Riferimenti bibliografici

American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. American Educational Research Association. https://www.testingstandards.net/uploads/7/6/6/4/76643089/standards_2014edition.pdf
Arden, R., Luciano, M., Deary, I. J., Reynolds, C. A., Pedersen, N. L., Plassman, B. L., McGue, M., Christensen, K., & Visscher, P. M. (2016). The association between intelligence and lifespan is mostly genetic. International Journal of Epidemiology, 45(1), 178–185. https://doi.org/10.1093/ije/dyv112
Association, A. P. (2022). Diagnostic and statistical manual of mental disorders. American Psychiatric Association Publishing. https://doi.org/10.1176/appi.books.9780890425787
Astle, D. E., & Fletcher-Watson, S. (2020). Beyond the core-deficit hypothesis in developmental disorders. Current Directions in Psychological Science, 29(5), 431–437.
Astle, D. E., Holmes, J., Kievit, R., & Gathercole, S. E. (2022). Annual research review: The transdiagnostic revolution in neurodevelopmental disorders. Journal of Child Psychology and Psychiatry, 63(4), 397–417. https://doi.org/10.1111/jcpp.13481
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Barrick, M. R., & Mount, M. K. (1991). The Big Five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44(1), 1–26. https://doi.org/10.1111/j.1744-6570.1991.tb00688.x
Bartholomew, D. J., Deary, I. J., & Lawn, M. (2009). A new lease of life for Thomson’s bonds model of intelligence. Psychological Review, 116(3), 567–579. https://doi.org/10.1037/a0016262
Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27. https://doi.org/10.1016/j.intell.2015.04.009
Basten, U., Stelzel, C., & Fiebach, C. J. (2013). Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network. Intelligence, 41(5), 517–528. https://doi.org/10.1016/j.intell.2013.07.006
Binet, A., & Simon, T. (1904). Méthodes nouvelles pour le diagnostic du niveau intellectuel des anormaux. L’année psychologique, 11(1), 191–244. https://doi.org/10.3406/psy.1904.3675
Binet, A., & Simon, T. (1916). The development of intelligence in children (the Binet-Simon scale) (E. S. Kite, Trans.). Williams & Wilkins.
Blau, N., van Spronsen, F. J., & Levy, H. L. (2010). Phenylketonuria. The Lancet, 376(9750), 1417–1427. https://doi.org/10.1016/S0140-6736(10)60961-0
Blum, D., & Holling, H. (2017). Spearman’s law of diminishing returns. A meta-analysis. Intelligence, 65, 60–66. https://doi.org/10.1016/j.intell.2017.07.004
Boring, E. G. (1923). Intelligence as the tests test it. New Republic, 36, 35–37.
Bratsberg, B., & Rogeberg, O. (2018). Flynn effect and its reversal are both environmentally caused. Proceedings of the National Academy of Sciences, 115(26), 6674–6678. https://doi.org/10.1073/pnas.1718793115
Brigham, C. C. (1923). A study of American intelligence. Princeton University Press.
Brigham, C. C. (1930). Intelligence tests of immigrant groups. Psychological Review, 37(2), 158–165. https://doi.org/10.1037/h0072570
Briley, D. A., & Tucker-Drob, E. M. (2013). Explaining the increasing heritability of cognitive ability across development: A meta-analysis of longitudinal twin and adoption studies. Psychological Science, 24(9), 1704–1713. https://doi.org/10.1177/0956797613478618
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376. https://doi.org/10.1038/nrn3475
Calvin, C. M., Batty, G. D., Der, G., Brett, C. E., Taylor, A., Pattie, A., Čukić, I., & Deary, I. J. (2017). Childhood intelligence in relation to major causes of death in 68 year follow-up: Prospective population study. BMJ, j2708. https://doi.org/10.1136/bmj.j2708
Calvin, C. M., Deary, I. J., Fenton, C., Roberts, B. A., Der, G., Leckenby, N., & Batty, G. D. (2011). Intelligence in youth and all-cause-mortality: Systematic review with meta-analysis. International Journal of Epidemiology, 40(3), 626–644. https://doi.org/10.1093/ije/dyq190
Campbell, F. A., Ramey, C. T., Pungello, E., Sparling, J., & Miller-Johnson, S. (2002). Early childhood education: Young adult outcomes from the Abecedarian project. Applied Developmental Science, 6(1), 42–57. https://doi.org/10.1207/S1532480XADS0601_05
Canivez, G. L., Watkins, M. W., & Dombrowski, S. C. (2017). Structural validity of the Wechsler Intelligence Scale for Children–Fifth Edition: Confirmatory factor analyses with the 16 primary and secondary subtests. Psychological Assessment, 29(4), 458–472. https://doi.org/10.1037/pas0000358
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press. https://doi.org/10.1017/CBO9780511571312
Caspi, A., Houts, R. M., Belsky, D. W., Goldman-Mellor, S. J., Harrington, H., Israel, S., Meier, M. H., Ramrakha, S., Shalev, I., Poulton, R., & Moffitt, T. E. (2014). The p factor: One general psychopathology factor in the structure of psychiatric disorders? Clinical Psychological Science, 2(2), 119–137. https://doi.org/10.1177/2167702613497473
Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1–22. https://doi.org/10.1037/h0046743
Cattell, R. B. (1987). Intelligence: Its structure, growth and action (Vol. 35). North-Holland.
Chabris, C. F., Lee, J. J., Cesarini, D., Benjamin, D. J., & Laibson, D. I. (2015). The fourth law of behavior genetics. Current Directions in Psychological Science, 24(4), 304–312. https://doi.org/10.1177/0963721415580430
Childhood cognitive ability and adult academic attainment: Evidence from three British cohort studies. (2010). Longitudinal and Life Course Studies, 1(3). https://doi.org/10.14301/llcs.v1i3.93
Cornoldi, C. (2007). L’intelligenza. Il Mulino.
Cornoldi, C., Belacchi, C., Giofrè, D., Martini, A., & Tressoldi, P. (2010). The mean Southern Italian children IQ is not particularly low: A reply to r. Lynn (2010). Intelligence, 38(5), 462–470. https://doi.org/10.1016/j.intell.2010.06.003
Cornoldi, C., & Vecchi, T. (2003). Visuo-spatial working memory and individual differences. Psychology Press.
Cox, S. R., Ritchie, S. J., Fawns-Ritchie, C., Tucker-Drob, E. M., & Deary, I. J. (2019). Structural brain imaging correlates of general intelligence in UK Biobank. Intelligence, 76, 101376. https://doi.org/10.1016/j.intell.2019.101376
Crespi, B. J. (2016). Autism as a disorder of high intelligence. Frontiers in Neuroscience, 10, 300. https://doi.org/10.3389/fnins.2016.00300
Crompton, C. J., Ropar, D., Evans-Williams, C. V. M., Flynn, E. G., & Fletcher-Watson, S. (2020). Autistic peer-to-peer information transfer is highly effective. Autism, 24(7), 1704–1712. https://doi.org/10.1177/1362361320919286
Cronbach, L. J. (1957). The two disciplines of scientific psychology. American Psychologist, 12(11), 671–684. https://doi.org/10.1037/h0043943
Cucina, J. M., Burtnick, S. K., De la Flor Musso, M. E., Walmsley, P. T., & Wilson, K. J. (2024). Meta-analytic validity of cognitive ability for hands-on military job proficiency. Intelligence, 104, 101818. https://doi.org/10.1016/j.intell.2024.101818
Davies, G., Lam, M., Harris, S. E., Trampush, J. W., Luciano, M., Hill, W. D., Hagenaars, S. P., Ritchie, S. J., Marioni, R. E., Fawns-Ritchie, C., Liewald, D. C. M., Okely, J. A., Ahola-Olli, A. V., Barnes, C. L. K., Bertram, L., Bis, J. C., Burdick, K. E., Christoforou, A., DeRosse, P., … Deary, I. J. (2018). Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nature Communications, 9(1), 2098. https://doi.org/10.1038/s41467-018-04362-x
Davies, G., Tenesa, A., Payton, A., Yang, J., Harris, S. E., Liewald, D., Ke, X., Le Hellard, S., Christoforou, A., Luciano, M., McGhee, K., Lopez, L., Gow, A. J., Corley, J., Redmond, P., Fox, H. C., Haggarty, P., Whalley, L. J., McNeill, G., … Deary, I. J. (2011). Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular Psychiatry, 16(10), 996–1005. https://doi.org/10.1038/mp.2011.85
Davies, N. M., Hill, W. D., Anderson, E. L., Sanderson, E., Deary, I. J., & Davey Smith, G. (2019). Multivariable two-sample Mendelian randomization estimates of the effects of intelligence and education on health. eLife, 8, e43990. https://doi.org/10.7554/eLife.43990
Day, A. L., & Carroll, S. A. (2008). Faking emotional intelligence (EI): Comparing response distortion on ability and trait-based EI measures. Journal of Organizational Behavior, 29(6), 761–784. https://doi.org/10.1002/job.485
Dearborn, W. F. (1921). Intelligence and its measurement: A symposium–XII. Journal of Educational Psychology, 12(4), 210–212. https://doi.org/10.1037/h0065003
Deary, I. J. (2012a). Intelligence. Annual Review of Psychology, 63(1), 453–482. https://doi.org/10.1146/annurev-psych-120710-100353
Deary, I. J. (2012b). Looking for “system integrity” in cognitive epidemiology. Gerontology, 58(6), 545–553. https://doi.org/10.1159/000341157
Deary, I. J., Cox, S. R., & Hill, W. D. (2022). Genetic variation, brain, and intelligence differences. Molecular Psychiatry, 27(1), 335–353. https://doi.org/10.1038/s41380-021-01027-y
Deary, I. J., Hill, W. D., & Gale, C. R. (2021). Intelligence, health and death. Nature Human Behaviour, 5(4), 416–430. https://doi.org/10.1038/s41562-021-01078-9
Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35(1), 13–21. https://doi.org/10.1016/j.intell.2006.02.001
Deary, I. J., Whalley, L. J., Lemmon, H., Crawford, J. R., & Starr, J. M. (2000). The stability of individual differences in mental ability from childhood to old age: Follow-up of the 1932 Scottish Mental Survey. Intelligence, 28(1), 49–55. https://doi.org/10.1016/S0160-2896(99)00031-8
Deary, I. J., Whiteman, M. C., Starr, J. M., Whalley, L. J., & Fox, H. C. (2004). The impact of childhood intelligence on later life: Following up the Scottish Mental Surveys of 1932 and 1947. Journal of Personality and Social Psychology, 86(1), 130–147. https://doi.org/10.1037/0022-3514.86.1.130
Demontis, D., Walters, G. B., Athanasiadis, G., Walters, R., Therrien, K., Nielsen, T. T., Farajzadeh, L., Voloudakis, G., Bendl, J., Zeng, B., Zhang, W., Grove, J., Als, T. D., Duan, J., Satterstrom, F. K., Bybjerg-Grauholm, J., Bækved-Hansen, M., Gudmundsson, O. O., Magnusson, S. H., … Børglum, A. D. (2023). Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nature Genetics, 55(2), 198–208. https://doi.org/10.1038/s41588-022-01285-8
Duncan, G. J., & Magnuson, K. (2013). Investing in preschool programs. Journal of Economic Perspectives, 27(2), 109–132. https://doi.org/10.1257/jep.27.2.109
Dutton, E., van der Linden, D., & Lynn, R. (2016). The negative Flynn effect: A systematic literature review. Intelligence, 59, 163–169. https://doi.org/10.1016/j.intell.2016.10.002
Dworak, E. M., Revelle, W., & Condon, D. M. (2023). Looking for Flynn effects in a recent online u. S. Adult sample: Examining shifts within the SAPA project. Intelligence, 98, 101734. https://doi.org/10.1016/j.intell.2023.101734
Ekstein, S., Glick, B., Weill, M., Kay, B., & Berger, I. (2011). Down syndrome and attention-deficit/hyperactivity disorder (ADHD). Journal of Child Neurology, 26(10), 1290–1295. https://doi.org/10.1177/0883073811405201
Elson, M., Hussey, I., Alsalti, T., & Arslan, R. C. (2023). Psychological measures aren’t toothbrushes. Communications Psychology, 1(1), 25. https://doi.org/10.1038/s44271-023-00026-9
Federal Trade Commission. (2016). Lumosity to pay $2 million to settle FTC deceptive advertising charges for its “brain training” program. Comunicato stampa. https://www.ftc.gov/news-events/news/press-releases/2016/01/lumosity-pay-2-million-settle-ftc-deceptive-advertising-charges-its-brain-training-program
Flore, P. C., & Wicherts, J. M. (2015). Does stereotype threat influence performance of girls in stereotyped domains? A meta-analysis. Journal of School Psychology, 53(1), 25–44. https://doi.org/10.1016/j.jsp.2014.10.002
Flynn, J. R. (1984). The mean IQ of Americans: Massive gains 1932 to 1978. Psychological Bulletin, 95(1), 29–51. https://doi.org/10.1037/0033-2909.95.1.29
Flynn, J. R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Psychological Bulletin, 101(2), 171–191. https://doi.org/10.1037/0033-2909.101.2.171
Flynn, J. R. (2020). Secular changes in intelligence: The “Flynn effect” (R. J. Sternberg, Ed.; 2nd ed., pp. 940–963). Cambridge University Press. https://doi.org/10.1017/9781108770422.040
Frazier, T. W., Demaree, H. A., & Youngstrom, E. A. (2004). Meta-analysis of intellectual and neuropsychological test performance in attention-deficit/hyperactivity disorder. Neuropsychology, 18(3), 543–555. https://doi.org/10.1037/0894-4105.18.3.543
Frinco, M., Sironi, E. M., & Frinco, R. (2024). The psychometric properties of test. Why they are essential for clinical and academic purposes. Psicologia Clinica Dello Sviluppo, 1/2024. https://doi.org/10.1449/113146
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. https://doi.org/10.1177/2515245919847202
Galton, F. (1869). Hereditary genius: An inquiry into its laws and consequences. Macmillan.
Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. Basic Books.
Genç, E., Fraenz, C., Schlüter, C., Friedrich, P., Hossiep, R., Voelkle, M. C., Ling, J. M., Güntürkün, O., & Jung, R. E. (2018). Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nature Communications, 9(1), 1905. https://doi.org/10.1038/s41467-018-04268-8
Gignac, G. E. (2014). Dynamic mutualism versus g factor theory: An empirical test. Intelligence, 42, 89–97. https://doi.org/10.1016/j.intell.2013.11.004
Gignac, G. E. (2016). Residual group-level factor associations: Possibly negative implications for the mutualism theory of general intelligence. Intelligence, 55, 69–78. https://doi.org/10.1016/j.intell.2016.01.007
Gignac, G. E., & Kretzschmar, A. (2017). Evaluating dimensional distinctness with correlated-factor models: Limitations and suggestions. Intelligence, 62, 138–147. https://doi.org/10.1016/j.intell.2017.04.001
Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences researchers. Personality and Individual Differences, 102, 74–78. https://doi.org/10.1016/j.paid.2016.06.069
Giofrè, D., Pastore, M., Cornoldi, C., & Toffalini, E. (2019). Lumpers vs. Splitters: Intelligence in children with specific learning disorders. Intelligence, 76, 101380. https://doi.org/10.1016/j.intell.2019.101380
Giofrè, D., Toffalini, E., Altoè, G., & Cornoldi, C. (2017). Intelligence measures as diagnostic tools for children with specific learning disabilities. Intelligence, 61, 140–145. https://doi.org/10.1016/j.intell.2017.01.014
Gobet, F., & Sala, G. (2023). Cognitive training: A field in search of a phenomenon. Perspectives on Psychological Science, 18(1), 125–141. https://doi.org/10.1177/17456916221091830
Goleman, D. (1995). Emotional intelligence. Bantam Books.
Gottfredson, L. S. (1997). Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography. Intelligence, 24(1), 13–23. https://doi.org/10.1016/S0160-2896(97)90011-8
Gottfredson, L. S. (2004). Intelligence: Is it the epidemiologists’ elusive "fundamental cause" of social class inequalities in health? Journal of Personality and Social Psychology, 86(1), 174–199. https://doi.org/10.1037/0022-3514.86.1.174
Grassi, M., & Talamini, F. (2025). Do musicians have better short-term memory than nonmusicians? A multi-lab study. Preprint, PsyArXiv. https://doi.org/10.31234/osf.io/kghua_v1
Gregory, H. (2015). McNamara’s folly: The use of low-IQ troops in the Vietnam War. Infinity Publishing.
Grieco, J., Pulsifer, M., Seligsohn, K., Skotko, B., & Schwartz, A. (2015). Down syndrome: Cognitive and behavioral functioning across the lifespan. American Journal of Medical Genetics Part C: Seminars in Medical Genetics, 169(2), 135–149. https://doi.org/10.1002/ajmg.c.31439
Guilford, J. P. (1967). The nature of human intelligence. McGraw-Hill.
Haier, R. J. (2017). The neuroscience of intelligence. Cambridge University Press.
Haier, R. J., Siegel, B. V., MacLachlan, A., Soderling, E., Lottenberg, S., & Buchsbaum, M. S. (1992). Regional glucose metabolic changes after learning a complex visuospatial/motor task: A positron emission tomographic study. Brain Research, 570(1-2), 134–143. https://doi.org/10.1016/0006-8993(92)90573-R
Haier, R. J., Siegel, B. V., Nuechterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., Browning, H. L., & Buchsbaum, M. S. (1988). Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12(2), 199–217. https://doi.org/10.1016/0160-2896(88)90016-5
Halpern, D. F., Benbow, C. P., Geary, D. C., Gur, R. C., Hyde, J. S., & Gernsbacher, M. A. (2007). The science of sex differences in science and mathematics. Psychological Science in the Public Interest, 8(1), 1–51. https://doi.org/10.1111/j.1529-1006.2007.00032.x
Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. https://doi.org/10.1037/a0038889
Hamilton, A. F. de C. (2013). Reflecting on the mirror neuron system in autism: A systematic review of current theories. Developmental Cognitive Neuroscience, 3, 91–105. https://doi.org/10.1016/j.dcn.2012.09.008
Haworth, C. M. A., Wright, M. J., Luciano, M., Martin, N. G., de Geus, E. J. C., van Beijsterveldt, C. E. M., Bartels, M., Posthuma, D., Boomsma, D. I., Davis, O. S. P., Kovas, Y., Corley, R. P., DeFries, J. C., Hewitt, J. K., Olson, R. K., Rhea, S.-A., Wadsworth, S. J., Iacono, W. G., McGue, M., … Plomin, R. (2010). The heritability of general cognitive ability increases linearly from childhood to young adulthood. Molecular Psychiatry, 15(11), 1112–1120. https://doi.org/10.1038/mp.2009.55
Heckman, J. J., Moon, S. H., Pinto, R., Savelyev, P. A., & Yavitz, A. (2010). The rate of return to the HighScope Perry Preschool program. Journal of Public Economics, 94(1-2), 114–128. https://doi.org/10.1016/j.jpubeco.2009.11.001
Howe, L. J., Nivard, M. G., Morris, T. T., Hansen, A. F., Rasheed, H., Cho, Y., Chittoor, G., Ahlskog, R., Lind, P. A., Palviainen, T., van der Zee, M. D., Cheesman, R., Mangino, M., Wang, Y., Li, S., Klaric, L., Ratliff, S. M., Bielak, L. F., Nygaard, M., … Davies, N. M. (2022). Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nature Genetics, 54(5), 581–592. https://doi.org/10.1038/s41588-022-01062-7
Ilieva, I. P., Hook, C. J., & Farah, M. J. (2015). Prescription stimulants’ effects on healthy inhibitory control, working memory, and episodic memory: A meta-analysis. Journal of Cognitive Neuroscience, 27(6), 1069–1089. https://doi.org/10.1162/jocn_a_00776
Intelligence and its measurement: A symposium. (1921). Journal of Educational Psychology, 12(3), 123–147. https://doi.org/10.1037/h0076078
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences, 105(19), 6829–6833. https://doi.org/10.1073/pnas.0801268105
Jensen, A. R. (1989). The Milwaukee Project: Preventing mental retardation in children at risk. Developmental Review, 9(3), 234–258. https://doi.org/10.1016/0273-2297(89)90030-0
Johnson, W., Bouchard, T. J., Krueger, R. F., McGue, M., & Gottesman, I. I. (2004). Just one g: Consistent results from three test batteries. Intelligence, 32(1), 95–107. https://doi.org/10.1016/S0160-2896(03)00062-X
Johnson, W., Carothers, A., & Deary, I. J. (2008). Sex differences in variability in general intelligence: A new look at the old question. Perspectives on Psychological Science, 3(6), 518–531. https://doi.org/10.1111/j.1745-6924.2008.00096.x
Joseph, D. L., & Newman, D. A. (2010). Emotional intelligence: An integrative meta-analysis and cascading model. Journal of Applied Psychology, 95(1), 54–78. https://doi.org/10.1037/a0017286
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154. https://doi.org/10.1017/S0140525X07001185
Kaufman, A. S. (2009). IQ testing 101. Springer Publishing Company.
Kell, H. J., Lubinski, D., & Benbow, C. P. (2013). Who rises to the top? Early indicators. Psychological Science, 24(5), 648–659. https://doi.org/10.1177/0956797612457784
Kendler, K. S., Turkheimer, E., Ohlsson, H., Sundquist, J., & Sundquist, K. (2015). Family environment and the malleability of cognitive ability: A Swedish national home-reared and adopted-away cosibling control study. Proceedings of the National Academy of Sciences, 112(15), 4612–4617. https://doi.org/10.1073/pnas.1417106112
Kievit, R. A., Lindenberger, U., Goodyer, I. M., Jones, P. B., Fonagy, P., Bullmore, E. T., the Neuroscience in Psychiatry Network, & Dolan, R. J. (2017). Mutualistic coupling between vocabulary and reasoning supports cognitive development during late adolescence and early adulthood. Psychological Science, 28(10), 1419–1431. https://doi.org/10.1177/0956797617710785
Kilpeläinen, T. O., Qi, L., Brage, S., Sharp, S. J., Sonestedt, E., Demerath, E., Ahmad, T., Mora, S., Kaakinen, M., Sandholt, C. H., Holzapfel, C., Autenrieth, C. S., Hyppönen, E., Cauchi, S., He, M., Kutalik, Z., Kumari, M., Stančáková, A., Meidtner, K., … Loos, R. J. F. (2011). Physical activity attenuates the influence of FTO variants on obesity risk: A meta-analysis of 218,166 adults and 19,268 children. PLoS Medicine, 8(11), e1001116. https://doi.org/10.1371/journal.pmed.1001116
Klingberg, T., Fernell, E., Olesen, P. J., Johnson, M., Gustafsson, P., Dahlström, K., Gillberg, C. G., Forssberg, H., & Westerberg, H. (2005). Computerized training of working memory in children with ADHD-a randomized, controlled trial. Journal of the American Academy of Child & Adolescent Psychiatry, 44(2), 177–186. https://doi.org/10.1097/00004583-200502000-00010
Knyspel, J., & Plomin, R. (2024). Comparing factor and network models of cognitive abilities using twin data. Intelligence, 104, 101833. https://doi.org/10.1016/j.intell.2024.101833
Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., Bagby, R. M., Brown, T. A., Carpenter, W. T., Caspi, A., Clark, L. A., Eaton, N. R., Forbes, M. K., Forbush, K. T., Goldberg, D., Hasin, D., Hyman, S. E., Ivanova, M. Y., Lynam, D. R., Markon, K., … Zimmerman, M. (2017). The hierarchical taxonomy of psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126(4), 454–477. https://doi.org/10.1037/abn0000258
Kovacs, K., & Conway, A. R. A. (2016). Process overlap theory: A unified account of the general factor of intelligence. Psychological Inquiry, 27(3), 151–177. https://doi.org/10.1080/1047840X.2016.1153946
Kovas, Y., & Plomin, R. (2006). Generalist genes: Implications for the cognitive sciences. Trends in Cognitive Sciences, 10(5), 198–203. https://doi.org/10.1016/j.tics.2006.03.001
Kush, J. C., & Canivez, G. L. (2019). Construct validity of the WISC–IV Italian edition: A bifactor examination of the standardization sample. International Journal of School & Educational Psychology, 9(1), 73–87. https://doi.org/10.1080/21683603.2019.1638854
Lawson, K. M., & Robins, R. W. (2021). Sibling constructs: What are they, why do they matter, and how should you handle them? Personality and Social Psychology Review, 25(4), 344–366. https://doi.org/10.1177/10888683211047101
Lee, J. J., Wedow, R., Okbay, A., et al. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112–1121. https://doi.org/10.1038/s41588-018-0147-3
Lee, Y., Berry, C. M., & Gonzalez-Mulé, E. (2019). The importance of being humble: A meta-analysis and incremental validity analysis of the relationship between honesty-humility and job performance. Journal of Applied Psychology, 104(12), 1535–1546. https://doi.org/10.1037/apl0000421
Levy, F., Hay, D. A., McStephen, M., Wood, C., & Waldman, I. (1997). Attention-deficit hyperactivity disorder: A category or a continuum? Genetic analysis of a large-scale twin study. Journal of the American Academy of Child & Adolescent Psychiatry, 36(6), 737–744. https://doi.org/10.1097/00004583-199706000-00009
Lovakov, A., & Agadullina, E. R. (2021). Empirically derived guidelines for effect size interpretation in social psychology. European Journal of Social Psychology, 51(3), 485–504. https://doi.org/10.1002/ejsp.2752
Lubinski, D., & Benbow, C. P. (2006). Study of mathematically precocious youth after 35 years: Uncovering antecedents for the development of math-science expertise. Perspectives on Psychological Science, 1(4), 316–345. https://doi.org/10.1111/j.1745-6916.2006.00019.x
Lynn, R. (2010). In Italy, north–south differences in IQ predict differences in income, education, infant mortality, stature, and literacy. Intelligence, 38(1), 93–100. https://doi.org/10.1016/j.intell.2009.07.004
Machts, N., Kaiser, J., Schmidt, F. T. C., & Möller, J. (2016). Accuracy of teachers’ judgments of students’ cognitive abilities: A meta-analysis. Educational Research Review, 19, 85–103. https://doi.org/10.1016/j.edurev.2016.06.003
Maenner, M. J., Warren, Z., Williams, A. R., Amoakohene, E., Bakian, A. V., Bilder, D. A., Durkin, M. S., Fitzgerald, R. T., Furnier, S. M., Hughes, M. M., Ladd-Acosta, C. M., McArthur, D., Pas, E. T., Salinas, A., Vehorn, A., Williams, S., Esler, A., Grzybowski, A., Hall-Lande, J., … Shaw, K. A. (2023). Prevalence and characteristics of autism spectrum disorder among children aged 8 years — autism and developmental disabilities monitoring network, 11 sites, United States, 2020. MMWR. Surveillance Summaries, 72(2), 1–14. https://doi.org/10.15585/mmwr.ss7202a1
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., Cho, J. H., Guttmacher, A. E., Kong, A., Kruglyak, L., Mardis, E., Rotimi, C. N., Slatkin, M., Valle, D., Whittemore, A. S., … Visscher, P. M. (2009). Finding the missing heritability of complex diseases. Nature, 461(7265), 747–753. https://doi.org/10.1038/nature08494
Marek, S., Tervo-Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., Hatoum, A. S., Donohue, M. R., Foran, W., Miller, R. L., Hendrickson, T. J., Malone, S. M., Kandala, S., Feczko, E., Miranda-Dominguez, O., Graham, A. M., Earl, E. A., Perrone, A. J., Cordova, M., Doyle, O., … Dosenbach, N. U. F. (2022). Reproducible brain-wide association studies require thousands of individuals. Nature, 603(7902), 654–660. https://doi.org/10.1038/s41586-022-04492-9
Martens, M. A., Wilson, S. J., & Reutens, D. C. (2008). Research review: Williams syndrome: A critical review of the cognitive, behavioral, and neuroanatomical phenotype. Journal of Child Psychology and Psychiatry, 49(6), 576–608. https://doi.org/10.1111/j.1469-7610.2008.01887.x
Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44. https://doi.org/10.1037/1082-989X.12.1.23
Mayer, J. D., Roberts, R. D., & Barsade, S. G. (2008). Human abilities: Emotional intelligence. Annual Review of Psychology, 59(1), 507–536. https://doi.org/10.1146/annurev.psych.59.103006.093646
McArdle, J. J., Ferrer-Caja, E., Hamagami, F., & Woodcock, R. W. (2002). Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Developmental Psychology, 38(1), 115–142. https://doi.org/10.1037/0012-1649.38.1.115
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. https://doi.org/10.1016/j.intell.2008.08.004
McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23(3), 412–433. https://doi.org/10.1037/met0000144
Melby-Lervåg, M., Redick, T. S., & Hulme, C. (2016). Working memory training does not improve performance on measures of intelligence or other measures of “far transfer”. Perspectives on Psychological Science, 11(4), 512–534. https://doi.org/10.1177/1745691616635612
Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525–543. https://doi.org/10.1007/BF02294825
Milton, D. E. M. (2012). On the ontological status of autism: The “double empathy problem.” Disability & Society, 27(6), 883–887. https://doi.org/10.1080/09687599.2012.710008
Molloy, M. F., Taxali, A., Angstadt, M., Greathouse, T., Toda-Thorne, K., McCurry, K. L., Weigard, A., Kardan, O., Burchell, L., Dziubinski, M., Choi, J., Vandersluis, M., Michael, C., Heitzeg, M. M., & Sripada, C. (2025). Regional, but not brain-wide, graph theoretic measures are robustly and reproducibly linked to general cognitive ability. Cerebral Cortex, 35(4), bhaf074. https://doi.org/10.1093/cercor/bhaf074
Mutluer, T., Aslan Genç, H., Özcan Morey, A., Yapici Eser, H., Ertinmaz, B., Can, M., & Munir, K. (2022). Population-based psychiatric comorbidity in children and adolescents with autism spectrum disorder: A meta-analysis. Frontiers in Psychiatry, 13, 856208. https://doi.org/10.3389/fpsyt.2022.856208
Neisser, U., Boodoo, G., Bouchard, T. J., Jr., Boykin, A. W., Brody, N., Ceci, S. J., Halpern, D. F., Loehlin, J. C., Perloff, R., Sternberg, R. J., & Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51(2), 77–101. https://doi.org/10.1037/0003-066X.51.2.77
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience & Biobehavioral Reviews, 33(7), 1004–1023. https://doi.org/10.1016/j.neubiorev.2009.04.001
Nye, C. D., Ma, J., & Wee, S. (2022). Cognitive ability and job performance: Meta-analytic evidence for the validity of narrow cognitive abilities. Journal of Business and Psychology, 37(6), 1119–1139. https://doi.org/10.1007/s10869-022-09796-1
Oh, I.-S., Le, H., & Roth, P. L. (2023). Revisiting Sackett et al.’s (2022) rationale behind their recommendation against correcting for range restriction in concurrent validation studies. Journal of Applied Psychology, 108(8), 1300–1310. https://doi.org/10.1037/apl0001078
Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use. Nature Human Behaviour, 3(2), 173–182. https://doi.org/10.1038/s41562-018-0506-1
Owen, A. M., Hampshire, A., Grahn, J. A., Stenton, R., Dajani, S., Burns, A. S., Howard, R. J., & Ballard, C. G. (2010). Putting brain training to the test. Nature, 465(7299), 775–778. https://doi.org/10.1038/nature09042
Penke, L., Maniega, S. M., Bastin, M. E., Valdés Hernández, M. C., Murray, C., Royle, N. A., Starr, J. M., Wardlaw, J. M., & Deary, I. J. (2012). Brain white matter tract integrity as a neural foundation for general intelligence. Molecular Psychiatry, 17(10), 1026–1030. https://doi.org/10.1038/mp.2012.66
Pennington, B. (2006). From single to multiple deficit models of developmental disorders. Cognition, 101(2), 385–413. https://doi.org/10.1016/j.cognition.2006.04.008
Petrides, K. V., & Furnham, A. (2001). Trait emotional intelligence: Psychometric investigation with reference to established trait taxonomies. European Journal of Personality, 15(6), 425–448. https://doi.org/10.1002/per.416
Pietschnig, J., Penke, L., Wicherts, J. M., Zeiler, M., & Voracek, M. (2015). Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean? Neuroscience & Biobehavioral Reviews, 57, 411–432. https://doi.org/10.1016/j.neubiorev.2015.09.017
Pietschnig, J., & Voracek, M. (2015). One century of global IQ gains: A formal meta-analysis of the Flynn effect (1909–2013). Perspectives on Psychological Science, 10(3), 282–306. https://doi.org/10.1177/1745691615577701
Pietschnig, J., Voracek, M., & Formann, A. K. (2010). Mozart effect–shmozart effect: A meta-analysis. Intelligence, 38(3), 314–323. https://doi.org/10.1016/j.intell.2010.03.001
Pizzigallo, E., Cornoldi, C., Buono, S., Città, S., Viola, F., & Toffalini, E. (2023). The intellectual profile of adults with specific learning disabilities. Journal of Intelligence, 11(12), 223. https://doi.org/10.3390/jintelligence11120223
Pletzer, J. L., & Abrahams, L. (2025). Personality and job performance: A review of trait models and recent trends. Current Opinion in Psychology, 65, 102054. https://doi.org/10.1016/j.copsyc.2025.102054
Plomin, R., & Deary, I. J. (2015). Genetics and intelligence differences: Five special findings. Molecular Psychiatry, 20(1), 98–108. https://doi.org/10.1038/mp.2014.105
Plomin, R., DeFries, J. C., McClearn, G. E., & McGuffin, P. (2001). Behavioral genetics (4th ed.). Worth Publishers.
Plomin, R., & von Stumm, S. (2018). The new genetics of intelligence. Nature Reviews Genetics, 19(3), 148–159. https://doi.org/10.1038/nrg.2017.104
Pohl, K. M., Thompson, W. K., Adeli, E., & Linguraru, M. G. (Eds.). (2019). Adolescent brain cognitive development neurocognitive prediction: First challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, proceedings. Springer International Publishing. https://doi.org/10.1007/978-3-030-31901-4
Pokropek, A., Marks, G. N., Borgonovi, F., Koc, P., & Greiff, S. (2022). General or specific abilities? Evidence from 33 countries participating in the PISA assessments. Intelligence, 92, 101653. https://doi.org/10.1016/j.intell.2022.101653
Protzko, J. (2015). The environment in raising early intelligence: A meta-analysis of the fadeout effect. Intelligence, 53, 202–210. https://doi.org/10.1016/j.intell.2015.10.006
Psacharopoulos, G., & Patrinos, H. A. (2018). Returns to investment in education: A decennial review of the global literature. Education Economics, 26(5), 445–458. https://doi.org/10.1080/09645292.2018.1484426
Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004
Quintin, E.-M., Jo, B., Hall, S. S., Bruno, J. L., Chromik, L. C., Raman, M. M., Lightbody, A. A., Martin, A., & Reiss, A. L. (2016). The cognitive developmental profile associated with fragile X syndrome: A longitudinal investigation of cognitive strengths and weaknesses through childhood and adolescence. Development and Psychopathology, 28(4pt2), 1457–1469. https://doi.org/10.1017/S0954579415001200
Rauscher, F. H., Shaw, G. L., & Ky, C. N. (1993). Music and spatial task performance. Nature, 365(6447), 611–611. https://doi.org/10.1038/365611a0
Raven, J. (2000). The Raven’s Progressive Matrices: Change and stability over culture and time. Cognitive Psychology, 41(1), 1–48. https://doi.org/10.1006/cogp.1999.0735
Ritchie, S. J., Bates, T. C., & Deary, I. J. (2015). Is education associated with improvements in general cognitive ability, or in specific skills? Developmental Psychology, 51(5), 573–582. https://doi.org/10.1037/a0038981
Ritchie, S. J., Cox, S. R., Shen, X., Lombardo, M. V., Reus, L. M., Alloza, C., Harris, M. A., Alderson, H. L., Hunter, S., Neilson, E., Liewald, D. C. M., Auyeung, B., Whalley, H. C., Lawrie, S. M., Gale, C. R., Bastin, M. E., McIntosh, A. M., & Deary, I. J. (2018). Sex differences in the adult human brain: Evidence from 5216 UK Biobank participants. Cerebral Cortex, 28(8), 2959–2975. https://doi.org/10.1093/cercor/bhy109
Ritchie, S. J., & Tucker-Drob, E. M. (2018). How much does education improve intelligence? A meta-analysis. Psychological Science, 29(8), 1358–1369. https://doi.org/10.1177/0956797618774253
Robinaugh, D. J., Hoekstra, R. H. A., Toner, E. R., & Borsboom, D. (2020). The network approach to psychopathology: A review of the literature 2008–2018 and an agenda for future research. Psychological Medicine, 50(3), 353–366. https://doi.org/10.1017/S0033291719003404
Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21(2), 137–150. https://doi.org/10.1037/met0000045
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42. https://doi.org/10.1177/2515245917745629
Rong, Y., Yang, C.-J., Jin, Y., & Wang, Y. (2021). Prevalence of attention-deficit/hyperactivity disorder in individuals with autism spectrum disorder: A meta-analysis. Research in Autism Spectrum Disorders, 83, 101759. https://doi.org/10.1016/j.rasd.2021.101759
Roth, B., Becker, N., Romeyke, S., Schäfer, S., Domnick, F., & Spinath, F. M. (2015). Intelligence and school grades: A meta-analysis. Intelligence, 53, 118–137. https://doi.org/10.1016/j.intell.2015.09.002
Russell, G., Mandy, W., Elliott, D., White, R., Pittwood, T., & Ford, T. (2019). Selection bias on intellectual ability in autism research: A cross-sectional review and meta-analysis. Molecular Autism, 10(1), 9. https://doi.org/10.1186/s13229-019-0260-x
Sackett, P. R., Demeke, S., Bazian, I. M., Griebie, A. M., Priest, R., & Kuncel, N. R. (2024). A contemporary look at the relationship between general cognitive ability and job performance. Journal of Applied Psychology, 109(5), 687–713. https://doi.org/10.1037/apl0001159
Sackett, P. R., Zhang, C., Berry, C. M., & Lievens, F. (2022). Revisiting meta-analytic estimates of validity in personnel selection: Addressing systematic overcorrection for restriction of range. Journal of Applied Psychology, 107(11), 2040–2068. https://doi.org/10.1037/apl0000994
Sackett, P. R., Zhang, C., Berry, C. M., & Lievens, F. (2023). Revisiting the design of selection systems in light of new findings regarding the validity of widely used predictors. Industrial and Organizational Psychology, 16(3), 283–300. https://doi.org/10.1017/iop.2023.24
Sala, G., & Gobet, F. (2017). Does far transfer exist? Negative evidence from chess, music, and working memory training. Current Directions in Psychological Science, 26(6), 515–520. https://doi.org/10.1177/0963721417712760
Sala, G., & Gobet, F. (2020). Cognitive and academic benefits of music training with children: A multilevel meta-analysis. Memory & Cognition, 48(8), 1429–1441. https://doi.org/10.3758/s13421-020-01060-2
Sala, G., Tatlidil, K. S., & Gobet, F. (2018). Video game training does not enhance cognitive ability: A comprehensive meta-analytic investigation. Psychological Bulletin, 144(2), 111–139. https://doi.org/10.1037/bul0000139
Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition and Personality, 9(3), 185–211. https://doi.org/10.2190/DUGG-P24E-52WK-6CDG
Satterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., Stevens, C., Reichert, J., Mulhern, M. S., Artomov, M., Gerges, S., Sheppard, B., Xu, X., Bhaduri, A., Norman, U., … Buxbaum, J. D. (2020). Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell, 180(3), 568–584.e23. https://doi.org/10.1016/j.cell.2019.12.036
Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., de Leeuw, C. A., Nagel, M., Awasthi, S., Barr, P. B., Coleman, J. R. I., Grasby, K. L., Hammerschlag, A. R., Kaminski, J. A., Karlsson, R., Krapohl, E., Lam, M., Nygaard, M., Reynolds, C. A., Trampush, J. W., … Posthuma, D. (2018). Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nature Genetics, 50(7), 912–919. https://doi.org/10.1038/s41588-018-0152-6
Savi, A. O., Marsman, M., van der Maas, H. L. J., & Maris, G. K. J. (2019). The wiring of intelligence. Perspectives on Psychological Science, 14(6), 1034–1061. https://doi.org/10.1177/1745691619866447
Schmidt, F. L., & Hunter, J. (2004). General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86(1), 162–173. https://doi.org/10.1037/0022-3514.86.1.162
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), 262–274. https://doi.org/10.1037/0033-2909.124.2.262
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864
Schweinhart, L. J., Montie, J., Xiang, Z., Barnett, W. S., Belfield, C. R., & Nores, M. (2005). Lifetime effects: The High/Scope Perry Preschool study through age 40. High/Scope Press.
Serpico, D. (2022). L’intelligenza tra natura e cultura. Rosenberg & Sellier.
Shayer, M., & Ginsburg, D. (2009). Thirty years on – a large anti-Flynn effect/ (II): 13- and 14-year-olds. Piagetian tests of formal operations norms 1976–2006/7. British Journal of Educational Psychology, 79(3), 409–418. https://doi.org/10.1348/978185408X383123
Shaywitz, S. E., Escobar, M. D., Shaywitz, B. A., Fletcher, J. M., & Makuch, R. (1992). Evidence that dyslexia may represent the lower tail of a normal distribution of reading ability. New England Journal of Medicine, 326(3), 145–150. https://doi.org/10.1056/NEJM199201163260301
Shewach, O. R., Sackett, P. R., & Quint, S. (2019). Stereotype threat effects in settings with features likely versus unlikely in operational test settings: A meta-analysis. Journal of Applied Psychology, 104(12), 1514–1534. https://doi.org/10.1037/apl0000420
Simons, D. J., Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016). Do “brain-training” programs work? Psychological Science in the Public Interest, 17(3), 103–186. https://doi.org/10.1177/1529100616661983
Sjöwall, D., Roth, L., Lindqvist, S., & Thorell, L. B. (2013). Multiple deficits in ADHD: Executive dysfunction, delay aversion, reaction time variability, and emotional deficits. Journal of Child Psychology and Psychiatry, 54(6), 619–627. https://doi.org/10.1111/jcpp.12006
Skeels, H. M. (1966). Adult status of children with contrasting early life experiences: A follow-up study. Monographs of the Society for Research in Child Development, 31(3), 1. https://doi.org/10.2307/1165791
Snowling, M. J., Nash, H. M., Gooch, D. C., Hayiou-Thomas, M. E., & Hulme, C. (2019). Developmental outcomes for children at high risk of dyslexia and children with developmental language disorder. Child Development, 90(5), e548–e564. https://doi.org/10.1111/cdev.13216
Snyderman, M., & Herrnstein, R. J. (1983). Intelligence tests and the Immigration Act of 1924. American Psychologist, 38(9), 986–995. https://doi.org/10.1037/0003-066X.38.9.986
Spearman, C. (1904). "General intelligence," objectively determined and measured. The American Journal of Psychology, 15(2), 201. https://doi.org/10.2307/1412107
Spearman, C. (1927). The abilities of man: Their nature and measurement. Macmillan.
Stanovich, K. E. (1986). Matthew effects in reading: Some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21(4), 360–407. https://doi.org/10.1598/RRQ.21.4.1
Steenbergen-Hu, S., Makel, M. C., & Olszewski-Kubilius, P. (2016). What one hundred years of research says about the effects of ability grouping and acceleration on K–12 students’ academic achievement: Findings of two second-order meta-analyses. Review of Educational Research, 86(4), 849–899. https://doi.org/10.3102/0034654316675417
Stephenson, K. G., Beck, J. S., South, M., Norris, M., & Butter, E. (2021). Validity of the WISC-v in youth with autism spectrum disorder: Factor structure and measurement invariance. Journal of Clinical Child & Adolescent Psychology, 50(5), 669–681. https://doi.org/10.1080/15374416.2020.1846543
Stern, W. (1912). Die psychologischen Methoden der Intelligenzprüfung. Barth.
Stern, Y. (2012). Cognitive reserve in ageing and Alzheimer’s disease. The Lancet Neurology, 11(11), 1006–1012. https://doi.org/10.1016/S1474-4422(12)70191-6
Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press.
Sternberg, R. J. (Ed.). (2002). Why smart people can be so stupid. Yale University Press.
Strenze, T. (2007). Intelligence and socioeconomic success: A meta-analytic review of longitudinal research. Intelligence, 35(5), 401–426. https://doi.org/10.1016/j.intell.2006.09.004
Sundet, J. M., Barlaug, D. G., & Torjussen, T. M. (2004). The end of the Flynn effect?: A study of secular trends in mean intelligence test scores of Norwegian conscripts during half a century. Intelligence, 32(4), 349–362. https://doi.org/10.1016/j.intell.2004.06.004
Szucs, D., & Ioannidis, J. Pa. (2020). Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990–2012) and of latest practices (2017–2018) in high-impact journals. NeuroImage, 221, 117164. https://doi.org/10.1016/j.neuroimage.2020.117164
Talamini, F., Altoè, G., Carretti, B., & Grassi, M. (2017). Musicians have better memory than nonmusicians: A meta-analysis. PLOS ONE, 12(10), e0186773. https://doi.org/10.1371/journal.pone.0186773
te Nijenhuis, J., & van der Flier, H. (2013). Is the Flynn effect on g?: A meta-analysis. Intelligence, 41(6), 802–807. https://doi.org/10.1016/j.intell.2013.03.001
Terman, L. M. (1916). The measurement of intelligence. Houghton Mifflin.
Thiele, J. A., Faskowitz, J., Sporns, O., & Hilger, K. (2022). Multitask brain network reconfiguration is inversely associated with human intelligence. Cerebral Cortex, 32(19), 4172–4182. https://doi.org/10.1093/cercor/bhab473
Thompson, W. F., Schellenberg, E. G., & Husain, G. (2001). Arousal, mood, and the Mozart effect. Psychological Science, 12(3), 248–251. https://doi.org/10.1111/1467-9280.00345
Thomson, G. H. (1916). A hierarchy without a general factor. British Journal of Psychology, 8(3), 271–281. https://doi.org/10.1111/j.2044-8295.1916.tb00133.x
Thurstone, L. L. (1938). Primary mental abilities. University of Chicago Press.
Toffalini, E., Buono, S., & Cornoldi, C. (2022). The structure, profile, and diagnostic significance of intelligence in children with ADHD are impressively similar to those of children with a specific learning disorder. Research in Developmental Disabilities, 129, 104306. https://doi.org/10.1016/j.ridd.2022.104306
Toffalini, E., & Cornoldi, C. (2026). Oltre il QI globale nell’IDS-2: Proposta di due nuovi indici per l’abilità intellettiva. Presentazione al convegno ICAP 2026, Firenze. https://enricotoffalini.github.io/slides/Toffalini-ICAP2026-IT.pdf
Toffalini, E., & Feraco, T. (2026). Musicians, memory, and general cognitive ability: A commentary on Grassi et al. (2025). Advances in Methods and Practices in Psychological Science, 9(3), 25152459261452948. https://doi.org/10.1177/25152459261452948
Toffalini, E., Giofrè, D., & Cornoldi, C. (2017). Strengths and weaknesses in the intellectual profile of different subtypes of specific learning disorder: A study on 1,049 diagnosed children. Clinical Psychological Science, 5(2), 402–409. https://doi.org/10.1177/2167702616672038
Tucker-Drob, E. M., & Bates, T. C. (2016). Large cross-national differences in gene × socioeconomic status interaction on intelligence. Psychological Science, 27(2), 138–149. https://doi.org/10.1177/0956797615612727
Turkheimer, E. (2000). Three laws of behavior genetics and what they mean. Current Directions in Psychological Science, 9(5), 160–164. https://doi.org/10.1111/1467-8721.00084
Turkheimer, E., Haley, A., Waldron, M., D’Onofrio, B., & Gottesman, I. I. (2003). Socioeconomic status modifies heritability of IQ in young children. Psychological Science, 14(6), 623–628. https://doi.org/10.1046/j.0956-7976.2003.psci_1475.x
van Bergen, E., de Zeeuw, E. L., Hart, S. A., Boomsma, D. I., de Geus, E. J. C., & Kan, K.-J. (2025). Co-occurrence and causality among ADHD, dyslexia, and dyscalculia. Psychological Science, 36(3), 204–217. https://doi.org/10.1177/09567976241293999
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 7619–7624. https://doi.org/10.1523/JNEUROSCI.1443-09.2009
Van der Hallen, R., Evers, K., Brewaeys, K., Van den Noortgate, W., & Wagemans, J. (2015). Global processing takes time: A meta-analysis on local–global visual processing in ASD. Psychological Bulletin, 141(3), 549–573. https://doi.org/10.1037/bul0000004
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), 36–52. https://doi.org/10.1037/bul0000078
van der Maas, H. L. J., Dolan, C. V., Grasman, R. P. P. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. J. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113(4), 842–861. https://doi.org/10.1037/0033-295X.113.4.842
Van Hootegem, A., Røgeberg, O., Bratsberg, B., & Lyngstad, T. H. (2023). Correlation between cognitive ability and educational attainment weakens over birth cohorts. Scientific Reports, 13(1), 17747. https://doi.org/10.1038/s41598-023-44605-6
van IJzendoorn, M. H., Juffer, F., & Poelhuis, C. W. K. (2005). Adoption and cognitive development: A meta-analytic comparison of adopted and nonadopted children’s IQ and school performance. Psychological Bulletin, 131(2), 301–316. https://doi.org/10.1037/0033-2909.131.2.301
van IJzendoorn, M. H., Luijk, M. P. C. M., & Juffer, F. (2008). IQ of children growing up in children’s homes: A meta-analysis on IQ delays in orphanages. Merrill-Palmer Quarterly, 54(3), 341–366. https://doi.org/10.1353/mpq.0.0002
Vernon, P. E. (1950). The structure of human abilities. Methuen.
Visser, B. A., Ashton, M. C., & Vernon, P. A. (2006). Beyond g: Putting multiple intelligences theory to the test. Intelligence, 34(5), 487–502. https://doi.org/10.1016/j.intell.2006.02.004
Warne, R. T. (2020). In the know: Debunking 35 myths about human intelligence. Cambridge University Press. https://doi.org/10.1017/9781108593298
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. https://doi.org/10.1080/00031305.2016.1154108
Waterhouse, L. (2006). Multiple intelligences, the Mozart effect, and emotional intelligence: A critical review. Educational Psychologist, 41(4), 207–225. https://doi.org/10.1207/s15326985ep4104_1
Watkins, M. W. (2006). Orthogonal higher order structure of the Wechsler Intelligence Scale for Children–fourth edition. Psychological Assessment, 18(1), 123–125. https://doi.org/10.1037/1040-3590.18.1.123
Watkins, M. W., & Smith, L. G. (2013). Long-term stability of the Wechsler Intelligence Scale for Children—Fourth Edition. Psychological Assessment, 25(2), 477–483. https://doi.org/10.1037/a0031653
Wechsler, D. (1939). The measurement of adult intelligence. Williams & Wilkins.
Wechsler, D. (1958). The measurement and appraisal of adult intelligence (4th ed.). Williams & Wilkins.
Wechsler, D. (2008). WAIS-IV technical and interpretive manual. Pearson.
Whalley, L. J., & Deary, I. J. (2001). Longitudinal cohort study of childhood IQ and survival up to age 76. BMJ, 322(7290), 819. https://doi.org/10.1136/bmj.322.7290.819
Wicherts, J. M., Dolan, C. V., Hessen, D. J., Oosterveld, P., van Baal, G. C. M., Boomsma, D. I., & Span, M. M. (2004). Are intelligence tests measurement invariant over time? Investigating the nature of the Flynn effect. Intelligence, 32(5), 509–537. https://doi.org/10.1016/j.intell.2004.07.002
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. https://doi.org/10.1073/pnas.1908430116
Wissler, C. (1901). The correlation of mental and physical tests. The Psychological Review: Monograph Supplements, 3(6), i–62. https://doi.org/10.1037/h0092995
Wolfram, T. (2023). (Not just) intelligence stratifies the occupational hierarchy: Ranking 360 professions by IQ and non-cognitive traits. Intelligence, 98, 101755. https://doi.org/10.1016/j.intell.2023.101755
Woodrow, H. (1921). Intelligence and its measurement: A symposium–XI. Journal of Educational Psychology, 12(4), 207–210. https://doi.org/10.1037/h0067806
World Health Organization. (2019). International classification of diseases, 11th revision (ICD-11). https://icd.who.int/
Yerkes, R. M. (Ed.). (1921). Psychological examining in the United States Army (Vol. 15). Government Printing Office.
Zajonc, R. B. (1965). Social facilitation: A solution is suggested for an old unresolved social psychological problem. Science, 149(3681), 269–274. https://doi.org/10.1126/science.149.3681.269
Zanon, F., & Priftis, K. (2025). Psychologists must be against the use of tests without accessible norms. Giornale Italiano Di Psicologia, 2/2025. https://doi.org/10.1421/116921