Parieto-frontal integration theory
The parieto-frontal integration theory (P-FIT) considers intelligence to relate to how well different
The theory
General intelligence requires specific brain regions and incorporates:
- Sensory processing, primarily in the visual and auditory modalities, including specific temporal and parietal areas
- Sensory abstracting and elaboration by the parietal cortex (especially the supramarginal, superior parietal, and angular gyri)
- Interaction between the parietal cortex and frontal lobes for hypothesis testing available solutions
- Response selection and inhibition of competing responses by the anterior cingulate
This theory proposes that greater general intelligence in individuals results from the greater communication efficiency between the dorsolateral prefrontal cortex, parietal lobe, anterior cingulate cortex, and specific temporal and parietal cortex regions.
Neuroimaging evidence
Jung and Haier (2007)
Across functional studies, the authors found that more than 40% of the studies, included in the review, found correlations between bilateral activations in the frontal and occipital cortices and intelligence. In these studies, activation in the left hemisphere was usually significantly higher than that of the right hemisphere. Similarly, bilateral cortical areas in the occipital lobe, such as BA (Brodmann area) 19 were activated during reasoning tasks in more than 40% of studies. Here left activation tended also to be greater than activation in the right hemisphere.[3]
Across the functional imaging studies reviewed, the parietal lobe was consistently involved in reasoning tasks, with BA 7 activated in more than 70% of studies and BA 40 activation was observed in more than 60% of studies.[3]
In recognition of the correlational nature of neuroimaging, the authors complement their neuroimaging review with a shorter review of evidence from lesion studies and imaging genomics regarding the biological basis of intelligence. The authors conclude that the lesion evidence supports a P-FIT theory of intelligence. Further, based on the imaging genomic studies reviewed, the authors suggest a
Further structural imaging evidence
Haier et al. (2009) provided further neuroimaging evidence for the P-FIT by investigating the correlation between g and gray matter volume. This was in order to see whether psychometric g is consistently related to a certain neural substrate, or a neuro-g. The authors argue that previous studies examining the neural correlates of g have mostly used indirect measures of g, render the findings of these studies as inconclusive.[4] The scores of 6292 participants on eight cognitive tests were used to derive g, and a small subset of 40 participants were also scanned using voxel-based morphometry. The evidence indicates that the neural correlates of g depend on part on the type of test used to derive g, despite evidence indicating that g derived from different tests tap onto the same underlying psychometric construct.[5] The authors suggest that this may, in part, explain some of the variance in the neuroimaging findings reviewed by Jung and Haier (2007).
In the same year, a study by Colom and colleagues also measured the gray matter correlates of g in a sample of 100 healthy Spanish adults. Similar to Haier et al. (2009), a direct measure of g was derived from a battery measuring fluid, crystallized, and spatial aspects of intelligence.[6] Although finding some differences between the P-FIT theory and their results, the authors conclude that their findings support the P-FIT theory. The identified inconsistencies include voxel clusters in the frontal eye fields, the inferior and middle temporal gyrus, areas which are involved in planning complex movements, high-level visual processing, respectively.[6]
Functional imaging evidence
Vakhtin et al. (2014) determined to identify functional networks relating to fluid intelligence, as measured by both the standard and advanced versions of Raven's Progressive Matrices test. Using fMRI, Vakhtin et al. found a discrete set of networks associated with fluid reasoning, including the dorsolateral cortex, inferior and parietal lobule, anterior cingulate, as well as temporal and occipital regions.[7] The authors argue that this is "broadly consistent"[7] with the P-FIT theory. The authors scanned 79 American university students three times each, wherein one session was at 'resting state', and in the other two the participants were asked to complete problems taken from Raven's Standard and Advanced Progressive Matrices. Attentional, cognitive, sensorimotor, visual, and default-mode networks were activated during the reasoning task.
Evidence from lesion studies
The majority of studies providing lesion evidence to the P-FIT theory use voxel-based lesion symptom mapping, a method in which scores on an intelligence test battery are compared between participants with and without a lesion at every voxel. This allows for the identification of regions with a causal role in performance on test measures, as it maps where brain damage can impact performance.[8]
Gläscher et al. (2010) explored whether g has distinct neural substrates, or whether it is related to global neural properties such as total brain volume. Using voxel-based lesion symptom mapping, Gläscher et al. (2010) found significant relationships between g scores and regions in primarily the left hemisphere, and major white matter tract regions in temporal, parietal, and inferior frontal areas.
A study of 182 male
Issues with the theory
There is little published criticism of the P-FIT, and it stands as the best current model for the biological basis of human intelligence.[2] Nevertheless, questions remain regarding the biological functioning of intelligence. A review of the methods used to identify large-scale networks involved in cognition highlights the importance of multi-dimensional context in understanding the neural bases of cognitive processes.[1] Although this review does not directly criticize the P-FIT, the authors caution that structural imaging and lesion studies, although helpful in implicating specific regions in processes, do little to elucidate the dynamical nature of cognitive processes. Furthermore, a review of the neuroscience of intelligence emphasizes the need of studies to consider the different cognitive and neural strategies individuals may use in completing cognitive tasks.[2]
Compatibility with other biological correlates of intelligence
The P-FIT is highly compatible with the neural efficiency hypothesis, and is supported by evidence of the relationship between white matter integrity and intelligence. For example, a study indicates that white matter integrity provides the neural basis for the rapid processing of information, which is considered central to general intelligence.[11]
References
- ^ a b Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: emerging methods and principles. Trends in Cognitive Sciences, 14(6), 277–290. doi:10.1016/j.tics.2010.04.004
- ^ a b c Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201-211. [doi:10.1038/nrn2793]
- ^ a b c Jung, R. E., & Haier, R. J. (2007). The parieto-frontal integration theory (P-FIT) of intelligence: converging neuroimaging evidence. Behavioral and Brain Sciences, 30, 135–187.
- ^ Haier, R. J., Colom, R., Schroeder, D. H., Condon, C. A., Tang, C., Eaves, E., & Head, K. (2009). Gray matter and intelligence factors: is there a neuro-g? Intelligence, 37(2), 136-144. doi:10.1016/j.intell.2008.10.011
- ^ Johnson, W., te Nijenhuis, J., & Bouchard, T. J. (2008). Still just 1 g: Consistent results from five test batteries. Intelligence, 36, 81−95
- ^ a b Colom, R., Haier, R. J., Head, K., Alvarez-Linera, J., Ouiroga, M. A., Shih, P. C., & Jung, R. E. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: testing the P-FIT model. Intelligence, 37, 124–135. [doi:10.1016/j.intell.2008.07.007]
- ^ a b Vakhtin, A. A., Ryman, S. G., Flores, R. A., & Jung, R. E. (2014). Functional brain networks contributing to the parieto-frontal integration theory of intelligence. NeuroImage, 103, 349–354. doi:10.1016/j.neuroimage.2014.09.055
- ^ Deary, I. J. (2012). Intelligence. Annual Review of Psychology, 63(1), 453-482. doi:10.1146/annurev-psych-120710-100353
- ^ Gläscher, J., Rudrauf, D., Colom, R., Paul, L. K., Tranel, D., Damasio, H., & Adolphs, R. (2010). Distributed neural system for general intelligence revealed by lesion mapping. Proceedings of the National Academy of Sciences of the United States of America, 107(10), 4705-4709. doi:10.1093/scan/nss124
- ^ Barbey, A. K., Colom, R., Solomon, J., Krueger, F., & Forbes, C. (2012). An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain, 135, 1154-1164. doi:10.1093/brain/aws021
- ^ Penke, L., Muñoz Maniega, S., Bastin, M. E., Valdés Hernández, M. C., Murray, C., Royle, N. A., … Deary, I. J. (2012). Brain white matter tract integrity as a neural foundation for general intelligence. Molecular Psychiatry, 17, 1026–1030. doi:10.1038/mp.2012.66