The Jung & Haier (J&H) P-FIT model shows how far we have progressed toward understanding the biological basis of intelligence. Twenty-five years ago researchers in the field were engaged in an unedifying discussion of the relation between skull sizes and intelligence test scores. By taking advantage of the huge advances in measurement of the brain that have occurred in the past quarter century, J&H can take the far more sophisticated view that individual differences in intelligence depend, in part, upon individual differences in specific areas of the brain and in the connections between them.
J&H's view is consistent with convincing psychological evidence that general intelligence depends upon two information-processing functions: working memory capacity and general speediness. The P-FIT model provides a way of understanding why this might be true. Working memory is closely connected to the simultaneous abilities to store relevant information for temporary manipulation and at the same time suppress irrelevant information. The information being maintained or suppressed may either come directly from the environment or be the result of activation of memories stored throughout the cortex. There is some gross evidence that general speediness, as reflected in complex problem-solving behaviors, may be linked to neural conductance. J&H's discussion of the varied roles of gray and white matter, and of the importance of measuring connections between areas of the brain, points the way toward the use of new technologies that can much improve upon arguments that rely on crude measures such as simple reaction times, or on external measurements of nervous system responses, such as the galvanic skin response (GSR). The P-FIT model, with its emphasis on interaction between parts, is also consistent with recent theoretical modeling (van der Maas et al. Reference van der Maas, Dolan, Grasman, Wicherts, Huizenga and Raijmakers2006) that has shown that the statistical phenomenon of general intelligence (g) could be produced by interaction between component modules, rather than by a general property underlying a variety of cognitive skills.
In theory, the P-FIT model and others like it might move discussions of the genetic basis of intelligence beyond discussions of the percentage of variance in intelligence that is due to genetics (a measure that is restricted to the population measured) to an understanding of the mechanisms by which genes influence intelligence. However, this is likely to be a slow, hard process, because isolating the genes involved is not going to be easy. For example, J&H cite reports that the ASPM and microcephalin genes, which are involved in pathologically small brain sizes, might be involved in establishing variations in intelligence within the normal range. Alas, recent evidence, which probably was not available when J&H wrote their article, indicates that such involvement is complex, that the alleles involved are not clear, and that the overall involvement, if it exists at all, is very small (Mekel-Bobrov et al. Reference Mekel-Bobrov, Posthuma, Gilbert, Lind, Gosso, Luciano, Harris, Bates, Polderman, Whalley, Fox, Starr, Evans, Montgomery, Fernandes, Heutnik, Martin, Boomsma, Deary, Wright, de Geuss and Lahn2007; Rushton et al. Reference Rushton, Vernon and Bons2006; Woods et al. Reference Woods, Freimer, De Young, Fears, Sicotte, Service, Valention, Toga and Mazziota2006). Tracing out the biological pathways involved in the genetics of intelligence is going to be a long and arduous task, simply because the genetic component is likely to be due to very small contributions by very large numbers of genes.
The problem is made even more complicated by the fact that different brain mechanisms may be crucial for intelligence at different times, and that the relative importance of brain areas may differ between men and women. See, for instance, Gernsbacher's (Reference Gernsbacher2007) light-hearted discussion of whether it is good or bad to have a thick cortex.
All this tells us is that the P-FIT model is a very useful step toward a model of the biological causes of intelligence, but there are many steps to come. I believe J&H would agree.
Finally, it is important to remember that the P-FIT model, and similar models to come, are models of the biological causes of intelligence. Although there is a sense in which everything, even knowledge of the arcane rules of American football, must have a biological basis, socio-cultural effects, most definitely including education, are important too. A complete understanding of variations in human cognition will not be reached until we have models of both environmental and biological influences and their interactions.
Obviously the P-FIT model is addressed solely to biological concerns. However, its development has analogical meaning for studies of the environment. The P-FIT and similar models are possible only because of the major advances that have been made in biological measurements of brain processes. Models of social influences will require similar detailed measurements, both of intelligence and of related social phenomena. Given the amount of recording that is routinely done for social transactions today, the gathering of such data is conceivable. However, actually gathering the data, and for that matter, the measurement of intelligence itself, is hampered by bureaucratic concerns for privacy that go far beyond what is needed to satisfy a legitimate concern for confidentiality. Until this problem is solved, we are unlikely to have the data needed to match the sophistication of biological models with equally sophisticated environmental models.
