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Neuroscience and intelligence
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{{Short description|Neurological factors responsible for 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.<ref name="Luders2009">{{cite journal | last1 = Luders | first1 = E. | last2 = Narr | first2 = K. L. | last3 = Thompson | first3 = P. M. | last4 = Toga | first4 = A. W. | year = 2009 | title = Neuroanatomical correlates of intelligence | journal = Intelligence | volume = 37 | issue = 2| pages = 156β163 | doi=10.1016/j.intell.2008.07.002 | pmid=20160919 | pmc=2770698}}</ref> Post-mortem measures of brain weight and brain volume have also been used.<ref name=Luders2009 /> 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.<ref name=Luders2009 /> Researchers have been able to identify correlates of intelligence within the brain and its functioning. These include overall brain volume,<ref name="Meta2015">{{cite journal|vauthors=Pietschnig J, Penke L, Wicherts JM, Zeiler M, Voracek M | title=Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean? | journal=Neuroscience & Biobehavioral Reviews | year=2015 | pmid=26449760 | doi=10.1016/j.neubiorev.2015.09.017 | volume=57 | pages=411β32| s2cid=23180321 }}</ref> grey matter volume,<ref name="Narr2007">{{cite journal | last1 = Narr | first1 = K. L. | last2 = Woods | first2 = R. P. | last3 = Thompson | first3 = P. M. | last4 = Szeszko | first4 = P. | last5 = Robinson | first5 = D. | last6 = Dimtcheva | first6 = T. | last7 = Bilder | first7 = R. M. | year = 2007 | title = Relationships between IQ and regional cortical gray matter thickness in healthy adults | journal = Cerebral Cortex | volume = 17 | issue = 9| pages = 2163β2171 | doi=10.1093/cercor/bhl125 | pmid=17118969| doi-access = free }}</ref> white matter volume,<ref name="Gur1999">{{cite journal | last1 = Gur | first1 = R. C. | last2 = Turetsky | first2 = B. I. | last3 = Matsui | first3 = M. | last4 = Yan | first4 = M. | last5 = Bilker | first5 = W. | last6 = Hughett | first6 = P. | last7 = Gur | first7 = R. E. | year = 1999 | title = Sex differences in brain gray and white matter in healthy young adults: correlations with cognitive performance | journal = Journal of Neuroscience | volume = 19 | issue = 10| pages = 4065β4072 | doi = 10.1523/JNEUROSCI.19-10-04065.1999 | pmid = 10234034 | pmc = 6782697 }}</ref> white matter integrity,<ref name="Penke2012">{{cite journal | last1 = Penke | first1 = L. | last2 = Maniega | first2 = S. M. | last3 = Bastin | first3 = M. E. | last4 = Hernandez | first4 = M. V. | last5 = Murray | first5 = C. | last6 = Royle | first6 = N. A. | last7 = Deary | first7 = I. J. | year = 2012 | title = Brain white matter tract integrity as a neural foundation for general intelligence | journal = Molecular Psychiatry | volume = 17 | issue = 10| pages = 1026β1030 | doi=10.1038/mp.2012.66 | pmid=22614288| s2cid = 2334558 }}</ref> cortical thickness<ref name=Narr2007 /> and neural efficiency.<ref name="Haier1988">{{cite journal | last1 = Haier | first1 = R. J. | last2 = Siegel | first2 = B. V. | last3 = Nuechterlein | first3 = K. H. | last4 = Hazlett | first4 = E. | last5 = Wu | first5 = J. C. | last6 = Paek | first6 = J. | last7 = Buchsbaum | first7 = M. S. | year = 1988 | title = Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography | journal = Intelligence | volume = 12 | issue = 2| pages = 199β217 | doi=10.1016/0160-2896(88)90016-5}}</ref> 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 (~10<sup>11</sup> neurons), and to calculate the number of successful links responsible for cooperation (~10<sup>14</sup> synapses).<ref>{{Cite journal |last=Eryomin |first=A. L. |date=2022 |title=Biophysics of Evolution of Intellectual Systems |url=https://doi.org/10.1134/S0006350922020051 |journal=Biophysics |language=en |volume=67 |issue=2 |pages=320β326 |doi=10.1134/S0006350922020051 |pmc=9244026 |pmid=35789557}}</ref> 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.<ref name=Luders2009 /> The neural basis of intelligence has also been examined in animals such as [[primate]]s, [[cetacea]]ns, and [[Rat#Intelligence|rodents]].<ref name="Dunbar2007">{{cite journal | last1 = Dunbar | first1 = R. I. | last2 = Shultz | first2 = S. | year = 2007 | title = Evolution in the social brain | journal = Science | volume = 317 | issue = 5843| pages = 1344β1347 | doi=10.1126/science.1145463 | pmid=17823343| bibcode = 2007Sci...317.1344D | s2cid = 1516792 }}</ref>
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