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The neuroscience of human intelligence differences

Nature Reviews Neuroscience volume 11, pages 201–211 (2010)Cite this article

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Key Points

  • More than 100 years of empirical research provide conclusive evidence that a general factor of intelligence (also known as g, general cognitive ability, mental ability and IQ (intelligence quotient)) exists, despite some claims to the contrary. Intelligence can be reliably measured, is stable in rank-order across the lifespan, and is predictive of many important life outcomes, including educational and occupational success, health and longevity.

  • Intelligence shows high heritability in quantitative genetic studies; this heritability increases across the lifespan to mid-adulthood and partly overlaps with genetic variance that influences brain structure.

  • As with many other highly heritable complex traits, the genetic polymorphisms underlying normal-range intelligence differences remain elusive. One possible explanation is that many mildly harmful, lineage-specific, rare genetic variants ('mutation load') might be responsible for the heritability of intelligence.

  • The most robust finding in the neuroscience of intelligence is that larger brains, and a greater volume of grey matter in various regions in the brain, are associated with higher intelligence.

  • Intelligence does not reside in a single localized area in the brain. The available evidence suggests a widely distributed network of parieto-frontal brain areas underlies intelligence.

  • The distributed nature of intelligence in the brain suggests a crucial role of white matter integrity and an efficient neurological network structure. Both hypotheses have initial empirical support.

  • Functional efficiency (that is, low energy consumption in task-relevant brain areas) is also related to higher intelligence, especially when task difficulty is neither particularly high nor particularly low.

  • Various lines of evidence suggest that men and women might use their brains differently to achieve similar levels of cognitive performance. These sex differences might extend to individual differences: people might differ in how they use their brains to solve the same cognitive tasks.

Abstract

Neuroscience is contributing to an understanding of the biological bases of human intelligence differences. This work is principally being conducted along two empirical fronts: genetics — quantitative and molecular — and brain imaging. Quantitative genetic studies have established that there are additive genetic contributions to different aspects of cognitive ability — especially general intelligence — and how they change through the lifespan. Molecular genetic studies have yet to identify reliably reproducible contributions from individual genes. Structural and functional brain-imaging studies have identified differences in brain pathways, especially parieto-frontal pathways, that contribute to intelligence differences. There is also evidence that brain efficiency correlates positively with intelligence.

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Figure 1: The hierarchy of intelligence differences.
Figure 2: The loci of intelligence differences.

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References

  1. Johnson, W., Carothers, A. & Deary, I. J. Sex differences in variability in general intelligence: a new look at the old question. Perspect. Psychol. Sci. 3, 518–531 (2008).

    Article  PubMed  Google Scholar 

  2. Moffitt, T. E., Caspi, A., Harkness, A. R. & Silva, P. A. The natural history of change in intellectual performance: Who changes? How much? Is it meaningful? J. Child Psychol. Psychiatry 3, 455–506 (1993).

    Article  Google Scholar 

  3. Deary, I. J., Whalley, L. J., Lemmon, H., Crawford, J. R. & Starr, J. M. The stability of individual differences in mental ability from childhood to old age: follow-up of the 1932 Scottish Mental Survey. Intelligence 28, 49–55 (2000).

    Article  Google Scholar 

  4. Johnson, W., McGue, M. & Iacono, W. G. Genetic and environmental influences on academic achievement trajectories during adolescence. Dev. Psychol. 42, 513–542 (2006).

    Article  Google Scholar 

  5. Deary, I. J., Strand, S., Smith, P. & Fernandes, C. Intelligence and educational achievement. Intelligence 35, 13–21 (2007).

    Article  Google Scholar 

  6. Strenze, T. Intelligence and socioeconomic success: a meta-analytic review of longitudinal research. Intelligence 35, 401–426 (2007).

    Article  Google Scholar 

  7. Gottfredson, L. Why g matters: the complexity of everyday life. Intelligence 24, 79–132 (1997). A thorough documentation of the findings relating general intelligence to life outcomes, including a theoretical exposition of the reasons for the associations.

    Article  Google Scholar 

  8. Batty, G. D., Deary, I. J. & Gottfredson, L. S. Premorbid (early life) IQ and later mortality risk: systematic review. Ann. Epidemiol. 17, 278–288 (2007).

    Article  PubMed  Google Scholar 

  9. Batty, G. D. et al. IQ in late adolescence/early adulthood and mortality by middle age: cohort study of one million Swedish men. Epidemiology 20, 100–109 (2009).

    Article  PubMed  Google Scholar 

  10. Spearman, C. General intelligence, objectively determined and measured. Am. J. Psychol. 15, 201–293 (1904).

    Article  Google Scholar 

  11. Carroll, J. B. Human Cognitive Abilities: A Survey of Factor Analytic Studies. (Cambridge Univ. Press, Cambridge, 1993). A careful re-analysis of over 460 correlation matrices of cognitive ability tests, indicating a three-stratum hierarchical structure of intelligence with the g factor at the top.

    Book  Google Scholar 

  12. Deary, I. J. Looking Down on Human Intelligence: From Psychometrics to the Brain. (Oxford Univ. Press, Oxford, 2000).

    Book  Google Scholar 

  13. Deary, I. J., Johnson, W. & Houlihan, L. M. Genetic foundations of human intelligence. Hum. Genet. 126, 215–232 (2009). A detailed review of the quantitative and molecular genetic literature on intelligence, indicating that intelligence is heritable even though no robust association with a genetic variant has been found so far.

    Article  PubMed  Google Scholar 

  14. McDaniel, M. A. Big-brained people are smarter: a meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence 33, 337–346 (2005). A meta-analysis of the relationship between structural MRI measures of full brain size and intelligence, showing a robust positive relationship.

    Article  Google Scholar 

  15. Galton, F. Heredity, talent, and character. Macmillan's Magazine 12, 157–166; 318–327 (1865).

    Google Scholar 

  16. Plomin, R., DeFries, J. C., McClearn, G. E. & McGuffin, P. Behavioral Genetics 5th edn (Worth, New York, 2007).

    Google Scholar 

  17. Johnson, W. et al. Genetic and environmental influences on the Verbal-Perceptual-Image Rotation (VPR) model of the structure of mental abilities in the Minnesota Study of Twins Reared Apart. Intelligence 35, 542–562 (2007).

    Article  Google Scholar 

  18. Posthuma, D., de Geus, E. J. & Boomsma, D. I. Perceptual speed and IQ are associated through common genetic factors. Behav. Genet. 31, 593–602 (2001).

    Article  CAS  PubMed  Google Scholar 

  19. Posthuma, D. et al. Genetic correlations between brain volumes and the WAIS-III dimensions of verbal comprehension, working memory, perceptual organization, and processing speed. Twin Res. 6, 131–139 (2003).

    Article  PubMed  Google Scholar 

  20. Rijsdijk, F. V., Vernon, P. A. & Boomsma, D. I. Application of hierarchical genetic models to Raven and WAIS subtests: a Dutch twin study. Behav. Genet. 32, 199–210 (2002).

    Article  PubMed  Google Scholar 

  21. Finkel, D., Pedersen, N. L., McGue, M. & McClearn, G. E. Heritability of cognitive abilities in adult twins: comparison of Minnesota and Swedish data. Behav. Genet. 25, 421–431 (1995).

    Article  CAS  PubMed  Google Scholar 

  22. McCartney, K., Harris, M. J. & Bernieri, F. Growing up and growing apart: a developmental meta-analysis of twin studies. Psychol. Bull. 107, 226–237 (1990).

    Article  CAS  PubMed  Google Scholar 

  23. McGue, M., Bouchard, T. J., Iacono, W. G. & Lykken, D. T. in Nature, Nurture, and Psychology (eds Plomin, R. & McClearn, G. E.) 59–76 (American Psychological Association, Washington DC, 1993).

    Book  Google Scholar 

  24. Wilson, R. S. Synchronies in mental development: an epigenetic perspective. Science 202, 939–948 (1978).

    Article  CAS  PubMed  Google Scholar 

  25. Spinath, F., Ronald, A., Harlaar, N., Price, T. S. & Plomin, R. Phenotypic g early in life: on the etiology of general cognitive ability in a large population sample of twin children aged 2–4 years. Intelligence 31, 195–210 (2003).

    Article  Google Scholar 

  26. Edmonds, C. J. et al. Inspection time and cognitive abilities in twins aged 7 to 17 years: age-related changes, heritability, and genetic covariance. Intelligence 36, 210–225 (2008).

    Article  Google Scholar 

  27. Jacobs, N., van Os, J., Derom, C. & Thiery, E. Heritability of intelligence. Twin Res. Hum. Genet. 10, 11–14 (2007).

    Article  Google Scholar 

  28. Bartels, M., Rietveld, M. J. H., Van Baal, G. C. M. & Boomsma, D. I. Genetic and environmental influences on the development of intelligence. Behav. Genet. 32, 237–249 (2002).

    Article  CAS  PubMed  Google Scholar 

  29. Hulshoff Pol, H. E. et al. Genetic contributions to human brain morphology and intelligence. J. Neurosci. 26, 10235–10242 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Pennington, B. F. et al. A twin study of size variations in the human brain. J. Cogn. Neurosci. 12, 223–232 (2000).

    Article  CAS  PubMed  Google Scholar 

  31. Peper, J. S., Brouwer, R. M., Boomsma, D. I., Kahn, R. S. & Hulshoff Pol, H. E. Genetic influences on human brain structure: a review of brain imaging studies in twins. Hum. Brain Mapp. 28, 464–473 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Posthuma, D. et al. The association between brain volume and intelligence is of genetic origin. Nature Neurosci. 5, 83–84 (2002). The first empirical demonstration, using a twin study design and structural MRI, that the correlation between brain size and intelligence is genetically mediated.

    Article  CAS  PubMed  Google Scholar 

  33. Thompson, P. M. et al. Genetic influences on brain structure. Nature Neurosci. 4, 1253–1258 (2001).

    Article  CAS  PubMed  Google Scholar 

  34. Anokhin, A. P., Muller, V., Lindenberger, U., Heath, A. C. & Meyers, E. Genetic influences on dynamic complexity of brain oscillations. Neurosci. Lett. 397, 93–98 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Friedman, N. P. et al. Individual differences in executive function are almost entirely genetic in origin. J. Exp. Psychol. Gen. 137, 201–225 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Miller, G. F. & Penke, L. The evolution of human intelligence and the coefficient of additive genetic variance in human brain size. Intelligence 35, 97–114 (2007).

    Article  Google Scholar 

  37. Shaw, P. et al. Intellectual ability and cortical development in children and adolescents. Nature 440, 676–679 (2006). A groundbreaking study showing that developmental plasticity in cortical thickness showed a stronger association with intelligence than cortical thickness per se.

    Article  CAS  PubMed  Google Scholar 

  38. Sowell, E. R., Thompson, P. M., Holmes, C. J., Jernigan, T. L. & Toga, A. W. In vivo evidence for post-adolescence brain maturation in frontal and striatal regions. Nature Neurosci. 2, 859–861 (1999).

    Article  CAS  PubMed  Google Scholar 

  39. Giedd, J. N., Schmitt, J. E. & Neale, M. C. Structural brain magnetic imaging of pediatric twins. Hum. Brain Mapp. 28, 474–481 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Lenroot, R. K. et al. Differences in genetic and environmental influences on the human cerebral cortex associated with development in childhood and adolescence. Hum. Brain Mapp. 30, 163–174 (2009).

    Article  PubMed  Google Scholar 

  41. Chelly, J., Khelfaoui, M., Francis, F., Cherif, B. & Bienvenu, T. Genetics and pathophysiology of mental retardation. Eur. J. Hum. Genet. 14, 701–713 (2006).

    Article  CAS  PubMed  Google Scholar 

  42. Payton, A. The impact of genetic research on our understanding of normal cognitive ageing: 1995 to 2009. Neuropsychol. Rev. 19, 451–477 (2009).

    Article  PubMed  Google Scholar 

  43. Wisdom, N. M., Callahan, J. L. & Hawkins, K. A. The effects of apolipoprotein E on non-impaired cognitive functioning: a meta-analysis. Neurobiol. Aging 12 Mar 2009 (doi: 10.1016/j.neurobiolaging.2009.02.003).

    Article  CAS  PubMed  Google Scholar 

  44. Bu, G. Apolipoprotein E and its receptors in Alzheimer's disease: pathways, pathogenesis and therapy. Nature Rev. Neurosci. 10, 333–344 (2009).

    Article  CAS  Google Scholar 

  45. Barnett, J. H., Scoriels, L. & Munafo, M. R. Meta-analysis of the cognitive effects of the catechol-O-transferase gene Val158/108Met polymorphism. Biol. Psychiatry 64, 137–144 (2008).

    Article  CAS  PubMed  Google Scholar 

  46. Goldman, D., Weinberger, D. R., Malhotra, A. K. & Goldberg, T. E. The role of COMT Val158Met in cognition. Biol. Psychiatry 65, e1–2 (2009).

    Article  PubMed  Google Scholar 

  47. Miyajima, F. et al. Brain-derived neurotrophic factor polymorphism Val66Met influences cognitive abilities in the elderly. Genes Brain Behav. 7, 411–417 (2007).

    Article  PubMed  CAS  Google Scholar 

  48. Need, A. C. et al. A genome-wide study of common SNPs and CNVs in cognitive performance in the CANTAB. Hum. Mol. Genet. 18, 4650–4661 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Penke, L., Denissen, J. J. A. & Miller, G. F. The evolutionary genetics of personality. Eur. J. Pers. 21, 549–587 (2007). A theoretical argument that intergenerationally accumulated rare variants (mutation load) underlie much of the genetic variance in intelligence.

    Article  Google Scholar 

  50. Penke, L., Denissen, J. J. A. & Miller, G. F. Evolution, genes, and inter-disciplinary personality research. Eur. J. Pers. 21, 639–665 (2007).

    Article  Google Scholar 

  51. Visscher, P. M. Sizing up human height variation. Nature Genet. 40, 489–490 (2008).

    Article  CAS  PubMed  Google Scholar 

  52. Galton, F. Head growth in students at the University of Cambridge. Nature 38, 14–15 (1888).

    Article  Google Scholar 

  53. Spitzka, E. A. A study of the brains of six eminent scientists belonging to the American Anthropometric Society: together with a description of the skull of Professor, E. D. Cope. Trans. Am. Philos. Soc. 21, 175–308 (1907).

    Article  Google Scholar 

  54. Rushton, J. P. & Ankney, C. D. Whole brain size and general mental ability: a review. Int. J. Neurosci. 119, 691–731 (2009).

    Article  PubMed  Google Scholar 

  55. MacLullich, A. M. et al. Intracranial capacity and brain volumes are associated with cognition in healthy elderly men. Neurology 59, 169–174 (2002).

    Article  CAS  PubMed  Google Scholar 

  56. Witelson, S. F., Beresh, H. & Kigar, D. L. Intelligence and brain size in 100 post-mortem brains: sex, lateralization and age factors. Brain 129, 386–398 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Andreasen, N. C. et al. Intelligence and brain structure in normal individuals. Am. J. Psychiatry 150, 130–134 (1993).

    Article  CAS  PubMed  Google Scholar 

  58. Flashman, L. A., Andreasen, N. C., Flaum, M. & Swayze, V. W. Intelligence and regional brain volumes in normal controls. Intelligence 25, 149–160 (1997).

    Article  Google Scholar 

  59. Narr, K. L. et al. Relationships between IQ and regional cortical gray matter thickness in healthy adults. Cereb. Cortex 17, 2163–2171 (2007).

    Article  PubMed  Google Scholar 

  60. Jung, R. E. & Haier, R. J. The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav. Brain Sci. 30, 135–154; discussion 154–187 (2007). A detailed review of structural neuroimaging correlates of intelligence supporting the conclusion that, in addition to frontal areas, a network of frontal and posterior brain areas are involved in general cognitive functions.

    Article  PubMed  Google Scholar 

  61. Colom, R., Jung, R. E. & Haier, R. J. General intelligence and memory span: evidence for a common neuroanatomic framework. Cogn. Neuropsychol. 24, 867–878 (2007).

    Article  PubMed  Google Scholar 

  62. Colom, R. et al. Gray matter correlates of fluid, crystallized, and spatial intelligence: testing the P-FIT model. Intelligence 37, 124–135 (2009).

    Article  Google Scholar 

  63. Haier, R. J. et al. Gray matter and intelligence factors: is there a neuro-g? Intelligence 37, 136–144 (2009).

    Article  Google Scholar 

  64. Karama, S. et al. Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds. Intelligence 37, 145–155 (2009).

    Article  Google Scholar 

  65. Choi, Y. Y. et al. Multiple bases of human intelligence revealed by cortical thickness and neural activation. J. Neurosci. 28, 10323–10329 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Luders, E., Narr, K. L., Thompson, P. M. & Toga, A. W. Neuroanatomical correlates of intelligence. Intelligence 37, 156–163 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Nachev, P., Mah, Y. H. & Husain, M. Functional neuroanatomy: the locus of human intelligence. Curr. Biol. 19, R418–R420 (2009).

    Article  CAS  PubMed  Google Scholar 

  68. Luo, L. & O'Leary, D. D. M. Axon retraction and degeneration in development and disease. Annu. Rev. Neurosci. 28, 127–156 (2005).

    Article  CAS  PubMed  Google Scholar 

  69. Gläscher, J. et al. Lesion mapping of cognitive abilities linked to intelligence. Neuron 61, 681–691 (2009). The first brain-wide lesion study on intelligence based on a large sample, which allowed stronger inferences on the necessity of brain regions for general cognitive functions than other structural neuroimaging studies.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Sporns, O., Chialvo, D., Kaiser, M. & Hilgetag, C. C. Organization, development and function of complex brain networks. Trends Cogn. Sci. 8, 418–425 (2004).

    Article  PubMed  Google Scholar 

  71. Li, Y. et al. Brain anatomical network and intelligence. PLoS Comput. Biol. 5, e1000395 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Achard, S., Salvador, R., Whitcher, B., Suckling, J. & Bullmore, E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 26, 63–72 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Rev. Neurosci. 10, 186–198 (2009).

    Article  CAS  Google Scholar 

  74. Frisoni, G. B., Galluzzi, S., Pantoni, L. & Filippi, M. The effect of white matter lesions on cognition in the elderly: small but detectable. Nature Clin. Pract. Neurol. 3, 620–627 (2007).

    Article  Google Scholar 

  75. Turken, A. et al. Cognitive processing speed and the structure of white matter pathways: convergent evidence from normal variation and lesion studies. Neuroimage 42, 1032–1044 (2008).

    Article  PubMed  Google Scholar 

  76. Deary, I. J., Leaper, S. A., Murray, A. D., Staff, R. T. & Whalley, L. J. Cerebral white matter abnormalities and lifetime cognitive change: a 67 year follow up of the Scottish Mental Survey 1932. Psychol. Aging 18, 140–148 (2003).

    Article  PubMed  Google Scholar 

  77. Jung, R. E. et al. Imaging intelligence with proton magnetic resonance spectroscopy. Intelligence 37, 192–198 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Deary, I. J. et al. White matter integrity and cognition in childhood and old age. Neurology 66, 505–512 (2006).

    Article  CAS  PubMed  Google Scholar 

  79. Schmithorst, V. J., Wilke, M., Dardzinski, B. J. & Holland, S. K. Cognitive functions correlate with white matter architecture in a normal pediatric population: a diffusion tensor MRI study. Hum. Brain Mapp. 26, 139–147 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Chiang, M. C. et al. Genetics of brain fiber architecture and intellectual performance. J. Neurosci. 29, 2212–2224 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Charlton, R. A., McIntyre, D. J., Howe, F. A., Morris, R. G. & Markus, H. S. The relationship between white matter brain metabolites and cognition in normal aging: the GENIE study. Brain Res. 1164, 108–116 (2007).

    Article  CAS  PubMed  Google Scholar 

  82. Yu, C. et al. White matter tract integrity and intelligence in patients with mental retardation and healthy adults. Neuroimage 40, 1533–1541 (2008).

    Article  PubMed  Google Scholar 

  83. Neubauer, A. C. & Fink, A. Intelligence and neural efficiency. Neurosci. Biobehav. Rev. 33, 1004–1023 (2009). A detailed and critical review of the neural efficiency hypothesis of intelligence based on functional neuroimaging data.

    Article  PubMed  Google Scholar 

  84. Haier, R. J. et al. Cortical glucose metabolic-rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence 12, 199–217 (1988).

    Article  Google Scholar 

  85. van den Heuvel, M. P., Stam, C. J., Kahn, R. S. & Hulshoff Pol, H. E. Efficiency of functional brain networks and intellectual performance. J. Neurosci. 29, 7619–7624 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Song, M. et al. Brain spontaneous functional connectivity and intelligence. Neuroimage 41, 1168–1176 (2008).

    Article  PubMed  Google Scholar 

  87. Haier, R. J., Jung, R. E., Yeo, R. A., Head, K. & Alkire, M. T. The neuroanatomy of general intelligence: sex matters. Neuroimage 25, 320–327 (2005).

    Article  PubMed  Google Scholar 

  88. Schmithorst, V. J. Developmental sex differences in the relation of neuroanatomical connectivity to intelligence. Intelligence 37, 164–173 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Neubauer, A. C., Grabner, R. H., Fink, A. & Neuper, C. Intelligence and neural efficiency: further evidence of the influence of task content and sex on the brain–IQ relationship. Brain Res. Cogn. Brain Res. 25, 217–225 (2005).

    Article  PubMed  Google Scholar 

  90. Johnson, W. & Bouchard, T. J. Sex differences in mental abilities: g masks the dimensions on which they lie. Intelligence 35, 23–39 (2007).

    Article  Google Scholar 

  91. Chen, X., Sachdev, P. S., Wen, W. & Ansteyc, K. J. Sex differences in regional gray matter in healthy individuals aged 44–48 years: a voxel-based morphometric study. Neuroimage 36, 691–699 (2007).

    Article  PubMed  Google Scholar 

  92. de Courten-Myers, G. M. The human cerebral cortex: gender differences in structure and function. J. Neuropathol. Exp. Neurol. 58, 217–226 (1999).

    Article  CAS  PubMed  Google Scholar 

  93. Luders, E. et al. Gender differences in cortical complexity. Nature Neurosci. 7, 799–800 (2004).

    Article  CAS  PubMed  Google Scholar 

  94. Dykiert, D., Gale, C. G. & Deary, I. J. Are apparent sex differences in mean IQ scores created in part by sample restriction and increased male variance? Intelligence 37, 42–47 (2009).

    Article  Google Scholar 

  95. Penke, L. in The Evolution of Personality and Individual Differences (eds Buss, D. M. & Hawley, P. H.)(Oxford Univ. Press, New York, in the press).

  96. Johnson, W. & Bouchard, T. J. Sex differences in mental ability: a proposed means to link them to brain structure and function. Intelligence 35, 197–209 (2007).

    Article  Google Scholar 

  97. Johnson, W., Jung, R. E., Colom, R. & Haier, R. J. Cognitive abilities independent of IQ correlate with regional brain structure. Intelligence 36, 18–28 (2008).

    Article  Google Scholar 

  98. Park, D. C. & Reuter-Lorenz, P. The adaptive brain: aging and neurocognitive scaffolding. Annu. Rev. Psychol. 60, 173–196 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Cabeza, R. Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol. Aging 17, 85–100 (2002).

    Article  PubMed  Google Scholar 

  100. Lohman, D. in Handbook of Intelligence (ed. Sternberg, R. J.) 285–340 (Cambridge Univ. Press, New York, 2000).

    Book  Google Scholar 

  101. Iaria, G., Petrides, M., Dagher, A., Pike, B. & Bohbot, V. D. Cognitive strategies dependent on the hippocampus and caudate nucleus in human navigation: variability and change with practice. J. Neurosci. 23, 5945–5952 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Rypma, B., Berger, J. S., Genova, H. M., Rebbechi, D. & D'Esposito, M. Dissociating age-related changes in cognitive strategy and neural efficiency using event-related fMRI. Cortex 41, 582–594 (2005).

    Article  PubMed  Google Scholar 

  103. Koten, J. W. et al. Genetic contribution to variation in cognitive function: an fMRI study in twins. Science 323, 1737–1740 (2009). An empirical demonstration of heritable individual differences in fMRI activation patterns underlying distinct cognitive strategies to solve a working memory task in remembering exposure to digits.

    Article  CAS  PubMed  Google Scholar 

  104. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Boring, E. G. Intelligence as the tests test it. New Republic 35, 35–37 (1923).

    Google Scholar 

  106. Gottfredson, L. S. Mainstream science on intelligence: an editorial with 52 signatories, history, and bibliography. Intelligence 24, 13–23 (1997).

    Article  Google Scholar 

  107. Johnson, W. & Bouchard, T. J. The structure of human intelligence: it is verbal, perceptual, and image rotation (VPR) not fluid and crystallized. Intelligence 33, 393–416 (2005).

    Article  Google Scholar 

  108. Visser, B. A., Ashton, M. C. & Vernon, P. A. Beyond g: putting multiple intelligence theory to the test. Intelligence 34, 487–502 (2006).

    Article  Google Scholar 

  109. Horn, J. L. in Intelligence: Measurement, Theory, and Public Policy (ed. Linn, R. L.) 29–73 (Univ. Illinois Press, Urbana, 1989).

    Google Scholar 

  110. Johnson, W., te Nijenhuis, J. & Bouchard, T. J. Still just one g: consistent results from five test batteries. Intelligence 32, 81–95 (2008). An empirical demonstration that g is not dependent on specific cognitive test batteries as long as there is sufficient variety in the tests.

    Article  Google Scholar 

  111. Gould, S. J. The Mismeasure of Man (Penguin, Harmondsworth, 1981).

    Google Scholar 

  112. Bartholomew, D. J., Deary, I. J. & Lawn, M. A new lease of life for Thomson's bonds model of intelligence. Psychol. Rev. 116, 567–579 (2009).

    Article  PubMed  Google Scholar 

  113. van der Maas, H. L. J. et al. A dynamical model of general intelligence: the positive manifold of intelligence by mutualism. Psychol. Rev. 113, 842–861 (2006).

    Article  PubMed  Google Scholar 

  114. Bouchard, T. J. Genetic influence on human intelligence (Spearman's g): how much? Ann. Hum. Biol. 36, 527–544 (2009).

    Article  PubMed  Google Scholar 

  115. Bouchard, T. J. & McGue, M. Familial studies of intelligence: a review. Science 212, 1055–1059 (1981).

    Article  PubMed  Google Scholar 

  116. Turkheimer, E., Haley, A., Waldron, M., D'Onofrio, B. M. & Gottesman, I. I. Socioeconomic status modifies heritability of IQ in young children. Psychol. Sci. 14, 623–628 (2003).

    Article  PubMed  Google Scholar 

  117. van den Oord, E. J. & Rowe, D. C. An examination of genotype-environment interactions for academic achievement in a US national longitudinal survey. Intelligence 25, 205–228 (1998).

    Article  Google Scholar 

  118. Deary, I. J. et al. Intergenerational social mobility and mid-life status attainment: influences of childhood intelligence, childhood social factors, and education. Intelligence 33, 455–472 (2005).

    Article  Google Scholar 

  119. Johnson, W. Genetic and environmental influences on behavior: capturing all the interplay. Psychol. Rev. 114, 423–440 (2007).

    Article  PubMed  Google Scholar 

  120. Salthouse, T. A. Localizing age-related individual differences in a hierarchical structure. Intelligence 32, 541–561 (2004).

    Article  Google Scholar 

  121. Petrill, A. A. et al. The genetic and environmental relationship between general and specific cognitive abilities in twins age 80 and older. Psychol. Sci. 9, 183–189 (1998).

    Article  Google Scholar 

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Acknowledgements

The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative. Funding from the Biotechnology and Biological Sciences Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council and Medical Research Council is gratefully acknowledged.

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  1. Ian J. Deary, Lars Penke and Wendy Johnson: All authors contributed equally to the work.

Authors and Affiliations

  1. Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Scotland, EH4 2EE, Edinburgh, UK

    Ian J. Deary, Lars Penke & Wendy Johnson

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  1. Ian J. Deary
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  2. Lars Penke
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  3. Wendy Johnson
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Correspondence to Ian J. Deary.

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FURTHER INFORMATION

Ian J. Deary's homepage

Lars Penke's homepage

Wendy Johnson's homepage

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Deary, I., Penke, L. & Johnson, W. The neuroscience of human intelligence differences. Nat Rev Neurosci 11, 201–211 (2010). https://doi.org/10.1038/nrn2793

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