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-5447f9dfdb-5flt4 Total loading time: 0 Render date: 2025-07-30T04:20:38.001Z Has data issue: false hasContentIssue false

Chapter 4 - Cognitive Models of Intelligence and Information Processing

Published online by Cambridge University Press:  03 August 2023

Richard J. Haier ,
Roberto Colom  and
Earl Hunt
Richard J. Haier
Affiliation:
University of California, Irvine
Roberto Colom
Affiliation:
Universidad Autónoma de Madrid
Earl Hunt
Affiliation:
University of Washington
Get access

Summary

Psychometric models do not explain the processes that underlie thinking. They are not intended to do so, but they nevertheless contribute to understanding intelligence. This has been the case since at least 1923, when Charles Spearman wrote The Nature of Intelligence and the Principles of Cognition. As Sternberg (2016, p. 236) highlighted, “Spearman believed that apprehension of experience, education of relations, and education of correlates are the basic overlapping information processes of intelligence. … The great psychometricians of all time – Spearman and Carroll – were also astute cognitive psychologists.”

Information

Type
Chapter
Information
The Science of Human Intelligence , pp. 84 - 121
Publisher: Cambridge University Press
Print publication year: 2023

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.)

Book purchase

Temporarily unavailable

References

Ackerman, P. L. 2016. Process overlap and g do not adequately account for a general factor of intelligence. Psychological Inquiry, 27, 178–180.Google Scholar
Ackerman, P. L., Beier, M. E., & Boyle, M. O. 2002. Individual differences in working memory within a nomological network of cognitive and perceptual speed abilities. Journal of Experimental Psychology: General, 131, 567–589.CrossRefGoogle ScholarPubMed
Ackerman, P. L., Beier, M. E., & Boyle, M. O. 2005. Working memory and intelligence: The same or different constructs? Psychological Bulletin, 131, 30–60.Google Scholar
Anderson, N. H. 1996. Cognitive algebra versus representativeness heuristic. Behavioral and Brain Sciences, 19, 17.Google Scholar
Baddeley, A. D. 1986. Working memory. New York: Oxford University Press.Google Scholar
Baddeley, A. D., & Hitch, G. 1974. Working memory. In Bower, G. (ed.), Recent advances in learning and motivation. New York: Academic Press.Google Scholar
Bartholomew, D. J., Allerhand, M., & Deary, I. J. 2013. Measuring mental capacity: Thomson’s Bonds model and Spearman’s g-model compared. Intelligence, 41, 222–233.Google Scholar
Blanch, A. 2021. Chess and individual differences. New York: Cambridge University Press.Google Scholar
Botella, J., Privado, J., Suero, M., Colom, R., & Juola, J. F. 2019. Group analyses can hide heterogeneity effects when searching for a general model: Evidence based on a conflict monitoring task. Acta Psychologica, 193, 171–179.Google Scholar
Bowren, M., Adolphs, R., Bruss, J., et al. 2020. Multivariate lesion-behavior mapping of general cognitive ability and its psychometric constituents. Journal of Neuroscience, 40, 8924–8937.Google Scholar
Burton, L. J., & Fogarty, G. J. 2003. The factor structure of visual imagery and spatial abilities. Intelligence, 31, 289–318.Google Scholar
Carroll, J. B. 1993. Human cognitive abilities: A survey of factor-analytic studies. New York: Cambridge University Press.CrossRefGoogle Scholar
Chuderski, A. 2013. When are fluid intelligence and working memory isomorphic and when are they not? Intelligence, 41, 244–262.Google Scholar
Chuderski, A. 2014. The relational integration task explains fluid reasoning above and beyond other working memory tasks. Memory and Cognition, 42, 448–63.Google Scholar
Chuderski, A. 2019. Even a single trivial binding of information is critical for fluid intelligence. Intelligence, 77, 101396.Google Scholar
Chuderski, A. 2022. Fluid intelligence emerges from representing relations. Journal of Intelligence, 10, 51.Google Scholar
Chuderski, A., Taraday, M., Necka, E., & Smolen, T. 2012. Storage capacity explains fluid intelligence but executive control does not. Intelligence, 40, 278–295.Google Scholar
Colom, R. 2016. Advances in intelligence research: What should be expected in the XXI century (questions and answers). Spanish Journal of Psychology, 19, 1–8.Google Scholar
Colom, R., Abad, F. J., Quiroga, M. Á., Shih, P. C., & Flores-Mendoza, C. 2008. Working memory and intelligence are highly related constructs, but why? Intelligence, 36, 584–606.Google Scholar
Colom, R., Chuderski, A., & Santarnecchi, E. 2016. Bridge over troubled water: Commenting on Kovacs and Conway’s process overlap theory. Psychological Inquiry, 27, 181–189.Google Scholar
Colom, R., Privado, J., Garcia, L. F., et al. 2015. Fluid intelligence and working memory capacity: Is the time for working on intelligence problems relevant for explaining their large relationship? Personality and Individual Differences, 79, 75–80.Google Scholar
Colom, R., Rebollo, I., Abad, F. J., & Shih, P. C. 2006a. Complex span tasks, simple span tasks, and cognitive abilities: A reanalysis of key studies. Memory and Cognition, 34, 158–171.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, 277–296.Google Scholar
Colom, R., Shih, P. C., Flores-Mendoza, C., & Quiroga, M. Á. 2006b. The real relationship between short-term memory and working memory. Memory, 14, 804–813.Google Scholar
Cowan, N. 2001. The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–114; discussion 114–185.Google Scholar
Cowan, N. 2016. Process overlap theory and first principles of intelligence testing. Psychological Inquiry, 27, 190–191.CrossRefGoogle ScholarPubMed
Cowan, N. 2017. The many faces of working memory and short-term storage. Psychonomic Bulletin and Review, 24, 1158–1170.Google Scholar
Cronbach, L. J. 1957. The 2 disciplines of scientific psychology. American Psychologist, 12, 671–684.CrossRefGoogle Scholar
Daneman, M., & Merikle, P. M. 1996. Working memory and language comprehension: A meta-analysis. Psychonomic Bulletin & Review, 3, 422–433.Google Scholar
Deary, I. J. 2000. Looking down on human intelligence: From psychometrics to the brain. Oxford: Oxford University Press.Google Scholar
Deary, I. J., Cox, S. R., & Ritchie, S. J. 2016. Getting Spearman off the skyhook: One more in a century (since Thomson, 1916) of attempts to vanquish g. Psychological Inquiry, 27, 192–199.Google Scholar
Deary, I. J., & Der, G. 2005. Reaction time, age, and cognitive ability: Longitudinal findings from age 16 to 63 years in representative population samples. Aging Neuropsychology and Cognition, 12, 187–215.CrossRefGoogle Scholar
Demetriou, A., Kui, Z. X., Spanoudis, G., et al. 2005. The architecture, dynamics, and development of mental processing: Greek, Chinese, or universal? Intelligence, 33, 109–141.Google Scholar
Detterman, D. K. 1994. Theoretical possibilities: The relation of human intelligence to basic cognitive abilities. In Detterman, D. K. (ed.), Current topics in human intelligence: Vol. 4. Theories of intelligence. Norwood, NJ: Ablex.Google Scholar
Detterman, D. K. 2000. General intelligence and the definition of phenotypes. In Bock, G. R., Good, J. A., & Webb, K. (eds.), The nature of intelligence. New York: John Wiley.Google Scholar
Detterman, D. K., & Daniel, M. H. 1989. Correlations of mental tests with each other and with cognitive variables are highest for low IQ groups. Intelligence, 13, 349–359.Google Scholar
Detterman, D. K., Petersen, E., & Frey, M. C. 2016. Process overlap and system theory: A simulation of, comment on, and integration of Kovacs and Conway. Psychological Inquiry, 27, 200–204.Google Scholar
Duncan, J., Chylinski, D., Mitchell, D. J., & Bhandari, A. 2017. Complexity and compositionality in fluid intelligence. Proceedings of the National Academy of Sciences of the United States of America, 114, 5295–5299.Google Scholar
Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. 1999. Working memory, short-term memory, and general fluid intelligence: A latent-variable approach. Journal of Experimental Psychology: General, 128, 309–331.Google Scholar
Flynn, J. R. 2016. No population is frozen in time: The sociology of intelligence. Psychological Inquiry, 27, 205–209.Google Scholar
Friedman, N. P., Miyake, A., Corley, R. P., et al. 2006. Not all executive functions are related to intelligence. Psychological Science, 17, 172–179.Google Scholar
Frischkorn, G. T., Wilhelm, O., & Oberauer, K. 2022. Process-oriented intelligence research: A review from the cognitive perspective. Intelligence, 94, 101681.Google Scholar
Gignac, G. E. 2006. Evaluating subtest ‘g’ saturation levels via the single trait-correlated uniqueness (STCU) SEM approach: Evidence in favor of crystallized subtests as the best indicators of ‘g.’ Intelligence, 34, 29–46.Google Scholar
Goldberg, R. A., Schwartz, S., & Stewart, M. 1977. Individual differences in cognitive processes. Journal of Educational Psychology, 69, 9–14.Google Scholar
Gottfredson, L. S. 2016. A g theorist on why Kovacs and Conway’s process overlap theory amplifies, not opposes, g theory. Psychological Inquiry, 27, 210–217.Google Scholar
Grandy, T. H., Lindenberger, U., & Werkle-Bergner, M. 2017. When group means fail: Can one size fit all? bioRxiv, 126490; doi: https://doi.org/10.1101/126490.Google Scholar
Haier, R. J. 2017. The neuroscience of intelligence. New York: Cambridge University Press.Google Scholar
Haier, R. J., Colom, R., Schroeder, D. H., et al. 2009. Gray matter and intelligence factors: Is there a neuro-g? Intelligence, 37, 136–144.Google Scholar
Haier, R. J., & Jung, R. E. 2016. The psychometric brain. Psychological Inquiry, 27, 218–219.Google Scholar
Halford, G. S., Cowan, N., & Andrews, G. 2007. Separating cognitive capacity from knowledge: A new hypothesis. Trends in Cognitive Sciences, 11, 236–242.Google Scholar
Hegarty, M., Just, M. A., & Morrison, I. R. 1988. Mental models of mechanical systems: Individual-differences in qualitative and quantitative reasoning. Cognitive Psychology, 20, 191–236.Google Scholar
Hegarty, M., & Waller, D. A. 2005. Individual differences in spatial abilities. In Miyake, P. S. A. (ed.), The Cambridge handbook of visuospatial thinking. New York: Cambridge University Press.Google Scholar
Heitz, R. P., Unsworth, N., & Engle, R. W. 2005. Working memory capacity, attention control, and fluid intelligence. In Engle, O. W. R. W. (ed.), Handbook of understanding and measuring intelligence. Thousand Oaks, CA: SAGE.Google Scholar
Hornung, C., Brunner, M., Reuter, R. A. P., & Martin, R. 2011. Children’s working memory: Its structure and relationship to fluid intelligence. Intelligence, 39, 210–221.Google Scholar
Hunt, E. 1980. Intelligence as an information-processing concept. British Journal of Psychology, 71, 449–74.Google Scholar
Hunt, E. 1987. The next word on verbal ability. In Vernon, P. A. (ed.), Speed of information processing and intelligence. New York: Ablex.Google Scholar
Hunt, E., & Agnoli, F. 1991. The Whorfian hypothesis – a cognitive-psychology perspective. Psychological Review, 98, 377–389.Google Scholar
Hunt, E., Frost, N., & Lunneborg, C. E. 1973. Individual differences in cognition: A new approach to intelligence. In Bower, G. S. (ed.), Advances in learning and motivation. New York: Academic Press.Google Scholar
Hunt, E., & Lansman, M. 1986. Unified model of attention and problem-solving. Psychological Review, 93, 446–461.Google Scholar
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. New York: Elsevier.Google Scholar
Johnson, W., & Bouchard, T. J., Jr. 2005. The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized. Intelligence, 33, 393–416.Google Scholar
Jolly, A. E., Scott, G. T., Sharp, D. J., & Hampshire, A. H. 2020. Distinct patterns of structural damage underlie working memory and reasoning deficits after traumatic brain injury. Brain, 143, 1158–1176.Google Scholar
Juan-Espinosa, M., Abad, F. J., Colom, R., & Fernandez-Truchaud, M. 2000. Individual differences in large-spaces orientation: g and beyond? Personality and Individual Differences, 29, 85–98.Google Scholar
Jung, R. E., & Haier, R. J. 2007. The parieto-frontal integration theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Science, 30, 135–154; discussion 154–187.Google Scholar
Just, M. A., & Carpenter, P. A. 1992. A capacity theory of comprehension: Individual-differences in working memory. Psychological Review, 99, 122–149.Google Scholar
Kan, K.-J., van der Maas, H. L. J., & Kievit, R. A. 2016. Process overlap theory: Strengths, limitations, and challenges. Psychological Inquiry, 27, 220–228.CrossRefGoogle Scholar
Kane, M. J., Hambrick, D. Z., Conway, A. R. A., & Engle, R. W. 2001. The generality of working memory capacity: A latent variable approach. Journal of Experimental Psychology: General, 130, 169–183.Google Scholar
Kane, M. J., Hambrick, D. Z., Tuholski, S. W., et al. 2004. The generality of working memory capacity: A latent-variable approach to verbal and visuospatial memory span and reasoning. Journal of Experimental Psychology: General, 133, 189–217.Google Scholar
Kaufman, S. B. 2016. Commentary on Kovacs and Conway, process overlap theory: A unified account of the general factor of intelligence. Psychological Inquiry, 27, 229–230.Google Scholar
Kintsch, W. 1998. Comprehension: A paradigm for cognition. New York: Cambridge University Press.Google Scholar
Kosslyn, S. M. 1980. Mental images. Recherche, 11, 156–163.Google Scholar
Kovacs, K., & Conway, A. R. A. 2016. Process overlap theory: A unified account of the general factor of intelligence. Psychological Inquiry, 27, 151–177.Google Scholar
Kronman, A. T. 2007. Against political correctness: A liberal’s cri de coeur. Yale Alumni Magazine.Google Scholar
Kyllonen, P. C., & Christal, R. E. 1990. Reasoning ability is (little more than) working-memory capacity. Intelligence, 14, 389–433.Google Scholar
Lerche, V., Von Krause, M., Voss, A., et al. 2020. Diffusion modeling and intelligence: Drift rates show both domain-general and domain-specific relations with intelligence. Journal of Experimental Psychology: General, 149, 2207–2249.Google Scholar
Lohman, D. 2000. Complex information processing and intelligence. In Sternberg, R. J. (ed.), Handbook of intelligence. New York: Cambridge University Press.Google Scholar
Macdonald, M. C., Just, M. A., & Carpenter, P. A. 1992. Working memory constraints on the processing of syntactic ambiguity. Cognitive Psychology, 24, 56–98.CrossRefGoogle ScholarPubMed
Martínez, K., Burgaleta, M., Román, F. J., et al. 2011. Can fluid intelligence be reduced to “simple” short-term storage? Intelligence, 39, 473–480.Google Scholar
Mervis, C. B., Robinson, B. F., Rowe, M. L., Becerra, A. M., & Klein-Tasman, B. R. 2003. Language abilities of individuals with Williams syndrome. International Review of Research in Mental Retardation, 27, 35–81.Google Scholar
Meyer, D. E., & Kieras, D. E. 1997a. A computational theory of executive cognitive processes and multiple-task performance. 1. Basic mechanisms. Psychological Review, 104, 3–65.Google Scholar
Meyer, D. E., & Kieras, D. E. 1997b. A computational theory of executive cognitive processes and multiple-task performance. 2. Accounts of psychological refractory-period phenomena. Psychological Review, 104, 749–791.Google Scholar
Miyake, A., Friedman, N. P., Emerson, M. J., et al. 2000. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100.CrossRefGoogle ScholarPubMed
Nettelbeck, , T. 2001. Correlation between inspection time and psychometric abilities: A personal interpretation. Intelligence, 29, 459–474.Google Scholar
Newell, , A. 1990. Better models of the cognitive agent: Some prospects for management and organizational science. Mathematical Social Sciences, 20, 309–309.Google Scholar
Nosek, B. A., Hardwicke, T. E., Moshontz, H., et al. 2022. Replicability, robustness, and reproducibility in psychological science. Annual Review of Psychology, 73, 719–748.Google Scholar
Oberauer, K. 2003. Selective attention to elements in working memory. Experimental Psychology, 50, 257–269.Google Scholar
Oberauer, K. 2016. Parameters, not processes, explain general intelligence. Psychological Inquiry, 27, 231–235.Google Scholar
Oberauer, K., Schulze, R., Wilhelm, O., & Suss, H. M. 2005. Working memory and intelligence – their correlation and their relation: Comment on Ackerman, Beier, and Boyle (2005). Psychological Bulletin, 131, 61–65.Google Scholar
Palmer, J., Macleod, C. M., Hunt, E., & Davidson, J. E. 1985. Information-processing correlates of reading. Journal of Memory and Language, 24, 59–88.Google Scholar
Poltrock, S. E., & Brown, P. 1984. Individual-differences in visual-imagery and spatial ability. Intelligence, 8, 93–138.Google Scholar
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.Google Scholar
Rey-Mermet, A., Gade, M., Souza, A. S., Von Bastian, C. C., & Oberauer, K. 2019. Is executive control related to working memory capacity and fluid intelligence? Journal of Experimental Psychology: General, 148, 1335–1372.Google Scholar
Rimfeld, K., Shakeshaft, N. G., Malanchini, M., et al. 2017. Phenotypic and genetic evidence for a unifactorial structure of spatial abilities. Proceedings of the National Academy of Sciences of the United States of America, 114, 2777–2782.Google Scholar
Román, F. J., Abad, F. J., Escorial, S., et al. 2014. Reversed hierarchy in the brain for general and specific cognitive abilities: A morphometric analysis. Human Brain Mapping, 35, 3805–3818.Google Scholar
Salthouse, T. A. 1996. The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403–428.Google Scholar
Schleicher, A. 2018. PISA 2018. www.oecd.org/pisa/PISA%202018%20Insights%20and%20Interpretations%20FINAL%20PDF.pdf.Google Scholar
Schneider, W. J., & McGrew, K. S. 2019. Process overlap theory is a milestone achievement among intelligence theories. Journal of Applied Research in Memory and Cognition, 8, 273–276.Google Scholar
Schubert, A.-L., & Frischkorn, G. T. 2020. Neurocognitive psychometrics of intelligence: How measurement advancements unveiled the role of mental speed in intelligence differences. Current Directions in Psychological Science, 29, 140–146.Google Scholar
Schubert, A.-L., Hagemann, D., & Frischkorn, G. T. 2017. Is general intelligence little more than the speed of higher-order processing? Journal of Experimental Psychology: General, 146, 1498–1512.Google Scholar
Shahabi, S. R., Abad, F. J., & Colom, R. 2014. Short-term storage is a stable predictor of fluid intelligence whereas working memory capacity and executive function are not: A comprehensive study with Iranian schoolchildren. Intelligence, 44, 134–141.Google Scholar
Spearman, C. 1904. General intelligence objectively determined and measured. American Journal of Psychology, 15, 201–293.Google Scholar
Spearman, C. 1923. The nature of “intelligence” and the principles of cognition. London: Macmillan.Google Scholar
Sternberg, R. J. 2016. Groundhog Day: Is the field of human intelligence caught in a time warp? A comment on Kovacs and Conway. Psychological Inquiry, 27, 236–240.Google Scholar
Thomson, G. H. 1916. A hierarchy without a general factor. British Journal of Psychology, 8, 271–281.Google Scholar
Unsworth, N., & Engle, R. W. 2007. On the division of short-term and working memory: An examination of simple and complex span and their relation to higher order abilities. Psychological Bulletin, 133, 1038–1066.Google Scholar
Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. 2014. Working memory and fluid intelligence: Capacity, attention control, and secondary memory retrieval. Cognitive Psychology, 71, 1–26.Google Scholar
Waller, D., Beall, A. C., & Loomis, J. M. 2004. Using virtual environments to assess directional knowledge. Journal of Environmental Psychology, 24, 105–116.Google Scholar
Waller, D., Knapp, D., & Hunt, E. 2001. Spatial representations of virtual mazes: the role of visual fidelity and individual differences. Human Factors, 43, 147–58.Google Scholar
Wang, T., Li, C., Ren, X., & Schweizer, K. 2021. How executive processes explain the overlap between working memory capacity and fluid intelligence: A test of process overlap theory. Journal of Intelligence, 9, 21.Google Scholar
Willoughby, E. A., & Lee, J. J. 2021. Parsing information flow in speeded cognitive tasks: The role of g in perception and decision time. Journal of Experimental Psychology: Learning, Memory, and Cognition, 47, 1792–1809.Google Scholar

Accessibility standard: Unknown

Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@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
×