Neuroscience and intelligence

Neuroscience and intelligence refers to the various neurological factors that are partly responsible for the variation of intelligence within species or between different species. A large amount of research in this area has been focused on the neural basis of human intelligence. Historic approaches to studying the neuroscience of intelligence consisted of correlating external head parameters, for example head circumference, to intelligence.[1] Post-mortem measures of brain weight and brain volume have also been used.[1] More recent methodologies focus on examining correlates of intelligence within the living brain using techniques such as magnetic resonance imaging (MRI), functional MRI (fMRI), electroencephalography (EEG), positron emission tomography and other non-invasive measures of brain structure and activity.[1]

Researchers have been able to identify correlates of intelligence within the brain and its functioning. These include overall brain volume,[2] grey matter volume,[3] white matter volume,[4] white matter integrity,[5] cortical thickness[3] and neural efficiency.[6]

Analyses of the parameters of intellectual systems, patterns of their emergence and evolution, distinctive features, and the constants and limits of their structures and functions made it possible to measure and compare the capacity of communications (~100 m/s), to quantify the number of components in intellectual systems (~1011 neurons), and to calculate the number of successful links responsible for cooperation (~1014 synapses).[7]

Although the evidence base for our understanding of the neural basis of human intelligence has increased greatly over the past 30 years, even more research is needed to fully understand it.[1]

The neural basis of intelligence has also been examined in animals such as primates, cetaceans, and rodents.[8]

  1. ^ a b c d Luders, E.; Narr, K. L.; Thompson, P. M.; Toga, A. W. (2009). "Neuroanatomical correlates of intelligence". Intelligence. 37 (2): 156–163. doi:10.1016/j.intell.2008.07.002. PMC 2770698. PMID 20160919.
  2. ^ Pietschnig J, Penke L, Wicherts JM, Zeiler M, Voracek M (2015). "Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean?". Neuroscience & Biobehavioral Reviews. 57: 411–32. doi:10.1016/j.neubiorev.2015.09.017. PMID 26449760. S2CID 23180321.
  3. ^ a b Narr, K. L.; Woods, R. P.; Thompson, P. M.; Szeszko, P.; Robinson, D.; Dimtcheva, T.; Bilder, R. M. (2007). "Relationships between IQ and regional cortical gray matter thickness in healthy adults". Cerebral Cortex. 17 (9): 2163–2171. doi:10.1093/cercor/bhl125. PMID 17118969.
  4. ^ Gur, R. C.; Turetsky, B. I.; Matsui, M.; Yan, M.; Bilker, W.; Hughett, P.; Gur, R. E. (1999). "Sex differences in brain gray and white matter in healthy young adults: correlations with cognitive performance". Journal of Neuroscience. 19 (10): 4065–4072. doi:10.1523/JNEUROSCI.19-10-04065.1999. PMC 6782697. PMID 10234034.
  5. ^ Penke, L.; Maniega, S. M.; Bastin, M. E.; Hernandez, M. V.; Murray, C.; Royle, N. A.; Deary, I. J. (2012). "Brain white matter tract integrity as a neural foundation for general intelligence". Molecular Psychiatry. 17 (10): 1026–1030. doi:10.1038/mp.2012.66. PMID 22614288. S2CID 2334558.
  6. ^ Haier, R. J.; Siegel, B. V.; Nuechterlein, K. H.; Hazlett, E.; Wu, J. C.; Paek, J.; Buchsbaum, M. S. (1988). "Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography". Intelligence. 12 (2): 199–217. doi:10.1016/0160-2896(88)90016-5.
  7. ^ Eryomin, A. L. (2022). "Biophysics of Evolution of Intellectual Systems". Biophysics. 67 (2): 320–326. doi:10.1134/S0006350922020051. PMC 9244026. PMID 35789557.
  8. ^ Dunbar, R. I.; Shultz, S. (2007). "Evolution in the social brain". Science. 317 (5843): 1344–1347. Bibcode:2007Sci...317.1344D. doi:10.1126/science.1145463. PMID 17823343. S2CID 1516792.