Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-848d4c4894-ndmmz Total loading time: 0 Render date: 2024-05-08T03:47:30.820Z Has data issue: false hasContentIssue false

1 - Defining and Measuring Intelligence

The Psychometrics and Neuroscience of g

from Part I - Fundamental Issues

Published online by Cambridge University Press:  11 June 2021

By
Thomas R. Coyle
Edited by
Aron K. Barbey ,
Sherif Karama  and
Richard J. Haier
Aron K. Barbey
Affiliation:
University of Illinois, Urbana-Champaign
Sherif Karama
Affiliation:
McGill University, Montréal
Richard J. Haier
Affiliation:
University of California, Irvine
Get access

Summary

The purpose of this chapter is to review key principles and findings of intelligence research, with special attention to psychometrics and neuroscience. Following Jensen (1998), the chapter focuses on intelligence defined as general intelligence (g). g represents variance common to mental tests and arises from ubiquitous positive correlations among tests (scaled so that higher scores indicate better performance). The positive correlations indicate that people who perform well on one test generally perform well on all others. The chapter reviews measures of g (e.g., IQ and reaction times), models of g (e.g., Spearman’s model and the Cattell-Horn-Carroll model), and the invariance of g across test batteries.

Type
Chapter
Information
The Cambridge Handbook of Intelligence and Cognitive Neuroscience , pp. 3 - 25
Publisher: Cambridge University Press
Print publication year: 2021

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.)

References

Ackerman, P. L., Beier, M. E., & Boyle, M. O. (2005). Working memory and intelligence: The same or different constructs? Psychological Bulletin, 131(1), 30–60.CrossRefGoogle ScholarPubMed
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8–20.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Benedek, M., Jauk, E., Sommer, M., Arendasy, M., & Neubauer, A. C. (2014). Intelligence, creativity, and cognitive control: The common and differential involvement of executive functions in intelligence and creativity, Intelligence, 46, 73–83.Google Scholar
Bezzola, L., Mérillat, S., Gaser, C., & Jäncke, L. (2011). Training-induced neural plasticity in golf novices. Journal of Neuroscience, 31(35), 12444–12448.Google Scholar
Binet, A., & Simon, T. (1916). The development of intelligence in children. Baltimore, MD: Williams & Wilkins (reprinted 1973, New York: Arno Press).Google Scholar
Blum, D., & Holling, H. (2017). Spearman’s law of diminishing returns. A meta-analysis. Intelligence, 65, 60–66.Google Scholar
Bouchard, T. J. (1997). Experience producing drive theory: How genes drive experience and shape personality. Acta Paediatrica, 86(Suppl. 422), 60–64.Google Scholar
Brown, K. G., Le, H., & Schmidt, F. L. (2006). Specific aptitude theory revisited: Is there incremental validity for training performance? International Journal of Selection and Assessment, 14(2), 87–100.CrossRefGoogle Scholar
Brown, R. E. (2016) Hebb and Cattell: The genesis of the theory of fluid and crystallized intelligence. Frontiers in Human Neuroscience, 10, 1–11.Google Scholar
Canivez, G. L., & Watkins, M. W. (2010). Exploratory and higher-order factor analyses of the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) adolescent subsample. School Psychology Quarterly, 25(4), 223–235.Google Scholar
Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1–22.CrossRefGoogle Scholar
Cattell, R. B. (1987). Intelligence: Its structure, growth and action. New York: North-Holland.Google Scholar
Colom, R., Haier, R. J., Head, K., Álvarez-Linera, J., Ángeles Quiroga, M., Chun Shih, P., & Jung, R. E. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model. Intelligence, 37(2), 124–135.CrossRefGoogle Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2006a). Finding the g-factor in brain structure using the method of correlated vectors. Intelligence, 34(6), 561–570.Google Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2006b). Distributed brain sites for the g-factor of intelligence. Neuroimage, 31(3), 1359–1365.CrossRefGoogle ScholarPubMed
Colom, R., Rebollo, I., Palacios, A., Juan-Espinosa, M., & Kyllonen, P. C. (2004). Working memory is (almost) perfectly predicted by g. Intelligence, 32(3), 277–296.Google Scholar
Coyle, T. R. (2013). Effects of processing speed on intelligence may be underestimated: Comment on Demetriou et al. (2013). Intelligence, 41(5), 732–734.Google Scholar
Coyle, T. R. (2015). Relations among general intelligence (g), aptitude tests, and GPA: Linear effects dominate. Intelligence, 53, 16–22.Google Scholar
Coyle, T. R. (2018a). Non-g factors predict educational and occupational criteria: More than g. Journal of Intelligence, 6(3), 1–15.CrossRefGoogle ScholarPubMed
Coyle, T. R. (2018b). Non-g residuals of group factors predict ability tilt, college majors, and jobs: A non-g nexus. Intelligence, 67, 19–25.Google Scholar
Coyle, T. R. (2019). Tech tilt predicts jobs, college majors, and specific abilities: Support for investment theories. Intelligence, 75, 33–40.Google Scholar
Coyle, T. R., Elpers, K. E., Gonazalez, M. C., Freeman, J., & Baggio, J. A. (2018). General intelligence (g), ACT scores, and theory of mind: ACT(g) predicts limited variance among theory of mind tests. Intelligence, 71, 85–91.Google Scholar
Coyle, T. R., & Pillow, D. R. (2008). SAT and ACT predict college GPA after removing g. Intelligence, 36(6), 719–729.CrossRefGoogle Scholar
Coyle, T. R., Purcell, J. M., Snyder, A. C., & Kochunov, P. (2013). Non-g residuals of the SAT and ACT predict specific abilities. Intelligence, 41(2), 114–120.Google Scholar
Coyle, T. R., Snyder, A. C., Richmond, M. C., & Little, M. (2015). SAT non-g residuals predict course specific GPAs: Support for investment theory. Intelligence, 51, 57–66.CrossRefGoogle Scholar
Deary, I. J., Egan, V., Gibson, G. J., Brand, C. R., Austin, E., & Kellaghan, T. (1996). Intelligence and the differentiation hypothesis. Intelligence, 23(2), 105–132.CrossRefGoogle Scholar
Deary, I. J., Ferguson, K. J., Bastin, M. E., Barrow, G. W. S., Reid, L. M., Seckl, J. R., … MacLullich, A. M. J. (2007). Skull size and intelligence, and King Robert Bruce’s IQ. Intelligence, 35(6), 519–525.Google Scholar
Frey, M. C., & Detterman, D. K. (2004). Scholastic assessment or g? The relationship between the scholastic assessment test and general cognitive ability. Psychological Science, 15(6), 373–378.Google Scholar
Friedman, N. P., Miyake, A., Corley, R. P., Young, S. E., DeFries, J. C., & Hewitt, J. K. (2006). Not all executive functions are related to intelligence. Psychological Science, 17(2), 172–179.Google Scholar
Gardner, H. (1983/2003). Frames of mind. The theory of multiple intelligences. New York: Basic Books.Google Scholar
Gerber, P., Schlaffke, L., Heba, S., Greenlee, M. W., Schultz, T., & Schmidt-Wilcke, T. (2014). Juggling revisited – A voxel-based morphometry study with expert jugglers. Neuroimage, 95, 320–325.CrossRefGoogle ScholarPubMed
Gignac, G. E. (2015). Raven’s is not a pure measure of general intelligence: Implications for g factor theory and the brief measurement of g. Intelligence, 52, 71–79.Google Scholar
Gignac, G. E., & Bates, T. C. (2017). Brain volume and intelligence: The moderating role of intelligence measurement quality. Intelligence, 64, 18–29.Google Scholar
Gignac, G., Vernon, P. A., & Wickett, J. C. (2003). Factors influencing the relationship between brain size and intelligence. In Nyborg, H. (ed.), The scientific study of general intelligence: Tribute to Arthur R. Jensen (pp. 93–106). New York: Pergamon.Google Scholar
Gignac, G. E., & Watkins, M. W. (2015). There may be nothing special about the association between working memory capacity and fluid intelligence. Intelligence, 52, 18–23.CrossRefGoogle Scholar
Gladwell, M. (2008). Outliers: The story of success. New York: Little, Brown & Co.Google Scholar
Gottfredson, L. S. (1997). Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography. Intelligence, 24(1), 13–23.CrossRefGoogle Scholar
Haier, R. J. (2017). The neuroscience of intelligence. New York: Cambridge University Press.Google Scholar
Haier, R. J. & Jung, R. E. (2007). Beautiful minds (i.e., brains) and the neural basis of intelligence. Behavioral and Brain Sciences, 30(2), 174–178.Google Scholar
Haier, R. J., Siegel, B. V. Jr, Nuechterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., … Buchsbaum, M. S. (1988). Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12(2), 199–217.Google Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavior & Brain Sciences, 33(2–3), 61–83.Google Scholar
Horn, J. L., & Cattell, R. B. (1966). Refinement and test of the theory of fluid and crystallized general intelligences. Journal of Educational Psychology, 57(5), 253–270.CrossRefGoogle ScholarPubMed
Jensen, A. R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger.Google Scholar
Jensen, A. R. (2006). Clocking the mind: Mental chronometry and individual differences. Amsterdam, The Netherlands: Elsevier.Google Scholar
Johnson, W., Bouchard, T. J. Jr, Krueger, R. F., McGue, M., & Gottesman, I. I. (2004). Just one g: Consistent results from three test batteries. Intelligence, 32(1), 95–107.Google Scholar
Johnson, W., te Nijenhuis, J., & Bouchard, T. J. Jr. (2008). Still just 1 g: Consistent results from five test batteries. Intelligence, 36(1), 81–95.Google Scholar
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.Google Scholar
Kuncel, N. R., & Hezlett, S. A. (2007). Standardized tests predict graduate students’ success. Science, 315(5815), 1080–1081.Google Scholar
Lee, J. J., Wedow, R., Okbay, A., Kong, O., Maghzian, M., Zacher, M., … Cesarini, D. (2018). Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment. Nature Genetics, 50(8), 1112–1121.Google Scholar
Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C. & Jiang, T. (2009). Brain anatomical network and intelligence. PLoS Computational Biology, 5(5), 1–17.Google Scholar
Lubinski, D. (2016). From Terman to today: A century of findings on intellectual precocity. Review of Educational Research, 86(4), 900–944.Google Scholar
Major, J. T., Johnson, W., & Bouchard, T. J. (2011). The dependability of the general factor of intelligence: Why small, single-factor models do not adequately represent g. Intelligence, 39(5), 418–433.CrossRefGoogle Scholar
McDaniel, M. A. (2005). Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence, 33(4), 337–346.Google Scholar
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.CrossRefGoogle Scholar
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49–100.Google Scholar
Nave, G., Jung, W. H., Linnér, R. K., Kable, J. W., & Koellinger, P. D. (2019). Are bigger brains smarter? Evidence from a large-scale preregistered study. Psychological Science, 30(1), 43–54.Google Scholar
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33(7), 1004–1023.CrossRefGoogle ScholarPubMed
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 and Biobehavioral Reviews, 57, 411–432.Google Scholar
Ree, M. J., Earles, J. A., & Teachout, M. S. (1994). Predicting job performance: Not much more than g. Journal of Applied Psychology, 79(4), 518–524.CrossRefGoogle Scholar
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.Google Scholar
Sackett, P. R., Kuncel, N. R., Arneson, J. J., Cooper, S. R., & Waters, S. D. (2009). Does socioeconomic status explain the relationship between admissions tests and post-secondary academic performance? Psychological Bulletin, 135(1), 1–22.Google Scholar
Santarnecchi, E., Galli, G., Polizzotto, N. R., Rossi, A., & Rossi, S. (2014). Efficiency of weak brain connections support general cognitive functioning. Human Brain Mapping, 35(9), 4566–4582.CrossRefGoogle ScholarPubMed
Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype ➔ environment effects. Child Development, 54(2), 424–435.Google Scholar
Scarr-Salapatek, S. (1971). Race, social class, and IQ. Science, 174(4016), 1285–1295.Google Scholar
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.Google Scholar
Schmidt, F. L., & Hunter, J. E. (2004). General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86(1), 162–173.Google Scholar
Spearman, C. (1927). The abilities of man: Their nature and measurement. New York: The Macmillan Company.Google Scholar
Taubert, M., Draganski, B., Anwander, A., Müller, K., Horstmann, A., Villringer, A., & Ragert, P. (2010). Dynamic properties of human brain structure: Learning-related changes in cortical areas and associated fiber connections. Journal of Neuroscience, 30(35), 11670–11677.Google Scholar
Thomas, C., & Baker, C. I. (2013). Teaching an adult brain new tricks: A critical review of evidence for training-dependent structural plasticity in humans. Neuroimage, 73, 225–236.Google Scholar
Thorndike, R. L. (1984). Intelligence as information processing: The mind and the computer. Bloomington, IN: Center on Evaluation, Development, and Research.Google Scholar
Tucker-Drob, E. M. (2009). Differentiation of cognitive abilities across the life span. Developmental Psychology, 45(4), 1097–1118.Google Scholar
Tucker-Drob, E. M., & Bates, T. C. (2015). Large cross-national differences in gene × socioeconomic status interaction on intelligence. Psychological Science, 27(2), 138–149.Google Scholar
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.Google Scholar
Warne, R. T., & Burningham, C. (2019). Spearman’s g found in 31 non-Western nations: Strong evidence that g is a universal phenomenon. Psychological Bulletin, 145(3), 237–272.CrossRefGoogle ScholarPubMed
Wechsler, D. (1944). The measurement of adult intelligence (3rd ed.). Baltimore, MD: Williams & Wilkins.Google Scholar
Woodley of Menie, M. A., Pallesen, J., & Sarraf, M. A. (2018). Evidence for the Scarr-Rowe effect on genetic expressivity in a large U.S. sample. Twin Research and Human Genetics, 21(6), 495–501.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×