Spearman C. The Abilities of Man: Their Nature and Measurement. New York: Macmillan; 1927.
Jensen AR. The g Factor: The Science of Mental Ability. Westport: Praeger; 1998.
Sniekers S, Stringer S, Watanabe K, Jansen PR, Coleman JRI, Krapohl E, et al. Genome-wide association meta-analysis of 78 308 individuals identifies new loci and genes influencing human intelligence. Nat Genet. 2017;49:1107–12.
Savage JE, Jansen PR, Stringer S, Watanabe K, Bryois J, de Leeuw CA, et al. Genome-wide association meta-analysis in 269 867 individuals identifies new genetic and functional links to intelligence. Nat Genet. 2018;50:912–9.
Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet. 2018;50:1112–21.
de la Fuente J, Davies G, Grotzinger AD, Tucker-Drob EM, Deary IJ. A general dimension of genetic sharing across diverse cognitive traits inferred from molecular data. Nat Hum Behav. 2021;5:49–58.
Thurstone LL. Primary Mental Abilities. Chicago: University of Chicago Press; 1938.
Vernon PE. The Structure of Human Abilities. New York: Wiley; 1950.
Horn JL. Human abilities: a review of research and theory in the early 1970s. Annu Rev Psychol. 1976;27:437–85.
Cattell RB. Intelligence: Its Structure. Growth and Action. Amsterdam: Elsevier; 1987.
Carroll JB. Human Cognitive Abilities: A Survey of Factor-Analytic Studies. New York: Cambridge University Press; 1993.
Johnson W, Bouchard TJ Jr. The structure of human intelligence: it is verbal, perceptual, and image rotation (VPR), not fluid and crystallized. Intelligence. 2005;33:393–416.
Humphreys LG, Lubinski D, Yao G. Utility of predicting group membership and the role of spatial visualization in becoming an engineer, physical scientist, or artist. J Appl Psychol. 1993;78:250–61.
Gohm CL, Humphreys LG, Yao G. Underachievement among spatially gifted students. Am Educ Res J. 1998;35:515–31.
Shea DL, Lubinski D, Benbow CP. Importance of assessing spatial ability in intellectually talented young adolescent: A 20-year longitudinal study. J Educ Psychol. 2001;93:604–14.
Park G, Lubinski D, Benbow CP. Contrasting intellectual patterns predict creativity in the arts and sciences: Tracking intellectually precocious youth over 25 years. Psychol Sci. 2007;18:948–52.
Wai J, Lubinski D, Benbow CP. Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance. J Educ Psychol. 2009;101:817–35.
Jonsdottir GA, Einarsson GV, Thorleifsson G, Magnusson SH, Gunnarsson AF, Frigge ML, et al. Genetic propensities for verbal and spatial ability have opposite effects on body mass index and risk of schizophrenia. Intelligence. 2021;88:101565.
Wai J, Lee MH, Kell HJ. Distributions of academic math-verbal tilt and overall academic skill of students specializing in different fields: a study of 1.6 million Graduate Record Examination test takers. Intelligence. 2022;95:101701.
Werdelin I The Mathematical Ability: Experimental and Factorial Studies. Gleerup: Lund, 1958.
Keith TZ, Reynolds MR. Cattell-Horn-Carroll abilities and cognitive tests: What we’ve learned from 20 years of research. Psychol Sch. 2010;47:635–50.
Gignac GE. Raven’s is not a pure measure of general intelligence: Implications for g factor theory and the brief measurement of g. Intelligence. 2015;52:71–9.
Jensen AR. Bias in Mental Testing. New York: Free Press; 1980.
Raven J, Raven JC, Court JH. Manual for Raven’s Progressive Matrices and Vocabulary Scales. San Antonio: Harcourt; 1998.
Pokropek A, Marks GN, Borgonovi F. How much do students’ scores in PISA reflect general intelligence and how much do they reflect specific abilities? J Educ Psychol. 2022;114:1121–35.
Lubinski D, Benbow CP. Study of Mathematically Precocious Youth after 35 years: Uncovering antecedents for the development of math-science expertise. Perspect Psychol Sci. 2006;1:316–45.
Coyle TR. Non-g factors predict educational and occupational criteria: More than g. J Intell. 2018;6:43.
Aucejo E, James J. The path to college education. The role of math and verbal skills. J Politcal Econ. 2021;129:2905–46.
Lubinski D, Humphreys LG. A broadly based analysis of mathematical giftedness. Intelligence. 1990;14:327–55.
Alarcón M, Knopik VS, DeFries JC. Covariation of mathematics achievement and general cognitive ability in twins. J Sch Psychol. 2000;38:63–77.
Kovas Y, Harlaar N, Petrill SA, Plomin R. ‘Generalist genes’ and mathematics in 7-year-old twins. Intelligence. 2005;33:473–89.
Trzaskowski M, Davis OSP, DeFries JC, Yang J, Visscher PM, Plomin R. DNA evidence for strong genome-wide pleiotropy of cognitive and learning abilities. Behav Genet. 2013;43:267–73.
Procopio F, Zhou Q, Wang Z, Gidziela A, Rimfeld K, Malanchini M, et al. The genetics of specific cognitive abilities. Intelligence. 2022;95:101689.
Campbell DP, Hyne SA, Nilsen DL. Manual for the Campbell Interest and Skill Survey: CISS. Minneapolis: National Computer Systems; 1992.
Neubauer AC, Hofer G. Self-estimates of abilities are a better reflection of individuals’ personality traits than of their abilities and are also strong predictors of professional interests. Pers Indiv Differ. 2021;169:109850.
Rolfhus EL, Akcerman PL. Self-report knowledge. At the crossroads of ability, interest, and personality. J Educ Psychol. 1996;88:174–88.
Ackerman PL. A theory of adult intellectual development: process, personality, interests, and knowledge. Intelligence. 1996;22:227–57.
Bernstein BO, Lubinski D, Benbow CP. Psychological constellations assessed at age 13 predict distinct forms of eminence 35 years later. Psychol Sci. 2019;30:444–54.
Ackerman PL, Heggestad ED. Intelligence, personality, and interests: evidence for overlapping traits. Psychol Bull. 1997;121:219–45.
Robertson KF, Smeets S, Lubinski D, Benbow CP. Beyond the threshold hypothesis: Even among the gifted and top math/science graduate students, cognitive abilities, vocational interests, and lifestyle preferences matter for career choice, performance, and persistence. Curr Dir Psychol Sci. 2010;19:346–51.
Grtozinger AD, Rhemtulla M, de Vlaming R, Ritchie SJ, Mallard TT, Hill WD, et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav. 2019;3:513–25.
Starr A, Riemann R. Common genetic and environmental effects on cognitive ability, conscientiousness, self-perceived abilities, and school performance. Intelligence. 2022;93:101664.
Torppa M, Aro T, Eklund K, Parrila R, Eloranta AK, Ahonen T. Adolescent reading and math skills and self-concept beliefs as predictors of age 20 emotional well-being. Read Writ. 2024;37:2075–99.
Neubauer AC, Pribil A, Wallner A, Hofer G. The self-other knowledge asymmetry in cognitive intelligence, emotional intelligence, and creativity. Heliyon. 2018;4:e01061.
Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47:1236–41.
Lee JJ, McGue M, Iacono WG, Chow CC. The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome-wide association studies. Genet Epidemiol. 2018;42:783–95.
Yang J, Ferreira T, Morris AP, Medland SE, Genetic Investigation of Anthropometric Traits Consortium, Diabetes Genetics Replication and Meta-Analysis Consortium. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 2012;44:369–75.
Wilson S, Haroian K, Iacono WG, Krueger RF, Lee JJ, Luciano M, et al. Minnesota center for twin and family research. Twin Res Hum Genet. 2019;22:746–52.
Ge T, Chen CY, Ni Y, Feng YCA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun. 2019;10:1776.
Miller MB, Basu S, Cunningham J, Eskin E, Malone SM, Oetting WS, et al. The Minnesota Center for Twin and Family Research genome-wide association study. Twin Res Hum Genet. 2012;15:767–74.
Fisher RA. Statistical methods in genetics. Heredity. 1952;6:1–12.
Laird LM, Lange C. Family-based designs in the age of large-scale gene-association studies. Nat Rev Genet. 2006;7:385–94.
Lee JJ, Chow CC. The causal meaning of Fisher’s average effect. Genet Res. 2013;95:89–109.
Okbay A, Wu Y, Wang N, Jayashankar H, Bennett M, Nehzati SM, et al. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nat Genet. 2022;54:437–49.
Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet. 2015;47:1228–35.
GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–60.
Finucane HK, Reshef YA, Anttila V, Slowikowski K, Gusev A, Byrnes A, et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat Genet. 2018;50:621–9.
Weeks EM, Ulirsch JC, Cheng NY, Trippe BL, Fine RS, Miao J, et al. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nat Genet. 2021;55:1267–76.
Pers TH, Karjalainen J, Chan Y, Westra HJ, Wood AR, Yang J, et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun. 2015;6:5890.
Bulik-Sullivan B, Loh PR, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics Consortium. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.
Demange PA, Malanchini M, Mallard TT, Biroli P, Cox SR, Grotzinger AD, et al. Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction. Nat Genet. 2021;53:35–44.
Pokropek A, Marks GN, Borgonovi F, Koc P, Grieff S. General or specific abilities? Evidence from 33 countries participating in the PISA assessments. Intelligence. 2022;92:101653.
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.
Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533:539–42.
Kim Y, Saunders GRB, Giannelis A, Willoughby EA, DeYoung GC, Lee JJ. Genetic and neural bases of the neuroticism general factor. Biol Psychol. 2023;184:108692.
Rajagopal VM, Ganna A, Coleman JR, Allegrini A, Voloudakis G, Grove J, et al. Genome-wide association study of school grades identifies genetic overlap between language ability, psychopathology and creativity. Sci Rep. 2023;13:429.
Tonizzi I, Usai MC. Math abilities in autism spectrum disorder: a meta-analysis. Res Dev Disabil. 2023;139:104559.
de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: Generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11:e1004219.
Frey MC, Detterman DK. Scholastic assessment or g? The relationship between the Scholastic Aptitude Test and general cognitive ability. Psychol Sci. 2004;15:373–8.
Malanchini M, Rimfeld K, Gidziela A, Cheesman R, Allegrini AG, Shakeshaft N, et al. Pathfinder: A gamified measure to integrate general cognitive ability into the biological, medical, and behavioural sciences. Mol Psychiatry. 2021;26:7823–37.
Landers RN, Armstrong MB, Collmus AB, Mujcic S, Blaik J. Theory-driven game-based assessment of general cognitive ability: design theory, measurement, prediction of performance, and test fairness. J Appl Psychol. 2022;107:1655–77.
Keivit RA, van Rooijen H, Wicherts JM, Waldorp LJ, Kan KJ, Scholte HS, et al. Intelligence and the brain: a model-based approach. Cogn Neurosci. 2012;3:89–97.
Lee JJ, McGue M, Iacono WG, Michael AM, Chabris CF. The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling. Intelligence. 2019;75:48–58.
Cox SR, Ritchie SJ, Fawns-Ritchie C, Tucker-Drob EM, Deary IJ. Structural brain imaging correlates of general intelligence in UK Biobank. Intelligence. 2019;76:101376.
Pietschnig J, Gerdesmann D, Zeiler M, Voracek M. Of differing methods, disputed estimates and discordant interpretations: the meta-analytical multiverse of brain volume and IQ associations. R Soc Open Sci. 2022;9:211621.
Stanek KC, Ones DS. Meta-analytic relations between personality and cognitive ability. Proc Natl Acad Sci USA. 2023;120:e2212794120.
Vedel A. Big Five personality group differences across academic majors: a systematic review. Pers Indiv Differ. 2016;92:1–10.
Zajenkowski M. How do teenagers perceive their intelligence? Narcissism, intellect, well-being and gender as correlates of self-assessed intelligence among adolescents. Pers Indiv Differ. 2021;169:109978.
Coenen J, Borghans L, Diris R. Personality traits, preferences and educational choices: a focus on STEM. J Econ Psychol. 2021;84:102361.
Cheesman R, Anapaz V, Ebeltoft JC, Porneso R, Ayorech Z, Demange P, et al. Genetic associations with educational fields in >460,000 individuals. Nat Genet. [in press].
Edwards T, Dawes CT, Willoughby EA, McGue M, Lee JJ. More than g: Verbal and performance IQ as predictors of socio-political attitudes. Intelligence. 2025;108:101876.
Hillygus DS. The missing link: Exploring the relationship between higher education and political engagement. Polit Behav. 2005;27:25–47.
Frey BJ, Dueck D. Clustering by passing messages between data points. Science. 2007;315:972–6.