“Ultimately, it would certainly be desirable to have an algorithm for the selection of an intelligence, such that any trained researcher could determine whether a candidate’s intelligence met the appropriate criteria. At present, however, it must be admitted that the selection (or rejection) of a candidate’s intelligence is reminiscent more of an artistic judgment than of a scientific assessment.” – Howard Gardner, educational psychologist
A while back, I wrote a post on general intelligence, noting that experts disagree as to what it actually is and emphasizing a broad conceptual analysis of the idea. Yet, for some reason, I kept having nagging doubts: “Why not keep this simple? An intelligent person is simply one with greater neural efficiency. Measure that factor correctly and you will have a quantification of this concept.” Perhaps a more efficient nervous system is more “fluid”, more adaptable, and thus more fit and functional in general. Raymond Cattell had two divisions of intelligence: Fluid and Crystallized, the latter being our acquired knowledge and skills. But the former is about a more “basic” sort of intelligence, how we process information without relying on a storehouse of previously acquired knowledge , but instead using abstract reasoning, pattern recognition, and general problem solving ability. Thrown into an unfamiliar situation with a requirement to think fast, all that stuff we’ve picked up over the years might avail us little or nothing. As Cattell put it, “It is apparent that one of these powers…has the ‘fluid’ quality of being directable to almost any problem.” Fluid intelligence (FI) is measured using a non-verbal, multiple choice picture completion test called the Raven Progressive Matrices, which focuses on detecting relationships among images.(Also used are the Cattell Culture Fair Test and the performance subscale of the Wechsler Adult Intelligence Scale (WAIS).
Peter Schonemann, who studied under Cattell, stated that the g factor (the letter standing for general/basic smarts) does not exist, and that those who emphasized it, like Arthur Jensen, were distorting the original findings of Charles Spearman with the effect, intentional or not, of putting racial minorities at a disadvantage in terms of policy decisions based on pro-hereditarian research. Canadian psychologist, Keith Stanovich argues that IQ tests, or their proxies (e.g., the SAT), do not effectively measure cognitive functioning, because they fail to assess real-world skills of judgement and decision-making. But he does not say the tests should be abandoned, just revised to encompass measurement of the aforementioned skills. Going back to the basic semantic question, Australian educational psychologist, R. W. Howard breaks intelligence down into three categories: Spearman’s g; a property of behavior; and a set of abilities. ‘Each concept,’ he writes, ‘contains different information, refers to a different category, and should be used in different ways…A concept is never right or wrong, but is only more or less useful.’ Matching a given use of the term, ‘intelligence’, with its appropriate category would eliminate much confusion. Furthermore, recent research on IQ testing suggests that at least part of what is being measured is motivation.
Brighter individuals display lower brain energy expenditure while performing cognitive tasks: this is the neural efficiency hypothesis in a nutshell. Such expenditure can be measured using PET scans with cortical glucose metabolic rate used as a correlate of abstract reasoning and attention. Considering “reconfiguration” to reflect said expenditure, recent research has found that “brain network configuration at rest was always closer to a wide variety of task configurations in intelligent individuals. This suggests that the ability to modify network connectivity efficiently when task demands change is a hallmark of high intelligence (Shultz and Cole, 2016). Dix et al (2016) have noted that high fluid intelligence and learning cause fast, accurate analogical reasoning. Yet,”for low FI, initially low cortical activity points to mental overload in hard tasks [and that] learning-related activity increases might reflect an overcoming of mental overload.” Swanson & McMurran (2017) conclude from a randomized control study that “improvement in working memory, as well as the positive transfer in learning outcomes, are moderated by fluid intelligence.” On the other hand, Neubauer and Fink (2009) note that, as opposed to moderate-difficulty tasks, when the more able individuals have to deal with very complex ones, they will invest more cognitive resources. “It is not clear,” write the authors, “if this reversal of the brain activation-intelligence relationship is simply due to brighter individuals’ volitional decision to invest more effort as compared to the less able ones, who might have simply ‘given up’ as they experience that the task surpasses their ability.” It is concluded that new study designs are necessary to explore this volitional factor of cortical effort.
While Howard Gardner’s multiple intelligence scheme seems to stretch the concept too far, a unitary notion centering on neural efficiency as an operational definition seems problematic due to limited explanatory force. Despite the compelling evidence for the NE hypothesis, the semantic issue remains. There is no fact of the matter which determines the “proper” use of the concept of intelligence. Nevertheless, in the light of more recent research (from neuroimaging, especially), the opponents of NE reductionism are clearly on the defensive today.
Wm. Doe, Ph.D., February, 2017
A. Dix et al (2016). The role of fluid intelligence in analogical reasoning: How to become neurally efficient? Neurobiology of Learning and Memory, 134B, 236-247.
D.H. Schultz, M.W. Cole (2016). Higher intelligence is associated with less task-related brain network reconfiguration. J. Neurosci., 36 (33).
H.L. Swanson, M. McMurran (2017). The impact of working memory on near and far transfer measures: Is it all about fluid intelligence? Child Neuropsychology, online pub 2/1/2017.
A. C. Neubauer, A. Fink (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33; 1004-23.