Fluid intelligence is the ability to


Fluid vs. Crystallized Intelligence: Examples and More

General intelligence consists of two types: fluid and crystallized.

You use both types of intelligences. If you’ve ever encountered a new problem and solved it by recalling a previous experience, your fluid and crystallized intelligences have teamed up to your advantage.

Fluid intelligence is your ability to learn, assess, and navigate new situations. Crystallized intelligence is accumulated knowledge you can recall as needed. Problem-solving uses both intelligences.

Although they’re different, fluid and crystallized intelligence are both equally important — and in some ways connected.

Fluid intelligence, or general fluid intelligence (Gf), is your ability to:

  • reason
  • recognize patterns
  • solve problems
  • adapt to your environment and the world

You use Gf when you encounter new information and learn.

Gf can sometimes predict a person’s academic and job success. When educators or employers conduct aptitude testing, they’re measuring fluid intelligence.

It’s Gf that allows young people to learn faster and more easily than adults. An analysis from 2018 that included 680 studies found a connection between fluid intelligence and reading and math development.

There was a stronger relation to math and complex skills over reading and foundation skills. Yet, math uses both fluid and crystallized intelligence.

What is an example of fluid intelligence?

When you think of a strategy to solve a complicated math problem, you’re using fluid intelligence.

At what age does fluid intelligence peak?

Fluid intelligence increases until adolescence, then gradually declines.

General crystallized intelligence (Gc), or crystallized intelligence, is information you’ve learned in the past and stored. Gc results from experience, education, and cultural background.

School testing of learned facts and job candidate testing of software knowledge are examples of crystallized knowledge assessments.

What is an example of crystallized intelligence?

When you solve a math problem, your existing knowledge of formulas and symbols is an example of crystallized intelligence.

At what age does crystallized intelligence peak?

Crystallized intelligence increases as you age and gain more life experience. It slows in adulthood but can either stabilize or continue to increase throughout most of your life.

Fluid intelligenceCrystallized intelligence
new informationstored information
short-term memorylong-term memory
increases through childhood, peaks at adolescence, then declinesincreases through childhood, and slows with aging, then stabilizes or continues to increase throughout life
functions include working memory, processing speed, reasoning, cognitive control, inhibition, complex skills, attention tasks, creativityfunctions include procedural (practical), declarative (factual), general and specialized knowledge, wisdom

Although fluid and crystallized intelligences are different, they complement each other.

For example, an emergency room doctor uses both. When a patient arrives, the doctor’s fluid intelligence assesses the situation. Much like when solving a puzzle, the doctor’s fluid intelligence uses clues to arrive at an answer — in this case, a diagnosis.

Then, the doctor’s crystallized intelligence provides the previously acquired procedural knowledge needed for care and treatment.

Fluid and crystallized intelligences are also connected in some ways.

For example, high fluid intelligence allows you to learn faster, which results in more accumulated knowledge and in turn higher crystallized intelligence, as one 2019 study explores.

Changes in fluid intelligence can affect the expression of crystallized intelligence.

If you’re an older adult who has experienced a decline in fluid intelligence, you may not be as skilled at applying crystallized intelligence from your long-term memory to some current or new situations.

Crystallized intelligence testing measures general knowledge. Test types include vocabulary, verbal, and language use assessments. Research from 2015 points to text completion examinations as one measure of crystallized intelligence.

Fluid intelligence includes multiple cognitive qualities, so it requires a more varied testing approach. Assessment options include:

  • Progressive matrices: to test for pattern recognition, as stated in research from 2013
  • Digital span sequencing: to assess working memory, as explained in a 2019 study
  • Trail making test: to measure cognitive flexibility, as described in 2008 research
  • Digital fluency test: to assess creativity, problem solving, and inhibition, as noted in a 2016 study
  • Tower test: to measure planning ability, as explained in another 2016 study
  • Cognitive failures questionnaire: to assess attention, memory, distractibility, and executive function, as suggested in a 2017 study

You can increase both types of intelligences.

To increase your crystallized intelligence, you simply need to acquire new knowledge. Education and experience can help it take root in your long-term memory.

According to 2016 research, improving fluid intelligence requires a more comprehensive approach. It helps to think of fluid intelligence as a reflection of the health and fitness of your brain. So to increase this type of intelligence, you’ll need to nourish it well and exercise it often.

Brain health strategies include:

  • improved diet
  • regular exercise
  • adequate sleep
  • mindfulness training
  • stress reduction

Brain fitness activities include:

  • learning a new language
  • playing strategy games like chess
  • trying cognitive training
  • changing your routine
  • learning new skills and hobbies
  • socializing more
  • learning a musical instrument
  • challenging yourself mentally
  • using your mind to remember things rather than apps and smart devices

General intelligence has two main categories: fluid and crystallized.

Fluid intelligence is your ability to process new information, learn, and solve problems. Crystallized intelligence is your stored knowledge, accumulated over the years.

The two types work together and are equally important. They both increase through childhood. Fluid intelligence decreases with age and crystallized intelligence remains stable or continues to increase with age.

You can increase both types of intelligence. You could improve your fluid intelligence by keeping your brain healthy and fit. Adopting a lifelong learning habit can help you build up more crystallized intelligence.

Fluid intelligence is related to capacity in memory as well as attention: Evidence from middle childhood and adulthood

1. Gottfredson LS. Why g matters: The complexity of everyday life. Intelligence. 1997. January;24[1]:79–132. [Google Scholar]

2. Hunter JE. Cognitive ability, cognitive aptitudes, job knowledge, and job performance. J Vocat Behav. 1986. December;29[3]:340–62. [Google Scholar]

3. Neisser U, Boodoo G, Bouchard TJ Jr, Boykin AW, Brody N, Ceci SJ, et al. Intelligence: Knowns and unknowns. Am Psychol. 1996;51[2]:77–101. [Google Scholar]

4. Binet A, Simon Th. The development of intelligence in the child In: The development of intelligence in children (The Binet-Simon Scale) [Internet]. Baltimore: Williams & Wilkins Co; 1916. [cited 2016 Jul 17]. p. 182–273. Available from: http://content.apa.org/books/11069-004 [Google Scholar]

5. Schmidt FL, Hunter J. General Mental Ability in the World of Work: Occupational Attainment and Job Performance. J Pers Soc Psychol. 2004;86[1]:162–73. 10.1037/0022-3514.86.1.162 [PubMed] [CrossRef] [Google Scholar]

6. Spearman C. “General Intelligence,” Objectively Determined and Measured. Am J Psychol. 1904. April;15[2]:201. [Google Scholar]

7. Bachelder BL, Ray Denny M. A theory of intelligence: I. span and the complexity of stimulus control. Intelligence. 1977. April;1[2]:127–50. [Google Scholar]

8. Engle RW, Tuholski SW, Laughlin J, Conway ARA. Working memory, short-term memory and general fluid intelligence: A latent variable model approach. J Exp Psychol Gen. 1999;128:309–31. 10.1037/0096-3445.128.3.309 [PubMed] [CrossRef] [Google Scholar]

9. Fukuda K, Vogel E, Mayr U, Awh E. Quantity, not quality: the relationship between fluid intelligence and working memory capacity. Psychon Bull Rev. 2010. October;17[5]:673–9. 10.3758/17.5.673 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

10. Kyllonen PC, Christal RE. Reasoning ability is (little more than) working-memory capacity?! Intelligence. 1990. October;14[4]:389–433. [Google Scholar]

11. Miyake A, Friedman NP, Rettinger DA, Shah P, Hegarty M. How are visuospatial working memory, executive functioning, and spatial abilities related? A latent-variable analysis. J Exp Psychol Gen. 2001;130[4]:621–40. 10.1037/0096-3445.130.4.621 [PubMed] [CrossRef] [Google Scholar]

12. Burnett Heyes S, Zokaei N, van der Staaij I, Bays PM, Husain M. Development of visual working memory precision in childhood. Dev Sci. 2012;15[4]:528–539. 10.1111/j.1467-7687.2012.01148.x [PMC free article] [PubMed] [CrossRef] [Google Scholar]

13. Simmering VR. Working memory capacity in context: Modeling dynamic processes of behavior, memory, and development. Monogr Soc Res Child Dev. 2016;1:168. [PubMed] [Google Scholar]

14. D’Esposito M, Postle BR. The cognitive neuroscience of working memory. Annu Rev Psychol. 2015. January 3;66:115–42. 10.1146/annurev-psych-010814-015031 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

15. Alloway TP, Alloway RG. Investigating the predictive roles of working memory and IQ in academic attainment. J Exp Child Psychol. 2010. May;106[1]:20–9. 10.1016/j.jecp.2009.11.003 [PubMed] [CrossRef] [Google Scholar]

16. Au J, Sheehan E, Tsai N, Duncan GJ, Buschkuehl M, Jaeggi SM. Improving fluid intelligence with training on working memory: a meta-analysis. Psychon Bull Rev. 2015. April;22[2]:366–77. 10.3758/s13423-014-0699-x [PubMed] [CrossRef] [Google Scholar]

17. Jaeggi SM, Studer-Luethi B, Buschkuehl M, Su Y-F, Jonides J, Perrig WJ. The relationship between n-back performance and matrix reasoning—implications for training and transfer. Intelligence. 2010. November;38[6]:625–35. [Google Scholar]

18. Baddeley AD. Working Memory: Theories, Models, and Controversies. Annu Rev Psychol. 2012. January 10;63[1]:1–29. [PubMed] [Google Scholar]

19. Baddeley AD, Hitch G. Working Memory. In: Psychology of Learning and Motivation [Internet]. Elsevier; 1974. [cited 2015 Oct 21]. p. 47–89. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0079742108604521 [Google Scholar]

20. Broadbent DE. Listening between and during practised auditory distractions. Br J Psychol. 1956;47:51–60. [PubMed] [Google Scholar]

21. Chun null. Contextual cueing of visual attention. Trends Cogn Sci. 2000. May;4[5]:170–8. [PubMed] [Google Scholar]

22. Posner MI. Orienting of attention. Q J Exp Psychol. 1980;32:3–25. [PubMed] [Google Scholar]

23. Pylyshyn ZW, Storm RW. Tracking multiple independent targets: evidence for a parallel tracking mechanism. Spat Vis. 1988;3[3]:179–97. [PubMed] [Google Scholar]

24. Wolfe JM. Guided search 2.0: A revised model of visual search. Psychon Bull Rev. 1994;1:202–38. 10.3758/BF03200774 [PubMed] [CrossRef] [Google Scholar]

25. Shipstead Z, Lindsey DRB, Marshall RL, Engle RW. The mechanisms of working memory capacity: Primary memory, secondary memory, and attention control. J Mem Lang. 2014. April;72:116–41. [Google Scholar]

26. Cowan N. Attention and Memory New York: Oxford University Press; 1995. [Google Scholar]

27. Cowan N, Fristoe NM, Elliott EM, Brunner RP, Saults JS. Scope of attention, control of attention, and intelligence in children and adults. Mem Cognit. 2006;34[8]:1754–68. 10.3758/bf03195936 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

28. Engle RW. Working Memory Capacity as Executive Attention. Curr Dir Psychol Sci. 2002. February;11[1]:19–23. [Google Scholar]

29. Markant J, Amso D. Leveling the playing field: Attention mitigates the effects of intelligence on memory. Cognition. 2014. May;131[2]:195–204. 10.1016/j.cognition.2014.01.006 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

30. Spencer JP, Barich K, Goldberg J, Perone S. Behavioral dynamics and neural grounding of a dynamic field theory of multi-object tracking. J Integr Neurosci. 2012;11:339–62. 10.1142/S0219635212500227 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

31. Ma WJ, Huang W. No capacity limit in attentional tracking: Evidence for probabilistic inference under a resource constraint. J Vis. 2009. October 1;9[11]:3–3. 10.1167/9.11.3 [PubMed] [CrossRef] [Google Scholar]

32. Allen R, Mcgeorge P, Pearson D, Milne AB. Attention and expertise in multiple target tracking. Appl Cogn Psychol. 2004. April;18[3]:337–47. [Google Scholar]

33. Fry AF, Hale S. Processing speed, working memory, and fluid intelligence: Evidence for a developmental cascade. Psychol Sci. 1996. July;7[4]:237–41. [Google Scholar]

34. Kranzler JH, Jensen AR. Inspection time and intelligence: A meta-analysis. Intelligence. 1989. October;13[4]:329–47. [Google Scholar]

35. Salthouse TA. The processing-speed theory of adult age differences in cognition. Psychol Rev. 1996. July;103[3]:403–28. [PubMed] [Google Scholar]

36. Blum D, Holling H. Spearman’s law of diminishing returns. A meta-analysis. Intelligence. 2017. November;65:60–6. [Google Scholar]

37. Karmiloff-Smith A. Development itself is the key to understanding developmental disorders. Trends Cogn Sci. 1998;2[10]:389–98. [PubMed] [Google Scholar]

38. Cattell RB. The theory of fluid and crystallized general intelligence checked at the 5–6 year-old level. Br J Educ Psychol. 1967. June;37[2]:209–24. [PubMed] [Google Scholar]

39. Cattell RB. Theory of fluid and crystallized intelligence: A critical experiment. J Educ Psychol. 1963;54[1]:1–22. [Google Scholar]

40. Horn JL, Cattell RB. Refinement and test of the theory of fluid and crystallized general intelligences. J Educ Psychol. 1966. October;57[5]:253–70. [PubMed] [Google Scholar]

41. Gathercole SE, Willis CS, Emslie H, Baddeley AD. Phonological memory and vocabulary development during the early school years: A longitudinal study. Dev Psychol. 1992;28[5Journal Article U Lancaster, Memory Research Unit, England UK. Sep English):887–98. [Google Scholar]

42. Brainard DH. The psychophysics toolbox. Spat Vis. 1997;10:433–6. [PubMed] [Google Scholar]

43. Kleiner M, Brainard DH, Pelli DG. What’s new in psychtoolbox-3? Percept 36 ECVP Abstr Suppl. 2007; [Google Scholar]

44. Unsworth N, Engle RW. Simple and complex memory spans and their relation to fluid abilities: Evidence from list-length effects. J Mem Lang. 2006. January;54[1]:68–80. [Google Scholar]

45. Kessels RPC, van den Berg E, Ruis C, Brands AMA. The Backward Span of the Corsi Block-Tapping Task and Its Association With the WAIS-III Digit Span. Assessment. 2008. May 15;15[4]:426–34. 10.1177/1073191108315611 [PubMed] [CrossRef] [Google Scholar]

46. Klingberg T, Forssberg H, Westerberg H. Increased brain activity in frontal and parietal cortex underlies the development of visuospatial working memory capacity during childhood. J Cogn Neurosci. 2002;14[1]:1–10. 10.1162/089892902317205276 [PubMed] [CrossRef] [Google Scholar]

47. Mackey AP, Hill SS, Stone SI, Bunge SA. Differential effects of reasoning and speed training in children: Effects of reasoning and speed training in children. Dev Sci. 2011. May;14[3]:582–90. 10.1111/j.1467-7687.2010.01005.x [PubMed] [CrossRef] [Google Scholar]

48. Thorell LB, Lindqvist S, Bergman Nutley S, Bohlin G, Klingberg T. Training and transfer effects of executive functions in preschool children. Dev Sci. 2009. January;12[1]:106–13. 10.1111/j.1467-7687.2008.00745.x [PubMed] [CrossRef] [Google Scholar]

49. Jaeggi SM, Buschkuehl M, Perrig WJ, Meier B. The concurrent validity of the N-back task as a working memory measure. Memory. 2010. May;18[4]:394–412. 10.1080/09658211003702171 [PubMed] [CrossRef] [Google Scholar]

50. Basak C, Verhaeghen P. Aging and Switching the Focus of Attention in Working Memory: Age Differences in Item Availability But Not in Item Accessibility. J Gerontol B Psychol Sci Soc Sci. 2011. September 1;66B[5]:519–26. [PMC free article] [PubMed] [Google Scholar]

51. Dale G, Green CS. Associations Between Avid Action and Real-Time Strategy Game Play and Cognitive Performance: a Pilot Study. J Cogn Enhanc. 2017. September;1[3]:295–317. [Google Scholar]

52. Price JM, Colflesh GJH, Cerella J, Verhaeghen P. Making working memory work: The effects of extended practice on focus capacity and the processes of updating, forward access, and random access. Acta Psychol (Amst). 2014. May;148:19–24. 10.1016/j.actpsy.2013.12.008 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

53. Berch DB, Krikorian R, Huha EM. The Corsi Block-Tapping Task: Methodological and Theoretical Considerations. Brain Cogn. 1998. December;38[3]:317–38. 10.1006/brcg.1998.1039 [PubMed] [CrossRef] [Google Scholar]

54. Colom R, Abad FJ, Rebollo I, Chun Shih P. Memory span and general intelligence: A latent-variable approach. Intelligence. 2005. November;33[6]:623–42. [Google Scholar]

55. Kane MJ, Hambrick DZ, Tuholski SW, Wilhelm O, Payne TW, Engle RW. The generality of working memory capacity: a latent-variable approach to verbal and visuospatial memory span and reasoning. J Exp Psychol Gen. 2004. June;133[2]:189–217. 10.1037/0096-3445.133.2.189 [PubMed] [CrossRef] [Google Scholar]

56. Orsini A, Grossi D, Capitani E, Laiacona M, Papagno C, Vallar G. Verbal and spatial immediate memory span: Normative data from 1355 adults and 1112 children. Ital J Neurol Sci. 1987. December;8[6]:537–48. [PubMed] [Google Scholar]

57. Luck SJ, Vogel EK. The capacity of visual working memory for features and conjunctions. Nature. 1997;390[6657]:279–81. 10.1038/36846 [PubMed] [CrossRef] [Google Scholar]

58. Rouder JN, Morey RD, Morey CC, Cowan N. How to measure working memory capacity in the change detection paradigm. Psychon Bull Rev. 2011. April;18[2]:324–30. 10.3758/s13423-011-0055-3 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

59. Aaronson D, Watts B. Extensions of Grier’s computational formulas for A’ and B” to below-chance performance. Psychol Bull. 1987. November;102[3]:439–42. [PubMed] [Google Scholar]

60. Machizawa MG, Driver J. Principal component analysis of behavioural individual differences suggests that particular aspects of visual working memory may relate to specific aspects of attention. Neuropsychologia. 2011. May;49[6]:1518–26. 10.1016/j.neuropsychologia.2010.11.032 [PubMed] [CrossRef] [Google Scholar]

61. Simmering VR, Patterson R. Models provide specificity: Testing a proposed mechanism of visual working memory capacity development. Cogn Dev. 2012;27[4]:419–39. 10.1016/j.cogdev.2012.08.001 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

62. Atkinson J, Campbell FW, Francis MR. The magic number 4 +/- 0: a new look at visual numerosity judgements. Perception. 1976;5[3]:327–34. 10.1068/p050327 [PubMed] [CrossRef] [Google Scholar]

63. Gallistel CR, Gelman R. Preverbal and verbal counting and computation. Cognition. 1992. August;44[1–2]:43–74. [PubMed] [Google Scholar]

64. Kaufman EL, Lord MW, Reese TW, Volkmann J. The Discrimination of Visual Number. Am J Psychol. 1949. October;62[4]:498 [PubMed] [Google Scholar]

65. Trick LM, Pylyshyn ZW. Why are small and large numbers enumerated differently? A limited-capacity preattentive stage in vision. Psychol Rev. 1994;101[1]:80–102. [PubMed] [Google Scholar]

66. Green CS, Bavelier D. Enumeration versus multiple object tracking: the case of action video game players. Cognition. 2006. August;101[1]:217–45. 10.1016/j.cognition.2005.10.004 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

67. Rueda MR, Fan J, McCandliss BD, Halparin JD, Gruber DB, Lercari LP, et al. Development of attentional networks in childhood. Neuropsychologia. 2004. January;42[8]:1029–40. 10.1016/j.neuropsychologia.2003.12.012 [PubMed] [CrossRef] [Google Scholar]

68. Dye MWG, Bavelier D. Differential development of visual attention skills in school-age children. Vision Res. 2010. February;50[4]:452–9. 10.1016/j.visres.2009.10.010 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

69. Fan J, McCandliss BD, Sommer T, Raz A, Posner MI. Testing the efficiency and independence of attentional networks. J Cogn Neurosci. 2002. April 1;14[3]:340–7. 10.1162/089892902317361886 [PubMed] [CrossRef] [Google Scholar]

70. Ball K, Owsley C. The useful field of view test: a new technique for evaluating age-related declines in visual function. J Am Optom Assoc. 1993. January;64[1]:71–9. [PubMed] [Google Scholar]

71. Boot WR, Basak C, Erickson KI, Neider M, Simons DJ, Fabiani M, et al. Transfer of skill engendered by complex task training under conditions of variable priority. Acta Psychol (Amst). 2010. November;135[3]:349–57. [PubMed] [Google Scholar]

72. Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ. Improving fluid intelligence with training on working memory. Proc Natl Acad Sci. 2008. May 13;105[19]:6829–33. 10.1073/pnas.0801268105 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

73. Klingberg T, Forssberg H, Westerberg H. Training of working memory in children with ADHD. J Clin Exp Neuropsychol. 2002. September;24[6]:781–91. 10.1076/jcen.24.6.781.8395 [PubMed] [CrossRef] [Google Scholar]

74. Unsworth N, Engle RW. On the division of short-term and working memory: An examination of simple and complex span and their relation to higher order abilities. Psychol Bull. 2007;133[6]:1038–66. 10.1037/0033-2909.133.6.1038 [PubMed] [CrossRef] [Google Scholar]

75. Dunn DM, Dunn DL. Peabody picture vocabulary test: Manual. Pearson; 2007. [Google Scholar]

76. Borsboom D. The attack of the psychometricians. Psychometrika. 2006. September;71[3]:425–40. 10.1007/s11336-006-1447-6 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

77. Borsboom D, Mellenbergh GJ, van Heerden J. The theoretical status of latent variables. Psychol Rev. 2003;110[2]:203–19. 10.1037/0033-295X.110.2.203 [PubMed] [CrossRef] [Google Scholar]

78. Bürkner P-C. brms: An R Package for Bayesian Multilevel Models Using Stan. J Stat Softw [Internet]. 2017. [cited 2018 Nov 5];80[1]. Available from: http://www.jstatsoft.org/v80/i01/ [Google Scholar]

79. Adey P, Csapó B, Demetriou A, Hautamäki J, Shayer M. Can we be intelligent about intelligence?: Why education needs the concept of plastic general ability. Educ Res Rev. 2007. January;2[2]:75–97. [Google Scholar]

80. Harrison TL, Shipstead Z, Engle RW. Why is working memory capacity related to matrix reasoning tasks? Mem Cognit. 2015. April;43[3]:389–96. 10.3758/s13421-014-0473-3 [PubMed] [CrossRef] [Google Scholar]

81. Li S-C, Lindenberger U, Hommel B, Aschersleben G, Prinz W, Baltes PB. Transformations in the couplings among intellectual abilities and constituent cognitive processes across the life span. Psychol Sci. 2004. March;15[3]:155–63. 10.1111/j.0956-7976.2004.01503003.x [PubMed] [CrossRef] [Google Scholar]

82. Ahissar M, Nahum M, Nelken I, Hochstein S. Reverse hierarchies and sensory learning. Philos Trans R Soc B Biol Sci. 2009. February 12;364[1515]:285–99. [PMC free article] [PubMed] [Google Scholar]

83. Fahle M. Perceptual learning: A case for early selection. J Vis. 2004. October 1;4[10]:879–90. 10.1167/4.10.4 [PubMed] [CrossRef] [Google Scholar]

84. Woodworth RS, Thorndike EL. The influence of improvement in one mental function upon the efficiency of other functions. Psychol Rev. 1901;8[3]:247–61. [Google Scholar]

85. Klingberg T. Is working memory capacity fixed? J Appl Res Mem Cogn. 2012;1[3]:194–6. [Google Scholar]

86. Redick TS, Shipstead Z, Harrison TL, Hicks KL, Fried DE, Hambrick DZ, et al. No evidence of intelligence improvement after working memory training: A randomized, placebo-controlled study. J Exp Psychol Gen. 2013;142[2]:359–79. 10.1037/a0029082 [PubMed] [CrossRef] [Google Scholar]

87. Shipstead Z, Redick TS, Engle RW. Is working memory training effective? Psychol Bull. 2012;138[4]:628–54. 10.1037/a0027473 [PubMed] [CrossRef] [Google Scholar]

88. Wass SV, Scerif G, Johnson MH. Training attentional control and working memory–Is younger, better? Dev Rev. 2012. December;32[4]:360–87. [Google Scholar]

89. Harrison TL, Shipstead Z, Hicks KL, Hambrick DZ, Redick TS, Engle RW. Working Memory Training May Increase Working Memory Capacity but Not Fluid Intelligence. Psychol Sci. 2013. December 1;24[12]:2409–19. 10.1177/0956797613492984 [PubMed] [CrossRef] [Google Scholar]

90. Jaeggi SM, Buschkeuhl M, Jonides J, Shah P. Cogmed and working memory training—Current challenges and the search for underlying mechanisms. J Appl Res Mem Cogn. 2012;1[3]:211–3. [Google Scholar]

91. Logie RH. Cognitive training: Strategies and the multicomponent cognitive system. J Appl Res Mem Cogn. 2012;1[3]:206–7. [Google Scholar]

92. Thompson TW, Waskom ML, Garel K-LA, Cardenas-Iniguez C, Reynolds GO, Winter R, et al. Failure of Working Memory Training to Enhance Cognition or Intelligence. de Beeck HPO p., editor. PLoS ONE. 2013. May 22;8[5]:e63614 10.1371/journal.pone.0063614 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

Mobile and crystallized intellect | it's... What is Fluctuating and crystallized intellect?

According to the theory of intelligence of psychologist Raymond Cattell [1] intelligence can be conditionally divided into fluid intelligence (eng. fluid intelligence , Gf) - the ability to think logically, analyze and solve problems regardless of previous experience; and crystallized intelligence (eng. crystallized intelligence , Gc) - accumulated experience and the ability to use acquired knowledge and skills.

Contents

  • 1 Mobile intelligence
  • 2 Crystallized Intelligence
  • 3 Notes
  • 4 See also

Fluid intelligence

Fluid intelligence, according to theory, applies to all areas of life, but most of all applies to science and technology, mathematics, programming. It includes the ability to learn, inductive and deductive thinking, abstract thinking, recognizing connections and patterns. Fluid intelligence uses working memory, and often its level in humans is generally closely related to the size of working memory. In this regard, a theory arose about the possibility of developing fluid intelligence through the development of working memory, using techniques like n-backward. [2] A person's mobile intelligence increases until about 30-40 years of age, after which it begins to decline. [3] Fluid intelligence predominates, for example, in patients with autism. [4]

Crystallized intellect

Crystallized intellect is responsible for retrieving knowledge and skills from long-term memory and applying them in habitual conditions where the inclusion of mobile intellect is not required. Crystallized intelligence is determined by the volume and depth of accumulated knowledge, vocabulary, it includes verbal abilities and is often measured precisely by the level of verbal abilities. Usually the level of a person's crystallized intellect is different in different areas of life. The crystallized intelligence of a person increases until old age, after which it usually begins to decline.

Crystallized intelligence is not a “crystallized” form of fluid intelligence — these are functionally different processes, although the level of crystallized intelligence depends on the level of fluid intelligence, because people with high fluid intelligence, learning faster, accumulate crystallized intelligence faster, which is confirmed by IQ tests .

Notes

  1. Cattell, R. B. (1971). Abilities: Their structure, growth, and action . New York: Houghton Mifflin. ISBN 0395042755.
  2. Kirchner, W. K. (1958), Age differences in short-term retention of rapidly changing information. Journal of Experimental Psychology, 55(4), 352-358
  3. Fluid Intelligence vs. Crystallized Intelligence
  4. Hayashi, Mika; Kato, Motoichiro; Igarashi, Kazue & Kashima, Haruo (2007), ""Superior fluid intelligence in children with Asperger's disorder"", Brain and Cognition (Elsevier) . — T. 66 (3): 306–310, doi:10.1016/j.bandc.2007.09.008,

See also

  • n-back task

Movable Intelligence - Mind Generator | by Sergey Karelov

Credit: CC0 Public Domain

Intelligence depends both on HW ( structure of the brain — the size of its regions and their connectivity) and on SW ( "firmware" of the brain - switching of the neural network that changes in the process of learning and experience).

According to the dominant theory of cognitive abilities (Cattell-Horn-Carroll), the SW of intelligence includes:

1) fluid intelligence — the ability to think logically, perceive and memorize new things, solve new unusual problems;

2) and crystallized intelligence accumulated experience and the ability to use acquired knowledge and skills.

1st is a kind of framework for developing applications, and 2nd is the applications themselves that can do various useful things.

But these applications are just the result of using (it is not clear yet by whom) the framework. And for different people, the complexity and perfection of this framework is very different.

And so much so that the same training:

• allows one person to equip themselves with a chic set of new applications;

• others - get a simpler set of applications;

• the third is just to increase the number of bugs in the applications they already have.

All of the above for a long time (at least since 1971, when the theory of fluid and crystallized intelligence was developed) remained a theory. But with the development of all kinds of brain scanning and mapping techniques, breakthroughs began.

Earlier this year, using data from hundreds of participants in the Human Connectome project, several of the world's leading laboratories simultaneously demonstrated that brain connectivity patterns create "individual fingerprints" that distinguish each person. People with strong functional ties between certain regions have an extensive vocabulary and exhibit higher fluid intelligence. They tend to have better education and life satisfaction, as well as better memory and attention.

People with weaker functional connections among the same areas of the brain have lower fluid intelligence, which reduces the ability to concentrate and, in general, to learn.

The 2nd wave of breakthroughs followed.

A new study on 424 volunteers answered the question

Is it possible, by analyzing the size of individual areas of the brain, to assess the level of human fluid intelligence (ie the complexity and sophistication of its application development framework)?

The researchers unequivocally answered yes.

At the same time, to identify significant differences in the level of mobile intelligence, it is sufficient to analyze only two structures in the brain - the parahippocampal cortex and the caudate body.

In other words,

can be determined in advance,

• who should be taught complex skills,

• and who should only be wasted time.

Excuse me, this is almost neuro-eugenics HW (which does not negate the significance of SW's socio-cultural firmware).

The authors of the study have already set the next goal - to find a way to enhance mobile intelligence on the existing HW. All sorts of neuro-implants, etc.

- Popular

- Paywall scientific

- Plaintext

P.S. This news has an important implication. If our intelligence still depends so much on the inherited HW, then Harari's fears about the imminent appearance of super-humans due to flashing their algorithms (SW) turn out to be somewhat premature.

P.P.S. Information about the study was sent by a channel reader who plans to use the research method in a very promising new project. If it takes off, I'll write.


Learn more