Abstract
An extensive literature has developed over the nature of human intellectual functioning, with more than a century of research to date. This chapter surveys this field, starting with its historical roots and following the trajectory of these ideas to contemporary views on the nature and structure of intelligence. Dating back to Spearman’s and Thurstone’s original factor-analytic studies, there has been an ongoing debate as to whether intelligence is best captured by one unitary factor (psychometric g), a group of independent broad abilities, or another alternative. Whereas structural models emphasize the interrelationships among different abilities (e.g., verbal and visuospatial skills, speed of processing, etc.), conceptual theories have been more concerned with the fundamental nature of intelligence. Current issues in the field concern the origins and significance of g, the centrality of particular cognitive processes to overall intelligence, and the debate between reductive and more holistic views. The chapter concludes with an overview of the best-supported principles regarding theories of intelligence, along with their implications for intellectual disabilities.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The name “two-factor” theory can be somewhat confusing, in the sense that it strongly emphasizes the importance of the single g factor. Nevertheless, the name refers to the assertion that any task involves contributions from two factors: g, which is common to all tasks, and s, which is unique to particular tests.
- 2.
- 3.
- 4.
Primary simulations were based on 16 hypothetical neurocognitive processes, which were each independently sampled from pre-specified distributions to define 1000 simulated subjects. Different models specified various constraints and relationships among model parameters.
- 5.
Though see Deary’s (2000, Chap. 2, and p. 68) account of several authors who espoused similar views but predated Galton
References
Acton, G. S., & Schroeder, D. H. (2001). Sensory discrimination as related to general intelligence. Intelligence, 29(3), 263–271. https://doi.org/10.1016/S0160-2896(01)00066-6
Almeida, L. S., Prieto, M. D., Ferreira, A. I., Bermejo, M. R., Ferrando, M., & Ferrándiz, C. (2010). Intelligence assessment: Gardner multiple intelligence theory as an alternative. Learning and Individual Differences, 20(3), 225–230. https://doi.org/10.1016/j.lindif.2009.12.010
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5). Arlington, VA: American Psychiatric Pub.
Barkley, R. A., & Fischer, M. (2011). Predicting impairment in major life activities and occupational functioning in hyperactive children as adults: Self-reported executive function (EF) deficits versus EF tests. Developmental Neuropsychology, 36(2), 137–161. https://doi.org/10.1080/87565641.2010.549877
Bartholomew, D. J. (2004). Measuring intelligence: Facts and fallacies. Retrieved from https://books.google.com/books?hl=en&lr=&id=sPaS7R5-Wk4C&oi=fnd&pg=PR9&dq=Bartholomew+measuring+intelligencce&ots=33RGo6Zgnb&sig=X3H3VntF0Fa1Re0Vx6PFgel10Zs
Bartholomew, D. J., Allerhand, M., & Deary, I. J. (2013). Measuring mental capacity: Thomson’s bonds model and Spearman’s g-model compared. Intelligence, 41(4), 222–233. https://doi.org/10.1016/j.intell.2013.03.007
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.
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
Beaujean, A. (2015). John Carroll’s views on intelligence: Bi-factor vs. higher-order models. Journal of Intelligence, 3(4), 121–136. https://doi.org/10.3390/jintelligence3040121
Bellgrove, M. A., Hester, R., & Garavan, H. (2004). The functional neuroanatomical correlates of response variability: Evidence from a response inhibition task. Neuropsychologia, 42(14), 1910–1916. https://doi.org/10.1016/j.neuropsychologia.2004.05.007
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 c. Intelligence, 46, 73–83.
Benson, N., Hulac, D. M., & Kranzler, J. H. (2010). Independent examination of the Wechsler adult intelligence scale—Fourth edition (WAIS-IV): What does the WAIS-IV measure? Psychological Assessment, 22(1), 121–130.
Binet, A., & Henri, V. (1894). Le développement de la mémoire visuelle chez les enfants. Revue Générale Des Sciences Pures et Appliquées, 5, 162–169. Retrieved from https://scholar.google.com/scholar?hl=en&as_sdt=0%2C45&q=binet+henri+1894+visuelle&btnG=#d=gs_cit&p=&u=%2Fscholar%3Fq%3Dinfo%3AQJ9OesbTwCkJ%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D0%26hl%3Den
Binet, A., & Simon, T. (1916). The development of intelligence in children: The Binet-Simon scale. In J. J. Jenkins & D. G. Paterson (Eds.), Studies in individual differences: The search for intelligence (pp. 81–111). East Norwalk, CT: Appleton-Century-Crofts. https://doi.org/10.1037/11491-008
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
Boake, C. (2002). From the Binet-Simon to the Wechsler-Bellevue: Tracing the history of intelligence testing. Journal of Clinical and Experimental Neuropsychology, 24(3), 383–405. https://doi.org/10.1076/jcen.24.3.383.981
Borsboom, D., Mellenbergh, G. J., & Van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110(2), 203–219. https://doi.org/10.1037/0033-295X.110.2.203
Brody, N. (2003). Construct validation of the Sternberg Triarchic abilities test comment and reanalysis. Intelligence, 31(4), 319–329. https://doi.org/10.1016/S0160-2896(01)00087-3
Burt, C. (1940). The factors of the mind. London: University of London Press.
Carroll, J. (1993). Human cognitive abilities: A survey of factor-analytic studies. Retrieved from https://books.google.com/books?hl=en&lr=&id=jp9dt4_0_cIC&oi=fnd&pg=PA3&dq=Carroll+higher+order+cognitive+ability&ots=dBCUQdPkR3&sig=11w_WN2WUrtcYQbni9H0F8ZI78U
Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1.
Cattell, R. B. (1987). Intelligence: Its structure, growth, and action. North-Holland. Retrieved from https://books.google.com/books?hl=en&lr=&id=flX770mG2HcC&oi=fnd&pg=PP1&dq=Catell+intelligence+growth,+structure,+action&ots=8WbUnyTDvG&sig=7E5O5M7XplQWUHXrinFn2HaBBbM#v=onepage&q=Catell intelligence growth%2C structure%2C action&f=false.
Chooi, W.-T., Long, H., & Thompson, L. (2014). The Sternberg Triarchic abilities test (level-H) is a measure of g. Journal of Intelligence, 2(3), 56–67. https://doi.org/10.3390/jintelligence2030056
Clark, C., Prior, M., & Kinsella, G. (2002). The relationship between executive function abilities, adaptive, behaviour, and academic achievement in children with externalising behaviour problems. Journal of Child Psychology and Psychiatry and Allied Disciplines, 43(6), 785–796. https://doi.org/10.1111/1469-7610.00084
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. https://doi.org/10.1016/j.intell.2003.12.002
Coslett, H. B. (2003). Acquired dyslexia. In K. M. Heilman & E. Valenstein (Eds.), Clinical Neuropsychology (4th ed., pp. 108–128). New York: Oxford.
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. https://doi.org/10.1016/j.intell.2012.12.001
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. https://doi.org/10.1016/j.intell.2015.05.003
Cucina, J., & Byle, K. (2017). The Bifactor model fits better than the higher-order model in more than 90% of comparisons for mental abilities test batteries. Journal of Intelligence, 5(3), 27. https://doi.org/10.3390/jintelligence5030027
Deary, I. J. (2000). Looking down on human intelligence: From psychometrics to the brain (Oxford Psychology Series). Oxford: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198524175.001.0001
Deary, I. J. (2012). Intelligence. Annual Review of Psychology, 63(1), 453–482. https://doi.org/10.1146/annurev-psych-120710-100353
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(3), 192–199. https://doi.org/10.1080/1047840X.2016.1186525
Deary, I. J., Der, G., & Ford, G. (2001). Reaction times and intelligence differences: A population-based cohort study. Intelligence, 29(5), 389–399. https://doi.org/10.1016/S0160-2896(01)00062-9
Demetriou, A., Spanoudis, G., Shayer, M., Mouyi, A., Kazi, S., & Platsidou, M. (2013). Intelligence cycles in speed-working memory-G relations: Towards a developmental – Differential theory of the mind. Intelligence, 41(1), 34–50. https://doi.org/10.1016/j.intell.2012.10.010
Detterman, D. K. (1987). Theoretical notions of intelligence and mental retardation. American Journal of Mental Deficiency, 92(1), 2–11. Retrieved from http://psycnet.apa.org/record/1987–31598-001.
Detterman, D. K. (2002). General intelligence: Cognitive and biological explanations. In R. J. Sternberg & E. L. Grigorenko (Eds.), The general factor of intelligence: How general is it (pp. 223–243). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.
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(4), 349–359. https://doi.org/10.1016/S0160-2896(89)80007-8
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(3), 200–204. https://doi.org/10.1080/1047840X.2016.1181514
Doebler, P., & Scheffler, B. (2016). The relationship of choice reaction time variability and intelligence: A meta-analysis. Learning and Individual Differences, 52, 157–166. https://doi.org/10.1016/j.lindif.2015.02.009
Duckworth, A. L., Quinn, P. D., Lynam, D. R., Loeber, R., & Stouthamer-Loeber, M. (2011). Role of test motivation in intelligence testing. Proceedings of the National Academy of Sciences of the United States of America, 108(19), 7716–7720.
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14(4), 172–179. https://doi.org/10.1016/j.tics.2010.01.004
Edgin, J. O. (2013). Cognition in down syndrome: A developmental cognitive neuroscience perspective. Wiley Interdisciplinary Reviews: Cognitive Science, 4(3), 307–317. https://doi.org/10.1002/wcs.1221
Euler, M. J., McKinney, T. L., Schryver, H. M., & Okabe, H. (2017). ERP correlates of the decision time-IQ relationship: The role of complexity in task- and brain-IQ effects. Intelligence, 65, 1–10. https://doi.org/10.1016/j.intell.2017.08.003
Fox, M. C., & Mitchum, A. L. (2012). A knowledge-based theory of rising scores on “culture-free” tests. Journal of Experimental Psychology: General, 142(3), 979–1000.
Galton, F. (1883). Inquiries into human faculty and its development. Inquiries into Human Faculty and Its Development. Galton: https://doi.org/10.1037/10913-000
Galton, F. (1907). Inquiries into human faculty and its development (2nd ed.). London: Dent.
Gardner, H. (2006). Frames of mind: The theory of multiple intelligences. New York: Basic Books.
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. (2016a). 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. (2016b). The higher-order model imposes a proportionality constraint: That is why the bifactor model tends to fit better. Intelligence, 55, 57–68. https://doi.org/10.1016/J.INTELL.2016.01.006
Gottfredson, L. S. (1997a). 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. (1997b). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79–132. https://doi.org/10.1016/S0160-2896(97)90014-3
Gottfredson, L. S. (2003). Dissecting practical intelligence theory: Its claims and evidence. Intelligence, 31(4), 343–397. https://doi.org/10.1016/S0160-2896(02)00085-5
Grigsby, J. (2016). The fragile X mental retardation 1 gene (FMR1): Historical perspective, phenotypes, mechanism, pathology, and epidemiology. The Clinical Neuropsychologist, 30(6), 815–833. https://doi.org/10.1080/13854046.2016.1184652
Gustafsson, J.-E. (1984). A unifying model for the structure of intellectual abilities. Intelligence, 8(3), 179–203. https://doi.org/10.1016/0160-2896(84)90008-4
Guttman, L. (1954). A new approach to factor analysis: The Radex. In P. Lazarsfeld (Ed.), Mathematical thinking in the social science (pp. 258–348). New York: The Free Press. Retrieved from http://doi.apa.org/psycinfo/1955–02329-001
Haier, R. J. (2016). The neuroscience of intelligence. Cambridge: Cambridge University Press.
Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4(1), 11–26.
Jauk, E., Benedek, M., Dunst, B., & Neubauer, A. C. (2013). The relationship between intelligence and creativity: New support for the threshold hypothesis by means of empirical breakpoint detection. Intelligence, 41(4), 212–221. https://doi.org/10.1016/j.intell.2013.03.003
Jensen, A. R. (1981). Reaction time and intelligence. Intelligence and Learning, 39–50.
Jensen, A. R. (1982). Reaction time and psychometric g. A Model for Intelligence, 93–132. https://doi.org/10.1007/978-3-642-68664-1_4
Jensen, A. R. (1992). The importance of intraindividual variation in reaction time. Personality and Individual Differences, 13(8), 869–881. https://doi.org/10.1016/0191-8869(92)90004-9
Jensen, A. R. (1993). Why is reaction time corelated with psychometric g? Current Directions in Psychological Science, 2(2), 53–56.
Jensen, A. R. (1998a). The g factor: The science of mental ability. Westport, CT: Praeger.
Jensen, A. R. (1998b). The suppressed relationship between IQ and the reaction time slope parameter of the Hick function. Intelligence, 26(l), 43–52.
Johnson, W., & Bouchard, T. J. (2005a). Constructive replication of the visual - perceptual-image rotation model in Thurstone’s (1941) battery of 60 tests of mental ability. Intelligence, 33(4), 417–430. https://doi.org/10.1016/j.intell.2004.12.001
Johnson, W., & Bouchard, T. J. (2005b). The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized. Intelligence, 33(4), 393–416.
Johnson, W., te Nijenhuis, J., & Bouchard, T. J. (2007). Replication of the hierarchical visual-perceptual-image rotation model in de Wolff and Buiten’s (1963) battery of 46 tests of mental ability. Intelligence, 35(1), 69–81. https://doi.org/10.1016/j.intell.2006.05.002
Jung, R. E., & Haier, R. J. (2007). The Parieto-frontal integration theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behav Brain Sci, 30(2), 135–187. https://doi.org/10.1017/S0140525X07001185
Kan, K. J., Kievit, R. A., Dolan, C., & van der Maas, H. (2011). On the interpretation of the CHC factor Gc. Intelligence, 39(5), 292–302.
Kan, K. J., Wicherts, J. M., Dolan, C. V., & van der Maas, H. L. J. (2013). On the nature and nurture of intelligence and specific cognitive abilities: The more heritable, the more culture dependent. Psychological Science, 24(12), 2420–2428. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/24104504
Kane, M. J., & Engle, R. W. (2002). The role of prefrontal cortex in working memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic Bulletin & Review, 9(4), 637–671.
Kane, M. J., & Engle, R. W. (2003). Working-memory capacity and the control of attention: The contributions of goal neglect, response competition, and task set to Stroop interference. Journal of Experimental Psychology: General, 132(1), 47–70. https://doi.org/10.1037/0096-3445.132.1.47
Keith, T., Kranzler, J., & Flanagan, D. (2001). What does the cognitive assessment system (CAS) measure? Joint confirmatory factor analysis of the CAS and the Woodcock-Johnson tests of cognitive ability (3rd edition). School Psychology Review, 30(1), 89–119.
Kievit, R. A., Lindenberger, U., Goodyer, I. M., Jones, P. B., Fonagy, P., Bullmore, E. T., & Dolan, R. J. (2017). Mutualistic coupling between vocabulary and reasoning supports cognitive development during late adolescence and early adulthood. Psychological Science, 28(10), 95679761771078. https://doi.org/10.1177/0956797617710785
Kievit, R. A., Romeijn, J.-W., Waldorp, L. J., Wicherts, J. M., Scholte, H. S., & Borsboom, D. (2011). Mind the gap: A psychometric approach to the reduction problem. Psychological Inquiry, 22(2), 67–87. https://doi.org/10.1080/1047840X.2011.550181
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
Kranzler, J. H., Benson, N., & Floyd, R. G. (2015). Using estimated factor scores from a bifactor analysis to examine the unique effects of the latent variables measured by the WAIS-IV on academic achievement. Psychological Assessment, 27(4), 1402–1416. https://doi.org/10.1037/pas0000119
Lee, D. G., & Harris, J. C. (2006). Intellectual disability: Understanding its development, causes, classification, evaluation, and treatment. New York, NY: Oxford University Press.
Lezak, M. D., Howieson, D. B., Bigler, E. D., & Tranel, D. (2012). Neuropsychological Assessment (5th ed.). New York: Oxford University Press.
Lichtenberger, E., & Kaufman, A. (2013). Essentials of WAIS-IV assessment (2nd ed.). Hoboken, NJ: John Wiley & Sons Inc. Retrieved from https://books.google.com/books?hl=en&lr=&id=4iHTzkqdQYQC&oi=fnd&pg=PR11&dq=lichtenberger+kaufman+essentials+WAIS-IV&ots=DoAKgKQZo0&sig=ngwhgFRGScK0HrpVyw7OI8-Gp4Y.
Lubinski, D. (2004). Introduction to the special section on cognitive abilities: 100 years after Spearman’s (1904) ‘general intelligence,’ objectively determined and measured. Journal of Personality and Social Psychology, 86(1), 96–111. https://doi.org/10.1037/0022-3514.86.1.96
Major, J. T., Johnson, W., & Deary, I. J. (2012). Comparing models of intelligence in project TALENT: The VPR model fits better than the CHC and extended Gf–Gc models. Intelligence, 40(6), 543–559. https://doi.org/10.1016/j.intell.2012.07.006
Makris, N., Tachmatzidis, D., Demetriou, A., & Spanoudis, G. (2017). Mapping the evolving core of intelligence: Changing relations between executive control, reasoning, language, and awareness. Intelligence, 62, 12–30. https://doi.org/10.1016/J.INTELL.2017.01.006
Marshalek, B., Lohman, D. F., & Snow, R. E. (1983). The complexity continuum in radex and hierarchical models of intelligence. Intelligence, 4, 107–127. Retrieved from http://www.sciencedirect.com/science/article/pii/0160289683900235
Matarazzo, J. D. (1972). Wechsler’s measurement and appraisal of adult intelligence (5th ed.). Oxford: Williams & Wilkins.
McDaniel, M. A., & Whetzel, D. L. (2005). Situational judgment test research: Informing the debate on practical intelligence theory. Intelligence, 33(5), 515–525. https://doi.org/10.1016/j.intell.2005.02.001
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
Melnick, M. D., Harrison, B. R., Park, S., Bennetto, L., & Tadin, D. (2013). A strong interactive link between sensory discriminations and intelligence. Current Biology, 23(11), 1013–1017.
Miyake, A., Friedman, N. P., Rettinger, D. A., Shah, P., & Hegarty, M. (2001). How are visuospatial working memory, executive functioning, and spatial abilities related? A latent-variable analysis. Journal of Experimental Psychology: General, 130(4), 621–640. https://doi.org/10.1037//0096-3445.130.4.621
Morgan, G., Hodge, K., Wells, K., & Watkins, M. (2015). Are fit indices biased in favor of bi-factor models in cognitive ability research?: A comparison of fit in correlated factors, higher-order, and bi-factor models via Monte Carlo simulations. Journal of Intelligence, 3(1), 2–20. https://doi.org/10.3390/jintelligence3010002
Murray, A. L., & Johnson, W. (2013). The limitations of model fit in comparing the bi-factor versus higher-order models of human cognitive ability structure. Intelligence, 41(5), 407–422. https://doi.org/10.1016/j.intell.2013.06.004
Naglieri, J. A., & Bornstein, B. T. (2003). Intelligence and achievement: Just how correlated are they? Journal of Psychoeducational Assessment, 21, 244–260.
Naglieri, J. A., & Das, J. P. (1990). Planning, attention, simultaneous, and successive (PASS) cognitive processes as a model for intelligence. Journal of Psychoeducational Assessment, 8(3), 303–337. https://doi.org/10.1177/073428299000800308
Naglieri, J. A., Goldstein, S., Delauder, B. Y., & Schwebach, A. (2005). Relationships between the WISC-III and the cognitive assessment system with Conners’ rating scales and continuous performance tests. Archives of Clinical Neuropsychology, 20(3), 385–401. https://doi.org/10.1016/j.acn.2004.09.008
Naglieri, J. A., Goldstein, S., Iseman, J. S., & Schwebach, A. (2003). Performance of children with attention deficit hyperactivity disorder and anxiety/depression on the WISC-III and cognitive assessment system (CAS). Journal of Psychoeducational Assessment, 21(1), 32–42. https://doi.org/10.1177/073428290302100103
Newton, J. H., & McGrew, K. S. (2010). Introduction to the special issue: Current research in Cattell-Horn-Carroll-based assessment. Psychology in the Schools, 47(7), 621–634. https://doi.org/10.1002/pits.20495
Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., & Turkheimer, E. (2012). Intelligence: New findings and theoretical developments. American Psychologist, 67(2), 130–159.
Oberauer, K., Schulze, R., Wilhelm, O., & Süß, H. (2005). Working memory and intelligence--their correlation and their relation: Comment on Ackerman, Beier, and Boyle. Retrieved from http://psycnet.apa.org/record/2004-22408-003
Papathanassiou, D., Etard, O., Mellet, E., Zago, L., Mazoyer, B., & Tzourio-Mazoyer, N. (2000). A common language network for comprehension and production: A contribution to the definition of language epicenters with PET. NeuroImage, 11(4), 347–357. https://doi.org/10.1006/nimg.2000.0546
Protzko, J. (2017). Effects of cognitive training on the structure of intelligence. Psychonomic Bulletin & Review, 24(4), 1022–1031. https://doi.org/10.3758/s13423-016-1196-1
Rammsayer, T. H., & Troche, S. (2016). Validity of the worst performance rule as a function of task complexity and psychometric g: On the crucial role of g saturation. Journal of Intelligence, 4(1), 5. Retrieved from http://www.mdpi.com/2079–3200/4/1/5/htm
Ratcliff, R., Schmiedek, F., & McKoon, G. (2008). A diffusion model explanation of the worst performance rule for reaction time and IQ. Intelligence, 36(1), 10–17. https://doi.org/10.1016/j.intell.2006.12.002
Raven, J. C., & Court, J. H. (1998). Raven’s progressive matrices and vocabulary scales. Oxford: Oxford Psychologists Press.
Redick, T. S., Shipstead, Z., Harrison, T. L., Hicks, K. L., Fried, D. E., Hambrick, D. Z.,... & Engle, R. W. (2013). No evidence of intelligence improvement after working memory training: a randomized, placebo-controlled study. Journal of Experimental Psychology: General, 142(2), 359. https://doi.org/10.1037/a0029082
Salthouse, T. A., & Davis, H. P. (2006). Organization of cognitive abilities and neuropsychological variables across the lifespan. Developmental Review, 26(1), 31–54. https://doi.org/10.1016/j.dr.2005.09.001
Sattler, J. M. (2008). Assessment of children: Cognitive foundations (5th ed.). San Diego, CA: Jerome M Sattler Publisher, Inc..
Schmiedek, F., Oberauer, K., Wilhelm, O., Süß, H.-M., & Wittmann, W. W. (2007). Individual differences in components of reaction time distributions and their relations to working memory and intelligence. Journal of Experimental Psychology: General, 136(3), 414–429. https://doi.org/10.1037/0096-3445.136.3.414
Schneider, J., & McGrew, K. S. (2012). The Cattell-Horn-Carroll (CHC) Model of Intelligence. In Contemporary intellectual assessment: Theories, tests, and issues (3rd ed.). https://doi.org/10.3233/978-1-60750-588-4-1344
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(10), 1498–1512. https://doi.org/10.1037/xge0000325
Sheppard, L. D., & Vernon, P. A. (2008). Intelligence and speed of information-processing: A review of 50 years of research. Personality and Individual Differences, 44(3), 535–551. https://doi.org/10.1016/j.paid.2007.09.015
Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. https://doi.org/10.2307/1412107
Spearman, C. (1927). The abilities of man. Retrieved from http://doi.apa.org/psycinfo/1927-01860-000
Stankov, L. (2017). Overemphasized “g”. Journal of Intelligence, 5(33), 1–10. Retrieved from http://www.mdpi.com/2079-3200/5/4/33
Stankov, L., & Crawford, J. D. (1993). Ingredients of complexity in fluid intelligence. Learning and Individual Differences, 5(2), 73–111.
Stankov, L., & Raykov, T. (1995). Modeling complexity and difficulty in measures of fluid intelligence. Structural Equation Modeling, 2(4), 335–366.
Stankov, L., & Roberts, R. D. (1997). Mental speed is not the basic process of intelligence. Personality and Individual Differences, 22(1), 69–84.
Sternberg, R. J. (1999). The theory of successful intelligence. Review of General Psychology, 3(4), 292–316. https://doi.org/10.1037/1089-2680.3.4.292
Sternberg, R. J. (2012). The Triarchic theory of successful intelligence. In D. Flanagan & P. Harrison (Eds.), Contemporary intellectual assessment (3rd ed., pp. 156–177). New York, NY: Guidlford Press.
Suchy, Y. (2015). Executive Functioning.
Thurstone, L. (1938). Primary mental abilities. Chicago, IL: University of Chicago Press. Retrieved from http://psycnet.apa.org/record/1938-15070-000.
Thurstone, L. (1947). Multiple factor analysis. Chicago, IL: University of Chicago Press. Retrieved from http://doi.apa.org/psycinfo/1947-15068-000
Tucker-Drob, E. M. (2009). Differentiation of cognitive abilities across the life span. Developmental Psychology, 45(4), 1097–1118. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2855504&tool=pmcentrez&rendertype=abstract
van de Vijver, F., & Tanzer, N. K. (2004). Bias and equivalence in cross-cultural assessment: An overview. Revue Europeenne de Psychologie Appliquee, 54(2), 119–135. https://doi.org/10.1016/j.erap.2003.12.004
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.
van der Maas, H. L. J., & Kan, K. J. (2016). Comment on “residual group-level factor associations: Possibly negative implications for the mutualism theory of general intelligence” by Gilles E. Gignac (2016). Intelligence, 57, 81–83. https://doi.org/10.1016/j.intell.2016.03.008
van der Maas, H. L. J., Kan, K.-J., & Borsboom, D. (2014). Intelligence is what the intelligence test measures. Seriously. Journal of Intelligence, 2(1), 12–15. https://doi.org/10.3390/jintelligence2010012
Vernon, P. (2014). The structure of human abilities (psychology revivals). Retrieved from https://books.google.com/books?hl=en&lr=&id=i8y2AgAAQBAJ&oi=fnd&pg=PP1&dq=Phillip+vernon+abilities&ots=NdTRZ5TxVT&sig=4As0OIfnlDicZ_7v-HliNK1dw1A
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., Astle, M. C., & Hill, J. C. (2018). What do undergraduates learn about human intelligence? An analysis of introductory psychology textbooks. Archives of Scientific Psychology, 6, 32–50. https://doi.org/10.1037/arc0000038
Wasserman, J. (2012). A history of intelligence assessment: The unfinished tapestry. In D. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (3rd ed., pp. 3–55). New York: Guilford Press.
Wechsler, D. (1943). Non-intellective factors in general intelligence. The Journal of Abnormal and Social Psychology, 38, 101. Retrieved from.
Wechsler, D. (2008). Wechsler adult intelligence scale-4th edition: Technical and interpretative manual. San Antonio, TX: Psychological Corporation.
Wechsler, D., & Edwards, A. J. (1974). Selected papers of David Wechsler. (A. Edwards, Ed.).
Woodcock, R. W. (1990). Theoretical foundations of the WJ-R measures of cognitive ability. Journal of Psychoeducational Assessment, 8(3), 231–258. https://doi.org/10.1177/073428299000800303
Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Woodcock-Johnson III tests of cognitive abilities. Test. Itasca, IL: Riverside.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Euler, M.J., McKinney, T.L. (2019). Theories of Intelligence. In: Matson, J.L. (eds) Handbook of Intellectual Disabilities. Autism and Child Psychopathology Series. Springer, Cham. https://doi.org/10.1007/978-3-030-20843-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-20843-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20842-4
Online ISBN: 978-3-030-20843-1
eBook Packages: Behavioral Science and PsychologyBehavioral Science and Psychology (R0)