Neuroscience and intelligence

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Neuroscience and intelligence concerns the various neurological factors that may be responsible for the variation of intelligence within a species or between different species. Much of the work in this field is concerned with the variation in human intelligence, but other intelligent species such as the non-human primates, cetaceans and rodents are also of interest. The basic mechanisms by which the brain produces complex phenomena such as consciousness and intelligence are still poorly understood.[1]

The research into the neuroscience of intelligence has involved indirect approaches, such as searching for correlations between psychometric test scores and variables associated with the anatomy and physiology of the brain. Historically, research was conducted on non-human animals or on postmortem brains as well as on skulls (Craniometry). More recent studies have involved non-invasive techniques such as MRI scans as they can be conducted on living subjects. MRI scans can be used to measure the size of various structures within the brain, or they can be used to detect areas of the brain that are active when subjects perform certain mental tasks.

Anatomy[edit]

Some of the anatomical variables that have been studied in association with psychometric test scores include total brain volume, the size and shape of the frontal lobes, the amount of grey and white matter, and the overall thickness of the cortex.

Brain size[edit]

Primates[edit]

Another theory of brain size in vertebrates is that it may relate to social rather than mechanical skill. Cortical size relates directly to a pairbonding life style and among primates cerebral cortex size varies directly with the demands of living in a large complex social network.[2]

Humans[edit]

Within human population, studies have been conducted to determine whether there is a relationship between brain size and a number of cognitive measures. Early anthropologists and psychometricians found correlations, but modern studies began with Van Valen 1974, which suggested a correlation of 0.3. Jensen & Johnson's 1994 "Race and sex differences in head size and IQ" wrote of the successive literature that

"In all of the 25 independent studies we have found in the literature, nonzero positive correlations between head measurements and intelligence measurements have been found, all but five with correlations significant beyond the .05 confidence level. The average correlation between various external measures of head size and IQ is close to + .15. But external head size is a rather weak proxy for brain size. Two recent studies have measured brain size per se by means of magnetic resonance imaging (MRI) and found correlations with IQ in the .30 to .40 range (Andreasen et al., 1993; Willerman, Schultz, Rutledge, & Bigler, 1991)."

Nguyen & McDaniel's 2000 meta-analysis[3] found similarly, as did McDaniel's 2005 "Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence":

"...based on 37 samples across 1530 people, the population correlation was estimated at 0.33...For external head measures, Vernon et al. (2000) reported the population correlation between head size and intelligence to be 0.19. Nguyen and McDaniel (2000) reported population correlations from 0.17 to 0.26 for three different sub-categories of external head size measures. Studies assessing the correlation between in vivo brain volume and intelligence are more rare. Vernon et al. (2000) reported data on 15 such correlations and obtained a population correlation of 0.33. Nguyen and McDaniel (2000) reported the same population correlation based on 14 correlations. Gignac, Vernon, and Wickett (2003) reported data published in 2000 or earlier with a mean correlation of 0.37."

Many correlations come in at 0.3 or 0.4[4] Imaging studies continued to report the correlation; Rushton & Ankney 2009 (some methods of analysis returning correlations as high or higher than 0.6[5]):

"Among humans, in 28 samples using brain imaging techniques, the mean brain size/GMA correlation is 0.40 (N = 1,389; p < 10−10); in 59 samples using external head size measures it is 0.20 (N = 63,405; p < 10−10)."

Some scientists prefer to look at more qualitative variables to relate to the size of measurable regions of known function, for example relating the size of the primary visual cortex to its corresponding functions, that of visual performance.[6][7]

In a study of the head growth of 633 term-born children from the Avon Longitudinal Study of Parents and Children cohort, it was shown that prenatal growth and growth during infancy were associated with subsequent IQ. The study’s conclusion was that the brain volume a child achieves by the age of 1 year helps determine later intelligence. Growth in brain volume after infancy may not compensate for poorer earlier growth.[8]

There is an association between IQ and myopia. One suggested explanation is that one or several pleiotropic gene(s) affect the size of the neocortex part of the brain and eyes simultaneously.[9]

Specific regions[edit]

Luders and colleagues in a literature review (2009) write that the majority of data shows that both gray matter and white matter volume correlate with IQ but the correlation is stronger for gray matter. Increased number of neurons in the gray matter may explain the higher correlation but not necessarily so since glucose consumption and intelligence measures correlate negatively which may mean intelligent individuals use their neurons more efficiently, such as being more efficient in their formation of synapses between neurons which help to create more efficient neural circuitry. The white matter correlation may be due to more myelination or better control of pH and thus enhanced neural transmission. For more specific regions, the most frequently replicated positive correlations appear localized in the lateral and medial frontal lobe cortex. Positive correlations are also found with volume in many other areas. Cortical thickness may be a better measure than gray matter volume although this may vary with age with an initially negative correlation in early childhood becoming positive later. The explanation may again be that more intelligent individuals manage their synapses better. During evolution not only brain size but also brain folding has increased which has increased the surface area. Convolution data may support the "The Parieto-Frontal Integration Theory" which see medial cortex structures as particularly important. Volume of the corpus callosum or subareas were found to be important in several studies which may be due to more efficient inter-hemispheric information transfer.[10]

In 2007, Behavioral and Brain Sciences published a target article that put forth a biological model of intelligence based on 37 peer-reviewed neuroimaging studies (Jung & Haier, 2007). Their review of a wealth of data from functional imaging (functional magnetic resonance imaging and positron emission tomography) and structural imaging (diffusion MRI, voxel-based morphometry, in vivo magnetic resonance spectroscopy) argues that human intelligence arises from a distributed and integrated neural network comprising brain regions in the frontal and parietal lobes.[11]

Brain injuries at an early age isolated to one side of the brain typically results in relatively spared intellectual function and with IQ in the normal range.[12]

Glucose metabolic rate[edit]

Other neurological parameters have been associated with IQ. Haier et al. (1995) found a correlation of -0.58 between glucose metabolic rate "GMR" (an indicator of energy use) and IQ. This suggested that intelligence is associated with more efficient brains. Others found a positive correlation between IQ and GMR (DeLeon et al. 1983; Chase et al. 1984). It seems like difference in results comes from different cognitive tasks (complicated vs. simple) that were performed by examinees (Fidelman, 1993).

Health[edit]

Several environmental factors related to health can lead to significant cognitive impairment, particularly if they occur during pregnancy and childhood when the brain is growing and the blood–brain barrier is less effective. Developed nations have implemented several health policies regarding nutrients and toxins known to influence cognitive function. These include laws requiring fortification of certain food products and laws establishing safe levels of pollutants (e.g. lead, mercury, and organochlorides). Comprehensive policy recommendations targeting reduction of cognitive impairment in children have been proposed.[13]

See also[edit]

References[edit]

  1. ^ Deary (2000). Testing Versus Understanding Human Intelligence. 
  2. ^ Dunbar RI, Shultz S (2007-09-07). "Evolution in the social brain". Science 317 (5843): 1344–1347. doi:10.1126/science.1145463. PMID 17823343. 
  3. ^ "Brain size and intelligence: A meta-analysis"
  4. ^ Witelson, SF; Beresh, H; Kigar, DL (2006). "Intelligence and Brain Size in 100 Postmoterm Brains". Brain: a journal of neurology 129 (Pt 2): 386–98. doi:10.1093/brain/awh696. PMID 16339797. 
  5. ^ Rushton & Ankney: "Six studies that used Jensen's (1998) method of correlated vectors to distill g from the subtests of an IQ test found the correlation with brain size is even higher (r = 0.63). The procedure consists of calculating the association between a column of quantified elements (such as g loadings) and any parallel column of independently derived scores (such as the correlation between GMA subtests and brain size). For example, Jensen (1994) found a simple correlation of 0.19 between head circumference and g on 17 cognitive tests among 286 adolescents became 0.64 using the method of correlated vectors. Wickett, Vernon, and Lee (2000) correlated brain volume by means of MRI in 68 adults and found r = 0.38 with g extracted from a battery of cognitive tests, but 0.59 when using correlated vectors. Wickett et al.'s head perimeter measure similarly went from 0.19 to 0.34. Schoenemann et al. (2000) obtained a simple correlation of 0.45 between brain volume and g, which Jensen (1998, p. 147) found to be 0.51 using the method of correlated vectors. Jensen (personal communication, August 8, 2002) carried out a vector analysis of the MRI study by MacLullich et al. (2002) and raised the correlation between g and cognitive ability from 0.42 to 0.78. Finally, Colom et al. (2006a) reanalyzed published data on 47 adults and found a correlation of 0.89 between g loadings and number of gray matter clusters, which was higher than the baseline correlations of 0.28–0.51 (Haier, Jung, Yeo, Head, & Alkire, 2004; Wilke, Sohn, Byars, & Holland, 2003)."
  6. ^ Brain size does not predict cognitive abilities within families
  7. ^ Brain size and intelligence
  8. ^ Catharine R. Gale, PhD, Finbar J. O'Callaghan, PhD, Maria Bredow, MBChB, Christopher N. Martyn, DPhil and the Avon Longitudinal Study of Parents and Children Study Team (October 4, 2006). "The Influence of Head Growth in Fetal Life, Infancy, and Childhood on Intelligence at the Ages of 4 and 8 Years". PEDIATRICS Vol. 118 No. 4 October 2006, pp. 1486-1492. Retrieved August 6, 2006. 
  9. ^ Czepita, D.; Lodygowska, E.; Czepita, M. (2008). "Are children with myopia more intelligent? A literature review". Annales Academiae Medicae Stetinensis 54 (1): 13–16; discussion 16. PMID 19127804.  edit
  10. ^ Luders, Eileen; Narr, Katherine L.; Thompson, Paul M.; Toga, Arthur W. (2009). "Neuroanatomical correlates of intelligence". Intelligence 37 (2): 156–163. doi:10.1016/j.intell.2008.07.002. PMC 2770698. PMID 20160919.  edit
  11. ^ Richard Haier & Rex Jung (July 26, 2007). "The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence". Cambridge University Press. Retrieved September 28, 2009. 
  12. ^ Bava, Sunita; Ballantyne, Angela O; Trauner, Doris A (2005). "Disparity of Verbal and Performance IQ Following Early Bilateral Brain Damage". Cognitive and Behavioral Neurology 18 (3): 163–70. doi:10.1097/01.wnn.0000178228.61938.3e. PMID 16175020. 
  13. ^ Olness, K. (2003). "Effects on brain development leading to cognitive impairment: a worldwide epidemic". Journal of Developmental and Behavioral Pediatrics 24 (2): 120–30. doi:10.1097/00004703-200304000-00009. PMID 12692458. 

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