Hostname: page-component-77f85d65b8-9nbrm Total loading time: 0 Render date: 2026-03-28T10:15:51.500Z Has data issue: false hasContentIssue false
  • English
  • Français

Genomic data can illuminate the architecture and evolution of cognitive abilities

Published online by Cambridge University Press:  15 August 2017

James J. Lee  and
Christopher F. Chabris
James J. Lee
Affiliation:
Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN 55455 leex2293@umn.edu https://umn.academia.edu/JamesLee
Christopher F. Chabris
Affiliation:
Institute for Advanced Application, Geisinger Health System, Lewisburg, PA 17837 chabris@gmail.com Department of Psychology, Union College, Schenectady, NY 12308 http://www.chabris.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Does general intelligence exist across species, and has it been a target of natural selection? These questions can be addressed with genomic data, which can rule out artifacts by demonstrating that distinct cognitive abilities are genetically correlated and thus share a biological substrate. This work has begun with data from humans and can be extended to other species; it should focus not only on general intelligence but also specific capacities like language and spatial ability.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e209
Check for updates
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Chabris, C. F., Lee, J. J., Cesarini, D., Benjamin, D. B. & Laibson, D. I. (2015) The fourth law of behavior genetics. Current Directions in Psychological Science 24:304–12.CrossRefGoogle ScholarPubMed
Dong, X., Wang, X., Zhang, F. & Tian, W. (2016) Genome-wide identification of regulatory sequences undergoing accelerated evolution in the human genome. Molecular Biology and Evolution 33:2565–75.Google Scholar
Hampshire, A., Highfield, R. R., Parkin, B. L. & Owen, A. M. (2012) Fractionating human intelligence. Neuron 76:1225–37.Google Scholar
Hauser, M. D., Chomsky, N. & Fitch, W. T. (2002b) The faculty of language: What is it, who has it, and how did it evolve? Science 298:1569–79.CrossRefGoogle ScholarPubMed
Heath, A. C., Berg, B., Eaves, L. J., Solass, M. H., Corey, L. A., Sundet, J. M., Magnus, P. & Nance, W. E. (1985) Nature 314:734–36.Google Scholar
Hurford, J. R. (2007) The origins of meaning: Language in the light of evolution. Oxford University Press.Google Scholar
Johnson, V. E., Deaner, R. O. & van Schaik, C. P. (2002) Bayesian analysis of rank data with application to primate intelligence experiments. Journal of the American Statistical Association 97(457):8–17.Google Scholar
Kell, H. J., Lubinski, D., Benbow, C. P. & Steiger, J. H. (2013) Creativity and technical innovation: Spatial ability's unique role. Psychological Science 24:1831–36.CrossRefGoogle ScholarPubMed
Kovas, Y. & Plomin, R. (2006) Generalist genes: Implications for the cognitive sciences. Trends in Cognitive Sciences 10:198–203.Google Scholar
Lee, J. J. (2012) Correlation and causation in the study of personality (with discussion). European Journal of Personality 26:372–412.Google Scholar
Lee, J. J. & Chow, C. C. (2013) The causal meaning of Fisher's average effect. Genetics Research 95:89–109.Google Scholar
Lee, J. J. & Kuncel, N. R. (2015) The determinacy and predictive power of common factors. Industrial and Organizational Psychology 8:467–72.CrossRefGoogle Scholar
Lee, J. J., Vattikuti, S. & Chow, C. C. (2016) Uncovering the genetic architectures of quantitative traits. Computational and Structural Biotechnology Journal 14:28–34.Google Scholar
Loehlin, J. C., Hansell, N. K., Wright, M. J. & Martin, N. G. (2016) Genetic and environmental contributions to cognitive structure in Australian twins: A reappraisal. Intelligence 59:24–31.Google Scholar
Okbay, A., Beauchamp, J. P., Fontana, M. A., Lee, J. J., Pers, T. H., Rietveld, C. A., Turley, P., Chen, G-B., Emilsson, V., Meddens, S. Fleur, W., Oskarsson, S., Pickrell, J. K., Thom, K., Timshel, P., de Vlaming, R., Abdellaoui, A., Ahluwalia, T. S., Bacelis, J., Baumbach, C., Bjornsdottir, G., Brandsma, J. H., Pin Concas, M., Derringer, J., Furlotte, N. A., Galesloot, T. E., Girotto, G., Gupta, R., Hall, L. M., Harris, S. E., Hofer, E., Horikoshi, M., Huffman, J. E., Kaasik, K., Kalafati, I. P., Karlsson, R., Kong, A., Lahti, J., van der Lee, S. J., deLeeuw, C., Lind, P. A., Liu, T., Mangino, M., Marten, J., Mihailov, E., Miller, M. B., van der Most, P. J. Oldmeadow, C., Payton, A., Pervjakova, N., Peyrot, W. J. Qian, Y., Raitakari, O., Rueedi, R., Salvi, E., Schmidt, B., Schraut, K. E., Shi, J., Smith, A. V., Poot, R. A., St Pourcain, B., Teumer, A., Thorleifsson, G., Verweij, N., Vuckovic, D., Wellmann, J., Westra, H.-J., Yang, J., Zhao, W., Zhu, Z., Alizadeh, B. Z., Amin, N., Bakshi, A., Baumeister, S. E., Biino, G., Bønnelykke, K., Boyle, P. A., Campbell, H., Cappuccio, F. P., Davies, G., De Neve, J.-E., Deloukas, P., Demuth, I., Ding, J., Eibich, P., Eisele, L., Eklund, N., Evans, D. M., Faul, J. D., Feitosa, M. F., Forstner, A. J., Gandin, I., Gunnarsson, B., Halldórsson, B. V., Harris, T. B., Heath, A. C., Hocking, L. J., Holliday, E. G., Homuth, G., Horan, M. A., Hottenga, J.-J., de Jager, P. L., Joshi, P. K., Jugessur, A., Kaakinen, M. A., Kähönen, M., Kanoni, S., Keltigangas-Järvinen, L., Kiemeney, L. A. L. M., Kolcic, I., Koskinen, S., Kraja, A. T., Kroh, M., Kutalik, Z., Latvala, A., Launer, L. J., Lebreton, M. P., Levinson, D. F., Lichtenstein, P., Lichtner, P., Liewald, D. C. M. LifeLines Cohort Study, Loukola, A., Madden, P. A., Mäki-Opas, T., Marioni, R. E., Marques-Vidal, P., Meddens, G. A., McMahon, G., Meisinger, C., Meitinger, T., Milaneschi, Y., Milani, L., Montgomery, G. W., Myhre, R., Nelson, C. P., Nyholt, D. R., Ollier, W. E. R., Palotie, A., Paternoster, L., Pedersen, N. L., Petrovic, K. E., Porteous, D. J., Räkkönen, K., Ring, S. M., Robino, A., Rostapshova, O., Rudan, I., Rustichini, A., Salomaa, V., Sanders, A. R., Sarin, A.-P., Schmidt, H., Scott, R. J., Smith, B. H., Smith, J. A., Staessen, J. A., Steinhagen-Thiessen, E., Strauch, K., Terracciano, A., Tobin, M. D., Ulivi, S., Vaccargiu, S., Quaye, L., van Rooij, F. J. A., Venturini, C., Vinkhuyzen, A. A. E., Völker, U., Völzke, H., Vonk, J. M., Vozzi, D., Waage, J., Ware, E. B., Willemsen, G., Attia, J. R., Bennett, D. A., Berger, K., Bertram, L., Bisgaard, H., Boomsma, D. I., Boreck, I. B., Bültmann, U., Chabris, C. F., Cucca, F., Cusi, D., Deary, I. J., Dedoussis, G. V., van Duijn, C. M., Eriksson, J. G., Franke, B., Franke, L., Gasparini, P., Gejman, P. V., Gieger, C., Grabe, H.-J., Gratten, J., Groenen, P. J. F., Gudnason, V., van der Harst, P., Hayward, C., Hinds, D. A., Hoffmann, W., Hyppönen, E., Iacono, W. G., Jacobsson, B., Järvelin, M.-R., Jöckel, K.-H., Kaprio, J., Kardia, S. L. R., Lehtimäki, T., Lehrer, S. F., Magnusson, P. K. E., Martin, N. G., McGue, M., Metspalu, A., Pendleton, N., Penninx, B. W. J. H., Perola, M., Pirastu, N., Pirastu, M., Polasek, O., Posthuma, D., Power, C., Province, M. A., Samani, N. J., Schlessinger, D., Schmidt, R., Sørensen, T. I. A., Spector, T. D., Stefansson, K., Thorsteinsdottir, U., Thurik, A. R., Timpson, N. J., Tiemeier, H., Tung, J. Y., Uitterlinden, A. G., Vitart, V., Vollenweider, P., Weir, D. R., Wilson, J. F., Wright, A. F., Conley, D. C., Krueger, R. F., Smith, G. D., Hofman, A., Laibson, D. I., Medland, S. E., Meyer, M. N., Yang, J., Johannesson, M., Visscher, P. M., Esko, T., Koillinger, P. D., Cesarini, D. & Benjamin, D. B. (2016) Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533:539–42.CrossRefGoogle ScholarPubMed
Pinker, S. & Jackendoff, R. (2005) The faculty of language: What's special about it? Cognition 95:201–36.CrossRefGoogle Scholar
Trzaskowski, M., Davis, O. S. P., DeFries, J. C., Yang, J., Visscher, P. M. & Plomin, R. (2013) DNA evidence for strong genome-wide pleiotropy of cognitive and learning abilities. Behavior Genetics 43:267–73.Google Scholar
van der Sluis, S., Verhage, M., Posthuma, D. & Dolan, C. (2010) Phenotypic complexity, measurement bias, and poor phenotypic resolution contribute to the missing heritability problem in genetic association studies. PLoS One 5:e13929.Google Scholar