Posts Tagged 'Fluid and crystallized intelligence'

Mapping and Predicting Fluid Intelligence

British-American psychologist Raymond Cattell

Raymond Cattell – photo Wikipedia

The idea of dividing Spearman‘s theory of general intelligence (g) into two separate concepts; fluid (gF) and crystallized (gC) intelligence, is not particularly new. Psychologist Raymond Cattell, best known for his work on defining and analysing personality types and the Cattell Culture Fair Test, published a paper on his theories on intelligence back in 1941 (Cattell, 1941).  Horn and Cattell (1967) later described fluid intelligence as “…the ability to perceive relationships independent of previous specific practice or instruction concerning those relationships.” That is, it’s the type of intelligence we bring to bear on previously-unseen problems we encounter in daily life that we need to find solutions for, and that may require abstract thinking.  Contrast this with crystallized intelligence, which relies on previous experience and long-term memory, and may also be influenced by cultural factors.

In a previous post from early 2009 I discussed the use of functional Magnetic Resonance Imaging (fMRI) techniques to map human neural activity to visual recognition of  previously-viewed scenes. Back then, researcher John-Dylan Haynes used Blood Oxygenation Level Dependent (BOLD-fMRI) imaging to determine what item a subject might be thinking about.  More recently, researchers at  universities in the US and Slovenia have used similar BOLD scanning techniques to map global connectivity of the prefrontal cortex to predict cognitive control and intelligence. Their research is based on the premise that “individuals with higher intelligence ha[ve] more efficient whole-brain network organization (van den Heuval et. al., 2009)”.  The researchers first tested their 94 young adult subjects for general intelligence using Cattell’s Culture Fair Test. They subsequently mapped the global connectivity of their subjects’ brains between specific regions in the Lateral Prefrontal Cortex (LPFC) and then correlated their findings with the results of the fluid intelligence tests mentioned above. This allowed them to make statistically-significant predictions about their subjects’ levels of intelligence.  The research concluded that “a specific region’s global connectivity [i.e. the relative strength of its neural connections] predicts intelligence” (Cole et. al., 2012).

The implications of this research for learning and teaching are not entirely clear.  Intelligence testing using any methodology can be contentious at the best of times.  Cattell’s attempt to ameliorate the effects of culture on intelligence testing, the Culture Fair Test, has its fair share of both supporters and critics, the latter generally commenting that measures of intelligence should not be based on single assessments, no matter how well designed to counteract the effects of biases such as culture and economically-deprived education.

From my own perspective, an undesirable outcome of the research discussed above would be the use of such quantitative measures of predicted intelligence to stream children into a range of vocational education programs supposedly best suited to their predicted future abilities and consequent needs.  This approach assumes that young adults are likely to be incapable of making wise choices regarding their own futures  (which is, regrettably, already the situation for some children whose parents see them as a projection of themselves and their social status).  The freedom to choose one’s own path, and to make changes along the way is, in my opinion, a fairly basic human right.  Fortunately, the ability to successfully re-invent yourself has some recent research to back it up; neural plasticity studies, as discussed by Norman Doidge (2007) in “The Brain that Changes Itself“, at least offer the hope that most human brains can successfully re-organize, even in late adulthood, according to changes in a person’s circumstances, immediate environment, or due to training specifically designed with that purpose in mind. Stanford’s Carol Dweck (2000) has her own take on how people see their own level of intelligence (as a personal “mindset”) and how they can realise their own potential through approaches based on personal growth.

Cattell, R. B. (1941). Some theoretical issues in adult intelligence testing. Psychology Bulletin 38, p.592

Horn J. L. & Cattell R. B. (1967). “Age differences in fluid and crystallized intelligence.” Acta Psychologica, 26, pp. 107-129.

Cole, M. W. , Yarkoni,T., Repovš, G., Anticevic, A. and Braver, T.S. (2012). Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence. The Journal of Neuroscience 27 June 2012, 32 (26), pp. 8988-8999

van den Heuvel M. P., Stam C.J. , Kahn R.S., Hulshoff Pol H.E. (2009).  Efficiency of functional brain networks and intellectual performance. The Journal of  Neuroscience 29: pp. 7619–7624.

Doidge, N. (2007). The Brain that Changes Itself. Penguin Books, London.

Dweck, C. (2000). Self-theories: their role in motivation, personality, and development. Hove: Psychology Press

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