Fluid and crystallized intelligence

Last updated

The concepts of fluid intelligence (gf) and crystallized intelligence (gc) were introduced in 1963 by the psychologist Raymond Cattell. [1] [2] According to Cattell's psychometrically-based theory, general intelligence (g) is subdivided into gf and gc. Fluid intelligence is the ability to solve novel reasoning problems and is correlated with a number of important skills such as comprehension, problem-solving, and learning. [3] Crystallized intelligence, on the other hand, involves the ability to deduce secondary relational abstractions by applying previously learned primary relational abstractions. [4]

Contents

History

Fluid and crystallized intelligence are constructs originally conceptualized by Raymond Cattell. [1] The concepts of fluid and crystallized intelligence were further developed by Cattell and his former student John L. Horn. [5] [6] [2] Most of the intelligence testing had mainly been focused on children, and young adults. Cattell and Hebb wanted to see how intelligence changed and developed when aging took place on an individual. When they realized that some memories and concepts remained, some diminished. Thus, the need to delineate two types of intelligence. [7]

Fluid versus crystallized intelligence

Fluid intelligence (gf) involved basic processes of reasoning and other mental activities that depend only minimally on prior learning (such as formal and informal education) and acculturation. Horn notes that it is formless and can "flow into" a wide variety of cognitive activities. [8] Tasks measuring fluid reasoning require the ability to solve abstract reasoning problems. Examples of tasks that measure fluid intelligence include figure classifications, figural analyses, number and letter series, matrices, and paired associates. [6]

Crystallized intelligence (gc) includes learned procedures and knowledge. It reflects the effects of experience and acculturation. Horn notes that crystallized ability is a "precipitate out of experience," resulting from the prior application of fluid ability that has been combined with the intelligence of culture. [8] Examples of tasks that measure crystallized intelligence are vocabulary, general information, abstract word analogies, and the mechanics of language. [6]

Example application of fluid and crystallized abilities to problem-solving

Horn [8] provided the following example of crystallized and fluid approaches to solving a problem. Here is the problem he described:

"There are 100 patients in a hospital. Some (an even number) are one-legged but wearing shoes. One-half of the remainder are barefooted. How many shoes are being worn?"

The crystallized approach to solving the problem would involve the application of high school-level algebra. Algebra is an acculturational product.

is the number of shoes worn, where x equals the number of one-legged patients. equals the number of two-legged patients. The solution boils down to 100 shoes.

In contrast to the crystallized approach to solving the problem, Horn provided a made-up example of a fluid approach to solving the problem, an approach that does not depend on the learning of high school-level algebra. In his made-up example, Horn described a boy who is too young to attend secondary school but could solve the problem through the application of fluid ability: "He may reason that if half the two-legged people are without shoes, and all the rest (an even number) are one-legged, then the shoes must average one per person, and the answer is 100."

Relationship to Piaget's theory of cognitive development

Researchers have linked the theory of fluid and crystallized abilities to Piaget's theory of cognitive development. [9] [10] Fluid ability and Piaget's operative intelligence both concern logical thinking and the "eduction of relations" (an expression Cattell used to refer to the inferring of relationships). Crystallized ability and Piaget's treatment of everyday learning reflects the impress of experience. Like fluid ability's relation to crystallized intelligence, Piaget's operativity is considered to be prior to, and ultimately provides the foundation for, everyday learning. [10]

Measurement of fluid intelligence

Various measures have been thought to assess fluid intelligence.

Raven's Progressive Matrices

The Raven's Progressive Matrices (RPM) [11] is one of the most commonly used measures of fluid ability. It is a non-verbal multiple-choice test. Participants have to complete a series of drawings by identifying relevant features based on the spatial organization of an array of objects and choosing one object that matches one or more of the identified features. [12] This task assesses the ability to consider one or more relationships between mental representations or relational reasoning.Propositional analogies and semantic decision tasks are also used to assess relational reasoning. [13] [14]

Woodcock–Johnson Tests of Cognitive Abilities, Third Edition

In the Woodcock–Johnson Tests of Cognitive Abilities, Third Edition (WJ-III), gf is assessed by two tests: Concept Formation and Analysis Synthesis. [15] Concept Formation tasks require the individual to use categorical thinking; Analysis Synthesis tasks require general sequential reasoning. [16]

Concept Formation

Individuals have to apply concepts by inferring the underlying "rules" for solving visual puzzles that are presented with increasing levels of difficulty. As the level of difficulty increases, individuals have to identify a key difference (or the "rule") for solving puzzles involving one-to-one comparisons. For more difficult items, individuals need to understand the concept of "and" (e.g., a solution must have some of this and some of that) and the concept of "or" (e.g., to be inside a box, the item must be either this or that). The most difficult items require fluid transformations and cognitive shifting between the various types of concept puzzles that the examinee had worked with previously. [16]

Analysis–Synthesis

In the Analysis–Synthesis test, the individual has to learn and orally state the solutions to incomplete logic puzzles that mimic a miniature mathematics system. The test also contains some of the features involved in using symbolic formulations in other fields such as chemistry and logic. The individual has presented a set of logic rules, a "key" that is used to solve the puzzles. The individual has to determine the missing colors within each of the puzzles using the key. Complex items presented puzzles that require two or more sequential mental manipulations of the key to deriving a final solution. Increasingly difficult items involve a mix of puzzles that requires fluid shifts in deduction, logic, and inference. [15]

Wechsler Intelligence Scales for Children, Fourth Edition

The Wechsler Intelligence Scales for Children, Fourth Edition (WISC-IV) [17] is used to have an overall measure in cognitive ability with five primary indexing scores. In the WISC-IV, the Perceptual Reasoning Index contains two subtests that assess gf: Matrix Reasoning, which involves induction and deduction, and Picture Concepts, which involves induction. [18]

Picture Concepts

In the Picture Concepts task, children have presented with a series of pictures on two or three rows and asked which pictures (one from each row) belong together based on some common characteristic. This task assesses the child's ability to discover the underlying characteristic (e.g., rule, concept, trend, class membership) that governs a set of materials. [18]

Matrix Reasoning

Matrix Reasoning also assesses this ability as well as the ability to start with stated rules, premises, or conditions and to engage in one or more steps to reach a solution to a novel problem (deduction). In the Matrix Reasoning test, children have presented with a series or sequence of pictures with one picture missing. Their task requires the child to choose the picture that fits the series or sequence from an array of five options. Since Matrix Reasoning and Picture Concepts involve the use of visual stimuli and do not require expressive language, they have been considered to be non-verbal tests of gf. [18]

In the workplace

Within the corporate environment, fluid intelligence is a predictor of a person's capacity to work well in environments characterised by complexity, uncertainty, and ambiguity. The Cognitive Process Profile (CPP) measures a person's fluid intelligence and cognitive processes. It maps these against suitable work environments according to Elliott Jaques's Stratified Systems Theory. [19] Fe et al. (2022) show that fluid intelligence measured in childhood predicts labor market earnings. [20]

Some authors have suggested that unless an individual is truly interested in a problem presented on an IQ test, the cognitive work required to solve the problem may not be performed owing to a lack of interest. These authors have contended that a low score on tests that are intended to measure fluid intelligence may reflect more of a lack of interest in the tasks than an inability to complete the tasks successfully. [21]

Development across life span

Fluid intelligence peaks at around age 20 and then gradually declines. [22] This decline may be related to local atrophy of the brain in the right cerebellum, a lack of practice, or the result of age-related changes in the brain. [23] [24]

Crystallized intelligence typically increases gradually, stays relatively stable across most of adulthood, and then begins to decline after age 65. [24] The exact peak age of cognitive skills remains elusive. [25]

Fluid intelligence and working memory

Working memory capacity is closely related to fluid intelligence, and has been proposed to account for individual differences in gf. [26] The linking of working memory and gf has been suggested that it could help resolve mysteries that have puzzled researchers concerning the two concepts. [27]

Neuroanatomy

According to David Geary, gf and gc can be traced to two separate brain systems. Fluid intelligence involves the dorsolateral prefrontal cortex, the anterior cingulate cortex, and other systems related to attention and short-term memory. Crystallized intelligence appears to be a function of brain regions that involve the storage and usage of long-term memories, such as the hippocampus. [28]

Research on training working memory and the training's indirect effect on fluid ability

Because working memory is thought to influence gf, then training to increase the capacity of working memory could have a positive impact on gf. Some researchers, however, question whether the results of training interventions to enhance gf are long-lasting and transferable, especially when these techniques are used by healthy children and adults without cognitive deficiencies. [29] A meta-analytical review published in 2012 concluded that "memory training programs appear to produce short-term, specific training effects that do not generalize." [30]

In a series of four individual experiments involving 70 participants (mean age of 25.6) from the University of Bern community, Jaeggi et al. found that, in comparison to a demographically matched control group, healthy young adults who practiced a demanding working memory task (dual n-back) approximately 25 minutes per day for between 8 and 19 days had significantly greater pre-to-posttest increases in their scores on a matrix test of fluid intelligence. [31] There was no long-term follow-up to assess how enduring the effects of training were.

Two later n-back studies [32] [33] did not support the findings of Jaeggi et al. Although participants' performance on the training task improved, these studies showed no significant improvement in the mental abilities tested, especially fluid intelligence and working memory capacity.

Thus the balance of findings suggests that training for the purpose of increasing working memory can have specific short-term effects but no effects on gf.

See also

Related Research Articles

Human intelligence is the intellectual capability of humans, which is marked by complex cognitive feats and high levels of motivation and self-awareness. Using their intelligence, humans are able to learn, form concepts, understand, and apply logic and reason. Human intelligence is also thought to encompass their capacities to recognize patterns, plan, innovate, solve problems, make decisions, retain information, and use language to communicate.

The g factor is a construct developed in psychometric investigations of cognitive abilities and human intelligence. It is a variable that summarizes positive correlations among different cognitive tasks, reflecting the fact that an individual's performance on one type of cognitive task tends to be comparable to that person's performance on other kinds of cognitive tasks. The g factor typically accounts for 40 to 50 percent of the between-individual performance differences on a given cognitive test, and composite scores based on many tests are frequently regarded as estimates of individuals' standing on the g factor. The terms IQ, general intelligence, general cognitive ability, general mental ability, and simply intelligence are often used interchangeably to refer to this common core shared by cognitive tests. However, the g factor itself is a mathematical construct indicating the level of observed correlation between cognitive tasks. The measured value of this construct depends on the cognitive tasks that are used, and little is known about the underlying causes of the observed correlations.

Intelligence has been defined in many ways: the capacity for abstraction, logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, and problem-solving. It can be described as the ability to perceive or infer information; and to retain it as knowledge to be applied to adaptive behaviors within an environment or context.

An aptitude is a component of a competence to do a certain kind of work at a certain level. Outstanding aptitude can be considered "talent", or "skill". Aptitude is inborn potential to perform certain kinds of activities, whether physical or mental, and whether developed or undeveloped. Aptitude is often contrasted with skills and abilities, which are developed through learning. The mass term ability refers to components of competence acquired through a combination of both aptitude and skills.

<span class="mw-page-title-main">Piaget's theory of cognitive development</span> Theory that discusses human intelligence from an epistemological perspective

Piaget's theory of cognitive development, or his genetic epistemology, is a comprehensive theory about the nature and development of human intelligence. It was originated by the Swiss developmental psychologist Jean Piaget (1896–1980). The theory deals with the nature of knowledge itself and how humans gradually come to acquire, construct, and use it. Piaget's theory is mainly known as a developmental stage theory.

<span class="mw-page-title-main">Raymond Cattell</span> British-American psychologist (1905–1998)

Raymond Bernard Cattell was a British-American psychologist, known for his psychometric research into intrapersonal psychological structure. His work also explored the basic dimensions of personality and temperament, the range of cognitive abilities, the dynamic dimensions of motivation and emotion, the clinical dimensions of abnormal personality, patterns of group syntality and social behavior, applications of personality research to psychotherapy and learning theory, predictors of creativity and achievement, and many multivariate research methods including the refinement of factor analytic methods for exploring and measuring these domains. Cattell authored, co-authored, or edited almost 60 scholarly books, more than 500 research articles, and over 30 standardized psychometric tests, questionnaires, and rating scales. According to a widely cited ranking, Cattell was the 16th most eminent, 7th most cited in the scientific journal literature, and among the most productive psychologists of the 20th century. He was a controversial figure due in part to his friendships with, and intellectual respect for, white supremacists and neo-Nazis.

Cognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test and the T maze test. Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence.

The Wechsler Intelligence Scale for Children (WISC) is an individually administered intelligence test for children between the ages of 6 and 16. The Fifth Edition is the most recent version.

<span class="mw-page-title-main">Three-stratum theory</span> Cognitive ability theory

The three-stratum theory is a theory of cognitive ability proposed by the American psychologist John Carroll in 1993. It is based on a factor-analytic study of the correlation of individual-difference variables from data such as psychological tests, school marks and competence ratings from more than 460 datasets. These analyses suggested a three-layered model where each layer accounts for the variations in the correlations within the previous layer.

<span class="mw-page-title-main">General knowledge</span> Type of information

General knowledge is information that has been accumulated over time through various media and sources. It excludes specialized learning that can only be obtained with extensive training and information confined to a single medium. General knowledge is an essential component of crystallized intelligence. It is strongly associated with general intelligence and with openness to experience.

The Kaufman Assessment Battery for Children (KABC) is a clinical instrument for assessing cognitive development. Its construction incorporates several recent developments in both psychological theory and statistical methodology. The test was developed by Alan S. Kaufman and Nadeen L. Kaufman in 1983 and revised in 2004. The test has been translated and adopted for many countries, such as the Japanese version of the K-ABC by the Japanese psychologists Tatsuya Matsubara, Kazuhiro Fujita, Hisao Maekawa, and Toshinori Ishikuma.

The Culture Fair Intelligence Test (CFIT) was created by Raymond Cattell in 1949 as an attempt to measure cognitive abilities devoid of sociocultural and environmental influences. Scholars have subsequently concluded that the attempt to construct measures of cognitive abilities devoid of the influences of experiential and cultural conditioning is a challenging one. Cattell proposed that general intelligence (g) comprises both fluid intelligence (Gf) and crystallized intelligence (Gc). Whereas Gf is biologically and constitutionally based, Gc is the actual level of a person's cognitive functioning, based on the augmentation of Gf through sociocultural and experiential learning.

Brain training is a program of regular activities purported to maintain or improve one's cognitive abilities. The phrase “cognitive ability” usually refers to components of fluid intelligence such as executive function and working memory. Cognitive training reflects a hypothesis that cognitive abilities can be maintained or improved by exercising the brain, analogous to the way physical fitness is improved by exercising the body. Cognitive training activities can take place in numerous modalities such as cardiovascular fitness training, playing online games or completing cognitive tasks in alignment with a training regimen, playing video games that require visuospatial reasoning, and engaging in novel activities such as dance, art, and music.

<span class="mw-page-title-main">Cattell–Horn–Carroll theory</span> Psychological theory

The Cattell–Horn–Carroll theory, is a psychological theory on the structure of human cognitive abilities. Based on the work of three psychologists, Raymond B. Cattell, John L. Horn and John B. Carroll, the Cattell–Horn–Carroll theory is regarded as an important theory in the study of human intelligence. Based on a large body of research, spanning over 70 years, Carroll's Three Stratum theory was developed using the psychometric approach, the objective measurement of individual differences in abilities, and the application of factor analysis, a statistical technique which uncovers relationships between variables and the underlying structure of concepts such as 'intelligence'. The psychometric approach has consistently facilitated the development of reliable and valid measurement tools and continues to dominate the field of intelligence research.

Domain-general learning theories of development suggest that humans are born with mechanisms in the brain that exist to support and guide learning on a broad level, regardless of the type of information being learned. Domain-general learning theories also recognize that although learning different types of new information may be processed in the same way and in the same areas of the brain, different domains also function interdependently. Because these generalized domains work together, skills developed from one learned activity may translate into benefits with skills not yet learned. Another facet of domain-general learning theories is that knowledge within domains is cumulative, and builds under these domains over time to contribute to our greater knowledge structure. Psychologists whose theories align with domain-general framework include developmental psychologist Jean Piaget, who theorized that people develop a global knowledge structure which contains cohesive, whole knowledge internalized from experience, and psychologist Charles Spearman, whose work led to a theory on the existence of a single factor accounting for all general cognitive ability.

<span class="mw-page-title-main">John L. Horn</span>

John Leonard Horn was a scholar, cognitive psychologist and a pioneer in developing theories of intelligence. The Cattell-Horn- Carroll (CHC) theory is the basis for many modern IQ tests. Horn's parallel analysis, a method for determining the number of factors to keep in an exploratory factor analysis, is also named after him.

Neurodevelopmental framework for learning, like all frameworks, is an organizing structure through which learners and learning can be understood. Intelligence theories and neuropsychology inform many of them. The framework described below is a neurodevelopmental framework for learning. The neurodevelopmental framework was developed by the All Kinds of Minds Institute in collaboration with Dr. Mel Levine and the University of North Carolina's Clinical Center for the Study of Development and Learning. It is similar to other neuropsychological frameworks, including Alexander Luria's cultural-historical psychology and psychological activity theory, but also draws from disciplines such as speech-language pathology, occupational therapy, and physical therapy. It also shares components with other frameworks, some of which are listed below. However, it does not include a general intelligence factor, since the framework is used to describe learners in terms of profiles of strengths and weaknesses, as opposed to using labels, diagnoses, or broad ability levels. This framework was also developed to link with academic skills, such as reading and writing. Implications for education are discussed below as well as the connections to and compatibilities with several major educational policy issues.

The following outline is provided as an overview of and topical guide to human intelligence:

g-VPR model

The g-VPR model is a model of human intelligence published in 2005 by psychology professors Wendy Johnson and Thomas J. Bouchard Jr. They developed the model by analyzing Gf-Gc theory, John Carroll’s Three-stratum theory and Vernon’s verbal-perceptual model.

The Hebb–Williams maze is a maze used in comparative psychology to assess the cognitive ability of small animals such as mice and rats. It was developed by Donald O. Hebb and his student Kenneth Williams in 1946, when both men were working at Queen's University at Kingston. A modified version, intended specifically to measure the intelligence of rats, was described in a 1951 paper by Hebb's students Rabinovitch and Rosvold. This modified version is the most commonly used in research where the aim is to measure animals' problem-solving abilities. In general, animals are tested in the Hebb–Williams maze's twelve separate mazes after acclimating to six practice mazes, though some studies have not used all twelve testing mazes. The two main procedures for the maze are the reward conditioning task and the water escape task. The maze has been used to investigate strain and sex differences in mice. A 2018 study argued that the maze is potentially useful for translational research in fragile X syndrome in humans.

References

  1. 1 2 Cattell, R. B. (1963). "Theory of fluid and crystallized intelligence: A critical experiment". Journal of Educational Psychology. 54: 1–22. doi:10.1037/h0046743.
  2. 1 2 Cattell, Raymond B. (1971). Abilities: Their structure, growth, and action. Boston: Houghton Mifflin. ISBN   0-395-04275-5. OCLC   159861.
  3. Unsworth, Nash; Fukuda, Keisuke; Awh, Edward; Vogel, Edward K. (2014). "Working memory and fluid intelligence: Capacity, attention control, and secondary memory retrieval". Cognitive Psychology. 71: 1–26. doi:10.1016/j.cogpsych.2014.01.003. PMC   4484859 . PMID   24531497.
  4. Cattell, Raymond B. (1987). Intelligence : its structure, growth, and action. Raymond B. Cattell. Amsterdam: North-Holland. ISBN   978-0-08-086689-5. OCLC   305506880.
  5. Horn, John L.; Cattell, Raymond B. (1967). "Age differences in fluid and crystallized intelligence". Acta Psychologica. 26 (2): 107–129. doi:10.1016/0001-6918(67)90011-X. PMID   6037305.
  6. 1 2 3 Horn, John L. (1968). "Organization of abilities and the development of intelligence" . Psychological Review. 75 (3): 242–259. doi:10.1037/h0025662. ISSN   1939-1471. PMID   4875815.
  7. Brown, Richard (15 December 2016). "Hebb and Cattell: The Genesis of he Theory of Fluid and Crystalized Intelligence". Frontiers in Human Neuroscience. 10: 606. doi: 10.3389/fnhum.2016.00606 . PMC   5156710 . PMID   28018191.
  8. 1 2 3 Horn, John L. (2020-03-12). "Intelligence—Why It Grows, Why It Declines". Human Intelligence. Routledge. pp. 53–74. doi:10.1201/9780429337680-5. ISBN   978-0-429-33768-0 . Retrieved 2022-10-11.
  9. Papalia, D.; Fitzgerald, J.; Hooper, F. H. (1971). "Piagetian Theory and the Aging Process: Extensions and Speculations". The International Journal of Aging and Human Development. 2: 3–20. doi:10.2190/AG.2.1.b. S2CID   143590129.
  10. 1 2 Schonfeld, Irvin Sam (1986). "The Genevan and Cattell-Horn conceptions of intelligence compared: The early implementation of numerical solution aids". Developmental Psychology . 22 (2): 204–212. doi:10.1037/0012-1649.22.2.204. S2CID   222275196.
  11. Raven, J.; Raven, J. C.; Court, J. H. (2003) [1998]. "Section 1: General Overview". Manual for Raven's Progressive Matrices and Vocabulary Scales. San Antonio, TX: Harcourt Assessment.[ page needed ]
  12. Bornstein, Joel C.; Foong, Jaime Pei Pei (2009). "MGluR1 Receptors Contribute to Non-Purinergic Slow Excitatory Transmission to Submucosal VIP Neurons of Guinea-Pig Ileum". Frontiers in Neuroscience. 3: 46. doi: 10.3389/neuro.21.001.2009 . PMC   2695390 . PMID   20582273.
  13. Wright, Samantha B.; Matlen, Bryan J.; Baym, Carol L.; Ferrer, Emilio; Bunge, Silvia A. (2007). "Neural correlates of fluid reasoning in children and adults". Frontiers in Human Neuroscience. 1: 8. doi: 10.3389/neuro.09.008.2007 . PMC   2525981 . PMID   18958222.
  14. Ferrer, Emilio; O'Hare, Elizabeth D.; Bunge, Silvia A. (2009). "Fluid reasoning and the developing brain". Frontiers in Neuroscience. 3 (1): 46–51. doi: 10.3389/neuro.01.003.2009 . PMC   2858618 . PMID   19753096.
  15. 1 2 Woodcock, R. W.; McGrew, K. S.; Mather, N (2001). Woodcock Johnson III. Itasca, IL: Riverside.[ page needed ]
  16. 1 2 Schrank, F. A.; Flanagan, D. P. (2003). WJ III Clinical use and interpretation. Scientist-practitioner perspectives. San Diego, CA: Academic Press.[ page needed ]
  17. Wechsler, D. (2003). WISC-IV technical and interpretive manual. San Antonio, TX: Psychological Corporation.[ page needed ]
  18. 1 2 3 Flanagan, D. P.; Kaufman, A. S. (2004). Essentials of WISC-IV assessment . Hoboken, NJ: John Wiley. ISBN   9780471476917.[ page needed ]
  19. Jaques, Elliott (October 1986). "The Development of Intellectual Capability: A Discussion of Stratified Systems Theory". The Journal of Applied Behavioral Science. 22 (4): 361–383. doi:10.1177/002188638602200402. ISSN   0021-8863. S2CID   145252823.
  20. Fe, Eduardo; Gill, David; Prowse, Victoria (October 2022). "Cognitive skills, strategic sophistication, and life outcomes" (PDF). Journal of Political Economy . 130 (10): 2643–2704. doi:10.1086/720460. S2CID   209472672.
  21. Messick, Samuel (1989). "Meaning and Values in Test Validation: The Science and Ethics of Assessment". Educational Researcher. 18 (2): 5–11. doi:10.3102/0013189X018002005. JSTOR   1175249. S2CID   146237448.
  22. Cacioppo, John T. (2013). Discovering psychology: the science of mind: briefer version. Wadsworth, Cengage Learning. ISBN   978-1-111-84129-4. OCLC   841668483.
  23. Lee, Jun-Young; Lyoo, In Kyoon; Kim, Seon-Uk; Jang, Hong-Suk; Lee, Dong-Woo; Jeon, Hong-Jin; Park, Sang-Chul; Cho, Maeng Je (2005). "Intellect declines in healthy elderly subjects and cerebellum". Psychiatry and Clinical Neurosciences. 59 (1): 45–51. doi: 10.1111/j.1440-1819.2005.01330.x . hdl:10371/27902. PMID   15679539. S2CID   45264214.
  24. 1 2 Cavanaugh, J. C.; Blanchard-Fields, F (2006). Adult development and aging (5th ed.). Belmont, CA: Wadsworth Publishing/Thomson Learning. ISBN   978-0-534-52066-3.[ page needed ]
  25. Desjardins, Richard; Warnke, Arne Jonas (2012). "Ageing and Skills" (PDF). OECD Education Working Papers. doi: 10.1787/5k9csvw87ckh-en . hdl:10419/57089.{{cite journal}}: Cite journal requires |journal= (help)
  26. Kyllonen, Patrick C.; Christal, Raymond E. (1990). "Reasoning ability is (little more than) working-memory capacity?!". Intelligence. 14 (4): 389–433. doi:10.1016/S0160-2896(05)80012-1.
  27. Fuster, Joaquin M. (2008). The prefrontal cortex (4th ed.). Amsterdam: Academic Press/Elsevier. ISBN   978-0-12-373644-4. OCLC   318353807.
  28. Geary, D. C. (2005). The origin of mind: Evolution of brain, cognition, and general intelligence. Washington, DC: American Psychological Association.
  29. Todd W. Thompson; et al. (2013). "Failure of Working Memory Training to Enhance Cognition or Intelligence". PLOS ONE. 8 (5): e63614. Bibcode:2013PLoSO...863614T. doi: 10.1371/journal.pone.0063614 . PMC   3661602 . PMID   23717453.
  30. Melby-Lervåg, Monica; Hulme, Charles (2012). "Is Working Memory Training Effective? A Meta-Analytic Review" (PDF). Developmental Psychology. 49 (2): 270–91. doi:10.1037/a0028228. PMID   22612437. S2CID   12370312.
  31. Jaeggi, Susanne M.; Buschkuehl, Martin; Jonides, John; Perrig, Walter J. (2008). "Improving fluid intelligence with training on working memory". Proceedings of the National Academy of Sciences. 105 (19): 6829–33. Bibcode:2008PNAS..105.6829J. doi: 10.1073/pnas.0801268105 . JSTOR   25461885. PMC   2383929 . PMID   18443283.
  32. Chooi, Weng-Tink; Thompson, Lee A. (2012). "Working memory training does not improve intelligence in healthy young adults". Intelligence. 40 (6): 531–42. doi:10.1016/j.intell.2012.07.004.
  33. Redick, Thomas S.; Shipstead, Zach; Harrison, Tyler L.; Hicks, Kenny L.; Fried, David E.; Hambrick, David Z.; Kane, Michael J.; Engle, Randall W. (2012). "No Evidence of Intelligence Improvement After Working Memory Training: A Randomized, Placebo-Controlled Study". Journal of Experimental Psychology: General. 142 (2): 359–379. doi:10.1037/a0029082. PMID   22708717. S2CID   15117431.