When Jung & Haier (J&H) provide a brief summarization of the P-FIT model in the target article (sect. 4, para. 3), they are describing one aspect of what can be considered intelligence. Their focus is on reasoning abilities and on what are referred to as effortful cognitive processes: early attentionally directed processing of information in the parietal cortex and coordination of information, information maintenance, and inhibition of prepotent or distracting information and responding through interconnected frontal and parietal cortical circuitry. The broad aspect of cognitive ability that is the focus of their review is generally referred to as working memory, executive function, or fluid cognition and can be considered quintessential higher-order cognition. However, although fluid abilities, broadly speaking, have been assumed to underlie general intelligence, a variety of information indicates this is not the case. As I outlined in a prior target article in this journal (Blair Reference Blair2006), despite numerous studies indicating near unity between working memory and general intelligence (e.g., Colom et al. Reference Colom, Rebollo, Palacios, Juan-Espinosa and Kyllonen2004), an equally numerous body of studies both with special populations (Duncan et al. Reference Duncan, Burgess and Emslie1995; Waltz et al. Reference Waltz, Knowlton, Holyoak, Boone, Mishkin, Santos, Thomas and Miller1999) and with historical cohort data (Flynn, in press) indicate dissociation between fluid cognitive abilities and general intelligence. Accordingly, the P-FIT model could more accurately be described as a theory of the neural basis for working memory/executive cognitive abilities, and as such generally has broad consensus.

As the data presented in the target article indicate, reasoning ability, measured in a variety of ways, is associated with a distributed cortical network primarily involving fronto-parietal circuitry. It is interesting to note, however, that early research on the neural basis for working memory focused primarily on the frontal cortex, and that the role of the parietal cortex in individual differences in working memory and reasoning ability has only recently become clearer. Specifically, several studies indicate that a higher level of ability/expertise, both within and between age groups, is associated with increased parietal – as much as or more so than frontal – cortical activation (Klingberg et al. Reference Klingberg, Forssberg and Westerberg2002; Lee et al. Reference Lee, Choi, Gray, Cho, Chae, Lee and Kim2006). An anterior to posterior shift with increasing expertise is a characteristic of learning and improved performance on a wide variety of cognitive tasks. As tasks become less difficult, individuals utilize posterior cortical regions more so than frontal ones. This focus on individual differences and task difficulty has been an important advance in research on the neural basis for fluid cognition. It is one that highlights the role that method and experimental design play in attempts to identify the neural bases for a given cognitive ability. In relation to the neural basis for individual differences in working memory or relational reasoning, it is necessary to ask: Should the focus of imaging research be on brain areas active in response to the most difficult problems that only high-IQ individuals can solve? Or should it be on differences in brain activity in high- and low-IQ individuals in response to problems that are solvable by most people? The two approaches are likely to lead to different conclusions about brain areas associated with intelligence. For working memory, prefrontal cortical activation primarily discriminates high- from low-IQ individuals when problem difficulty is increased by highly distracting elements (Gray et al. Reference Gray, Chabris and Braver2003). Among reasoning problems, however, when difficulty pertains primarily to complexity of relations among problem elements, activation in the parietal cortex primarily discriminates high- from low-IQ individuals (Lee et al. Reference Lee, Choi, Gray, Cho, Chae, Lee and Kim2006).

From the foregoing, one might conclude, contrary to J&H, that the neural basis for individual differences in intelligence relates not to particular brain areas but in the application of relevant brain areas to a given task or problem. As with one of the target author's prior findings for glucose metabolic rate using PET (Haier et al. Reference Haier, Siegel, Nuechterlein, Hazlett, Wu, Paek, Browning and Buchsbaum1988; Haier et al. Reference Haier, Chueh, Touchette, Lott, Buchsbaum, Macmillan, Sandman, Lacasse and Sosa1995), higher-IQ individuals require fewer, not greater, resources to solve problems that are generally solvable by most people. This likely has important implications for the investigation of the neural bases of other aspects of cognition associated with intelligent behavior, such as memory, language, inspection time, speed of processing, and so on. It may also suggest that there are no specific cortical areas that underlie intelligence, but that individual differences in intelligence reflect aspects of brain function that enable more efficient use of cortical structures and resources that are associated with specific cognitive abilities.

Future work that takes a resource utilization/efficiency approach to the study of the neural basis for reasoning could profitably consider processes of development and automaticity. Developmental imaging studies of reasoning abilities, such as simple relational reasoning or basic mathematical calculation (i.e., single-digit addition or subtraction), indicate a frontal to parietal shift with age. In these tasks, age is negatively correlated with frontal and striatal activation and positively correlated with parietal activation (Eslinger et al., submitted; Rivera et al. Reference Rivera, Reiss, Eckert and Menon2005). Significantly, these differences are observed even in the absence of a relation between accuracy in problem solving and age. These findings suggest a process in which the less-expert problem solver relies on active, more resource-intensive processes of information maintenance and coordination of procedural knowledge required for problem solution associated with the frontal cortex and striatum. In contrast, the more-expert problem solver requires presumably fewer cognitive resources and exhibits increased parietal activation associated with a more automatic and efficient arrival at problem solution. But does this mean that the more-expert, older problem solver is more intelligent than the less-expert, younger problem solver? The differences in brain activity are associated with age and experience, not intelligence – at least not as the construct is commonly understood. Traditional theories of general intelligence have struggled to incorporate development and experience in meaningful ways and have never really succeeded in doing so, despite an excellent start in this direction (Hunt Reference Hunt1961). Attempts to consider the neural basis for general intelligence must also clearly articulate a clear understanding of the role of experience and development.

In conclusion, consideration of development and automaticity in brain function points to an overarching issue for the P-FIT theory – specifically, the idea that general intelligence is a mathematical abstraction, not a thing in itself. As such, the search for its neural basis may ultimately prove futile. In contrast, the search for the neural basis for components of intelligence, for specific cognitive abilities, has been and will likely continue to be very productive.