conferring fitness in adapting to external, often social, environments.

The experiencing aspect of openness may also play a role in knowledge acquisition, as evidenced by positive, far from negligible relations with both quantitative and verbal acquired knowledge (mean p̂ = .17 and .28, respectively; see Supplementary Table 257 in Appendix I). This may reflect a motivational drive for individuals to explore and engage with the world through their senses to self-enrich and develop. Alternatively, acquisition of knowledge may have collateral benefits in developing characteristic adaptations related to experiencing. For example, persistent exposure to cognitive stimulation encountered during knowledge acquisition may help develop cognitive pliability that strengthens tendencies toward aesthetics, fantasy, and the like (see Supplementary Tables 257–266 in Appendix I for all openness aspect and facet results).

Complexes of Traits Indicating Fitness Strategies: Self-Preservation and Self-Evolution

The current quantitative synthesis investigated the overlap between two vast and consequential domains of human individual differences—cognitive ability and personality. By reviewing and meta-analyzing over 60,000 relations between the full range of cognitive abilities and personality traits from virtually all accessible published and unpublished research from the past 100 years, we were able to discern psychologically functional relations that highlight trait complexes for self-preservation and self-evolution. These complexes serve the function of maintaining psychological homeostasis (i.e., stability) and/or enabling change (i.e., development and growth). The tensile strength that results from this dichotomy strengthens individuals, social groups, and humankind; it’s where freedom meets security. The result is a stable and well-tested dynamic that preserves unity while driving progress.3

Contrary to what is commonly believed by behavioral scientists and lay people, human cognitive abilities and personality traits are intrinsically and intricately related. Cognitive ability-personality bonds documented in this research highlight clusters of abilities and personality traits involved in psychological fitness. These complexes represent key packages of individual differences that were shaped by evolutionary environments to form strategies tuned to address the fundamental goals of maintaining homeostasis and enabling change internally and externally. Each individual can be viewed as a bet, a set of tuning parameter values meant to maximize fitness and increase the probability of surviving and flourishing in an ever-changing environment. Cognitive ability-personality relations documented in this research expose these complexes.

Strengths of the Current Research

Others before us have examined some of these relations to varying degrees using primary studies as well as meta-analyses, but the present work differs from those efforts in terms of the (a) expansiveness of its database, (b) breadth, depth, and empirical grounding of the personality and cognitive ability taxonomies used, and (c) extent of brand new personality-ability links meta-analytically quantified. These strengths afford insights that are relevant to scholars and practitioners across fields.

The current investigation’s expansive database contained more than 24 times as many contributing effect sizes and 12 times as many participants as the largest previous meta-analysis of personality and cognitive ability (Anglim et al., 2022). Our data characterize more than two million individuals across more than 50 countries, represent a broad range of ages from 12 to over 100, and are nearly perfectly balanced by sex. The database we are making available offers a foundation to guide future research.

Secondly, the results of the current meta-analyses are reported at a more granular level than previous works. Such granular resolution was enabled by advancing taxonomic knowledge of personality and cognitive ability constructs. For example, evidence clarifying personality facets emerged in the past decade and a half for each Big Five factor: conscientiousness (Roberts et al., 2004, 2005), openness (Connelly, Ones, Davies, et al., 2014; Woo et al., 2014), extraversion (Davies, 2012; Wilmot et al., 2019), agreeableness (Davies, 2012; Ones et al., 2020), and neuroticism (Birkland & Ones, 2006; DeYoung et al., 2007). Likewise, the general factor of personality, meta-traits, and aspects were mostly unrecognized until Musek (2007), Digman (1997), van der Linden et al. (2017), and DeYoung et al.’s (2006) recent works. The use of a comprehensive, evidence-based taxonomy and compendium of associated measures enabled (1) the investigation of more personality constructs than all previous meta-analyses combined, including 28 additional personality constructs, and (2) recognition of theoretically meaningful patterns in the results (e.g., consideration of the hierarchical nature of personality traits).

Similarly, the use of the updated, unified Cattell-Horn-Carroll (CHC) taxonomy (Stanek & Ones, 2018) for cognitive abilities is also a major strength. Previous meta-analyses in this domain have also used the CHC in organizing their cognitive ability constructs. However, those analyses have not distinguished between specific abilities contained within each primary ability dimension, due to a lack of data or the assumption that they would similarly relate to personality constructs. We have examined relations with abilities at different levels of the cognitive abilities hierarchy: general mental ability, primary abilities, and specific abilities (McGrew, 2009a, 2009b, p. 10). We were also careful to distinguish between learning efficiency and retrieval fluency as distinct primary abilities, as well as non-invested and invested abilities, uncovering distinct personality correlates of each. Using taxonomically grounded cognitive ability dimensions enabled us to report on 84 more ability constructs and consider the hierarchical nature of cognitive ability to a greater degree than previous works. The meta-analytic findings for these cognitive ability constructs represent fresh conclusions and novel discoveries.

In all, of the 3,543 personality-cognitive ability construct relations, 3,310 had not been examined in previously published meta-analyses (Ackerman & Heggestad, 1997; von Stumm & Ackerman, 2013; Wolf & Ackerman, 2005; Anglim et al., 2022), and only 233 were directly comparable to previously published meta-analytic estimates. In nearly half of these comparable meta-analyses, effect sizes shifted by .10 or more correlational points; in 22% of comparisons (52 analyses), the change was .15 points or more. In sum, based on these directly comparable analyses, nearly half of the conclusions drawn from previous research merited revision based on the large-scale data presented in the current set of results.

In conclusion, both personality traits and cognitive abilities contribute to strategies for surviving and thriving in varying environments. Such individual differences help direct resources toward cybernetic activities and, ultimately, self-preservation and self-evolution. Like an anchor, cognitive abilities and personality traits for homeostasis confer stability and help maintain the individual across environments. Like a sail, cognitive abilities and personality traits for change underpin activation for adaptation as well as growth. Personality traits reflect strategies for sensing, evaluating, and behaving. Cognitive abilities explain how efficiently and proficiently goals are set, pursued, and achieved in complex environments. Clusters of personality traits and cognitive abilities coordinate the use of finite resources toward goal achievement as well as the generation of new goals, interpretations, and strategies as goals are attained or obstructed. Personality and cognitive ability domains are inseparably related, likely through several neurobiological, chemical, and environmental pathways. Many avenues beckon for further exploration. This book highlights important co-dependencies between these two fundamental domains of individual differences by providing the most comprehensive empirical review of cognitive ability-personality bonds. The fine resolution provided by the thousands of individual meta-analyses presented here provides encyclopedic quantification of their relations and affords a macro perspective on psychological fitness. The extensive data presented here deepen understanding of human psychological differences and pave the way for improved theoretical explanations of human behavior and applications for harnessing human potential and improving the human condition.

References

Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin, 121, 219–245.

Anglim, J., Dunlop, P. D., Wee, S., Horwood, S., Wood, J. K., & Marty, A. (2022). Personality and intelligence: A meta-analysis. Psychological Bulletin, 148(5–6), 301.

Birkland, A. S., & Ones, D. S. (2006, July). The structure of emotional stability: A meta-analytic investigation. International Congress of Applied Psychology.

Connelly, B. S., Ones, D. S., & Chernyshenko, O. S. (2014). Introducing the special section on openness to experience: Review of openness taxonomies, measurement, and nomological net. Journal of Personality Assessment, 96(1), 1–16.

Connelly, B. S., Ones, D. S., Davies, S. E., & Birkland, A. (2014). Opening up Openness: A theoretical sort following critical incidents methodology and a meta-analytic investigation of the trait family measures. Journal of Personality Assessment, 96(1), 17–28.

Davies, S. E. (2012). Lower and higher order facets and factors of the interpersonal traits among the big Five: Specifying, measuring, and understanding extraversion and agreeableness [Unpublished doctoral dissertation]. University of Minnesota.

DeYoung, C. G. (2006). Higher-order factors of the Big Five in a multi-informant sample. Journal of Personality and Social Psychology, 91(6), 1138.

DeYoung, C. G., Grazioplene, R. G., & Peterson, J. B. (2012). From madness to genius: The Openness/Intellect trait domain as a paradoxical simplex. Journal of Research in Personality.

DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology, 93(5), 880.

DeYoung, C. G., Shamosh, N. A., Green, A. E., Braver, T. S., & Gray, J. R. (2009). Intellect as distinct from openness: Differences revealed by fMRI of working memory. Journal of Personality and Social Psychology, 97(5), 883–892.

Digman, J. M. (1997). Higher-order factors of the Big Five. Journal of Personality and Social Psychology, 73(6), 1246.

Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2(2), 156–168.

McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 1–10.

McGrew, K. S. (2009b). Applied Psychometrics 101: IQ test score difference series #2—What does the WAIS-IV measure. http://www.iapsych.com/iapap101/iapap1012.pdf

Musek, J. (2007). A general factor of personality: Evidence for the Big One in the five-factor model. Journal of Research in Personality, 41(6), 1213–1233.

Ones, D. S., Dilchert, S., Giordano, C., Stanek, K. C., & Viswesvaran, C. (2020). Waking up Rip van Winkle: A meta-analytic data based evaluation of the HEXACO personality model and inventory. European Journal of Personality, 34(4), 538–541.

Roberts, B. W., Bogg, T., Walton, K. E., Chernyshenko, O. S., & Stark, S. E. (2004). A lexical investigation of the lower-order structure of conscientiousness. Journal of Research in Personality, 38(2), 164–178.

Roberts, B. W., Chernyshenko, O. S., Stark, S., & Goldberg, L. R. (2005). The structure of conscientiousness: An empirical investigation based on seven major personality questionnaires. Personnel Psychology, 58(1), 103–139.

Stanek, K. C., & Ones, D. S. (2018). Taxonomies and compendia of cognitive ability and personality constructs and measures relevant to industrial, work and organizational psychology. In D. S. Ones, C. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work & organizational psychology: Personnel psychology and employee performance (pp. 366–407). Sage.

van der Linden, D., Pekaar, K. A., Bakker, A. B., Schermer, J. A., Vernon, P. A., Dunkel, C. S., & Petrides, K. V. (2017). Overlap between the general factor of personality and emotional intelligence: A meta-analysis. Psychological Bulletin, 143(1), 36.

von Stumm, S., & Ackerman, P. L. (2013). Investment and intellect: A review and meta-analysis. Psychological Bulletin, 139(4), 841–869.

Wilmot, M. P., Wanberg, C. R., Kammeyer-Mueller, J. D., & Ones, D. S. (2019). Extraversion advantages at work: A quantitative review and synthesis of the meta-analytic evidence. Journal of Applied Psychology.

Wolf, M. B., & Ackerman, P. L. (2005). Extraversion and intelligence: A meta-analytic investigation. Personality and Individual Differences, 39(3), 531–542.

Woo, S. E., Chernyshenko, O. S., Longley, A., Zhang, Z.-X., Chiu, C.-Y., & Stark, S. E. (2014). Openness to experience: Its lower level structure, measurement, and cross-cultural equivalence. Journal of Personality Assessment, 96(1), 29–45.

Endnotes

1 Note that when discussing results, we focus on the most notable findings from meta-analyses based on at least 1,000 individuals or 10 effect sizes.

2 Throughout this book, the mean p̂ provides the average of meta-analytic correlations across a set of meta-analytic findings described in the same sentence (e.g., global conscientiousness’ relations with all abilities in this example). This metric characterizes trends in the meta-analyses and avoids giving undue weight to heavily-studied constructs.

3 Ironically, one of humanity’s utmost goals seems to be skirting the natural selection that has helped our species survive and thrive for millenia. We use every ingenuity to control our environments and subdue entropy, but such insulation erodes nature’s ability to direct our further evolution. Ultimately, achieving such ends may actually yield our end rather than our transcendence of frailty and wanting.

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Chapter 9

Boundaries of Understanding Personality-Ability Relations

CHAPTER SUMMARY

Our meta-analyses are orders of magnitude larger than previous investigations and illuminate novel areas of overlap between personality traits and cognitive abilities.

Nevertheless, we assessed potential limitations and considered opportunities for future exploration:

Publication bias is unlikely to have affected this book’s major conclusions since we made every effort to include the population of available studies and since most of the effect sizes came from unpublished sources, including diverse participants from around the world.

Some personality-intelligence correlations had to be estimated with limited data, because primary research has not yet comprehensively explored relations between these domains. Results based on sparse data should be interpreted as preliminary estimates and examined in future research.

Linear associations were examined in all contributing research. Non-linear associations are possible, but if they exist, then the linear estimates presented here are under-estimates of their true strength. Future research should examine potential non-linear associations, especially in clinical measures and populations.

Extremely large studies influenced some results. Project Talent was an outsized contributor to multiple analyses. As a nationally representative, professionally run study, its results should be an accurate representation of associations in the population, but if they are not, some relations may be biased. We encourage new, large-scale studies to further expand our knowledge.

Causality and longitudinal effects cannot be inferred from our investigations since we focus on cross-sectional associations. Longitudinal studies offer some of the greatest opportunity to make significant discoveries.

Unexamined moderators may influence some findings, but for many relations there was little heterogeneity across effect sizes to be explained.

The current findings highlight promising avenues for future investigation, and they also have implications for research, theory, and applications. In this chapter, we first place our results in context of other major studies of personality-cognitive ability relations. We then describe some potential limitations and boundary conditions of our research, providing directions for future research.

Interpreting Contributions of Findings

We will not rehash all the discoveries from our results here. While our research is not the first to examine personality-cognitive ability relations, it was the most detailed and comprehensive study to date.

Beyond documenting relations that are explainable by Cybernetic Trait Complexes Theory (CTCT), our illumination of “missing areas” of overlap between personality and cognitive ability are of significance for knowledge acquisition theories (e.g., Ackerman, 2018). For example, Ackerman and Heggestad’s (1997) math and spatial abilities complex did not include any personality constructs, leaving voids in understanding. The findings from our research add higher standing on the openness’ ideas facet, and lower standing on politeness and modesty traits (within agreeableness), and lower order and orderliness (within conscientiousness) to this math and spatial cluster. CTCT offers an explanation. It identifies knowledge acquisition as one enabler of self-evolution, and visuospatial and quantitative abilities are both likely to be involved in self-evolution and thus related to ideas facet of openness. In contrast, traits involving politeness and modesty (within agreeableness) or order and orderliness (within conscientiousness) support maintenance of self in social environments and are therefore likely to drain energy from other intensive pursuits (e.g., negative relations with quantitative and visuospatial abilities). The focus of these traits on stability is counter to strategies focused on change, such as those involving visuospatial abilities and/or fluid abilities like induction.

Another example is Ackerman and Heggestad’s (1997) social trait complex, which included extraversion and emotional stability (i.e., low neuroticism) but no major cognitive abilities. Processing abilities (e.g., perceptual speed) could be motivationally relevant for detecting social cues and therefore useful as part of a social trait complex. The current meta-analyses investigated these “missing” personality traits and cognitive abilities, filling gaps in the nomological networks of hundreds of personality and cognitive ability constructs. The results highlight areas of overlap, suggest common causes/effects, and deepen understanding of prior research that omitted important personality or cognitive ability constructs when examining phenomena related to both domains. Even in personality-ability relations that have been examined previously, nearly half of the conclusions warranted revision, which has implications for practice as well as research.

Potential Limitations and Future Research

Despite significant strengths of current research, some potential limitations need to be addressed. We view several of these as providing fruitful avenues for future research.

Publication Bias

An often-cited threat to the validity of meta-analytic conclusions is publication bias. Several factors immunized the current meta-analyses against publication bias. First, and most importantly, 63% of the data analyzed here were derived from unpublished sources (e.g., data archives, unpublished conference manuscripts, dissertations). By including these materials, not only did we safeguard against publication bias, but we also brought to light and made accessible findings from otherwise ignored studies. Thus, as another benefit of this research, we are making coded data from these unpublished sources and newly computed relations from data archives available to the scientific community as part of the publicly available dataset associated with this research (see Appendix F).

Second, in addition to more than half of the effect sizes being derived from unpublished sources, few of the studies included in this research were conducted for the express purpose of examining personality and cognitive ability relations. Rather, these two sets of variables were often included in broader examinations of other phenomena such as physical health, educational attainment, job success, development and aging, interpersonal relations, and so forth. Relations between the variables we analyzed here were often incidentally reported in the correlation matrices of other works. That is, many of the identified and included sources were not intended to be direct examinations of personality-cognitive ability relations, reducing the threat of underreporting of null relations (see supplement of Wilmot & Ones [2019]).

Third, the threat of publication bias was also mitigated by the depth of the search strategies: multiple databases and archives were searched, relevant studies from reference lists of contributing research were culled, authors were contacted directly for missing metrics, and sources were not excluded because they were in different languages. In this same vein, the contribution of non-United States-based materials and data from nationally representative, large-scale studies1 were a further means of inoculation against publication bias.

Data Availability

Despite being based on the largest meta-analytic database ever amassed on the relations in question, for some cognitive ability-personality pairings, data were scant (e.g., fluid ability with persistence, verbal ability with cooperation, quantitative ability with intellect, visual processing with introspection). Although these gaps limit our ability to draw inferences about those relations, they also point to areas worthy of future research. We encourage individual differences researchers to proactively bring fresh data to bear on the associations for which there are few or no existing data.

Psychological research including personality and cognitive ability variables continues to be produced at an increasing rate. The series of meta-analyses presented in this research represent which relations have been observed between the two domains during a century of research. It is a comparative baseline for the next hundred years of research. Another potential area of expansion is sample age and type. We did not include children younger than 12 or clinical populations in our database, as we expected that personality-cognitive ability relations could be distorted in such samples (e.g., less differentiated, some relations more magnified or muted). Future research should systematically examine relations in these populations to shed light onto developmental questions and gain insights into psychopathologies.

Linear Associations

We acknowledge that the effect sizes examined in the present meta-analyses assume linear associations. This is because most primary studies assumed and reported zero-order, bivariate correlations. The possibility of non-linear relations has been raised (Ackerman, 2018). All personality traits are characterized by bipolarity (Krueger & Markon, 2006). Whether or not a given personality measure will assess extrema of a personality construct depends on the extremity of the items (Dilchert et al., 2014, 2019). Measures with extreme items (e.g., clinical measures) result in non-normal distributions and can thereby produce non-linear relations with normally distributed scores, such as those from standard cognitive ability tests. Future research should systematically examine whether measures assessing extrema in both cognitive ability and personality domains produce evidence of non-linear effects. While we took care not to include such effect sizes in our meta-analytic database, even if non-linear relations exist between personality and cognitive ability constructs, the linear associations detected and presented in this research would be underestimates of such non-linear relations.

Impact of Extremely Large Studies

Outliers are an important consideration in meta-analytic research since contributing studies with large sample sizes can influence results. Such weight is warranted because larger studies have smaller sampling error and are less likely to produce spurious associations. However, a quandary emerges if large samples yield findings differing from the remaining studies. For some of our meta-analyses, results varied when an extremely large study (Project Talent) was excluded. In these cases, Project Talent contributed data for approximately twice as many participants as any other primary study. As a result, the Project Talent data imparted considerable influence on some of the reported relations when N or K were comparatively small (i.e., there were few studies or smaller other samples and Project Talent was the largest sample contributor). Project Talent was designed as a nationally representative study of 1960s American high school students. If its data are not representative of the rest of the population of studies in terms of factors that impact the relations between personality and cognitive ability, then the results of otherwise small-N meta-analyses including them may be biased.

Although we had no a priori reason to think that the Project Talent data were deviant, all meta-analyses were run a second time excluding Project Talent. While some results did not change substantially (e.g., relations with cautiousness), others did. In most instances, across personality constructs and cognitive ability constructs, these exclusions led to relations shrinking in magnitude or shifting toward smaller or negative values. In general, the pattern of conclusions remained unchanged. There were two conscientiousness constructs for which inclusion of Project Talent data substantially altered conclusions: order2 and industriousness. When Project Talent data were included, these traits were more highly correlated with cognitive abilities, especially with acquired verbal abilities. The exclusion of Project Talent data also muted some of the effects reported for some extraversion facets, including those for activity. Relations with openness deflated the least (see Appendix E’s “Impact of Outlier Samples” section for further discussion). Tables presenting all the results without Project Talent data can be found in supplementary materials (Supplementary Tables 100–196 and 276–354 in Appendices H and J).

Causality and Longitudinal Effects

One major limitation of the present research is that we are not able to draw direct causal inferences due to the reliance on correlational data from cross-sectional investigations in nonexperimental settings. Longitudinal meta-analyses would supplement the current results and provide valuable insights for causal inferences (e.g., Luchetti et al., 2016). For example, if youth rank on industriousness is more predictive of adult rank on verbal ability than adult rank on orderliness, it might indicate that industriousness helps individuals accumulate knowledge or at least maintain verbal ability across the lifespan. Although such examinations would be an ideal extension of the current results, the number of effect sizes required to adequately fill a four-dimensional matrix of personality construct by cognitive ability construct by initial age by follow-up age is immense, and many cells would be sparsely populated (e.g., those with long age lags). Still, as data continue to accumulate from the multitude of longitudinal investigations, meta-analyses of longitudinal relations are recommended since the perspective afforded by such investigations would offer insights about the longer-term development of personality-cognitive ability relations, as well as personality traits and cognitive abilities separately.

Other Potential Moderators

For most of the 3,543 meta-analytic relations, the associated true standard deviations were nil, negligible, or very small. Therefore, for many personality-cognitive ability relations, there is little room for moderators to influence the strengths of relations (Schmidt & Hunter, 2014). In cases where there was room for moderators, age, gender, and cultural context may be fertile areas for future inquiry. One other potential moderator worth mentioning is the impact of stimulus type (e.g., “types of content” in Structure of Intellect model, Guilford, 1956). For example, when measuring quantitative ability, the stimulus could be a word problem or a geometric drawing.

Measurement Methods

In studying the past century of relations between personality traits and cognitive abilities, we were struck by how similar modern measures are to those used a hundred years ago. This is especially true for personality measures, which still consist mostly of people indicating how much certain words or phrases describe them. Such approaches require dedicated time to complete, are limited by a single perspective, are susceptible to respondents interpreting words differently, and are burdensome to collect. Digital technology, from wearable sensors (Wiernik et al., 2020) to passive digital exhaust (Hall & Matz, 2020), offers massive potential for overcoming the limitations of traditional measures and catapulting our understanding.

Distillation of Boundaries to Our Understanding

We compiled a multitude of diverse sources to examine the relations between personality traits and cognitive abilities. By including samples from more than fifty countries, research from the past century, measures from across disciplines, and samples from myriad demographics we were able to reveal patterns of personality-ability relations that were previously obscured. As a result, we were able to synthesize a theory that accounts for how these domains of individuality are related and why.

Despite investing over a dozen years into this project and striving to be thorough in our efforts, numerous avenues beckon for further research. Non-linear relations, moderators, and sub-group analyses are perennial opportunities, but the investigation of longitudinal trends is particularly tantalizing. Exploring the accumulated data across existing longitudinal studies would further elucidate how personality traits and cognitive abilities develop over time. Similarly, there is an opportunity to initiate and extend large-scale longitudinal studies with better measures of personality and ability than ever before. Finally, there is a significant opportunity to better measure the environments of individuals alongside their traits, abilities, and goals.

References

Ackerman, P. L. (2018). The search for personality–intelligence relations: Methodological and conceptual issues. Journal of Intelligence, 6(1), 2.

Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin, 121, 219–245.

Dilchert, S., Ones, D. S., & Krueger, R. F. (2014). Maladaptive personality constructs, measures, and work behaviors. Industrial and Organizational Psychology, 7(1), 98–110.

Dilchert, S., Ones, D. S., & Krueger, R. F. (2019). Personality assessment for work: Legal, IO, and clinical perspective. Industrial and Organizational Psychology, 12(2), 143–150.

Guilford, J. P. (1956). The structure of intellect. Psychological Bulletin, 53(4), 267.

Hall, A. N., & Matz, S. C. (2020). Targeting item–level nuances leads to small but robust improvements in personality prediction from digital footprints. European Journal of Personality, 34(5), 873–884.

Hedges, L. V., & Nowell, A. (1995). Sex differences in mental test scores, variability, and numbers of high-scoring individuals. Science, 269(5220), 41–45.

Krueger, R. F., & Markon, K. E. (2006). Reinterpreting comorbidity: A model-based approach to understanding and classifying psychopathology. Annual Review of Clinical Psychology, 2, 111–133.

Luchetti, M., Terracciano, A., Stephan, Y., & Sutin, A. R. (2016). Personality and cognitive decline in older adults: Data from a longitudinal sample and meta-analysis. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 71(4), 591–601.

Schmidt, F. L., & Hunter, J. E. (2014). Methods of Meta-Analysis: Correcting Error and Bias in Research Findings (3rd ed.). Sage Publications.

Wiernik, B. M., Ones, D. S., Marlin, B. M., Giordano, C., Dilchert, S., Mercado, B. K., Stanek, K. C., Birkland, A., Wang, Y., Ellis, B., Yazar, Y., Kostal, J. W., Kumar, S., Hnat, T., Ertin, E., Sano, A., Ganesan, D. K., Choudhoury, T., & al’Absi, M. (2020). Using Mobile Sensors to Study Personality Dynamics. European Journal of Psychological Assessment.

Wilmot, M. P., & Ones, D. S. (2019). A century of research on conscientiousness at work. Proceedings of the National Academy of Sciences, 116(46), 23004–23010.

Endnotes

1 As recommended by previous authors (Hedges & Nowell, 1995).

2 Conclusions for other order-related constructs such as routine seeking and orderliness were not impacted by the exclusion/inclusion of Project Talent data.

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Chapter 10

Meaning and Future of Intelligence-Personality Relations

CHAPTER SUMMARY

Implications and future directions

Personality and intelligence research:

Many cognitive abilities are correlated with a variety of personality constructs. Therefore, measuring a given cognitive ability construct is likely to give some indication of a set of personality constructs.

Long term retrieval constructs have a distinct nomological network from long term learning constructs and therefore should be distinguished.

Several knowledge acquisition trait complexes identified in Ackerman and Heggestad’s previous meta-analyses (1997) are supported but with the addition of many personality traits from various levels of the personality hierarchy (e.g., factor beta, compassion, order, and achievement via independence).

Bandwidth fidelity: Relations between constructs at commensurate levels of bandwidth were not stronger than other relations (i.e., we did not find stronger relations between constructs at similar levels of the cognitive and personality hierarchies).

Etiology of personality and intelligence:

Personality constructs and their etiological bases (e.g., hormonal, genetic) should be incorporated into models of prefrontal cortex function along with the impact of environmental resources and threats (e.g., nutrition, domestic violence).

Estimates of trait overlap inform utility forecasts for personality and cognitive ability measurements.

Insights into human individuality and diversity:

Fit between the environment and the individual depends on the latter’s goals.

Fitness manifests across life domains as mental well-being, professional effectiveness, educational attainment, and longevity, among others.

At the individual level, psychological fitness may be maximized by modulating expression of trait complexes to meet varying environmental demands.

Our environment and goals are dynamic, so there is no single optimal human profile; diversity is optimal.

At the population level, odds of species survival are maximized by diversifying individuals’ predispositions toward certain strategies and their corresponding trait complexes to meet a wide range of environments.

Agility to adapt oneself and one’s goals to various environments or to find better-fitting environments is a common thread of effective individuals, but even agility requires tradeoffs.

Cybernetic Trait Complexes Theory (CTCT) acknowledges that each of us must prioritize finite resources among competing goals and strategies. Our trait constellations guide how we approach these challenges and help us balance homeostasis and change as the environment demands in order to survive and thrive.

Implications and Future Directions

This book reported 3,543 meta-analyses that indicate numerous associations between cognitive abilities and personality traits. These relations have implications for the etiology of these domains as well as for explanation and prediction.

Cognitive Ability and Personality Research

The current study has three major implications for individual differences research. First and foremost, many cognitive abilities are correlated with a variety of personality constructs. Therefore, measuring a given cognitive ability construct is likely to give some indication of a set of personality constructs. For example, scoring high on mathematical abilities tends to indicate higher standing on industriousness and the ideas facet of openness but lower standing on politeness, modesty, and orderliness. Second, the nomological network of relations with long term retrieval constructs supports its distinction from long term learning constructs. Third, the knowledge acquisition trait complexes identified by Ackerman and Heggestad (1997) were mostly supported but with the addition of many personality traits from various levels of the personality hierarchy (e.g., factor beta, compassion, order, and achievement via independence). Similarly, the findings inform investment theories of intelligence in how personality guides the acquisition of knowledge. For example, since conscientiousness’ industriousness and order components as well as neuroticism’s uneven tempered and suspiciousness facets correlate notably with verbal abilities, developmental and theoretical models should incorporate these traits. Future research should also add contemporary conceptualizations of interests, values, and preferences to the present findings (cf. Hansen & Wiernik, 2018; Wiernik, 2016) to fully document the constitution of each trait complex.

Additionally, the overarching pattern of results is relevant to the bandwidth-fidelity dilemma. Most notably, we did not find stronger relations between constructs at similar levels of the cognitive and personality hierarchies. Instead, personality constructs and cognitive ability constructs cluster in complexes indicative of diverse fitness strategies.

As mentioned previously, general fitness refers to “individuals’ agility in effectively moving between a focus on surviving and a focus on thriving as the environment demands/provides” (see Chapter 5). Other researchers have described a fitness factor as “an index of general genetic quality that predicts survival and reproductive success” (Arden et al., 2009 p. 581). In both cases, fitness is conceptualized as conferring surviving and thriving. However, Arden et al.’s explanations focus on genetic causality and are more geared toward population-level effects. Our conceptualizations are built upon data from individuals and thus describe the psychologically functional interplay between abilities and personality in response to environmental demands and affordances. Therefore, fitness is relative to a specific environment (Sober, 2001).1 There are no ability-personality constellations that uniformly imbue individuals with greater fitness across all environments. As Nettle (2006) points out, variation in personality traits has been maintained over human evolution because of benefits and trade-offs across the continua of those traits that may confer differential advantages/disadvantages depending on a given environment. As we established through the research presented in this volume, abilities and personality traits that galvanize cybernetic mechanisms to achieve homeostasis for self-preservation and to activate change for self-development covary and may casually explain behavior. As such, their relations are central to the CTCT perspective we proposed, and they deepen understanding of personality-cognitive ability relations. Future research should examine the precise casual mechanisms involved.

Etiology of Personality and Cognitive Ability

The notable clusters of individual differences related to homeostasis and change identified by our review may be useful for directing the attention of researchers to investigate shared roots. Genetic (Deary et al., 2006; Hill et al., 2018; Sanchez-Roige et al., 2018), neurobiological (Allen et al., 2017; Deary et al., 2010), cultural (Lynn & Mikk, 2007; Schaller & Murray, 2008), educational (Chen et al., 2018; Dahmann & Anger, 2014; Ritchie et al., 2015), and other factors constituting the etiology of cognitive abilities and personality traits have been examined. The personality-cognitive ability links documented in this research can help researchers examine additional etiological factors that may have been overlooked or understudied and organize these factors around theoretically and empirically meaningful clusters.

For example, it is acknowledged that, “genes do not act directly on behavior; genetic effects are mediated by brain function and structure.” (Wilt & Revelle, 2017 p. 34). For agentic traits, such as extraversion, dopaminergic activity is a primary etiological basis (Depue, 1995; Depue & Collins, 1999; DeYoung, 2013). Our finding that extraversion’s activity and dominance facets as well as behavioral activation traits (e.g., achievement via independence) are related to several cognitive abilities may suggest a role for the dopaminergic system in cognitive activation for change. In support of this idea, research has documented links between various aspects of the dopamine system and memory (e.g., Luciana et al., 1998) as well as learning (Klingberg, 2010; Schultz, 2002; Wise, 2004). Yet dopamine’s role in acquisition of knowledge and the investment theory of cognitive ability has yet to be fully described and empirically documented. Behavioral activation traits’ pervasive relations with cognitive abilities suggest a ripe area for future research. More generally, our findings underscore a need to incorporate personality constructs and their other known etiological bases (e.g., hormonal, genetic) into models of prefrontal cortex function (e.g., Miller & Cohen, 2001). Many other biological and environmental factors await identification of their influences on personality and cognitive abilities. As researchers from a diverse set of fields review and contemplate the personality-cognitive ability relations documented here, they will be in the best position to consider and research shared etiological factors.

Considering environmental factors, it is certain that some experiences can have a significant, negative impact on cognitive abilities (e.g., head trauma). Detrimental effects of malnutrition on cognitive development have also been documented (e.g., Gillberg et al., 2010; Neumann et al., 2007; Prado & Dewey, 2014). Given the sizable, negative correlations between cognitive abilities and depression, a natural question is whether conditions that result in resource deficiencies and reduce the availability of psychological energy (e.g., malnutrition and famine-like conditions such as anorexia nervosa) constitute a shared cause. Some evidence for this has been reported from the Dutch famine during World War II (e.g., Roseboom et al., 2011). In the same way, personality traits such as neuroticism are affected by environmental influences (e.g., divorce, job promotion; Laceulle et al., 2013). Such relations may indicate concerted, intra-life adaptations to reorient the organism’s life strategy in response to strong environmental influences. We expect that, as researchers from a diverse set of fields review and ponder the multitude of personality-cognitive ability relations documented here, there will be many other shared etiological factors that will eventually be identified and understood.

Prediction of Behavior and Outcomes

The current research informs estimates of the joint utility of personality and cognitive ability measurements in the prediction and understanding of important behaviors and outcomes. For example, even though the associations of personality traits and cognitive abilities with a variety of important life behaviors and outcomes are well-known (e.g., Gottfredson, 1997; Lubinski, 2009; Roberts et al., 2007), the incremental utility of individual differences from each domain over the other in explaining and predicting behaviors and outcomes can only be estimated and understood by knowing the overlap between the two. That is, establishing the direction and degree of overlap between personality and cognitive ability variables enables the estimation of the incremental usefulness of each.

For example, a university that uses aptitude tests to predict high school students’ future learning success (e.g., measured by university grades) may consider adding a personality assessment of conscientiousness. Similarly, a company that uses personality measures to predict employees’ leadership potential may consider adding an assessment of reasoning ability. The current meta-analyses provide information crucial to the estimation of how much additional predictive value would be gained and, therefore, if the personality assessment is worthwhile.

Similarly, both cognitive ability and personality traits display significant relations in predicting career success metrics, such as salary (Ng et al., 2005). Cognitive ability is the single most potent psychological predictor of job performance, across jobs, with strongest effects in complex jobs (Dilchert, 2018; Ones et al., 2016). Many organizations rely on verbal ability tests or general mental ability measures in selecting their employees and managing their talent. Based on the current findings, one would know that verbal abilities are more strongly related to ambition than general mental ability is, and this important consideration could be leveraged to build a selection system that is more predictive of job performance.

There are many insights among the more than 3,500 meta-analytic relations presented here that specialists could use to better predict and understand behaviors and outcomes in their own fields. By using a large amount of data to form robust estimates of personality-cognitive ability relations, it is now possible to estimate the unique contributions of each personality trait and cognitive ability in domains such as education, work, intrapersonal, and social, among others.

Beyond predicting outcomes, CTCT could be used to help people understand themselves and identify their optimal environment. Currently, most people are not in the optimal job, relationship, and residential environment because they do not fully understand themselves, the plethora of available options, or how to match effectively. The result is forfeited happiness, health, and effectiveness for individuals, societies, and humanity.

Energy, Information, Individuals, Environments, and Goals

In this concluding section, we aim to cogently synthesize the research findings and theory presented in this book. We are deliberately concise in our description; the rest of the book provides the details and nuance. Ultimately, we use the findings presented in this volume to formulate an explanation of why you are.

Many studies have examined differences between people’s traits and how those differences are correlated, and yet the fundamental reasons why such differences and correlations occur has remained a mystery. Even large-scale investigations using advanced neuroimaging and molecular genetics reveal only narrow, intermediary mechanisms like brain area activation and allele expression.

Integrating concepts from economics, genetics, psychology, biology, and other fields, we synthesized thousands of meta-analyses to propose a theory that explains why some traits co-occur in individuals more often. In the process, we also elucidated why traits like high aggression, high anxiety, and low intelligence persist and why such diversity is valuable in human populations. While other theories emphasize the importance of the individual or the environment, we posit that no trait or environment is inherently good or bad on its own.2 Instead, such evaluations depend on the fit of the individual with the environment and the fit of both with the goals of the individual. These fits define fitness. That is, fitness is how well the characteristics of the individual combine with the demands of their environment to achieve goals.3 Fitness manifests across life domains as mental well-being, professional effectiveness, educational attainment, and longevity.

Individual differences are tuning parameters. These parameters make individuals more/less likely to set, pursue, and achieve various goals when presented with varying environmental constraints and resources. These parameters also determine how the individual weighs risk vs. reward, values freedom and order, approaches intrapersonal and social relations, balances desire for power with corresponding resource constraints, and approaches other fundamental dimensions of life. The world, especially the future, is too complex to be optimized with a singular solution.4 This is true of physical characteristics (e.g., our species’ ability to achieve goals is more resilient when some people are tall and others are short). This is also true of psychological characteristics: individuals embody unique constellations of traits that represent bets for what will be most effective in the environments they encounter. Casting diverse bets ensures the durability of species.5 In fact, our adversaries and those we view as the antitheses of ourselves may be our greatest partners in ensuring the achievement of long-term goals (e.g., survival of our kin).

We go further by highlighting how the agility to adapt oneself and goals to various environments is a common thread of effective individuals, which is why traits like cognitive ability predict success across so many disparate domains (e.g., work, creative pursuits, health). Nevertheless, we maintain that even rigid trait constellations can be successful when fit with the right goals and environment. Such views are consistent with the largest meta-analyses of two of the most important domains of psychological differences: personality and cognitive ability.

We proposed Cybernetic Trait Complexes Theory (CTCT) to describe why certain traits are correlated. The term “cybernetic” denotes goal-directed systems that self-regulate via feedback. Individuals are cybernetic systems that must prioritize finite resources among competing goals and strategies. Individuals set goals, detect their current state and movement toward goal states, consider their options, can take action to try to advance toward their goal state, and iteratively adjust their behaviors and goals based on evaluative feedback loops.6 This process (depicted in Figure 18 in Chapter 6) encapsulates goals, strategies, and behaviors that reflect the values, interests, abilities, personality, history, and environment of the individual.

Personality traits reflect different cybernetic preferences for sensing, evaluating, and behaving (i.e., typical strategies for assigning meaning and values to internal and external stimuli). Cognitive abilities index how effectively information is leveraged to set goals and employ strategies that maximize the probability of goal achievement (i.e., how efficiently, proficiently, and successfully goals are set, and pursued). Essentially, the universe is full of entropy, which is to say full of information, and more intelligent brains are better able to decipher and leverage this information to employ strategies and behaviors that increase the probability of goal attainment.7

Constellations of these traits and others, including values, interests, and physical attributes, form strategies aimed at maximizing odds of surviving and thriving. That is, personality traits and cognitive abilities jointly direct resource prioritization to achieve self-preservation and self-evolution. In the process, people invest energy in strategies distributed across two fundamental axes: homeostasis versus change and self/internal focus versus environment/external focus (see Chapter 6’s Figure 19). These strategies are reflected in trait complexes. Complexes emphasizing homeostasis enable strategies focused on psychological stability and maintaining the individual across environments. Complexes emphasizing change enable strategies focused on growth and activation for adaptation. Individuals can display different complexes at different times, but the probability of each complex differs across individuals due to their genetic blueprint and life histories.8

Successful achievement of goals hinges on employing appropriate strategies to cull and weigh information about oneself and one’s context to decide when/what/how/where to preserve and when/what/how/where to evolve. That is, when to persevere and when to pivot. In addition to sensory perception and processing, feedback loops are the key mechanism providing this information, and they make cybernetic systems inherently adaptive.

Life is a constant interplay of diversification and concentration, ebb and flow, change and stability, where the odds of an individual surviving and thriving are maximized through the adaptive orchestration of energy to prioritize growth and homeostasis in environmentally sensitive ways. Similarly, the odds of species survival are maximized by diversifying individuals’ predispositions toward certain strategies and their corresponding trait complexes to meet a wide range of environments. To fully understand human behavior and outcomes it is important not only to understand and measure the individual’s traits and outcomes but also what contexts best fit different trait constellations and goals. People are not types; instead, they manifest complex constellations of tuning parameters. A person might navigate by one constellation today and another tomorrow.9 Different levels of each parameter can be differentially valuable in different environments. Diversity makes us more resilient as a group, but at the same time it is important to acknowledge that some constellations are more likely to succeed in certain environments. Currently, comprehensive measures of the environment, taxonomies of goals, and studies of person-environment-goal fit are almost completely absent from the scientific literature, which is leading to misunderstandings about what traits predict success, why diversity is important, and what methods are most effective for changing behavior.

The current research proposes that success is a matter of effectively setting, pursuing, and attaining goals, and that this depends on the fit between the resources and constraints of the environment as well as the trait parameters of the individual. The current research also reinforces the proposal of DeYoung and Krueger (2018) that psychopathology ensues from malfunctioning cybernetic feedback loops (e.g., poorly set or pursued goals) as well as from mismatches between the trait parameters of the individual, their goals, and/or their environment. Finally, the current results indicate that traits bundle together to form optimal strategies for certain types of environments. Since these trait bundles are likely to be bounded by evolution and genetics and hence, by biology, the best way to increase the effectiveness of behaviors may actually be to help individuals set better-fitting goals and choose or create more suitable environments. After all, our choices are how we anchor and sail.

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Endnotes

1 Reproductive success may or may not be adaptive for any given individual, given environmental resources and threats.

2 Indeed, both genes and environment must be considered since nature equipping an unborn child for fitness is like trying to pack your bag for a camping trip when you do not know where you are going; some things are generally useful, like drinking water, but others are best suited to certain environments, like ice picks.

3 Successful people tend to be those who find or put themselves in situations whose demands fully align to their strengths.

4 For example, every vice from lust to aggressiveness to greed was once a virtue.

5 Put differently, you are the steward of one of nature’s bets. The ultimate purpose of these bets is not to accomplish your goals but rather to ensure that we continually find paths through the crucible of the ever-changing environment. Some have posited that evolutionary effectiveness is how well you can accomplish your goals, but there is a meta-purpose to your goals, strivings, and failings: to accomplish nature’s goal of finding viable feature sets to fit with the environment.

6 While our model bears some similarity to some decision-making models (e.g., observe-orient-decide-act; Boyd, 1986), it was developed independently and appears to be the most generalizable template of goal pursuit based on individual differences and environmental circumstances.

7 Entropy describes a state where it is very difficult to find patterns or predict outcomes. Complexity is when seemingly independent elements appear to spontaneously form a coherent pattern. In both cases, the patterns are there all along, but our limited intelligence makes it difficult to grasp them, giving the appearance of chaos. Therefore, the reduction of entropy, which is known as information, is actually just a measure of our understanding.

8 One motivating dynamic in the balance between change and homeostasis is the fact that most changes might have a low success probability but making no changes ever will lead to certain failure in the long run.

9 Indeed, the behavioral records of individuals in intense environments across history has shown that ‘good’ and ‘evil’ cut not just through every society and family tree, but every human heart. This is not to say that individuals lack predispositions toward behaviors that society deems good or evil. On the contrary, individuals’ behaviors are the result of both their inner predispositions and external environments.

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Appendix A. Cognitive Ability Construct Definitions

Appendix A. is available as a PDF download at: https://umnlibraries.manifoldapp.org/projects/of-anchors-and-sails/resource/appendix-a

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Appendix B. Measures and References

Appendix B. is available as a CSV download available at: https://umnlibraries.manifoldapp.org/projects/of-anchors-and-sails/resource/appendix-b and references are available as CSV download at: https://umnlibraries.manifoldapp.org/projects/of-anchors-and-sails/resource/appendix-b-references.

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Appendix C. Personality Construct Definitions

Appendix C. is available as a PDF download at: https://umnlibraries.manifoldapp.org/projects/of-anchors-and-sails/resource/appendix-c

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Appendix D. Measures and References

Appendix D. is available as a CSV download at: https://umnlibraries.manifoldapp.org/projects/of-anchors-and-sails/resource/appendix-d and references are available as a CSV file at: https://umnlibraries.manifoldapp.org/projects/of-anchors-and-sails/resource/appendix-d-references.

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Appendix E. Detailed Methodology

In this appendix, we provide details about our methodology that go further than the brief overview presented in Chapter 3. We first detail how our database was created (e.g., how studies were identified, screened, and coded). We then describe how coded data were processed (e.g., how personality measures and ability tests were assigned to constructs). Lastly, we specify meta-analytic features (e.g., artifact distributions) of our research that are important to the psychometric meta-analytic methods used in this work (Schmidt & Hunter, 2014).

Database Creation

In creating a meta-analytic database, three areas are of critical importance: how primary studies are identified, which inclusion and exclusion criteria are used to screen studies, and how studies are coded. These areas are delineated below.

Study Identification

To ensure our results were representative of people across regions, ages, races, and backgrounds; transcendent of specific measurement instruments; and as accurate and precise as possible, we systematically employed nine diverse source identification strategies based on the methods devised in the dissertation of the first author (Stanek, 2014). Figure 3 in Chapter 3 summarizes the counts of studies identified, sifted, and eventually included in/excluded from the meta-analyses.

The identification of studies for potential inclusion in these meta-analyses began with studies included in relevant, previous meta-analyses and reviews (Aamodt, 2004; Ackerman & Heggestad, 1997; Chen et al., 2001; Cohn & Westenberg, 2004; Credé et al., 2010; DeRue et al., 2011; Hembree, 1988; Lange, 2013; Meriac et al., 2008; O’Boyle et al., 2013; Payne et al., 2007; Robbins et al., 2004; Signorella & Jamison, 1986; Steel, 2007; von Stumm et al., 2011; von Stumm & Ackerman, 2013; Wolf & Ackerman, 2005). From these materials we identified 1,945 studies, which constituted the starting point for our research.

Second, these initial studies were supplemented with database searches in PsycINFO, Web of Science, and Google Scholar. Keyword searches were conducted with the following cognitive ability and personality terms: intelligence, cognitive ability, general mental ability, mental ability, aptitude, g, g loaded, intellect, WAIS, Wechsler, Raven, Bennett, Bennett Mechanical Aptitude Test, Differential Aptitude Test, and DAT; personality, extraversion, extroversion, neuroticism, emotional stability, conscientiousness, agreeableness, openness, openness to experience, Big 5, Big Five, disposition, Myers Briggs, NEO PI-R, California Psychological Inventory, and CPI. Recognizing that strong research is conducted in many countries and languages, studies did not need to be written in English to be included in our meta-analyses. Trained research assistants and volunteers proficient in all required languages screened the identified articles according to the inclusion criteria and then assisted with processing and coding. To cover the countries with the most scientific psychology output in the world and the languages with the most speakers, Google Scholar was queried in English, German, Chinese, and Spanish (“Scimago Journal & Country Rank for Psychology 1996-2018,” 2018; Simons & Fennig, 2018). These procedures identified over 3 million studies that were then initially screened by 31 trained research assistants and volunteers over a five-year period.

The third study identification strategy queried WorldCat, the Educational Resources Information Center (ERIC), and the Network of Libraries, as well as search portals/databases specific to Africa, Australia, Brazil, Britain, Canada, Chile, China, Europe, Finland, France, Germany, Hong Kong, India, Korea, Latin America, Mexico, Netherlands, New Zealand, Portugal, Russia, Scandinavia, Singapore, Scotland (University of Edinburgh), South Africa, South America, Spain, Taiwan, and Wales. Beyond these continent-, country-, and even university-specific search portals, we also examined “open” databases and online repositories including Open Thesis, Journal of Articles in Support of the Null Hypothesis, Journal of Negative Results, Journal of Null Results, Social Science Research Network, The Data, Data Dryad, and Open Science Framework.

The fourth study identification strategy has been largely overlooked in previous meta-analyses: examination of governmental organizations’, especially military, research and databases. We inspected the online database for all public research conducted by the United States military, the Defense Technical Information Center, as well as the online database for Defence Research and Development Canada. Specific agencies’ databases were also searched (e.g., United States Federal Aviation Association). To incorporate research from other nations’ militaries, we examined all presentations that had been given at the International Military Testing Association and made available online (2004–2011). Many of these authors were also contacted via e-mail, phone, and/or in-person to request access to materials, clarify details, and investigate the existence of other relevant data. Fourteen studies were identified through this strategy.

The fifth strategy went beyond database searches to manually examine each article in high-yield journals for all issues across 20 years. Given their relevance, the following journals were selected: Intelligence, Journal of Personality and Social Psychology, Journal of Applied Psychology, European Journal of Personality, Personality and Individual Differences, Journal of Personality, Personality and Social Psychology Bulletin, Psychological Bulletin, Psychological Assessment, Journal of Experimental Psychology, Journal of Individual Differences, Personnel Psychology, Journal of Research in Personality, Annual Review of Psychology, and Medical Education.

The sixth strategy further extended the search to conference materials. Digitally archived programs of several high-relevance conferences were examined for any materials (e.g., posters, symposia, presentations, debates) that appeared to contain or cite relevant data. In most cases, authors had to be contacted for the relevant data/statistics since personality-cognitive ability relations were not the focus of their studies. All obtainable materials were examined.1 Conference programs examined were from the following: International Society for the Study of Individual Differences (1993–2013, excluding 2007, which was not available online), International Society for Intelligence Research (2000–2011), Society for Industrial and Organizational Psychology (1998–2013), European Conference on Personality (2008, 2010, 2012), World Conference on Personality (2013), and Association for Research in Personality (2000–2009, 2011, 2013). Through this strategy, 1,214 potentially usable materials were identified. Upon request, some of these organizations also sent their members invitations on our behalf to contribute to this meta-analysis (i.e., International Society for Intelligence Research, International Personality Assessment Council, International Military Testing Association). A couple organizations (i.e., International Society for the Study of Individual Differences, Association for Research in Personality) posted the invitation to contribute on their websites, but response to these indirect solicitations was very weak. Of the studies identified in this vein, less than 10% (i.e., 48) were obtained using solicitations and appeals.

The seventh study identification strategy involved contacting publishers of relevant assessments and other organizations involved in applied psychological measurement. In striving to be comprehensive, we contacted all members of the Association of Test Publishers (n = 179) individually with bespoke messages. Some members provided raw data, others provided summary statistics, and still others provided manuals or technical reports that contained useful information (n = 11). In instances when no response was received from test publishers, we searched through any test manuals/reports/notes we could otherwise obtain (more than 1,000). The personality and cognitive ability test manuals archive of Professor Deniz Ones was an invaluable resource in this endeavor. This archive incorporates manuals from the former University of Minnesota Psychology Library as well as from the personal libraries of Professors Thomas Bouchard, John P. Campbell, David Campbell, Marvin Dunnette, Stephan Dilchert, Jo-Ida-Hansen, and Deniz S. Ones. Based on examination of this archive, requests were sent to 42 additional test publishers of personality and cognitive ability measures. Relatedly, e-mail, phone, and postal mail requests were also sent to consulting firms, independent consultants, contacts in corporations, and practitioners at other institutions who the authors thought had access to datasets containing both personality and cognitive ability measures. Test manuals were pored over page-by-page, since relevant correlations and data were often reported for multiple samples within a single manual. We noticed that test manuals are especially likely to report data for the same sample in multiple publications (e.g., editions of the manual), though the sample sizes and even effect sizes sometimes changed, usually indicating updating of original samples by the addition of (a) new study participants and/or (b) additional measures. In such cases, we took care to include non-overlapping data in the present meta-analyses so as not to violate the independence of samples within a given analysis.

An eighth strategy to locate relevant data was to sift through the protocols of professionally run, large-scale studies (185 studies), since these projects tend to recognize the importance of individual difference domains such as personality and cognitive ability. Such studies were identified by examining reference lists of other materials found for this research, manually sifting through datasets in repositories compiled by others (e.g., Henry A. Murray Research Archive, Inter-university Consortium for Political and Social Research), and inspecting published directories of such studies (e.g., Hur & Craig, 2013; van Dongen et al., 2012). Most of these raw datasets have not been included in prior meta-analytic work, and their inclusion was paramount, given their quality, quantity, and relevance to our research questions. Twenty-five such studies provided personality trait and cognitive ability raw data. These studies often included multiple samples, waves, and/or datasets, which contained thousands of variables that required examining tens of thousands of pages of scanned codebooks and other relevant documents to be used.

Finally, the reference lists of all obtained materials were examined for citations of materials including relevant data/statistics. Relevant materials were retrieved from publication portals or by contacting study authors/project managers.

Inclusion and Exclusion Criteria

To be included in the current meta-analyses, studies had to provide at least one correlation coefficient or convertible effect size between a personality trait and a cognitive ability as well as an associated sample size. Secondary summaries that aggregated primary effects were excluded. Many studies collected relevant personality and ability measurements in usable samples but did not report effect sizes relating personality constructs to cognitive ability constructs. For 673 such studies, we spent a year attempting to individually contact the authors and obtain the raw data or usable statistics. This enabled 100 more materials to be included.

Study Design Characteristics

Bivariate relations had to be reported in the contributing material or calculable based on individual-level data. Studies reporting personality-cognitive ability relations at group (e.g., team), organization (e.g., school, company), or country levels of analysis were excluded. Within-individual personality-cognitive ability relations were also excluded. We also excluded studies with experimental manipulations that may have impacted personality or cognitive ability scores, as well as contrasted/extreme group studies where participants were selected based on having extreme personality and/or cognitive ability scores. After being converted to Pearson correlations and correcting for dichotomization, phi coefficients from non-extreme group contrasted studies were included (Hunter & Schmidt, 1990).

Samples

Clinical or pathological samples and samples with participants younger than 12 years old were not included. These criteria were implemented to report only on relatively stable (Briley & Tucker-Drob, 2013) and normative trait variation not affected by psychoactive medications or psychiatric settings.

When processing raw data and the information was provided, we excluded data from individuals who received more than minimal prompting/assistance on cognitive ability tests, who had their testing sessions interrupted (e.g., by a phone ringing), or about whom the original researchers had noted a significant impairment (e.g., “unable to read the test due to a visual impairment; glasses were not sufficiently helpful”).

Statistical Criteria

In contrast to some previous personality-cognitive ability meta-analyses (e.g., von Stumm & Ackerman, 2013), we included studies that presented results from a non-select set of all relevant correlations/effect sizes. Including studies reporting only statistically significant results (e.g., Lukey & Baruss, 2005) would have inflated personality-ability relations (Ones et al., 1993). Studies that only reported partial or semipartial correlations (e.g., path coefficients) controlling for other variables were excluded if bivariate Pearson correlation coefficients could not be calculated retroactively or obtained from the authors. This is important because partial and semipartial correlations are not directly comparable to zero order correlations.

Personality Trait Variables

To be included, personality variables had to meet multiple criteria. Measures of attitudes, values, self-efficacy, state affect, mood, response bias, and validity scales were not included in the current analyses. Only personality scales that measured personality using self-reports were included (i.e., we did not include others’ ratings or objectively recorded behavioral indicators of personality [cf. Wiernik et al., 2020]). Self-report and other-report measurements of personality each have their own strengths and weaknesses and therefore are complementary approaches to measuring personality (Connelly & Ones, 2010). However, combining self- and other-reports in the same meta-analysis would have created heterogeneity and muddled results. Without conducting two separate sets of meta-analyses, one focusing on self-report personality-cognitive ability relations and the other on other-report personality-cognitive ability relations, there was no conceptually and psychometrically clean way of including other-report measures in this research. We also focused on studies that measured personality with scales (i.e., studies that reported relations with single personality items were avoided as we wished to examine latent personality traits, and, without repeated measurements, single item measures of personality tend to be too unreliable and typically construct deficient to assess broad personality factors or sub-factors (Epstein, 1979; Ones et al., 2016)). We encourage future researchers to undertake item-level meta-analyses (Mõttus et al., 2020), if robust data become available.

For multiple reasons, ipsative personality measures were also excluded. Ipsative measures force respondents to make choices between traits (i.e., “Are you higher on extraversion or openness?”). First, ipsative measures are not designed to yield normative scores. In fact, ipsativity hampers between-individual normative comparisons and biases the rank ordering of individuals. Such issues of fully ipsative measures are well-documented in the literature (Dunlap & Cornwell, 1994; Hicks, 1970; Tenopyr, 1988). Second, some evidence indicates that ipsative measures are more cognitively demanding for respondents and therefore more highly correlated with cognitive ability than normative measures of personality (e.g., Vasilopoulos et al., 2006). Normative measures of personality require respondents to make a single decision: how much they agree or disagree with an item. Forced choice measures require multiple decisions: indicating how much they agree or disagree with an item and ranking the item relative to the rest of the alternatives. Quasi-ipsative measures were included (Salgado et al., 2015).

Cognitive Ability Variables

Only studies that utilized objective measures of cognitive abilities were included. We excluded subjective ratings of cognitive ability (e.g., self-estimates of ability). Measures of the consequences or outcomes of cognitive ability were also excluded (e.g., school grades/grade point average, performance on situational judgement tests and assessment center exercises). We also excluded cognitive style, metacognition, attributional complexity, and executive functioning measures (e.g., Stroop, trail-making, tower of London, Wisconsin card sort). Finally, we excluded measures of emotional intelligence, practical intelligence, cultural intelligence, rational intelligence, etc. since they have been too heterogeneously defined and measured. They are also not yet fully understood and incorporated into models of human intelligence (e.g., CHC [Schneider & McGrew, 2012] and Unified CHC [Stanek & Ones, 2018]). Literatures are emerging around each of these constructs (Cabrera & Nguyen, 2001; Dilchert & Ones, 2004; Joseph & Newman, 2010; McDaniel et al., 2007; Van Rooy et al., 2006; Van Rooy & Viswesvaran, 2004), but including them in the present effort would have injected undesirable heterogeneity in the meta-analytic estimates.

Final Set of Included Studies

The above search strategies and inclusion/exclusion criteria yielded 1,325 studies contributing independent data to the meta-analyses in this research. We were as thorough as possible in our approach to minimize the impact of publication bias; to investigate beyond Western, educated, industrialized, rich, and democratic societies; and to avoid the biases inherent in the paradigm of any one field/research perspective (Henrich et al., 2010; Simmons et al., 2011). Even after more than five years of searching, the current study is unlikely to be fully exhaustive. That is, there were likely studies in existence that we did not locate, but it is hoped that the search methods employed yielded a representative majority of the relevant, available, quantitative information on the relations between personality traits and cognitive ability constructs.2 Comparison of the number of studies included in this set of meta-analyses with prior meta-analyses of personality-cognitive ability relations indicates that we created the largest and most comprehensive database to date on this topic, and one of the largest meta-analytic databases on any topic (cf. only larger meta-analysis [Polderman et al., 2015]). The meta-analytic database is publicly available in Appendix F. A full list of studies contributing effect sizes to the present meta-analyses is presented in Appendix K.

Coding of Studies and Data Entry

A supervised team of trained researchers including the authors, graduate students, undergraduate students, and university graduates coded the studies. More than 90% of the entries were double-checked by the lead author or via blind double-entry by multiple research assistants. These double-entry and checking procedures identified a small number of typographical errors, which were corrected before the analyses were run.

Each contributing study’s year, authors, publication type, source name, sample size, cognitive ability measure used, personality measure used, reliabilities for the personality and cognitive ability measures observed in the sample, type of reliability calculated, and correlation between the cognitive ability and personality variables were coded. To the extent they were reported, sample demographics were coded in order to help characterize the meta-analytic database.

We also conducted a final, independent check on our database using the database from the most cited previous meta-analyses examining personality and cognitive ability (Ackerman & Heggestad, 1997). Dr. Heggestad graciously provided the database from that investigation to allow for comparisons of data entry consistency and accuracy. The database of Ackerman and Heggestad included 119 materials, while the current meta-analyses examined 1,325 materials. Several studies included by Ackerman and Heggestad were excluded from the current analyses because they did not meet our inclusion criteria (e.g., included children younger than 12, utilized ipsative measures). Comparisons of the overlapping studies’ sample sizes and effect sizes coded revealed 98% agreement. Disagreements were due to ambiguities in the original primary study reporting (e.g., sample size reported as a range). This approach of independently coding then checking against previous meta-analyses’ database is a rare but important method for quality assurance. We advocate for this approach to become a routine method for assessing coding quality, especially as meta-analytic reporting standards become more widespread and updates are conducted.

Data Preparation

Prior to running meta-analyses, the data had to be further processed and prepared. While Pearson correlations were reported in many cases, in other instances they had to be computed from raw data or converted from other types of effect sizes. Additionally, each of the myriad personality and cognitive ability measures needed to be mapped to the construct they assessed. After assigning measures to constructs, we checked for independence of effect sizes for each meta-analysis to be conducted. These data preparation steps are described in detail below.

Construct Categorization: Personality

A consistent personality framework had to be implemented to organize the personality measures and constructs for analysis. Stanek and Ones (2018) studied a variety of personality taxonomies (e.g., Ashton et al., 2004; Cattell, 1946; Costa & McCrae, 1992; Davies, 2012; DeYoung et al., 2007; Edmonds, 1929; H. J. Eysenck, 1959; H. J. Eysenck & Himmelweit, 1947; M. Eysenck, 1992; S. B. G. Eysenck, 1965; Galton, 1884; Goldberg, 1992; Hofstee et al., 1992; Hough & Ones, 2002; John et al., 1991; Norman, 1963; Stark et al., 2014; Tellegen, 1982; Wundt, 1897), as well as emerging factor analytic and nomological network evidence (Soto & John, 2017), to synthesize the Pan-Hierarchical Five Factor Model and create an empirically rooted compendium of personality measures. Although it can be tempting to ascribe constructs to measures based on the scale name or a primary study author’s description, such methods suffer from significant flaws, including the jingle-jangle fallacy, disparate usages of common terms (e.g., extraversion), and common misunderstandings by researchers of what constructs are actually captured by the measures they employ.

Across all levels of the personality hierarchy, there were 79 personality constructs included in the present meta-analyses. Definitions of each construct and a compendium of scales assessing each may be found in Stanek and Ones (2018) and Appendices C and D.

The Pan-Hierarchical Five Factor Model (Stanek & Ones, 2018) was used in the present meta-analytic investigations (see Chapter 2, Figure 2 for a depiction). In our classifications for this study, each personality measure was identified as an indicator of a single personality factor, meta-trait, aspect, facet, or compound trait according to the extensive Stanek and Ones (2018) compendium listing measures of each personality construct. Particularly challenging scale classification decisions benefited from the input of Dr. Colin DeYoung. Overall, 2,861 personality scales contributed data to the present analyses, and the use of transparent, open access, well-validated compendia for both the personality domain and the cognitive ability domain supports the replicability and future extension of the analyses presented.

Construct Categorization: Cognitive Ability

Cognitive ability is a domain of interrelated abilities that are hierarchically organized, with general mental ability at the apex (see Figure 1 in Chapter 2 for depiction). The general mental ability factor (g) stems from the positive manifold among specific indicators of cognitive ability (Carroll, 1993; Spearman, 1904). General mental ability can be measured via the g factor extracted from a sufficiently broad battery of cognitive tests or via the overall score of a cognitive ability test assessing different primary abilities. The present research endeavored to examine the relations between constructs of personality and cognitive ability (e.g., general mental ability, extraversion), not specific scales (e.g., Wonderlic, Big Five Aspects Scales). To achieve this, we used a consistent cognitive ability framework to organize constructs and map measures: the Stanek and Ones’ (2018) Unified CHC taxonomy and compendium of associated measures, which is an update of the Cattell-Horn-Carroll (CHC) taxonomy of cognitive abilities (McGrew, 1997, 2005, 2009).

Ninety-seven cognitive abilities were included. These include 70 specific abilities (e.g., number facility and quantitative reasoning), which are grouped into 10 dimensions of primary abilities (e.g., processing speed and fluid abilities), which in turn can be organized into four groups: domain independent general capacities (i.e., fluid abilities, memory), sensory-motor domain specific abilities (e.g., visual processing, auditory processing), speed capacities (e.g., processing speed, reaction and decision speed), and invested abilities (i.e., acquired knowledge). There were also compound ability measures, which include two or three primary abilities.

Another goal of the current research was to examine personality-cognitive ability relations in fine detail. Therefore, each contributing ability scale was assigned to a single ability by the authors according to the Stanek and Ones (2018) compendium. Particularly challenging scale classifications benefited from the input of Dr. Kevin McGrew, the current intellectual custodian of the CHC taxonomy. Overall, 2,388 cognitive ability scales contributed to the present analyses. 97 cognitive ability constructs across all levels of the ability hierarchy were included.3 Definitions of each construct and a compendium of scales assessing each can be found in Stanek and Ones (2018) and Appendices A and B

Effect Size Independence

To avoid issues of non-independence, each sample was permitted to contribute only one effect size to each personality-cognitive ability construct meta-analysis. All meta-analyses presented contain only effect sizes obtained from independent samples. In instances where one sample would have contributed two effect sizes to the same meta-analysis, a composite was formed to ensure the sample was only counted once but all available information was incorporated (Schmidt & Hunter, 2014).

Correlations for the same sample and measures were reported in multiple sources in several cases (e.g., Furnham et al., 2007, 2008). Most sources failed to properly indicate sample dependence/reuse, so our team had to manually examine materials for clues of non-independence. To do so, we examined sample descriptions, demographics of the sample (if reported), sources’ authors, and publication/data-collection year. The majority of these cases were not straightforward investigations, in part because authors tended to drop cases and combine measures differentially across publications (e.g., Aitken Harris, 1999; Ashton et al., 2000). When needed, we contacted authors directly in attempts to verify.

Meta-Analytic Approach

Psychometric meta-analysis (Schmidt & Hunter, 2014) methods were used to combine effect sizes across studies and improve statistical power, reduce error variance of the estimated effect sizes, and estimate the degree to which results might generalize to populations and situations beyond those investigated in individual primary studies. The technique has its origins in applied psychology (Schmidt & Hunter, 1977), but has now become ubiquitous in every domain of science. Psychometric meta-analysis is a random effects approach to cumulating findings across studies that, in addition to minimizing the influence of sampling error, also accounts for differences across studies in reliability of measurement as well as other applicable statistical artifacts.

In meta-analysis, the mean observed correlation r̄ indicates the average, sample-size-weighted observed correlation across studies’ effect sizes. Like most other meta-analytic approaches, psychometric meta-analysis reduces the impact of sampling error by pooling data across many studies and thus increasing the sample size associated with effect sizes produced. However, most other meta-analytic approaches do not account for the systematic effects that downwardly bias effect sizes (Schmidt & Hunter, 2014). For example, no measure is perfectly reliable, and this is particularly true for psychological measures. Measurement error in both personality and cognitive ability measures (e.g., due to misreading an item) systematically lowers the magnitudes of observed correlations, masking correlations that exist between constructs from the two domains (Wiernik & Ones, 2017). Psychometric meta-analysis corrects for this source of systematic bias, thereby producing accurate estimates of the relations between personality and cognitive ability constructs. r̄ is corrected for unreliability as well as other applicable statistical artifacts (e.g., dichotomization) to attain the estimated mean corrected correlation p̂ (i.e., estimated true-score correlation). In our meta-analyses, correlations were corrected for sampling error and unreliability in both the cognitive ability and personality measures. Such corrections result in estimates of the construct-level relations free of measurement error, which are described further in the next section. For the interested reader, Supplementary Tables 3–99 and 197–275 report the observed as well as the psychometrically-corrected relations (see Appendices G and I).

The variability of observed correlations across effect sizes is indicated by SDr. Observed standard deviations reflect the fact that unreliability and other statistical artifacts inflate variation in effects observed across studies. Thus, corrections are necessary to control for the differential reliabilities of various scales contributing to each meta-analysis in order to estimate true variability. The estimated standard deviation of corrected correlations (SDp̂) indicates the degree of true (i.e., non-artifactual) variability associated with the mean, corrected correlation. It indexes true heterogeneity. Other meta-analysts sometimes refer to this as τ (e.g., Hedges & Vevea, 1998).4 The present meta-analyses also produced precise estimates of the degree of true variability in the meta-analytic distributions. Beyond just a dichotomous pronouncement of heterogeneity being present/null, these SDp̂ values are important indicators of the degree of variability beyond that due to sampling error and measure unreliability in the relations examined.

We also computed 80% credibility values for each meta-analytic result. The credibility value range indicates the range in which most individual true-score correlations would be expected to fall (e.g., when new studies are conducted; Schmidt & Hunter, 2014). Reporting credibility value ranges reveals whether the examined relations are expected to generalize (Ones et al., 2017; Wiernik et al., 2017). Overall, the results from these meta-analyses constitute a large-scale quantification of the generalizability of personality-cognitive ability relations. To estimate these various statistics, we used the psychmeta package in R (Dahlke & Wiernik, 2018).

Corrections for Unreliability in Cognitive Ability and Personality Measures: Artifact Distributions

As noted above, observed correlations are systematically downwardly biased, and differences in reliabilities across studies inflate observed variation in effect sizes (Schmidt & Hunter, 2014). To correct for this attenuation due to measurement unreliability, robust artifact distributions were compiled drawing on several sources: the individual effect sizes in the current meta-analytic database, previous meta-analyses that compiled reliability distributions for various personality constructs (Davies, 2012), technical manuals of the respective measures, and articles containing representative samples. The means, standard deviations, and number of reliability estimates for each construct’s artifact distribution are reported in Table S1 (for cognitive ability constructs) and Table S2 (for personality constructs). For cognitive ability constructs, these distributions were constructed exclusively from internal consistency reliability estimates and short-term (less than two months) test-retest reliability values.5

Table S1. Reliability distribution statistics for cognitive ability constructs.

Construct

K

M√rxx

SD√rxx

r̄xx

SDrxx

General Mental Ability

134

.94

.04

.88

.07

Fluid

73

.88

.06

.78

.11

Induction

119

.88

.07

.78

.11

General Sequential Reasoning

26

.88

.06

.78

.10

Quantitative Reasoning

36

.91

.06

.83

.11

Memory

115

.87

.08

.76

.12

Long Term Storage and Retrieval

60

.84

.08

.71

.12

Learning Efficiency

55

.88

.08

.77

.13

Associative Memory

22

.90

.05

.81

.08

Meaningful Memory

23

.87

.10

.76

.16

Episodic Memory

55

.88

.08

.77

.13

Free Recall Memory

55

.88

.08

.77

.13

Long Term Visual Memory

55

.88

.08

.77

.13

Retrieval Fluency

163

.85

.08

.72

.13

Ideational Fluency

28

.87

.07

.76

.12

Associational Fluency

21

.88

.06

.78

.10

Expressional Fluency

20

.84

.07

.71

.11

Sensitivity to Problems and Alt. Solutions

21

.86

.06

.75

.10

Originality and Creativity

21

.85

.07

.73

.11

Naming Facility and Speed of Lexical Access

28

.77

.12

.61

.18

Word Fluency

21

.85

.05

.73

.08

Short Term Memory

55

.90

.06

.82

.10

Memory Span

23

.91

.06

.83

.11

Working Memory Capacity

24

.91

.05

.84

.08

Attentional Executive Control

20

.93

.07

.87

.12

Visual Processing

105

.90

.05

.81

.09

Visualization

41

.93

.02

.81

.10

Closure Speed

20

.89

.04

.79

.07

Flexibility of Closure

23

.89

.05

.80

.08

Spatial Scanning

20

.90

.06

.81

.10

Perceptual Illusions

9

.93

.04

.87

.07

Visual Memory

21

.85

.10

.74

.16

Auditory Processing

45

.86

.07

.74

.12

Processing Speed

20

.92

.04

.85

.07

Perceptual Speed

34

.92

.04

.84

.08

Scanning

21

.86

.08

.74

.13

Pattern Recognition

21

.89

.04

.79

.07

Number Facility

22

.93

.03

.87

.05

Reaction and Decision Speed

150

.88

.10

.79

.14

Simple Reaction Time

20

.83

.22

.73

.29

Choice Reaction Time

20

.86

.12

.75

.19

Decision Time

20

.86

.12

.75

.19

Movement Time

20

.86

.12

.75

.19

Semantic Processing Speed

2

.96

.01

.92

.02

Inspection Time

101

.90

.02

.81

.03

Mental Comparison Speed

150

.88

.10

.79

.14

Acquired Knowledge

20

.93

.04

.86

.07

Quantitative Ability

25

.92

.04

.85

.07

Mathematics Knowledge

20

.92

.02

.85

.03

Mathematics Achievement

52

.86

.10

.74

.15

Verbal Ability

39

.93

.05

.87

.08

Reading and Writing

34

.90

.06

.81

.11

Reading Comprehension

23

.87

.05

.76

.09

Reading Decoding

20

.96

.03

.91

.06

Reading Speed

20

.90

.05

.82

.09

Native Language Usage

34

.86

.08

.74

.13

Writing Ability

24

.92

.04

.85

.08

Spelling Ability

20

.85

.10

.74

.16

Comprehension Knowledge

50

.91

.06

.83

.10

General Verbal Information

21

.91

.06

.83

.10

Language Development

20

.91

.03

.84

.06

Lexical Knowledge

34

.91

.06

.83

.11

Listening Ability

20

.85

.08

.72

.14

Domain Specific Knowledge

35

.84

.12

.57

.31

Foreign Language Proficiency

20

.91

.04

.83

.07

Arts and Humanities

40

.78

.04

.57

.18

Behavioral Content Knowledge

14

.52

.28

.35

.30

Business Knowledge

2

.72

.02

.52

.03

Occupational

20

.86

.10

.74

.16

Military & Police

20

.86

.10

.74

.16

Realistic Knowledge

20

.77

.08

.60

.13

General Science Knowledge

56

.73

.11

.55

.15

Life Sciences Knowledge

21

.81

.13

.67

.21

Mechanical Knowledge

35

.89

.07

.80

.12

Natural Sciences Knowledge

44

.84

.11

.72

.17

Physical Sciences Knowledge

23

.87

.07

.77

.11

compound (Nat. Sci. Knwl. & Phy. Sci. Knwl.)

44

.84

.11

.72

.17

Social Studies Knowledge

24

.82

.11

.69

.17

Psychomotor Ability

64

.88

.10

.79

.15

Aiming

24

.93

.05

.87

.09

Finger Dexterity

20

.85

.10

.73

.16

Manual Dexterity

20

.90

.09

.83

.16

Psychomotor Speed

4

.87

.17

.77

.26

Writing Speed

20

.86

.09

.74

.14

Compounds

Acquired Knowledge & Auditory Processing

65

.88

.07

.78

.12

Acquired Knowledge & Memory

91

.92

.04

.85

.06

Acquired Knowledge & Processing Speed

40

.93

.04

.86

.07

Acquired Knowledge & Visual Processing

125

.90

.05

.82

.09

Acquired Knwl., Psychomotor Speed, & Vis. Proc.

129

.90

.06

.81

.10

Fluid & Memory

144

.90

.05

.81

.09

Fluid & Processing Speed

93

.89

.06

.80

.10

Fluid & Visual Processing

178

.89

.06

.80

.10

Memory & Processing Speed

91

.92

.04

.84

.06

Memory & Visual Processing

220

.88

.07

.78

.11

Processing Speed & Reaction and Decision Spd.

170

.89

.10

.80

.13

Processing Speed & Visual Processing

126

.90

.05

.81

.09

Psychomotor Speed & Visual Processing

109

.90

.06

.80

.10

Note: K is the number of independent reliability values. Some values are based on fewer than 20 primary values due to lack of availability in the extant literature. In some instances, the same reliability distribution was used for child constructs of a parent. M√rxx is the average of the square rooted primary reliability values. SD√rxx is the standard deviation of the square rooted primary reliability values. r̄xx is the average of the primary reliability values. SDrxx is the standard deviation of the primary reliability values.

Table S2. Reliability distribution statistics for personality constructs.

Construct

K

M√rxx

SD√rxx

r̄xx

SDrxx

General Factor of Personality

20

.89

.05

.79

.09

Factor Alpha

37

.87

.07

.75

.12

Factor Beta

23

.88

.07

.77

.12

Neuroticism

290

.90

.05

.81

.09

Withdrawal

23

.89

.05

.79

.09

Volatility

20

.89

.04

.79

.07

Negative Affect

23

.89

.07

.80

.12

Depression

30

.87

.08

.76

.13

Anxiety

74

.90

.06

.81

.10

Anxiety (Test)

21

.93

.02

.86

.05

Uneven Tempered

33

.89

.05

.79

.08

Suspiciousness

29

.82

.10

.69

.16

Agreeableness

216

.88

.05

.78

.09

Compassion

20

.85

.08

.73

.12

Politeness

23

.83

.11

.71

.16

Tender Mindedness

33

.79

.10

.63

.16

Nurturance

21

.86

.04

.75

.07

Cooperation

20

.84

.04

.71

.08

Lack of Aggression

20

.89

.04

.79

.08

Modesty

22

.86

.05

.75

.08

Non Manipulative

20

.83

.08

.69

.12

Conscientiousness

264

.89

.06

.80

.10

Industriousness

43

.88

.05

.78

.09

Orderliness

44

.84

.09

.71

.14

Achievement

60

.86

.06

.75

.10

Persistence

21

.85

.11

.74

.17

Dependability

63

.82

.07

.68

.11

Cautiousness

55

.86

.06

.74

.11

Order

36

.86

.06

.75

.11

Procrastination Avoidance

22

.85

.11

.74

.17

Extraversion

241

.90

.05

.81

.08

Enthusiasm

27

.86

.06

.75

.10

Assertiveness

20

.90

.03

.81

.06

Dominance

57

.87

.06

.75

.10

Activity

23

.85

.06

.72

.10

Positive Emotionality

30

.87

.07

.77

.11

Sociability

32

.89

.04

.79

.07

Sensation Seeking

38

.86

.06

.75

.11

Openness

216

.87

.06

.77

.10

Experiencing

28

.84

.07

.71

.11

Intellect

38

.88

.06

.77

.10

Need for Cognition

20

.94

.02

.88

.04

Ideas

31

.85

.09

.72

.13

Curiosity

20

.87

.06

.77

.10

Introspection

23

.80

.07

.64

.11

Fantasy

20

.86

.07

.75

.11

Aesthetics

20

.87

.03

.76

.06

Non Traditional

42

.82

.07

.67

.12

Variety Seeking

24

.79

.06

.63

.10

Compounds

Interpersonal Sensitivity

27

.83

.06

.70

.10

Achievement via Independence

25

.88

.09

.78

.13

Innovation

20

.91

.04

.82

.07

Type A

20

.86

.04

.73

.08

Managerial Potential

30

.88

.06

.78

.10

Narcissism

107

.91

.03

.82

.06

Self Esteem

56

.87

.06

.76

.10

Locus of Control

47

.82

.06

.67

.09

Optimism

20

.86

.08

.74

.13

Routine Seeking

20

.89

.07

.80

.13

Resourcefulness

20

.87

.03

.76

.06

Machiavellianism

113

.87

.05

.75

.09

Self Monitoring

20

.85

.04

.73

.07

Customer Service

20

.89

.06

.80

.10

Stress Tolerance

21

.87

.07

.76

.11

Trust

22

.86

.07

.74

.12

Self Control

36

.89

.04

.79

.06

Risk Taking

24

.87

.09

.77

.13

Openness to Emotions

20

.84

.04

.71

.07

Warmth

27

.86

.07

.74

.12

Grandiosity and Intimidation

24

.76

.05

.58

.07

Ambition

27

.88

.04

.78

.07

Ambitious Risk Taking

5

.90

.03

.82

.05

Restrained Expression

20

.87

.06

.76

.10

Tolerance

23

.86

.04

.73

.06

Independent of Conventions and Others

21

.82

.05

.67

.09

Creative Personality

20

.85

.05

.72

.08

Judging-Perceiving

23

.81

.14

.68

.20

Cold Efficiency

20

.78

.08

.61

.11

Rugged Individualism

23

.85

.10

.73

.15

Note: K is the number of independent reliability values. Some values are based on fewer than 20 primary values due to lack of availability in the extant literature. In some instances, the same reliability distribution was used for child constructs of a parent. M√rxx is the average of the square rooted primary reliability values. SD√rxx is the standard deviation of the square rooted primary reliability values. r̄xx is the average of the primary reliability values. SDrxx is the standard deviation of the primary reliability values.

Although less common, some psychometric meta-analyses also correct for the attenuating effect of range restriction. This is appropriate in cases where range restriction, direct or indirect, is known to affect variation in either of the variables of study. In the present meta-analyses, information about the levels of direct or indirect range restriction was not available and could not be computed based on information provided in most contributing studies. To the degree that range restriction existed, it would result in conservative estimates of personality-cognitive ability relations and larger associated variabilities. In any case, we do not expect the degree of underestimation of true-score correlations and overestimation of variability to be large. Based on the contributing studies’ sample descriptions, direct range restriction was only even possible in 8% of samples (e.g., occupational samples). Previous research has shown that gravitation to jobs reduces variability by about 10% for cognitive ability (Sackett & Ostgaard, 1994) and about 4% for personality (Ones & Viswesvaran, 2003). While there is convincing empirical evidence that differential gravitation into occupations affects mean cognitive ability and personality levels, variabilities are far less affected, especially for personality traits. Therefore, the impact of not correcting for direct or indirect range restriction on cognitive ability and personality variables can be expected to be a rather small attenuating influence on the results. If full information were available in the primary studies and appropriate corrections could be undertaken, the result would be slightly larger personality-cognitive ability relations than those we report, since range restriction depresses correlation coefficients. Finally, because most previous meta-analytic investigations of personality-cognitive ability relations did not correct for range restriction, leaving our results uncorrected for range restriction makes them directly comparable to previous research.

Potential Impact of Publication Bias

An often-cited potential threat to the validity of meta-analytic conclusions is publication bias. The meta-analyses presented in this volume are unlikely to have been affected by publication bias for three reasons. First, almost two-thirds (63%) of the effect sizes contributing to these analyses are from unpublished sources (e.g., data archives, unpublished conference manuscripts, dissertations). By including these materials, not only did we safeguard against publication bias, but we also brought to light and made accessible findings from otherwise missed primary studies, research samples, and databases. These freshly computed metrics are now surfaced from archives, mapped to constructs, and available to the scientific community as part of the publicly available dataset associated with the current meta-analyses.

Second, few of the studies included in this research were conducted for the express purpose of examining personality and cognitive ability relations. Rather, these two sets of variables were often included in broader examinations of other phenomena such as physical health, educational attainment, job success, development and aging, interpersonal relations, and decision-making. That is, relations between personality and cognitive ability were often incidentally reported in the correlation matrices, reducing the threat of underreporting of null relations (see supplement of Wilmot & Ones, 2019). Thus, in the case of the published studies (i.e., one-third of the database), we have no reason to suspect that only significant or sizable personality-intelligence relations are reported since they were rarely the focus of the investigations.

Third, the threat of publication bias was also mitigated by the depth of our search strategies: multiple electronic databases were scrutinized, relevant studies from reference lists of examined studies were culled, and sources were not excluded because they were in different languages. In this vein, the contribution of non-USA-based materials and data from nationally representative, large-scale studies6 was another means of protection against publication bias.

Impact of Outlier Samples

For some meta-analyses, results varied when an extremely large study (Project Talent) was excluded. In some of these instances, Project Talent contributed data for approximately twice as many participants as other primary studies, which influenced some of the reported relations. Project Talent was a nation-wide study of 1960s American high schoolers. However, if it is not representative of the broader population of studies in terms of factors that impact the relations between personality and cognitive ability, then the results of otherwise small-N meta-analyses including them may be biased.

We had no a priori reason to think that the Project Talent data were deviant. However, we ran all meta-analyses excluding Project Talent to examine its influence. Some results did not change substantially (e.g., relations with cautiousness), but others did. The general trend was toward smaller relations. In general, the pattern of conclusions did not change. Relations with two conscientiousness constructs, however, were significantly altered by the exclusion of Project Talent data: order and industriousness. With Project Talent data included, these traits were more highly correlated with cognitive abilities, especially with acquired verbal abilities. Excluding Project Talent data also muted some of the effects reported for some extraversion facets, including the activity facet. Relations with openness deflated the least. As an additional check on our data, a non-author PhD-level psychologist at a different research university independently computed and verified the effect sizes included from Project Talent. Other researchers have also found sizable uncorrected relations for personality and intelligence relations in the Project Talent data (Reeve et al., 2006). All the technical results without Project Talent data can be found in Supplementary Tables 100–196 and 276–354 in Appendices H and J.

To further explore the potential impact of Project Talent we examined various features of the data. Scales used as part of Project Talent are well-established (Flanagan et al., 1964; Pozzebon et al., 2013). Although some of the cognitive ability measures in Project Talent displayed very poor reliability, that fact would have attenuated the observed correlations when the data were included, rather than made them unusually strong (e.g., in the case of relations with industriousness). We also examined whether distributional properties of the measures (e.g., ceiling and floor effects) could produce aberrant results. To the extent that they exist, floor and ceiling effects also attenuate correlations. However, we found no evidence of these effects. Instead, we observed distributions that would be expected from nationally representative samples. On the other hand, range enhancement could result in stronger relations (Schmidt & Hunter, 2014, p. 37). We were not able to ascertain the degree of any potential range enhancement in any specific variable. We note, however, that variances in study variables are typically not restricted or enhanced in nationally representative samples such as the one studied in Project Talent. The national origin, age, and generation of the sample are also unlikely to be the driving force behind stronger results, as other studies of individuals from this country, age group, and era showed more typical personality-cognitive ability relations. One possible explanation is that youths, especially those high on industriousness, participating in this once-in-an-era study may have felt particularly motivated to do their best on the cognitive ability assessments. In contrast, those lower in industriousness may have felt that the results of the assessments had no impact on their future and were therefore not worth maximal effort. If both effects occurred simultaneously, range enhancement might be a possible explanatory mechanism. Comparisons of motivated versus less motivated samples' personality-cognitive ability relations could shed light on the question. We also encourage a new Project Talent-like study so that research on individual differences and their life consequences are not limited to one such study, half a century old.

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Endnotes

1 Some conferences were not held every year or did not post agendas, which led to gaps in the years searched.

2 Less than 1% of the effect sizes come from studies conducted prior to 1950, since few standardized personality measures were available. Consistent with increasing use of personality measurements in research and applied settings, 20% of the effect sizes are from 1950–1979, 31% are from 1980–2009, and the remainder are from since 2010.

3 Some ability measures from primary studies we analyzed tapped variance from multiple primary ability domains. Scales tapping several primary abilities were classified as measuring general mental ability. Otherwise, they were analyzed separately as “compound” ability scales, clearly noting their primary ability connections.

4 Some meta-analyses also report a Q statistic to indicate the presence/absence of heterogeneity in contributing effect sizes. However, when the number of effect sizes contributing to a meta-analysis is large, Q tests can also appear “significant” even if the amount of heterogeneity is tiny (Schmidt & Hunter, 2014). We report standard deviations instead of these metrics or I2 in order to more clearly quantify the degree of heterogeneity and, thus, the potential for meaningful moderators.

5 Large-scale, empirical evidence indicates that short-term test-retest reliabilities are well-approximated by internal consistency reliabilities such as Cronbach’s alpha (Davies, 2012; Dilchert, 2008; Viswesvaran & Ones, 2000)

6 As recommended by previous authors (Hedges & Nowell, 1995).

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Appendix F. Data Availability and Description

The dataset used for the analyses presented in this book is available in this book’s online supplementary materials (https://hdl.handle.net/11299/257780), along with the annotated R code for our meta-analyses.

The database is the result of five years of searching for effect sizes describing the relations between personality traits and cognitive abilities. This project involved scanning thousands of pages; partnering with dozens of librarians, researchers, companies, and translators around the globe; and entering, mapping, and re-checking millions of database values. Myriad sources of information, including many unpublished datasets, were utilized to minimize file drawer biases. The contributing materials were found through electronic literature searches; liaising with relevant investigators; and systematic searches of often overlooked sources like dissertations, grant-funded studies, psychological test manuals, and large applied-setting databases (e.g., military entrance examinations). See Appendix K for citations of all contributing materials.

Each sample was inspected to be sure it was independent of other included samples. However, this task was challenging since many studies did not clearly report if their data overlapped with other reports. Meta-Analysis Sample ID reflects our best understanding of which samples are unique or overlapping in the database.

Some values in this database show “<Not Reported>” because the contributing material did not report the value (e.g., country of the participants). In other cases, the correct value was unclear (e.g., some authors referred to a personality scale by a different name or not all pages of the original material could be obtained or read). When we could not be reasonably sure of the correct value, we put a “?” after the value.

Personality and cognitive ability measures were mapped to constructs according to Stanek and Ones’ (2018) compendia, which are themselves based on examination of several lines of evidence (Hough et al., 2015) (e.g., convergent and divergent validities, item content, scale descriptions). Other works have attempted to explore the relations between constructs across measures (Hough & Ones, 2002; Schwaba et al., 2020), but Stanek and Ones (2018) is the most comprehensive cross-sample examination. Dashes in our construct names indicate different levels of the construct hierarchy, with higher-level constructs listed further left. For example, Acquired Knowledge--Domain Specific Knowledge---Sciences----Life Sciences Knowledge indicates the lineage of the granular Life Sciences Knowledge construct, which is within the broader Sciences construct, which is within the broader Domain Specific Knowledge construct, which is within the broader Acquired Knowledge construct.

In the contributing materials, measures were sometimes scored in the opposite direction of the Stanek and Ones’ (2018) constructs. Effect sizes from such measures were reversed before being included in the current meta-analytic database. Reversals are noted separately for cognitive ability and personality measures since one or both might necessitate reversing the effect size sign.

Unique Row ID is included to provide a common ID for each row across users.

References

Hough, L. M., & Ones, D. S. (2002). The structure, measurement, validity, and use of personality variables in industrial, work, and organizational psychology. In Handbook of industrial, work, and organizational psychology (Vol. 1, pp. 233–277). Sage.

Hough, L. M., Oswald, F. L., & Ock, J. (2015). Beyond the Big Five: New directions for personality research and practice in organizations. Annual Review of Organizational Psychology and Organizational Behavior, 2(1), 183–209.

Schwaba, T., Rhemtulla, M., Hopwood, C. J., & Bleidorn, W. (2020). A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure. Plos One, 15(7), e0236893.

Stanek, K. C., & Ones, D. S. (2018). Taxonomies and compendia of cognitive ability and personality constructs and measures relevant to industrial, work and organizational psychology. In D. S. Ones, C. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work & organizational psychology: Personnel psychology and employee performance (pp. 366–407). Sage.

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Appendix G. Intelligence-Personality Relations

Appendix G. is available as a PDF download at: https://umnlibraries.manifoldapp.org/projects/of-anchors-and-sails/resource/appendix-g.

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Appendix H. Intelligence-Personality Relations Excluding Project Talent

Appendix H. is available as a PDF download at: https://umnlibraries.manifoldapp.org/projects/of-anchors-and-sails/resource/appendix-h.

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Appendix I. Personality-Intelligence Relations

Appendix I. is available as a PDF download at: https://umnlibraries.manifoldapp.org/projects/of-anchors-and-sails/resource/appendix-i.

<span data-node-type="text" data-node-uuid="1f02518ec8957d00b0e56720ee3b15fbd973eaf1" data-text-digest="8c23da62c6086b2ebdb0dbf0604799cde83abb4d">20_Appendix_J</span>

Appendix J. Personality-Intelligence Relations Excluding Project Talent

Appendix J. is available as a PDF download at: https://umnlibraries.manifoldapp.org/projects/of-anchors-and-sails/resource/appendix-j.

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Appendix K. List of Materials Included in the Current Meta-Analyses

Appendix K. is available as a PDF download at: https://umnlibraries.manifoldapp.org/projects/of-anchors-and-sails/resource/appendix-k.

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List of Figures and Tables

List of Figures

Unified Cattell-Horn-Carroll (CHC) Cognitive Abilities Taxonomy (Chapter 2)

Stanek & Ones (2018) Pan-Hierarchical Five Factor Model of Personality (Chapter 2)

Study Inclusion Flow Diagram (Chapter 3)

Number of Effect Sizes by Samples’ Age Groups (Chapter 3)

Number of Meta-Analyses Involving each Personality Construct (Chapter 3)

Number of Meta-Analyses Involving each Cognitive Ability Construct (Chapter 3)

Domain Independent General Capacities Correlate with Personality Traits (Chapter 4)

Sensory-Motor Domain Specific Abilities Correlate with Personality Traits (Chapter 4)

Speed Abilities Correlate with Personality Traits (Chapter 4)

Invested Abilities Correlate with Personality Traits (Chapter 4)

General Mental Ability and Compound Abilities Correlate with Personality Traits (Chapter 4)

Neuroticism-Related Traits Correlate with Cognitive Abilities (Chapter 5)

Agreeableness-Related Traits Correlate with Cognitive Abilities (Chapter 5)

Conscientiousness-Related Traits Correlate with Cognitive Abilities (Chapter 5)

Extraversion-Related Traits Correlate with Cognitive Abilities (Chapter 5)

Openness-Related Traits Correlate with Cognitive Abilities (Chapter 5)

Higher Order Personality Traits Correlate with Cognitive Abilities (Chapter 5)

Stages And Steps of Cybernetic Self-Regulation (Chapter 6)

Cognitive Ability-Personality Trait Fitness Complexes (Chapter 6)

Personality-Ability Relations for Homeostasis (Chapter 7)

Personality-Ability Relations for Change (Chapter 7)

Personality-Ability Relations for Individual Development and Growth (Chapter 7)

Personality-Ability Relations for General Fitness (Chapter 7)

Examples of Non-Openness Personality Traits with Sizable Relations with Cognitive Abilities (Chapter 8)

Cognitive Ability-Personality Relations (Chapter 8)

Cognitive Ability-Compound Personality Trait Relations (Chapter 8)

Personality Aspects’ Differential Relations with Cognitive Abilities (Chapter 8)

List of Tables

Compound Personality Traits and Their Sources of Variance (Chapter 2)

Current Study Compared to Previous Meta-Analyses (Chapter 3)

Sources of Effect Sizes (Chapter 3)

Participants in Contributing Research (Chapter 3)

Effect Sizes by Country (Chapter 3)

S1. Reliability Distribution Statistics for Cognitive Ability Constructs (Appendix E)

S2. Reliability Distribution Statistics for Personality Constructs (Appendix E)

S3–S99. Relations of each Cognitive Ability with Personality Traits (Appendix G)

S100–S196. Relations of each Cognitive Ability with Personality Traits Excluding Project Talent (Appendix H)

S197–S275. Relations of each Personality Trait with Cognitive Abilities (Appendix I)

S276–S354. Relations of each Personality Trait with Cognitive Abilities Excluding Project Talent (Appendix J)

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Acknowledgements for Data and Database Assistance

This research would not have been possible without the unpublished and typically unavailable data from a very large number of colleagues and organizations. In addition, the effort required to complete an endeavor of this magnitude usually requires assistance for many aspects of the research such as study curation, data coding, entering, checking, classifying, analysis and the like. Here we would like formally to acknowledge and extend our deepest thanks to everyone involved in these activities.

Providers of raw data kindly shared data that we subsequently analyzed before including in our meta-analyses. Individual researchers, grant-funded studies, consultants, and archives provided raw data, including:

Add Health (This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (addhealth@unc.edu). No direct support was received from grant P01-HD31921 for this analysis.)

Anonymous biopharmaceutical company

Australian [Adelaide] Longitudinal Study of Aging (Australian [Adelaide] Longitudinal Study of Aging: South Australian Health Commission, Australian Rotary Health Research Fund, and United States Department of Health and Human Services. National Institutes of Health (AG-08523-02))

Laura Baker & Pan Wang (Southern California Twin Project: NIMH (R01 MH58354), NIMH (Independent Scientist Award K02 MH01114-08), & Institute of Governmental Affairs, University of California, Davis)

Paul T. Barrett

Alexandre Baudet & Thibaud Latour

British Cohort Study 1970 (British Cohort Study 1970: Centre for Longitudinal Studies, Institute of Education and UK Data Archive and Economic and Social Data Service. We are grateful to the Centre for Longitudinal Studies, Institute of Education for the use of these data and to the UK Data Archive and Economic and Social Data Service for making them available. However, they bear no responsibility for the analysis or interpretation of these data.)

Dana Broach (Data were provided by the Federal Aviation Administration’s Civil Aerospace Medical Institute)

Dana Carney

David Condon & Bill Revelle (Synthetic Aperture Personality Assessment (SAPA))

Kelly Dages

Colin DeYoung

Stephan Dilchert

Herb Eber

Education Longitudinal Study (Education Longitudinal Study 2002: This research utilized publicly available and restricted-use data from the National Center for Education Statistics (NCES). The content is the responsibility of the authors and does not necessarily represent the views of IES, the U.S. Department of Education, NIH, or NCES.)

Valerie Elsässer & Hans-Werner Wahl (Interdisciplinary Study on Adult Aging/Longitudinal Study of Adult Development: Bundesministerium für Familie, Senioren, Frauen und Jugend (BMFSFJ); AZ 301-1720-295/2)

English Longitudinal Study of Aging (English Longitudinal Study of Aging: The data were made available through the UK Data Archive. ELSA was developed by a team of researchers based at the NatCen Social Research, University College London, and the Institute for Fiscal Studies. The data were collected by NatCen Social Research. The funding is provided by the National Institute of Aging in the United States, and by a consortium of UK government departments coordinated by the Office for National Statistics. The developers and funders of ELSA and the Archive do not bear any responsibility for the analyses or interpretations presented here.)

Greg Feist

Lew Goldberg (Eugene Springfield Community Sample: Funds for this project were provided by a research grant from the National Institute of Mental Health, U.S. Public Health Service (Grant MH49227: Mapping personality trait structure))

Irv Gottesman

Jeremy Gray

Cathy Hall

Health and Lifestyle Survey (Cox, B. D., Blaxter, M., Buckle, A. L. J., Fenner, N. P., Golding, J. F., Gore, M., et al. (1987). The Health and Lifestyle Survey. London: Health Promotion Research Trust. Disclaimer: Although all efforts are made to ensure the quality of the materials, neither the original data creators, depositors or copyright holders, the funders of the Data Collections, nor the UK Data Archive bear any responsibility for the accuracy or comprehensiveness of these materials. All rights reserved. No part of these materials may be reproduced, stored in, or introduced into a retrieval system, or transmitted, in any form, or by any means (electronic, mechanical, photocopying, recording, or otherwise) without the prior written permission of the UK Data Archive.)

Health and Retirement Study (HRS (Health and Retirement Study): sponsored by the National Institute on Aging (grant number NIA U01AG009740) and conducted by the University of Michigan.)

High School and Beyond (United States Department of Education. National Center for Education Statistics. https://nces.ed.gov/surveys/hsb/)

Jason Huang

Interuniversity Consortium for Political and Social Research (ICPSR)

The Irish Longitudinal Study on Ageing (The Irish Longitudinal Study on Ageing: Trinity College Dublin. TILDA data have been co-funded by the Government of Ireland through the Office of the Minister for Health, by Atlantic Philanthropies, and by Irish Life, and have been collected under the Statistics Act, 1993, of the Central Statistics Office. The project has been designed and implemented by the TILDA Study Team. © Department of Health.)

Dan Ispas & Dragos Iliescu

Norbert Jaušovec

Wendy Johnson

Maciek Karwowski

Kelly Longitudinal Study (This research used the Kelly Longitudinal Study, 1935–1955 data set [made accessible in 1978, original paper records and electronic data files]. These data were collected by E. Lowell Kelly and are available through the archive of the Henry A. Murray Research Center of the Radcliffe Institute for Advanced Study, Harvard University, Cambridge, Massachusetts [Producer and Distributor].)

Roger Kievet

Michal Kosinski & David Stillwell (MyPersonality Project)

Filip Lievens & Britt De Soete

Long Beach Longitudinal Study (Long Beach Longitudinal Study: R01AG10569/AG/NIA NIH HHS/United States)

Lothian Birth Cohorts of 1921 and 1936 (Lothian Birth Cohorts of 1921 and 1936: Royal Society Wolfson Research Merit Award, Chief Scientist Office, Age UK, and Medical Research Council)

Matt McGue & Shandell Pahlen (University of Minnesota Twin Aging: University of Minnesota Twin Aging Study)

Gerhard Meisenberg

Mexican Health and Aging Study (MHAS (Mexican Health and Aging Study): partly sponsored by the National Institutes of Health/National Institute on Aging (grant number NIH R01AG018016). Data files and documentation are public use and available at www.MHASweb.org.)

Midlife in the United States (MIDUS I study (Midlife in the U.S.): supported by the John D. and Catherine T. MacArthur Foundation Research Network on Successful Midlife Development. The MIDUS II research was supported by a grant from the National Institute on Aging (P01-AG020166) to conduct a longitudinal follow-up of the MIDUS I investigation. Data used for this research were provided by the longitudinal study titled “Midlife in the United States,” (MIDUS), managed by the Institute on Aging, University of Wisconsin. This research was supported by a grant from the National Institute on Aging (P01-AG020166).)

Minnesota Twin Study of Adult Development and Aging (National Institute on Aging Grant R01 AG06886)

Murray Archive

National Child Development Survey (Power, C., & Elliott, J. (2006). Cohort profile: 1958 British birth cohort (National Child Development Study). International Journal of Epidemiology, 35, 34−41. Any publication, whether printed, electronic or broadcast, based wholly or in part on these materials, should acknowledge the original data creators, depositors or copyright holders, the funders of the Data Collections (if different) and the UK Data Archive, and acknowledge Crown Copyright where appropriate. Copyright: Centre for Longitudinal Studies. Although all efforts are made to ensure the quality of the materials, neither the original data creators, depositors or copyright holders, the funders of the Data Collections, nor the UK Data Archive bear any responsibility for the accuracy or comprehensiveness of these materials. All rights reserved. No part of these materials may be reproduced, stored in, or introduced into a retrieval system, or transmitted, in any form, or by any means (electronic, mechanical, photocopying, recording or otherwise) without the prior written permission of the UK Data Archive.)

National Educational Longitudinal Study 1988 (United States Department of Education. National Center for Education Statistics. https://nces.ed.gov/surveys/nels88/)

National Longitudinal Survey of Youth 1997 (United States Bureau of Labor Statistics. National Longitudinal Surveys. https://www.bls.gov/nls/nlsy97.htm)

National Survey for Health and Development (National Survey of Health and Development: Funded by the Medical Research Council)

Emily C. Nusbaum & Paul Silvia

Deniz S. Ones

Miranda Peeters

Jakob Pietschnig

Ricardo Primi

Project Talent (American Institutes for Research. Project Talent. https://www.air.org/project/project-talent)

Max Roberts

Chet Robie

Second Duke Adaptation Study (Duke University Data Archive for Aging and Adult Development was the data source. The Duke Adaptation Study was supported by grants from the National Institute on Aging, AG00364 (HD00668), to the Duke University Center for the Study of Aging and Human Development. Busse, E. W., Siegler, I. C., George, L. K., Palmore, E., Maddox, G. L., & Nowlin, J. B. (2006). Second Duke Adaptation Study, 1968-1976. http://hdl.handle.net/1902.1/00743 UNF:3:UEsAw9ofVLXOyHo+f6c3pA== Murray Research Archive [Distributor] V3 [Version])

Amitai Shenhav

Judy Silberg (Virginia Twin Study of Adolescent Behavioral Development)

Claire Sira

Survey of Health, Ageing and Retirement in Europe (SHARE) (Survey of Health, Ageing and Retirement in Europe (SHARE): This paper uses data from SHARE wave 4 release 1.1.1, as of March 28th 2013 or SHARE wave 1 and 2 release 2.6.0, as of November 29th 2013. The SHARE data collection has been primarily funded by the European Commission through the 5th Framework Programme (project QLK6-CT-2001-00360 in the thematic programme Quality of Life), through the 6th Framework Programme (projects SHARE-I3, RII-CT-2006-062193, COMPARE, CIT5-CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812) and through the 7th Framework Programme (SHARE-PREP, N° 211909, SHARE-LEAP, N° 227822 and SHARE M4, N° 261982). Additional funding from the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, R21 AG025169, Y1-AG-4553-01, IAG BSR06-11 and OGHA 04-064) and the German Ministry of Education and Research as well as from various national sources is gratefully acknowledged (see www.share-project.org for a full list of funding institutions).)

Kurtis Swope

Aaro Toomela

John Trent

United Kingdom Data Archive

Well Elderly 2 (Well Elderly 2: National Archive of Computerized Data on Aging and United States Department of Health and Human Services. National Institutes of Health. National Institute on Aging (R01 AG021108))

Jelte Wicherts

Kevin Williams

Wisconsin Longitudinal Study (This document contains materials from the Wisconsin Longitudinal Study (WLS) of the University of Wisconsin-Madison. Since 1991, the WLS has been supported principally by the National Institute on Aging (AG-9775, AG-21079 and AG-033285), with additional support from the Vilas Estate Trust, the National Science Foundation, the Spencer Foundation, and the Graduate School of the University of Wisconsin-Madison. Any opinions expressed herein are those of the authors.)

Matthias Ziegler

Providers of unpublished materials (other than raw data) generously shared unpublished studies and materials that expanded the meta-analytic database. These individuals supplied us with unpublished studies and otherwise inaccessible materials but did not provide raw data. We are grateful.

Mike Aamodt

Kim Barchard

Vic Catano

Megan Crowley

Christen Dovalina, Chris Berry, & Paul Sackett

Carmen Flores-Mendoza

Irv Gottesman

Jen Harvel

Eric Heggestad

Daniel Higgins

Jason Huang

Dan Ispas & Dragos Iliescu

Roza Jankovic, Elizabeth Teets, & Michael Knies

Reidar Källström

Sebastian Lange

Morrie Mullins

Türker Özkan

Maria Rotundo

Anjum Sibia

Jürgen Strohhecker

Robert Votruba

Eric Weis

Paul Wood

Ruopeng Wu

Providers of supplemental information or statistics contributed statistical or otherwise critical information that were not included in published materials. This made it possible to include additional studies in the database. We are grateful for their generosity and dedication to collaborative science.

Angel Aguilar Alonso

Julie Aitken Schermer

Anne-Grit Albrecht

Jüri Allik

Manfred Amelang & Ricarda Steinmayr

Alana Andrade

Hubert Annen

Anonymous pharmaceutical company

Michela Balsamo

Laura Barron

Paul Bartone

Philip Batterham, Helen Christensen, & Andrew Mackinnon

Alexandre Baudet & Thibaud Latour

Alex Beaujean & Michael Firmin

Michael Biderman & Nhung Nguyen

Tanja Bipp

Angel Blanch

Tim Bogg

Silvia Bonaccio

Jan Booij & Sarah M. Burke

Peter Borkenau

Adam Butler

Anne Campbell

Barbara Carretti

Ben Chapman

Gilad Chen

Wen Cheng

John Chibnall

Ole Christian Lang-Ree, Nina Margrethe Isaksen, & Tom Hilding Skoglund as well as the Norwegian military

Hannie Comijs

Katie Corker

Megan Crowley

Kobi Dayan & Shaul Fox

Ian Deary & Sarah Harris

Ana Delgado & Gerardo Prieto

Andreas Demetriou

Amelia Diaz

Stephan Dilchert

Nermin Djapo

Stefano Di Domenico

Christen Dovalina, Chris Berry, & Paul Sackett

Paul Duberstein

Jan Duchek

Angela Duckworth, Sarah Patrick, & Benjamin Plummer

Michael Dufner

Amy DuVernet

Jill Ellingson

Andrew Elliot

Juliana Gottschling (Cognitive Ability, Self-Reported Motivation, and School Achievement (CoSMoS) and Saarland Secondary School Study)

Eileen Graham

Anett Gyurak & Ozlem Ayduk

John Hattie

Guido Heineck

Laura Helle

Benjamin E. Hilbig

Scott M. Hofer

John Horwood (Christchurch Health and Development Study: Health Research Council of New Zealand, National Child Health Research Foundation, Canterbury Medical Research Foundation, and New Zealand Lottery Grants Board)

Renate Houts & Terrie Moffitt (Dunedin Longitudinal Study: New Zealand Health Research Council and New Zealand Ministry of Business, Innovation and Employment (MBIE). US National Institute on Aging (NIA) grants AG032282, AG048895, AG049789, UK MRC grant MR/K00381X, UK ESRC grant ES/M010309/1, Additional support was provided by the Jacobs Foundation and the Avielle Foundation.)

Ann Howard

Gizem Hülür

Yoon-Mi Hur (South Korean Twin Registry: South Korean data were collected by financial support of the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2011-371-B00047))

Dan Ispas & Dragos Iliescu

Ando Juko & team (Keio Twin Registry)

Jaakko Kaprio, Eero Vuoksimaa, & Antti Latvala (FinnTwin: supported by National Institute on Alcohol Abuse and Alcoholism grants AA–00145, AA–08315, AA–09203, and AA–12502; by the Academy of Finland; and by the Yrjö Jahnsson Foundation)

Heidi Keiser

Rachael Klein

Donald H. Kluemper

Katja Kokko & Anna-Liisa Lyyra (Jyvaskyla Longitudinal Study of Personality and Social Development: Academy of Finland grants 127125 and 118316)

Jack Kostal

Kaia Laidra

Richard Landers

James Lee & Christopher Chabris

Rhys Lewis

Richard Lippa

Eleni Lobene

Jan-Erik Lönnqvist

David Lubinski

Michelle Luciano & Narelle Hansell & Margie Wright

Mary Ann Lumsden & Miles Bore

Erik Lykke Mortensen

Don Lynam

Karen Mackinnon

Greg Manley & Tom Carretta

Tom Marlow & Ingmar Skoog (Göteborg Studies of Aging)

Jack Mayer

Robert W. McCarley & Ronald Gurrera

Robert McCrae & John Loehlin (National Merit Twin Study and the Texas Adoption Study: Grant MH 24280 from the National Institute of Mental Health)

Matt McLarnon

Lonneke de Meijer

Thomas Meyer

Benjamin Morasco

René Mõttus

John Mueller

Craig Nagoshi (Hawaii Family Study of Cognition: National Science Foundation Grant GB-34720 and National Institute of Child Health and Human Development Grant HD-06669)

Lori Nathanson & Marc Brackett

Jenae Neiderhiser & Paul Lichtenstein (Twin and Offspring Study in Sweden: Supported by the National Institute of Mental Health (NIMH) Grant R01MH54601)

Aljoscha Neubauer & Emanuel Jauk

Aljoscha C. Neubauer, Emanuel Jauk, & Sylvia Opriessnig

Janneke Oostrom

Frederick L. Oswald & Neal Schmitt

Ayça Özen & H. Canan Sümer

Robert Plomin & Andy McMillan (Twins Early Development Study programme grant from the UK Medical Research Council (G0901245; and previously G0500079).)

Chuck Reeve

Frühling Rijsdijk & Harriëtte Riese (Twin Interdisciplinary Neuroticism Study: NWO, Medische Wetenschappen (0804/904-57-130))

Heiner Rindermann

Stuart Ritchie

Jonah Rockoff

Pia Rosander

Tim Salthouse

Naomi Samimi Sadeh

Katja Schlegel

Suzanne Segerstrom

Anjum Sibia

Luke Smillie

Markus Sommer

Alma Sörberg

Andrea Soubelet

George Spanoudis

Mike Stallings (Colorado Twin Sample: CRCW grant from the University of Colorado; National Institutes of Health grants HD19802, HD010333, HD18426, HD038526, MH43899, MH63207, MH001865, DA05131, and DA011015; the John D. and Catherine T. MacArthur Foundation; and Veterans Administration grant 1296.07.1629B)

Heidrun Stoeger

Kristian Tambs

Ian Tharp

Yonca Toker

Annette Towler, Daniel Stanhope, & Eric Surface

Irina Trofimova

Susan Trumbetta

Travis Tubre

Fredrik Ullén & Guy Madison

Beth Visser

Jeffrey R. Vittengl

Dina Vojinovic & Najaf Amin (Erasmus Rucphen Family Study: European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947); funding also received from the European Community’s Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the programme “Quality of Life and Management of the Living Resources” of 5th Framework Programme (no.~QLG2-CT-2002-01254). The ERF study was further supported by ENGAGE consortium and CMSB)

Jan Wacker

Gerd Waldhauser

David Watson

Aaron Weidman & Jessica Tracy

Alex Weiss, Catharine Gale, & G. David Batty

Brett Wells

Jelte Wicherts

Marc Zajenkowski

Providers of manuals and technical materials shared published and unpublished test manuals, technical reports, and other technical materials that contained either personality-ability correlations that were included in the database or information that provided details about measures in the meta-analytic database. We thank them for their contributions.

Amy Fernandes, Stela Ivanova, & Scott Mackintosh

Gareth Hagger-Johnson

Rainer Kurz

Damian O’Keefe

Maria Pollai

Markus Sommer

Auke Tellegen

Brenton M. Wiernik

Cheryl Zink

Providers of published materials assisted access to relevant, published materials that contributed to the meta-analytic database. We are thankful for their help.

Tanja Bipp

Tom Brothen

Jeff Cucina

Michael Cullen

Andreas Demetriou

Stephan Dilchert

Olivier Dionne & Tania Robinson

Jan Duchek

Madeline Ehrman

Andrea Gaye-Valentine

Eileen Graham

Annemarie Hiemstra & Eva Derous

Lian Hortensius Arwen Hunter

Jodie Illies

Kevin Impelman

Dan Ispas & Dragos Iliescu

Mavis Kung

John Palmatier

Jody Reiter-Palmon

Markus Sommer

Debra Steele-Johnson

Ekaterina Valueva

Parul Wasan

Ruopeng Wu

Library assistance was provided by several institutions that shared rare materials, scanned unloanable studies, and helped us maximize search methodologies. We are immensely grateful to the staff who relentlessly helped us retrieve these otherwise unreachable materials.

Melissa Ann Hubbard, David Bond, & Martha Jean Kallal

Sonia Barbarosa

Karen Fleet

Michelle Gait

Karen Lodge & Vicky Wallace

Linda Maddux & Gay Walker

Marie Paiva

Emily Veenstra-Ott

Alice Welch, Emily Riha, & Cherie Weston

Maura Williams-Freier

Scott Zimmer

Study translations were performed for dozens of non-English materials by the authors, research assistants, and generous volunteers. The authors and their research assistants (see separate section below for a listing) were proficient in multiple languages and were able to code directly from materials in Chinese, English, French, German, Korean, Spanish, and Turkish. We would like to thank the following individuals for their assistance with materials that were not in a language that could be coded directly or that contained portions that proved to be too complex for our team’s language expertise.

Paulina Birgiolaite

Stephan Dilchert

Neda Gould & Shahzad Kavoossi Farzad

Nataliya Harkins Balabanova

Lian Hortensius

Arsena Ianeva-Lockney

Mahdad Majd

Mariska Rebière

Tara Tetzlaff

Abhishek Wadhwa

Research assistants devoted thousands of tireless hours to searching for, obtaining, entering, and checking study information that contributed to the analyses. Without their conscientious work and countless hours of volunteer assistance, this project would not have been possible. Thank you!

Kelsey Aponas

Christine Cao

Usain Yat Lun Chan

Elizabeth Dippel

Steven Hunter Fritz

Anne Gale

Abraham Gibbons

Kayla Groehler

Ryan Hanna

Taryn Ibach

Qing Han Lau

Franky Chun Hei Li

Amanda Song Yu Low

Harry (Ting Fung) Luk

Atessa Majd

Chengxi Peng

Napoleon Petsoulis

Domingo Ramirez

Heather Roesslein

Yi Ying Sack

Sam Sevett

Ryan Shea

Bowei Song

Dylan Sorman

Paula Stanek

Glenn Trussell

Kevin Tully

James Updyke

Natalia Velenchenko

Ashley Wong

Classification contributions for identifying constructs measured by rare and unusual measures were provided by Auke Tellegen and Colin DeYoung (personality) as well as Kevin McGrew (cognitive ability). We thank them for their important contribution.

Meta-analysis scripts were written by Brenton Wiernik (for the beta version of the psychmeta package in R) and Jeffrey Dahlke (for the psychmeta R package). We appreciate their expert guidance and commitment to open science.

Publishing partners were Emma Molls and Amy Riegelman of the University of Minnesota Libraries Press, who guided us through the publication process and consideration of features, accessibility, and preservation. Thank you for your support.

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Special Thanks

This book could never have been created by two people alone. We acknowledge and express our gratitude to the hundreds of individuals and organizations that contributed to the data and to the analyses of the findings presented. They are properly acknowledged in the preceding section.

We are also grateful to several colleagues for their thought partnership, intellectual guidance, data, and editorial contributions. Chief among these individuals was Stephan Dilchert. From the inception to the completion of this manuscript and associated materials, our thinking and writing benefited greatly from his acumen and advice. His thoughtful editing significantly enhanced the clarity of our reporting. His guidance and support were indispensable.

External scholarly review was an important part of quality control for this volume. The following scientists and practitioners had deep expertise in personality, cognitive abilities, and the methodologies employed in our research. They read earlier outlines, shorter manuscript versions of this work, or current chapters relevant to their expertise, and provided comments and insights that improved this work. We are tremendously grateful. Any potential inaccuracies that may remain are our own.

Auke Tellegen, Ph.D., Professor Emeritus, University of Minnesota

Brenton Wiernik, Ph.D., Assistant Professor, University of Southern Florida

Brian S. Connelly, Ph.D., Associate Professor, University of Toronto

Chockalingam Viswesvaran, Ph.D., Professor, Florida International University

Colin DeYoung, Ph.D., Associate Professor, University of Minnesota

Irving Gottesman, Ph.D., Professor Emeritus, University of Virginia

Jack Kostal, Ph.D., Applied Researcher, General Mills

John P. Campbell, Ph.D., Professor Emeritus, University of Minnesota

Kevin McGrew, Ph.D., Founder, Institute for Applied Psychometrics

Matthew K. McGue Ph.D., Regents Professor, University of Minnesota

Michael Wilmot, Ph.D., Assistant Professor, University of Arkansas

Robert Eichinger, Ph.D., CEO TalentTelligent LLC

Roger Pearman, Ed.D., Applied Researcher, Leadership Performance Systems

Thomas Dohm, Ph.D., Director of the Office of Measurement, University of Minnesota

Projects of this magnitude are typically undertaken using sizable grants or sponsorship from organizations. This project had no funding or sponsorship. We only relied on our own efforts and the goodwill of volunteers. Deniz’ Hellervik Professorship afforded her time to devote to this work during summers, and her one-semester sabbatical leave during Fall 2022 enabled the successful completion of this long-running project. Kevin’s Eichinger Fellowship similarly provided critical time to devote to this work.

We were each influenced by those who came before us. We owe an intellectual debt to those whose ideas fueled our foundational knowledge and understanding of personality, abilities, and psychometric methods. We would like to acknowledge the influence of Harrison Gough, Auke Tellegen, Jim Butcher, Bob and Joyce Hogan, Marvin Dunnette, Leaetta Hough, Raymond Cattell, Claude Shannon, and John Carroll, as well as Frank Schmidt and John (Jack) Hunter. By inventing psychometric meta-analysis, the latter two individuals made research like ours possible. Their mentorship continues to provide scholarly sustenance. We are honored to count ourselves among their intellectual descendants.

We also are thankful for the personal support each of us received from our families and friends. The energy directed to this opus for over 13 years also reflects their personal contributions and sacrifices. For this, Kevin is immeasurably grateful to his parents and his partner, Taryn Ibach. Deniz would like to express her sincere appreciation to her intellectual family members, to the Ones and Haner families, and especially to her daughter, Daria M. Haner.

Science is built on the backs of scholars. We believed in this project with ardent fervor, forgoing leisure, friends, family, wealth, and health in the hope of contributing to science. We dedicate this to all those who produced the primary studies on which this work is founded. This is our collective monument to personality and cognitive ability constellations.

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Author Biographies

Dr. Kevin C. Stanek believes that humans occupy an untenable position. We cannot go back to being simpler animals in harmony with nature, and yet our pace of destruction exceeds our pace of progress. Kevin’s life is zealously dedicated to understanding our world and purpose, so we can find a sustainable existence for nature and ourselves. Beyond human behavior, his research seeks to link fundamental topics like energy, information, and time to uncover clues about our reason for being. His current research focuses on the many ways to traverse a life; how people differ and why; and the implications for individuals, societies, and humanity. His previous research includes twin studies, sensor studies, and real-world longitudinal investigations of human behavior and outcomes. Kevin is a scholar and editorial board member as well as a practitioner, leading the global People Analytics function at a Fortune 150 company. In addition, he consults with organizations, from to non-profits to multinational corporations, on maximizing the impact of their people-related information in areas such as talent management research, retention prediction, and employee experience design. Kevin holds a doctorate in individual differences and industrial/organizational psychology from the University of Minnesota, a master’s in behavioral genetics from the University of Minnesota, and bachelor’s degrees in economics and psychology from the University of Southern California. After 14 years of reclusive focus on this research, he is looking forward to plotting where to anchor or sail next.

Dr. Deniz S. Ones is a Distinguished McKnight University Professor and Distinguished University Teacher at the University of Minnesota. She also holds the Hellervik Professorship of Industrial Psychology. Her research, published in more than 250 journal articles, books, and chapters, focuses on assessment of individual differences for employee selection and on measurement of personality, integrity, and cognitive ability variables for the prediction of job performance as well as counterproductive work behaviors. Her quantitative research includes contributions to meta-analytic research methods, reliability generalization, criterion profile analysis, and natural language processing (NLP), as well as to deployment of mobile sensor technologies for assessing work and health relevant attributes. Her humanistic research examines environmental sustainability, ethical behavior, and social responsibility in and of organizations. She has studied and served on research projects and blue ribbon panels focusing on assessment and job performance of police officers, expatriates, engineers, managers (including C-suite executives), astronauts, nurses, politicians, agricultural workers, and R&D teams, among many others. She has received numerous awards for her work and accomplishments, including career distinguished scientific contributions awards in her fields. Her life’s work is to potentiate human talents in all their forms. She believes in work-life integration. She enjoys making discoveries, writing comprehensive research syntheses, collaborating with and nurturing her intellectual and genealogical family, traveling the world, experiencing exceptional food, and learning—preferably concurrently.