While the existence of a psychometric g is now firmly established, there is considerable controversy concerning the existence of a neurological and/ or genetic g. (See my previous post.) For those not familiar with the debate, let me just quote from Ian Deary’s excellent recent article, Intelligence:
Probably the strongest psychometric challenge to Spearman’s account of intelligence differences was from Godfrey Thomson (Bartholomew et al. 2009). Thomsonever denied Spearman’s positive manifold of correlations among mental tests, but he suggested a radically different reason for its occurring. Instead of g—perhaps, according to Spearman, the result of people having generally more or less of mental energy or power — Thomson found that the universally positive correlations among tests could also arise from each test’s sampling a subset of numerous, independent mental bonds; thus his “bonds” or “sampling” theory of intelligence. The Spearman-Thomson debates lasted from the First World War until almost the end of World War II. A fresh look at Thomson’s ideas concluded that his model of intelligence was not inferior to Spearman’s, either on statistical or biological grounds, though that was partly because both were vague biologically (Bartholomew et al. 2009). A related development is the mutual interaction model of intelligence, which also posits the emergence of a general factor without a general cause (van der Maas et al. 2006). The basic idea is that a statistical g emerges through the mutual interaction, over the course of their development, of several cognitive processes.
As Spearman’s model and that proposed by van der Maas et al. have radically different implications for individual (and group) differences, both with regards to their origin and stability, this issue is not an arcane one.
General intelligence, the g factor, is a major issue in psychology and neuroscience. However, the neural mechanism of the g factor is still not clear. It is suggested that the g factor should be non-modular (a property across the brain) and show good colinearity with various cogni- tive tests. This study examines the hypothesis that functional connectivity may be a good can- didate for the g factor. We recorded resting state eyes-closed EEG signals in 184 healthy young females. Coherence values of 38 selected channel pairs across delta, theta, alpha, beta and gamma frequencies were correlated with six intelligence quotient (IQ) subtests, including symbol search, block design, object assembly, digit span, similarity and arithmetic. A three- stage analytic flow was constructed to delineate common (g factor) and unique neural compo- nents of intelligence. It is noticed that the coherence pattern demonstrates good correlation with five of the IQ subtests (except symbol search) and non-modularity in the brain. Our com- monality analyses support connectivity strength in the brain as a good indicator of the g factor. For the digit span and arithmetic tests, the uniqueness analyses provide left-lateralized topog- raphy relevant to the operation of working memory. Performance on the arithmetic test is fur- ther correlated with strengths at left temporo-parietal and bilateral temporal connections. All the significant correlations are positive, indicating that the stronger the connectivity strengths, the higher the intelligence. Our analyses conclude that a smarter brain is associated with stronger interaction in the central nervous system. The implication and why the symbol search does not show parallel results are discussed.