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What Is Fluid Intelligence? Can It Be Improved?

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Cognitive Abilities and Educational Outcomes

Part of the book series: Methodology of Educational Measurement and Assessment ((MEMA))

Abstract

General fluid intelligence (Gf) is the ability used in inductive and deductive reasoning, particularly with novel material. It can be contrasted with general crystallized ability (Gc) which reflects schooling and acculturated learning, and the two abilities have different developmental trajectories, with Gf peaking earlier in the lifespan. Gustafsson has made key contributions to our understanding of Gf. He (Gustafsson 1984) introduced hierarchical confirmatory factor analytic models to reconcile Thurstonian (non-hierarchical) and Spearman and Cattell-Horn (hierarchical) models of intelligence and in so doing identified Gf as a second-order factor which perfectly correlated with the third-order factor, general ability (g). This has important implications for understanding the nature of general cognitive skill. Subsequent research showed that Gf can be identified separately from g through variation in culture-related opportunities to learn (Valentin Kvist and Gustafsson 2008). Gf has served both as a predictor (Gustafsson and Balke 1993) and outcome (Cliffordson and Gustafsson 2008) in the developmental, cognitive training, cognitive aging, international comparative assessment, genetics, neuropsychopharmacological, human capital theory, and behavioral economics literatures. Understanding the nature of fluid intelligence and how to improve it has become a topic of renewed and general interest for optimizing human performance in school and in the workplace.

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References

  • Ackerman, P. L. (1996). A theory of adult intellectual development: Process personality, interests, and knowledge. Intelligence, 22, 229–259.

    Google Scholar 

  • Ackerman, P. L. (1999). Traits and knowledge as determinants of learning and individual differences: Putting it all together. In P. L. Ackerman, P. C. Kyllonen, & R. D. Roberts (Eds.), Learning and individual differences: Process, trait, and content determinants (pp. 437–460). Washington, DC: American Psychological Association.

    Google Scholar 

  • 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 

  • Arendasy, M., & Sommer, M. (2005). Using psychometric technology in educational assessment: The case of a schema-based isomorphic approach to the automatic generation of quantitative reasoning items. Learning and Individual Differences, 17(4), 366–383.

    Google Scholar 

  • Arendasy, M., & Sommer, M. (2012). Using automatic item generation to meet the increasing item demands of high-stakes assessment. Learning and Individual Differences, 22, 112–117.

    Google Scholar 

  • Au, J., Sheehan, E., Tsai, N., Duncan, G. J., Buschkuehl, M., & Jaeggi, S. M. (2015). Improving fluid intelligence with training on working memory: A meta-analysis. Psychonomic Bulletin & Review, 22(2), 366–377.

    Google Scholar 

  • Baddeley, A. D. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4, 829–839. doi:10.1038/nrn1201

  • Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New York: Academic Press.

    Google Scholar 

  • Battleday, R. M., & Brem, A. K. (2015). Modafinil for cognitive neuroenhancement in healthy non-sleep-deprived subjects: A systematic review. European Neuropsychopharmacology, 25, 1865–1881.

    Google Scholar 

  • Becker, M., Lüdtke, O., Trautwein, U., Köller, O., & Baumert, J. (2012). The differential effects of school tracking on psychometric intelligence: Do academic-track schools make students smarter? Journal of Educational Psychology, 104, 682–699. doi:10.1037/a0027608

  • Birney, D. P., & Bowman, D. B. (2009). An experimental-differential investigation of cognitive complexity. Psychology Science Quarterly, 51(4), 449–469.

    Google Scholar 

  • Borghans, L., Duckworth, A. L., Heckman, J. J., & ter Weel, B. (2008, February). The economics and psychology of personality traits (NBER Working Paper No. 13810). Cambridge, MA: National Bureau of Economic Research.

    Google Scholar 

  • Borghans, L., Golsteyn, B. H. H., Heckman, J. J., & Humphries, J. E. (2015). What do grades and achievement tests measure? Unpublished manuscript, Human Capital and Economic Working Group, University of Chicago, Chicago, IL. Retrieved from https://hceconomics.uchicago.edu/sites/default/files/3_BGHH-Grades-Achievement-Measure-Paper_2015-12-30a_sjs.pdf

  • Boring, E. G. (1923). Intelligence as the tests test it. New Republic, 36, 35–37.

    Google Scholar 

  • Bridgeman, B., Strang, H. R., & Buttram, J. (1974). “Game” versus “test” instructions for the WISC. Journal of Educational Measurement, 11, 285–287.

    Google Scholar 

  • Brinch, C. N., & Galloway, T. A. (2011). Schooling in adolescence raises IQ scores. Proceedings of the National Academy of Sciences of the United States of America, 109(2), 425–430. doi:10.1073/pnas.1106077109

  • Burgess, G. C., Gray, J. R., Conway, A. R. A., & Braver, T. (2011). Neural mechanisms of interference control underlie the relationship between fluid intelligence and working memory span. Journal of Experimental Psychology: General, 140(4), 674–692.

    Google Scholar 

  • Buros, O. K. (Ed.). (1965). The sixth mental measurements yearbook. Lincoln, NE: University of Nebraska Press. ISBN 0-910674-06-X.

    Google Scholar 

  • Cahan, S., & Cohen, N. (1989). Age versus schooling effects on intelligence development. Child Development, 60(5), 1239–1249.

    Google Scholar 

  • Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test. Psychological Review, 97, 404–431.

    Google Scholar 

  • Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. New York, NY: Cambridge University Press.

    Google Scholar 

  • Carroll, J. B. (2003). The higher-stratum structure of cognitive abilities: Current evidence supports g and about ten broad factors. In H. Nyborg (Ed.), The scientific study of general intelligence: Tribute to Arthur R. Jensen (pp. 5–21). Boston, MA: Pergamon.

    Google Scholar 

  • Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1–22.

    Google Scholar 

  • Cattell, R. B. (1987). Intelligence: Its structure, growth, and action. New York: North-Holland.

    Google Scholar 

  • Cattell, R. B., & Horn, J. L. (1978). A check on the theory of fluid and crystallized intelligence with description of new subtest designs. Journal of Educational Measurement, 15, 139–164.

    Google Scholar 

  • Ceci, S. (1991). How much does schooling influence general intelligence and its cognitive components? A reassessment of the evidence. Developmental Psychology, 27(5), 703–722.

    Google Scholar 

  • Ceci, S. J., & Williams, W. M. (1997). Schooling, intelligence, and income. American Psychologist, 52, 1051–1058.

    Google Scholar 

  • Chuderski, A. (2015). The broad factor of working memory is virtually isomorphic to fluid intelligence tested under time pressure. Personality and Individual Differences, 85, 98–104.

    Google Scholar 

  • Cliffordson, C. (2010). Methodological issues in investigations of the relative effects of schooling and age on school performance: The between-grade regression discontinuity design applied to Swedish TIMSS 1995 data. Educational Research and Evaluation: An International Journal on Theory and Practice, 16(1), 39–52.

    Google Scholar 

  • Cliffordson, C., & Gustafsson, J.-E. (2008). Effects of age and schooling on intellectual performance: Estimates obtained from analysis of continuous variation in age and length of schooling. Intelligence, 36, 143–152.

    Google Scholar 

  • Conway, A. R. A., Jarrold, C., Kane, M. J., Miyake, A., & Towse, J. N. (Eds.). (2007). Variations in working memory. Oxford, England: Oxford University Press.

    Google Scholar 

  • Corno, L., Cronbach, L. J., Kupermintz, H., Lohman, D. F., Mandinach, E. B., Porteus, A. W., et al. (2002). Remaking the concept of aptitude: Extending the legacy of Richard E. Snow. Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Cronbach, L. J. (1971). Test validation. In R. L. Thorndike (Ed.), Educational measurement (2nd ed., pp. 443–507). Washington, DC: American Council on Education.

    Google Scholar 

  • Dance, A. (2016). A dose of intelligence. Nature, 536, S2–S3.

    Google Scholar 

  • Danziger, K. (2009). The holy grail of universality. In T. Teo, P. Stenner, A. Rutherford, E. Park, & C. Baerveldt (Eds.), Varieties of philosophical and practical concerns (pp. 2–11). Concord, Ontario, Canada: Captus University Publications.

    Google Scholar 

  • Deary, I. J., Starr, J. M., & MacLennan, W. J. (1998). Fluid intelligence, memory and blood pressure in cognitive aging. Personality and Individual Differences, 25(4), 605–619.

    Google Scholar 

  • Diehl, K. A. (2002). Algorithmic item generation and problem solving strategies in matrix completion problems. Unpublished doctoral dissertation, University of Kansas.

    Google Scholar 

  • 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, 7716–7720.

    Google Scholar 

  • Embretson, S. E. (2002). Generating abstract reasoning items with cognitive theory. In S. Irvine & P. Kyllonen (Eds.), Generating items for cognitive tests: Theory and practice. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Finn, A. S., Kraft, M., West, M. R., Leonard, J. A., Bish, C., Martin, R. E., … Gabrieli, J. D. E. (2014). Cognitive skills, student student achievement tests and schools. Psychological Science, 25, 736–744.

    Google Scholar 

  • Fiske, D. W., & Butler, J. M. (1963). The experimental conditions for measuring individual differences. Educational and Psychological Measurement, 26, 249–266.

    Google Scholar 

  • Flynn, J. R. (2007). What is intelligence? Beyond the Flynn effect. Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Flynn, J. R., & Weiss, L. G. (2007). American IQ gains from 1932 to 2002: The WISC subtests and educational progress. International Journal of Testing, 7, 209–224.

    Google Scholar 

  • Freund, P. A., & Holling, H. (2011). How to get really smart: Modeling retest and training effects in ability testing using computer-generated figural matrix items. Intelligence, 39, 233–243.

    Google Scholar 

  • Geggel, L. (2015). ‘Smart drug’ modafinil actually works, study shows. Live Science. Retrieved April 1, 2016 from http://www.livescience.com/51919-modafil-improves-attention-learning.html

  • Guilford, J. P. (1967). The nature of human intelligence. New York, NY: McGraw-Hill.

    Google Scholar 

  • Gustafsson, J.-E. (1984). A unifying model for the structure of intellectual abilities. Intelligence, 8, 179–203.

    Google Scholar 

  • Gustafsson, J.-E. (1998). Hierarchical models of individual differences in cooperative abilities. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 4, pp. 35–71).

    Google Scholar 

  • Gustafsson, J.-E. (1999). Measuring and understanding G: Experimental and correlational approaches. In P. L. Ackerman, P. C. Kyllonen, & R. D. Roberts (Eds.), Learning and individual differences: Process, trait, and content determinants (pp. 275–289). Washington, D.C.: American Psychological Association.

    Google Scholar 

  • Gustafsson, J.-E. (2001). Schooling and intelligence: Effects of track of study on level and profile of cognitive abilities. International Education Journal, 2(4), 166–186.

    Google Scholar 

  • Gustafsson, J.-E. (2002). Measurement from a hierarchical point of view. In H. L. Braun, D. G. Jackson, & D. E. Wiley (Eds.), The role of constructs in psychological and educational measurement (pp. 73–95). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Gustafsson, J.-E., & Balke, G. (1993). General and specific abilities as predictors of school achievement. Multivariate Behavioral Research, 28(4), 407–434.

    Google Scholar 

  • Halford, G. S., Wilson, W. H., & Phillips, S. (1998). Processing capacity defined by relational complexity: Implications for comparative, developmental, and cognitive psychology. Behavioral and Brain Sciences, 21, 803–865.

    Google Scholar 

  • Harden, K. P., Turkheimer, E., & Loehlin, J. C. (2007). Genotype by environment interaction in adolescents’ cognitive aptitude. Behavior Genetics, 37(2), 273–283.

    Google Scholar 

  • Harkins, S. G. (2006). Mere effort as the mediator of the evaluation-performance relationship. Journal of Personality and Social Psychology, 91(3), 436–455. doi:10.1037/0022-3514.91.3.436

  • Heckman, J. J., & Kautz, T. (2014). Achievement tests and the role of character in American life. In J. J. Heckman, J. E. Humphries, & T. Kautz (Eds.), The myth of achievement tests: The GED and the role of character in American life (pp. 3–56). Chicago, IL: University of Chicago Press.

    Google Scholar 

  • Herrnstein, R. J., & Murray, C. (1994). The bell curve: Intelligence and class structure in American life. New York, NY: The Free Press (Simon & Shuster).

    Google Scholar 

  • Horn, J. L. (1989). Models for intelligence. In R. Linn (Ed.), Intelligence: Measurement, theory, and public policy (pp. 29–73). Urbana, IL: University of Illinois Press.

    Google Scholar 

  • Horn, J. L. (2008). Spearman, g, expertise, and the nature of human cognitive capability. In P. C. Kyllonen, R. D. Roberts, & L. Stankov (Eds.), Extending intelligence: Enhancement and new constructs (pp. 185–230). New York: LEA.

    Google Scholar 

  • Horn, J. L., & Noll, J. (1997). Human cognitive capabilities: Gf-Gc theory. In D. P. Flanagan, J. L. Genshaft, & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests and issues (pp. 53–91). New York, NY: Guilford.

    Google Scholar 

  • Hornke, L. F., & Habon, M. W. (1986). Rule-based item bank construction and evaluation within the linear logistic framework. Applied Psychological Measurement, 10, 369–380.

    Google Scholar 

  • Hunt, E. (2011). Human intelligence. New York, NY: Cambridge University Press.

    Google Scholar 

  • Irvine, S. H. (2002). The foundations of item generation for mass testing. In S. H. Irvine & P. C. Kyllonen (Eds.), Item generation for test development (pp. 3–34). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Jacobs, P. I., & Vandeventer, M. (1972). Evaluating the teaching of intelligence. Educational and Psychological Measurement, 32(2), 235–238.

    Google Scholar 

  • Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences of the United States of America, 105(19), 6829–6833. doi:10.1073/pnas.0801268105

  • Jensen, E. (1998). The g factor: The science of mental ability (Human evolution, behavior, and intelligence). Westport, CT: Praeger.

    Google Scholar 

  • Kane, M. J., Hambrick, D. Z., Tuholski, S. W., Wilhelm, O., Payne, T. W., & Engle, R. W. (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 

  • Kanfer, R., & Ackerman, P. L. (2004). Aging, adult development, and work motivation. The Academy of Management Review, 29(3), 440–458.

    Google Scholar 

  • Kaufman, J. C. (2012). Self estimates of general, crystallized, and fluid intelligences in an ethnically diverse population. Learning and Individual Differences, 22, 118–122.

    Google Scholar 

  • Kyllonen, P. C. (1995). CAM: A theoretical framework for cognitive abilities measurement. In D. K. Detterman (Ed.), Current topics in human intelligence (Vol. 4, pp. 307–359). New York, NY: Springer.

    Google Scholar 

  • Kyllonen, P. C., & Christal, R. E. (1990). Reasoning ability is (little more than) working-memory capacity?! Intelligence, 14(4), 389–433.

    Google Scholar 

  • Lindqvist, E., & Vestman, R. (2011). The labor market returns to cognitive and noncognitive ability: Evidence from the Swedish enlistment. American Economic Journal: Applied Economics, 3, 101–128.

    Google Scholar 

  • Liu, O. L., Bridgeman, B., & Adler, R. (2012). Learning outcomes assessment in higher education: Motivation matters. Educational Researcher, 41, 352–362.

    Google Scholar 

  • McArdle, J. J., Ferrer-Caja, E., Hamagami, F., & Woodcock, R. W. (2002). Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Developmental Psychology, 38(1), 115–142.

    Google Scholar 

  • McGrew, K. S. (1997). Analysis of the major intelligence batteries according to a proposed comprehensive Gf-Gc framework. In D. P. Flanagan, J. L. Genshaft, & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (pp. 151–179). New York, NY: Guilford.

    Google Scholar 

  • McGrew, K. S. (2005). The Cattell-Horn-Carroll theory of cognitive abilities: Past, present, and future. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (2nd ed., pp. 136–182). New York, NY: Guilford.

    Google Scholar 

  • Melby-Lervåg, M., & Hulme, C. (2012). Is working memory training effective? A meta-analytic review. Developmental Psychology, 49(2), 270–291.

    Google Scholar 

  • Mellers, B., Stone, E., Murray, T., Minster, A., Rohrbaugh, N., Bishop, M., … Tetlock, P. (2015). Identifying and cultivating superforecasters as a method of improving probabilistic predictions. Perspectives on Psychological Science, 10(3), 267–281.

    Google Scholar 

  • 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.

    Google Scholar 

  • Meo, M., Roberts, M. J., & Marucci, F. S. (2007). Element salience as a predictor of item difficulty for Raven’s progressive matrices. Intelligence, 35(4), 359–368.

    Google Scholar 

  • Mulholland, T. M., Pellegrino, J. W., & Glaser, R. (1980). Components of geometric analogy solution. Cognitive Psychology, 12, 252–284.

    Google Scholar 

  • Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., et al. (2012). Intelligence: New findings and theoretical developments. American Psychologist, 67(2), 130–159.

    Google Scholar 

  • Oberauer, K., Schulze, R., Wilhelm, O., & Suess, H.-M. (2005). Working memory and intelligence—Their correlation and relation: Comment on Ackerman, Beier, and Boyle (2005). Pscyhological Bulletin, 131, 61–65.

    Google Scholar 

  • Owen, A. M., Hampshire, A., Grahn, J. A., Stenton, R., Dajani, S., Burns, A. S., et al. (2010). Putting brain training to the test. Nature, 465, 775–778.

    Google Scholar 

  • Pind, J., Gunnarsdóttir, E. K., Jóhannesson, H. S. (2003). Raven’s standard progressive matrices: New school age norms and a study of the test’s validity. Personality and Individual Differences, 34, 375–386.

    Google Scholar 

  • Postlethwaite, B. E. (2011). Fluid ability, crystallized ability, and performance across multiple domains: A meta-analysis. Doctoral dissertation. Retrieved from http://ir.uiowa.edu/etd/1255/

  • Preckel, F. (2003). Diagnostik intellektueller Hochbegabung. Testentwicklung zur Erfassung der fluiden Intelligenz [Assessment of intellectual giftedness. Development of a fluid intelligence test]. Göttingen, Germany: Hogrefe.

    Google Scholar 

  • Preckel, F., & Thiemann, H. (2003). Online- versus paper-pencil-version of a high potential intelligence test. Swiss Journal of Psychology, 62, 131–138.

    Google Scholar 

  • Primi, R. (2001). Complexity of geometric inductive reasoning tasks: Contribution to the understanding of fluid intelligence. Intelligence, 20, 41–70.

    Google Scholar 

  • Rindermann, H. (2007). The g-factor of international cognitive ability comparisons: The homogeneity of results in PISA, TIMSS, PIRLS and IQ-tests across nations. European Journal of Personality, 21, 667–706.

    Google Scholar 

  • Sackett, P. R. (2012). Cognitive tests, constructs, and content validity: A commentary on Schmidt (2012). International Journal of Selection and Assessment, 20(1)

    Google Scholar 

  • Schaie, K. W. (2012). Developmental influences on adult intelligence: Seattle Longitudinal Study (2nd ed.). New York, NY: Oxford University Press.

    Google Scholar 

  • Schalke, D., Brunner, M., Geiser, C., Preckel, F., Keller, U., Spendler, M., et al. (2013). Stability and change in intelligence from age 12 to age 52: Results for the Luxembourg MAGRIP study. Developmental Psychology, 49, 1529–1543.

    Google Scholar 

  • Schank, R. C. (1984). How much intelligence is there in artificial intelligence? Intelligence, 4(1), 1–14.

    Google Scholar 

  • Schmid, J., & Leiman, J. M. (1957). The development of hierarchical factor solutions. Psychometrika, 22(1), 53–61.

    Google Scholar 

  • Schmidt, F. L., & Crano, W. D. (1974). A test of the theory of fluid and crystallized intelligence in middle- and low-socioeconomic-status children: A cross-lagged panel analysis. Journal of Educational Psychology, 66(2), 255–261.

    Google Scholar 

  • Schrank, F. A. (2006). Specification of the cognitive processes involved in performance on the Woodcock-Johnson III (Assessment Service Bulletin No. 7). Itasca, IL: Riverside.

    Google Scholar 

  • Sechrest, L. (2005). Validity of measures is no simple matter. Health Services Research, 40(5), 1584–1604.

    Google Scholar 

  • Sells, S. B., Dailey, J. T., & Pickrel, E. W. (Eds.). (1984). Selection of air traffic controllers. Report no. FAA-AM-84-2. Washington, DC: U.S. Department of Transportation, Federal Aviation Administration.

    Google Scholar 

  • Snow, R. E., Kyllonen, P. C., & Marshalek, B. (1984). The topography of ability and learning correlations. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 2., pp. 47–103). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292.

    Google Scholar 

  • Spearman, C. (1924). The nature of “intelligence” and the principles of cognition. Journal of Philosophy, 21(11), 294–301.

    Google Scholar 

  • Spearman, C. (1927). The abilities of man. London, England: Macmillan and Co.

    Google Scholar 

  • Stankov, L., & Schweizer, K. (2007). Raven’s progressive matrices, manipulations of complexity and measures of accuracy, speed and confidence. Psychology Science, 49, 326–342.

    Google Scholar 

  • Stelzl, I., Merz, F., Ehlers, T., & Remer, H. (1995). The effect of schooling on the development of fluid and crystallized intelligence: A quasi-experimental study. Intelligence, 21, 279–296. doi:10.1016/0160-2896(95)90018-7

  • Terman, L. M. (1924). The mental test as a psychological method. Psychological Review, 31, 93–117.

    Google Scholar 

  • Thorsen, C., Gustafsson, J.-E., & Cliffordson, C. (2014). The influence of fluid and crystallized intelligence on the development of knowledge and skills. British Journal of Educational Psychology, 84(4), 556–570. doi:10.1111/bjep.12041

  • Thurstone, L. L. (1938). Primary mental abilities. Psychometric monographs, 1, ix–121.

    Google Scholar 

  • Tucker-Drob, E. M., & Salthouse, T. A. (2011). Individual differences in cognitive aging. In T. Chamorro-Premuzic, S. von Stumm, & A. Furnham (Eds.), The Wiley-Blackwell handbook of individual differences (pp. 242–267). Malden, MA: Wiley-Blackwell.

    Google Scholar 

  • Undheim, J. O. (1981). On Intelligence III: Examining developmental implications of Catail’s broad ability theory and of an alternative neo-Spearman model. Scandinavian Journal of Psychology, 22(1), 243–249.

    Google Scholar 

  • Undheim, J. O., & Gustafsson, J.-E. (1987). The hierarchical organization of cognitive abilities: Restoring general intelligence through the use of linear structural relations (LISREL). Multivariate Behavioral Research 22, 149–171.

    Google Scholar 

  • Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. (2014). Working memory and fluid intelligence: Capacity, attention control, and secondary memory. Cognitive Psychology, 71, 1–26.

    Google Scholar 

  • Valentin Kvist, A. V., & Gustafsson, J.-E. (2008). The relation between fluid intelligence and the general factor as a function of cultural background: A test of Cattell’s investment theory. Intelligence, 36, 422–436.

    Google Scholar 

  • Wai, J., & Putallaz, M. (2011). The Flynn effect puzzle: A 30-year examination from the right tail of the ability distribution provides some missing pieces. Intelligence, 39(6), 443–455.

    Google Scholar 

  • Wilhelm, O. (2000). Psychologie des schlussfolgernden Denkens: Differentialpsychologische Prfung von Strukturuberlegeungen [Psychology of reasoning: Testing structural theories]. Hamburg, Germany: Dr. Kovac.

    Google Scholar 

  • Wilhelm, O. (2006). Measuring reasoning ability. In O. Wilhelm & R. W. Engle (Eds.), Handbook of understanding and measuring intelligence (pp. 373–392). Los Angeles: Sage. doi:http://dx.doi.org/10.4135/9781452233529.n21

  • Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Woodcock-Johnson III. Itasca, IL: Riverside.

    Google Scholar 

  • Zigler, E., & Trickett, P. K. (1978). IQ, social competence, and the evaluation of early childhood intervention programs. American Psychologist, 33, 789–798.

    Google Scholar 

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    Patrick Kyllonen & Harrison Kell

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    Ulrika Wolff

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Kyllonen, P., Kell, H. (2017). What Is Fluid Intelligence? Can It Be Improved?. In: Rosén, M., Yang Hansen, K., Wolff, U. (eds) Cognitive Abilities and Educational Outcomes. Methodology of Educational Measurement and Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-43473-5_2

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