What is Intelligence?
About a century ago, psychologists found that in tests designed to measure mental abilities—such as mathematical ability, verbal ability, or memory—people who did well on one kind of test tended to also do well on other tests. And those who did poorly, did so across the board. From this overlap, or inter-correlation, it was discovered that there exists a general factor underlying all mental abilities. This general factor is called general mental ability, or simply, general intelligence, abbreviated as g.
g is the single best predictor of how an individual performs in life at-large, and it is for this reason that g is of such profound importance in psychology. g is highly correlated with academic and professional achievement, proficiency in military training, and to a lesser extent, with longevity, prosocial and industrious behaviour. Intelligence can thus be defined as the capacity to identify, understand and utilize resources in the environment.
Simply put, intelligence—to a certain extent—determines ‘destiny.’
g is a measure of neural processing efficiency. Nearly all mental abilities will therefore load on g to some extent, and the g loading increases with the complexity of information being processed. Neural processing power is determined by the architecture of the brain, such as the efficiency of connectivity within and across modules, and the efficiency of memory systems. g can thus be measured using processing tests that tap into these performance factors, and then graded in terms of ‘Intelligence Quotient (IQ)’ scores. Individuals with more efficient brain architecture will have higher g, or more “brain power,” and therefore greater IQ scores.
[g is broadly analogous to the processing power of the CPU in a computer, which acts as the general factor underlying all processing capabilities of that computer. CPU processing power is determined by the architecture of the CPU, such as the efficiency of connectivity within and across components, and the efficiency of its memory caching systems. CPUs can therefore be graded using processing tests that tap into these performance factors. Just as how high grade CPUs are necessary for complex computing tasks, high g is necessary for cognitively complex domains.]
Intelligence is one of the most heritable traits known: genetic influence accounts for about 80% of individual differences in g between adults. It is a highly polygenic trait controlled by small additive effects from a very large number of genes. However, the same large set of genes influence diverse mental abilities such as verbal and non-verbal abilities as well as learning abilities such as reading and mathematics. In other words, the same genes that influence one mental ability influence other mental abilities as well, indicating that there is a genetic general factor affecting neural processing efficiency.
The genetic influence on g increases with age—from about 40% among preschoolers, 60% by adolescence and to 80% in late adulthood—whereas the influence of shared environment decreases with age, becoming negligible after adolescence. One reason for this is that individuals attain peak neural processing capacity only by late adulthood, and another is that individuals increasingly seek out environments that indulge their mental abilities as they grow older. Unique environmental influences have no predictable effects on g, because they are indistinguishable from random miscellaneous effects.
Sex Differences in g
Adult human males have a mean advantage in g of 7 IQ equivalent points, and greater variance in distribution, compared to adult human females. The combined effect of higher mean and greater variance results in an exponentially increasing male over-representation from average g (IQ=100) and up, amounting to 9 males for each female at g=3 SD (IQ=145).
Alternate measures of g, such as Elementary Cognitive Tasks (ECT), also reveal similar sex differences. ECTs tap into basic mental processes to measure neural efficiency. As such, they have no specific intellectual content, and therefore have clear and correct outcomes that do not reflect differences in motivation, strategy, or personality traits.
- Reaction Time (RT) is one such measure. RT is the time that elapses between a individual being presented with a stimulus and the individual initiating a response to that stimulus. RT tests are so easy to do that even 9-year-old children can perform them in a second. Not to be confused with “quick reflexes” in the realm of athletics, RT taps into the efficiency of the central nervous system. Simple RT measures correlate with g ∼ 0.20, while complex RT measures correlate ∼ 0.40. In aggregate, RTs can correlate ~ 0.70 with g. Individuals with higher g have faster RTs, and males consistently have faster RTs than females.
- Temporal Processing (TP), which involves the perception of time, correlates ~ 0.45 with g. There exists in the brain a unitary timing mechanism that synchronizes processing across modules to maintain neural efficiency. This is broadly analogous to the internal clock in a CPU which synchronizes the execution of instructions across components. TP measures tap into this internal neural clock. Individuals with higher g have a faster neural clock, reflecting faster updation of information, and so perceive time more accurately. From milliseconds, through seconds, to minutes, males perceive time more accurately than females do.
ECTs reveal a male advantage of ~6 IQ equivalent points.
Nerve Conduction Velocity (NCV) is the speed at which electrical signals propagate down a neural pathway. NCV measures correlate ~ 0.35 with g. Individuals with higher g have faster NCVs due to more efficient brain architecture. Males have faster NCVs, increasing with age, than females.
It should be noted that males have faster RT, TP and NCV despite also having larger brain volumes and body size. This necessarily means that there is a substantial increase in neural efficiency in males compared to females.
Sex-related differences are found at every level of the brain. The most important differences—related to intelligence—are found at the level of organization and may reveal the underlying mechanisms that contribute to the male advantage in general intelligence.
As a general rule, the brain’s structure is an expression of its function, where larger size translates to greater functional capacity. To illustrate this, I take ‘absolute brain size’ as the starting point. Absolute brain size has been established as the best predictor of individual differences in intelligence across species. In humans, absolute brain size is moderately correlated with g ~ r=0.45.
The absolute brain size in men is about 10–12% larger than in women. Males average larger brains from birth and across all age groups—even though girls tend to be taller from ages 10–14. Results from autopsy studies and fMRI find only a weak brain size–body size correlation ~ r=0.20. This means that even after correcting for body size, the brains of men are larger by 8–10%. When brain/body ratios of men and women of equal size are compared, at any given size, the ratio is much higher in men than in women.
Thus, men with their larger brains, have higher g.
Unfortunately, the simplistic presentation of this argument fails to capture its significance, which I will unpack.
Brains may enlarge by adding more neurons, by making existing neurons larger or some combination of both. The most common method of enlargement is by adding more neurons. But, for this to work, the number of connections must increase much faster than the number of neurons in order to maintain connectivity. White matter is known to increase faster than gray matter, but this is not quite fast enough to resolve the connectivity problem. In addition to this design issue, a substantial increase in the ratio of glia–neurons is required to keep up with the increasing energy costs. Given these constraints, brains cannot enlarge without undergoing changes to their organization. Thus, as brains enlarge, they become increasingly modular: larger functions are broken down into smaller processes and processes that handle overlapping tasks are clustered together, enhancing the connectivity within these localized regions.
Then, there’s the other side of the coin: the increase in modularity is further exploited to increase the degree of hemispheric lateralization. ‘Hemispheric lateralization’ is the distribution of functions between the two hemispheres of the brain. The hemispheres have evolved qualitatively different biases in how they interact with each other: the right-hemisphere strongly interacts with both hemispheres whereas the left-hemisphere mostly interacts within itself. This bias is also expressed in their functionality: the right-hemisphere is dominant for ‘global’ information, it operates spatially, intuitively and is concerned with the ‘whole’; the left-hemisphere is dominant for ‘local’ information, it operates verbally, sequentially and is concerned with the constituent ‘parts.’ By distributing functions between the two hemispheres, the brain is able to perform parallel processing. Therefore, as brains enlarge, they become more lateralized to take advantage of enhanced parallel processing.
However, for parallel processing to work, it is necessary to minimise potential conflicts between the hemispheres. The corpus callosum (CC)—the white matter tract connecting the two hemispheres of the brain—functions as the ‘bridge’ between the hemispheres. As brains enlarge, they are optimised for intra-hemispheric connectivity (within-hemisphere) and the CC is scaled down to minimise inter-hemispheric (between-hemisphere) interference.
Putting this all together: larger brains are more modular, more lateralized and consequently, optimised for intra-hemispheric connectivity. Absolute brain size is thus properly understood as a proxy for the brain’s organizational complexity. The end result is a substantial enhancement to parallel processing which leads to a systematic increase in cognitive capacity.
It follows that men’s larger brains are packed with 19% more neocortical neurons for processing, 16% more white matter for connectivity and 28% more neocortical glia to keep up with energy costs. Consequently, men’s brains are more modular, more lateralized and optimised towards an intra-hemispheric configuration whereas women’s brains are more diffused and fall back to an inter-hemispheric configuration. While there are certain benefits to both configurations, the mechanical advantages to men is apparent in task performance:
- Language processing is represented bilaterally in women, but is left-lateralized in men. This necessitates a reliance on inter-hemispheric connectivity in women for processing language, resulting in slower performance and lower verbal intelligence. The intra-hemispheric configuration in men prevents this kind of “traffic jam,” even though they have longer inter-hemispheric transmission times in the left–right direction.
- Women show greater reliance on inter-hemispheric connectivity for non-verbal intelligence as well. Correlations between bi-manual temporal measures, inter-hemispheric transfer times and non-verbal intelligence are significant for women, but not men.
- When adjusted for brain size, women have 3–5% more grey matter than men. This excess, found in the parietal lobe, is associated with a disadvantage in visuospatial processing for women. In contrast, the greater surface area of the parietal lobe in men translates to an advantage.
The sex differences in brain organization manifest in preferred “cognitive styles”—i.e. men and women think differently—the most important aspect of sex differences in intelligence.
Men preferentially engage in a holistic, ‘global’ (“Gestalt”) style of processing taking advantage of their intra-hemispheric connectivity to integrate disparate streams of information into a coherent “whole,” whereas women preferentially engage in a decomposed, ‘local’ style of processing, assembling information piece-by-piece to optimise their reliance on inter-hemispheric connectivity. While there are certain advantages to both cognitive styles, the overall enhancement to cognitive capacity in men cannot be missed:
- Visuospatial ability (VS) is the ability to generate, retain, retrieve, and transform well-structured mental images. VS is a unified trait in men—they are able to integrate complex mental images in a bottom-up, automatic and holistic fashion. For women, VS is a diffused trait—they put together mental images in a top-down, forced and piecemeal fashion. Thus, VS is more of a talent in men and a learned skill for women. Consequently, the more complex the task, the more apparent the male advantage. Incredibly, the male advantage in VS can already be detected in 5-month-old infants.
- The male advantage in VS extends to spatial orientation. Here the global–local bias is immediately evident: when asked to navigate through maps, men focus primarily on global features, such as cardinal directions whereas women focus on local features, such as landmarks. This global–local bias can also be detected in children.
- The male advantage in VS also extends to audiospatial tasks. In binaural auditory processing tasks, the enhanced parallel processing in male brains contribute to a performance advantage.
- The male advantage in VS extends to visuospatial working memory (VSWM). When both object icons and words are presented together for VSWM processing, men outperform women even in the most demanding conditions.
- Mathematical ability is an enhanced trait in men, yet again extending from their advantage in VS. The global bias is also apparent here: the male advantage increases as the level of cognitive complexity increases and also as the content changes from arithmetic through algebra to geometry.
- Temporal Processing—perception of time—also reveals a male advantage. In tasks requiring temporal integration across a series of sensory events, men apply a holistic processing strategy to their advantage.
- Creative thinking is associated with ‘global’ right-hemispheric processing and selective parallel processing. Consequently, there exists a clear male superiority—found cross-culturally—in creativity.
- Men are also more ‘global’ when it comes to ‘social intelligence.’ The male social structure involves complex group-based interactions set up by automatic rank-ordering whereas the female social structure consists of triads, dyads and one-on-one interactions with related individuals. Consequently, men are more competitive, co-operative, collaborative, tolerant of and easily affiliate with genetically unrelated individuals.
The sex difference in ‘cognitive style’ is clearly one of the most profound differences between the sexes, the full extent of which goes well beyond the mere numbers measured by IQ tests (and beyond the scope of this article). It is because IQ tests tap into some of these mechanical differences, that they reveal a higher g score for males.
Thus, men with their more complex brains, have higher g.
Extrapolating from early findings of sex differences in the corpus callosum, it was claimed that because women had a differently shaped corpus callosum, they were better at “multitasking.”
Unfortunately for this claim, the inter-hemispheric setup of the female brain is their primary disadvantage. Even worse for this claim, the human brain is not even capable of true “multitasking.” The closest we come to multitasking is parallel processing, which is an enhanced feature in the male brain. Still worse for this claim, it inadvertently reveals women’s inability to tune out information irrelevant to the task at hand, resulting in sub-optimal performance in certain tasks.
Over the last few years, several publications have put forth claims such as “there are no sex differences in IQ” or that, “women and men are equally intelligent” and even “sex differences in cognitive abilities are small.”
Fully 100% of these publications manufacture their conclusions by either omitting or ignoring critical information regarding sex differences in mental abilities.
Most mental tests are constructed such that items that show a large advantage to one sex are removed, leaving behind a selection of sub-tests that cancel each other out. All popular IQ tests, including the many versions of the Wechsler Adult Intelligence Scales (WAIS), Raven’s Progressive Matrices (RPM) and the Wonderlic Cognitive Ability Test (WCAT) are ‘sex normalized’ in this manner. Since the late ’80s, this treatment has been extended to national and international assessments including the SAT, the Cognitive Abilities Test (CAT), the Programme for International Student Assessment (PISA), the Trends in International Mathematics and Science Study (TIMSS) and even the armed forces’ standardized tests.
[As Namae Nanka pointed out in the comments section, the practice of ‘sex normalization’ has been institutionalized at least since the time of the eminent educational psychologist Lewis Terman.]
It’s ridiculous to claim that sex differences in intelligence are small or non-existent when the tests themselves are constructed to render them invisible. One must look to other measures of intelligence to unveil sex differences in g, such as the ECTs mentioned in the previous section.
It is well known that males tend to show 10–20% greater variance in measures of intellectual capacity, even when mean differences are small. The sex difference in variance is found to emerge even before pre-school, and by age 10, boys have a mean advantage and greater variance. Even among opposite-sex siblings, there are at least two times more males at the extremes. The male tendency to be over-represented at the extremes and the female tendency to crowd around the center of the distribution is a fundamental aspect of sex differences in humans.
Studies that claim there are no sex differences in intellectual capacity tend to ignore these dispersion effects altogether. This is a serious omission which discounts the exponential increase in the proportion of males at higher levels of cognitive ability.
It is well known that boys mature several years after girls do. But the most conspicuous difference is the brain developmental lag seen in males compared to females. The ‘halfway’ point—the transitional point—in brain development for females is age 10.5, whereas the equivalent point for males is age 14.5. Females reach the end of their developmental trajectory at around age 22 and males at around age 30. Furthermore, as I’ve already discussed earlier, the brains of men are organized differently from the brains of women and this results in a substantial difference in processing capacity. Given that brain organization is a function of age, differences in cognitive capacity may not be fully apparent until adulthood.
It is nonsensical to use samples of children in Grades 3–12 to generalize sex differences in intelligence, because it is guaranteed to underestimate the male advantage – by a substantial margin – as such samples contain a much greater proportion of immature males.
There exists a clear and significant male advantage in g that can be revealed by proper testing, but this is hidden through the use of outdated models and incorrect methodology.
One would think that even the most incompetent social scientist would have figured out the critical factors mentioned in the previous section. Indeed, it has now become clear that incompetence is not the problem here—academic fraud of this scale originate from radicals who, disguised as scholars, act in service to misguided ideology.
With the identity politics filtered out:
- Measurable sex differences in general intelligence can largely explain the male dominance in the upper echelons of all known human societies, both in history and in the present day.
- The ‘gender gap’ extends well beyond ‘general intelligence,’ and is most certainly not diminishing. If anything, the proportion of males at the extremes seems to have increased.
- Contrary to various claims, women are not under-represented but over-represented in many domains. As a result of fixed quotas designed to bypass filters for ability, there is now an increasing number of mediocre women and a tragic paucity of gifted individuals in higher educational and S.T.E.M. spheres.
I intend to keep this post updated based on requests and any new data.
Gottfredson LS. (2003) g, jobs and life. In Nyborg H (Ed.) The Scientific Study of General Intelligence: Tribute to Arthur R. Jensen (pp. 293–342). Oxford: Elsevier. ↲
Jensen AR. (1998) The g Factor: The Science of Mental Ability. Praeger Publishers Inc. ↲
Plomin R, et al. (2013) Common DNA markers can account for more than half of the genetic influence on cognitive abilities. Psychological Science, 24(4):562–568. ↲
Trzaskowski M, et al. (2013) Intelligence indexes generalist genes for cognitive abilities. Intelligence, 41(5):560–565. ↲
Calvin CM, et al. (2012) Multivariate genetic analyses of cognition and academic achievement from two population samples of 174,000 and 166,000 school children. Behavior Genetics, 42(5):699–710. ↲
Davies G, et al. (2011) Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular Psychiatry, 16(10):996–1005. ↲
Hawort CMA, et al. (2009) Generalist genes and high cognitive abilities. Behavior Genetics, 39(4):437–445. ↲
Nyborg H. (2005) Sex-related differences in general intelligence g, brain size, and social status. Personality and Individual Differences, 39(3):497–509. ↲
Nyborg H. (2003) Sex differences in g. In Nyborg H (Ed.), The Scientific Study of General intelligence: Tribute to Arthur R. Jensen (pp. 187–222) Oxford: Elsevier. ↲
Nyborg H. (2002) IQ and g: The art of uncovering the sex difference in general intelligence. Presented at the third annual conference of the International Society for Intelligence Research, Vanderbilt University, Nashville, TN. ↲
Nyborg H. (2001) Early sex differences in general and specific intelligence: Pitting biological against chronological age (Addendum). Presented at the second annual conference of the International Society for Intelligence Research, Cleveland, OH. ↲
Jensen AR. (2006) Clocking the Mind: Mental Chronometry and Individual Differences. Oxford: Elsevier. ↲
Der G & Deary IJ. (2006) Age and sex differences in reaction time in adulthood: results from the United Kingdom Health and Lifestyle Survey. Psychology and Aging, 21(1):62–73. ↲
Deary IJ & Der G. (2005) Reaction time, age, and cognitive ability: Longitudinal findings from age 16 to 63 years in representative population samples. Aging, Neuropsychology, and Cognition, 12(2):187–215. ↲
Bleecker ML, et al. (1987) Simple visual reaction time: Sex and age differences. Developmental Neuropsychology, 3(2):165–172. ↲
Noble CE, et al. (1964) Age and sex parameters in psychomotor learning. Perceptual and Motor Skills, 19(3):935–945. ↲
Bellis CJ. (1933) Reaction time and chronological age. Experimental Biology and Medicine, 30(6):801–803. ↲
Rammsayer TH & Brandler S. (2007) Performance on temporal information processing as an index of general intelligence. Intelligence, 35(2):123–139. ↲
Helmbold N, et al. (2006) Temporal information processing and pitch discrimination as predictors of general intelligence. Canadian Journal of Experimental Psychology, 60(4):294–306. ↲
Rammsayer T & Troche S. (2010) Sex differences in the processing of temporal information in the sub-second range. Personality and Individual Differences, 49(8):923–927. ↲
Rostad K, et al. (2007) Sex-related differences in the correlations for tactile temporal thresholds, interhemispheric transfer times, and nonverbal intelligence. Personality and Individual Differences. 43(7):1733–1743. ↲
Wittmann M & Szelag E. (2003) Sex differences in perception of temporal order. Perceptual and Motor Skills, 96(1):105–112. ↲
Rammsayer T & Lustnauer S. (1989) Sex differences in time perception. Perceptual and Motor Skills, 68(1):195–198. ↲
Strang HR, et al. (1973) Sex differences in short-term time estimation. Perceptual and Motor Skills, 36(3):1109–1110. ↲
Roeckelein JE. (1972) Sex differences in time estimation. Perceptual and Motor Skills, 35(3):859–862. ↲
Pesta BJ, et al. (2008) Sex differences on elementary cognitive tasks despite no differences on the Wonderlic Personnel Test. Personality and Individual Differences, 45(5):429–431. ↲
Johnson AM, et al. (2005) Brain nerve conduction velocity is a valid and useful construct for studying human cognitive abilities: a reply to Saint-Amour et al. Neuropsychologia, 43(12):1845–1846. ↲
Reed TE, et al. (2004) Confirmation of correlation between brain nerve conduction velocity and intelligence level in normal adults. Intelligence, 32(6):563–572. ↲
Reed TE, et al. (2004) Sex difference in brain nerve conduction velocity in normal humans. Neuropsychologia, 42(12):1709–1714. ↲
Ngun TC, et al. (2011) The genetics of sex differences in brain and behavior. Frontiers in Neuroendocrinology, 32(2):227–246. ↲
Reinius B & Jazin E. (2009) Prenatal sex differences in the human brain. Molecular Psychiatry, 14(11):987, 988–989. ↲
Deaner RO, et al. (2007) Overall brain size, and not encephalization quotient, best predicts cognitive ability across non-human primates. Brain, Behavior and Evolution, 70(2):115–124. ↲
Anderson B. (1993) Evidence from the rat for a general factor that underlies cognitive performance and that relates to brain size: Intelligence? Neuroscience Letters, 153:98–102. ↲
Rushton JP & Ankney CD. (2009) Whole brain size and general mental ability: A review. International Journal of Neuroscience, 119(5):692–732. ↲
Rushton JP & Ankney CD. (2007) The evolution of brain size and intelligence. In Platek SM, Keenan JP & Shackelford TK (Eds.), Evolutionary Cognitive Neuroscience (pp. 121–161). Cambridge: MIT Press. ↲
Gignac G, et al. (2003) Factors influencing the relationship between brain size and intelligence. In Nyborg H (Ed.), The Scientific Study of General Intelligence: Tribute to Arthur R. Jensen (pp. 93–106. Oxford: Elsevier. ↲
Wickett JC, et al. (2000) Relationships between factors of intelligence and brain volume. Personality and Individual Differences, 29:1095–1122. ↲
Wickett JC, et al. (1994) In vivo brain size, head perimeter, and intelligence in a sample of healthy adult females. Personality and Individual Differences, 16:831–838. ↲
Rushton JP. (1997) Cranial size and IQ in Asian Americans from birth to age seven. Intelligence, 25:7–20. ↲
Giedd JN, et al. (2012) Review: magnetic resonance imaging of male/female differences in human adolescent brain anatomy. Biology of Sex Differences, 3(1):19. ↲
Groeschel S, et al. (2010) Developmental changes in cerebral grey and white matter volume from infancy to adulthood. International Journal of Developmental Neuroscience, 28(6):481–489. ↲
Lenroot RK, et al. (2007) Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage, 36(4):1065–1073. ↲
Rushton JP & Ankney CD, 2009. ↲
Giedd JN, et al. (1997). Sexual dimorphism of the developing human brain. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 21(8):1185–1201. ↲
Reiss AL, et al. (1996) Brain development, gender and IQ in children. A volumetric imaging study. Brain, 119(5): 1763–1774. ↲
Giedd JN, et al. (1996) Quantitative magnetic resonance imaging of human brain development: Ages 4–18. Cerebral Cortex, 6(4):551–559. ↲
Rushton JP & Ankney CD (1996). Brain size and cognitive ability: Correlations with age, sex, social class, and race. Psychonomic Bulletin & Review, 3(1):21–36. ↲
Rushton JP & Ankney CD. (1995) Brain size matters: a reply to Peters. Canadian Journal of Experimental Psychology, 49(4):562–569. ↲
Willerman L, et al. (1991) In vivo brain size and intelligence. Intelligence, 15(2):223–228. ↲
Kuczmarski RJ, et al. (2002). CDC growth charts for the United States: methods and development. Vital and Health Statistics Series, 11:1–190. ↲
Rushton JP & Ankney CD, 2009. ↲
Witelson SF, et al. (2006) Intelligence and brain size in 100 postmortem brains: Sex, lateralization and age factors. Brain, 129:386–398. ↲
Ho KC, et al. (1980) Analysis of brain weight: I & II. Archives of Pathology and Laboratory Medicine, 104:635–645. ↲
Dekaban AS & Sadowsky D. (1978) Changes in brain weights during the span of human life: Relation of brain weights to body heights and body weights. Annals of Neurology, 4:345–356. ↲
Rushton JP & Ankney CD, 2009. ↲
Wickett JC, et al, 1994. ↲
Pearlson GD, et al. (1989) Ventricle-brain ratio, computed tomographic density, and brain area in 50 schizophrenics. Archives of General Psychiatry, 46:690–697. ↲
Haug H. (1987) Brain sizes, surfaces, and neuronal sizes of the cortex cerebri: a stereological investigation of man and his variability and a comparison with some mammals (primates, whales, marsupials, insectivores, and one elephant). American Journal of Anatomy, 180, 126–42. ↲
Ringo JL. (1991) Neuronal interconnection as a function of brain size. Brain, Behavior and Evolution, 38, 1–6. ↲
Hofman MA. (1985) Size and shape of the cerebral cortex in mammals. I. The cortical surface. Brain, Behavior and Evolution, 27, 28–40. ↲
Ringo JL, 1991. ↲
Caceres M. (2003) Elevated gene expression levels distinguish human from non-human primate brains. Proceedings of the National Academy of Sciences, 100(22):13030–13035. ↲
Uddin M, et al. (2004) Sister grouping of chimpanzees and humans as revealed by genome-wide phylogenetic analysis of brain gene expression profiles. Proceedings of the National Academy of Sciences, 101(9):2957–2962. ↲
Kaas JH. (1993) Evolution of multiple areas and modules within neocortex. Perspectives on Developmental Neurobiology, 1(2):101–107. ↲
Gotts SJ, et al. (2013). Two distinct forms of functional lateralization in the human brain. Proceedings of the National Academy of Sciences, 110(36):E3435–3444. ↲
Alonso-Nanclares L, et al. (2008) Gender differences in human cortical synaptic density. Proceedings of the National Academy of Sciences, 105(38):14615–14619. ↲
Stark AK, et al. (2007) The effect of age and gender on the volume and size distribution of neocortical neurons. Neuroscience, 150(1):121–130. ↲
Pakkenberg B & Gundersen HJ. (1997) Neocortical neuron number in humans: effect of sex and age. The Journal of Comparative Neurology, 384(2):312–320. ↲
Marner L, et al. (2003) Marked loss of myelinated nerve fibers in the human brain with age. The Journal of Comparative Neurology, 462(2):144–152. ↲
Ingalhalikar M, et al. (2014) Sex differences in the structural connectome of the human brain. Proceedings of the National Academy of Sciences, 111(2):823–8. ↲
Wu K, et al. (2013) Topological organization of functional brain networks in healthy children: Differences in relation to age, sex, and intelligence. PLoS ONE, 8(2):e55347. ↲
Tomasi D & Volkow ND. (2012) Laterality patterns of brain functional connectivity: Gender effects. Cerebral Cortex, 22(6):1455–1462. ↲
Wang L, et al. (2012) Combined structural and resting-state functional MRI analysis of sexual dimorphism in the young adult human brain: an MVPA approach. NeuroImage, 61(4):931–940. ↲
Schmithorst VJ & Holland SK. (2007) Sex differences in the development of neuroanatomical functional connectivity underlying intelligence found using Bayesian connectivity analysis. NeuroImage, 35(1):406–419. ↲
Clements AM, et al. (2006) Sex differences in cerebral laterality of language and visuospatial processing. Brain and Language, 98(2):150–158. ↲
Schmithorst VJ & Holland SK. (2006) Functional MRI evidence for disparate developmental processes underlying intelligence in boys and girls. NeuroImage, 31(3):1366–1379. ↲
Vogel JJ, et al. (2003) Cerebral lateralization of spatial abilities: a meta-analysis. Brain and Cognition, 52(2):197–204. ↲
Kansaku K, et al. (2000) Sex differences in lateralization revealed in the posterior language areas. Cerebral Cortex, 10(9):866–872. ↲
Jaeger JJ, et al. (1998) Sex differences in brain regions activated by grammatical and reading tasks. NeuroReport, 9(12):2803–2807. ↲
Shaywitz BA, et al. (1995) Sex differences in the functional organization of the brain for language. Nature, 373(6515):607–609. ↲
Kulynych JJ, et al. (1994) Gender differences in the normal lateralization of the supratemporal cortex: MRI surface-rendering morphometry of Heschl’s gyrus and the planum temporale. Cerebral Cortex, 4(2):107–18. ↲
Bitan T, et al. (2010) Bidirectional connectivity between hemispheres occurs at multiple levels in language processing but depends on sex. Journal of Neuroscience, 30(35):11576–11585. ↲
Nowicka A & Fersten E. (2001) Sex-related differences in interhemispheric transmission time in the human brain. Neuroreport, 12(18):4171–4175. ↲
Rostad K, et al, 2007. ↲
Koscik T, et al. (2009) Sex differences in parietal lobe morphology: Relationship to mental rotation performance. Brain and Cognition, 69(3):451–459. ↲
Semrud-Clikeman M, et al. (2012) Gender differences in brain activation on a mental rotation task. International Journal of Neuroscience, 122(10):590–597. ↲
Heil M & Jansen-Osmann P. (2008) Sex differences in mental rotation with polygons of different complexity: Do men utilize holistic processes whereas women prefer piecemeal ones? Quarterly Journal of Experimental Psychology, 61(5):683–689. ↲
Butler T, et al. (2006) Sex differences in mental rotation: top-down versus bottom-up processing. NeuroImage, 32(1):445–456. ↲
Thomsen T, et al. (2000) Functional magnetic resonance imaging (fMRI) study of sex differences in a mental rotation task. Medical Science Monitor, 6(6):1186–1196. ↲
Moore DS & Johnson SP (2008) Mental rotation in human infants: a sex difference. Psychological Science, 19(11):1063–1066. ↲
Vederhus L & Krekling S. (1996) Sex differences in visual spatial ability in 9-year-old children. Intelligence, 23(1):33–43. ↲
Coluccia E, et al. (2007) The relationship between map drawing and spatial orientation abilities: A study of gender differences. Journal of Environmental Psychology, 27(2):135–144. ↲
Kramer JH, et al. (1996) Developmental sex differences in global-local perceptual bias. Neuropsychology, 10(3):402–407. ↲
Simon-Dack SL, et al. (2009) Sex differences in auditory processing in peripersonal space: an event-related potential study. NeuroReport, 20(2):105–110. ↲
McRoberts GW & Sanders B. (1992) Sex differences in performance and hemispheric organization for a nonverbal auditory task. Perception & Psychophysics, 51(2):118–122. ↲
Cattaneo Z, et al. (2006) Gender differences in memory for object and word locations. The Quarterly Journal of Experimental Psychology, 59(05):904–919. ↲
Geary DC, et al. (2000) Sex differences in spatial cognition, computational fluency, and arithmetical reasoning. Journal of Experimental Child Psychology, 77(4):337–353. ↲
Engelhard G. (1990) Gender differences in performance on mathematics items: Evidence from the United States and Thailand. Contemporary Educational Psychology, 15(1):13–26. ↲
Brunner M, et al. (2008) Gender differences in mathematics: Does the story need to be rewritten? Intelligence, 36(5):403–421. ↲
Rammsayer T & Troche S, 2010. ↲
He W, et al. (2013) A study of the greater male variability hypothesis in creative thinking in Mainland China: Male superiority exists. Personality and Individual Differences, 55(8):882–886. ↲
Markovits H & Benenson JF (2010). Males outperform females in translating social relations into spatial positions. Cognition, 117(3):332–340. ↲
Benenson JF, et al. (2007) Explaining sex differences in infants’ preferences for groups. Infant Behavior & Development, 30(4):587–595. ↲
Maddux WW & Brewer MB. (2005) Gender differences in the relational and collective bases for trust. Group Processes & Intergroup Relations, 8(2):159–171. ↲
Seeley EA, et al. (2003) Circle of friends or members of a group? Sex differences in relational and collective attachment to groups. Group Processes & Intergroup Relations, 6(3):251–263. ↲
Gabriel S & Gardner WL. (1999) Are there “his” and “hers” types of interdependence? The implications of gender differences in collective versus relational interdependence for affect, behavior, and cognition. Journal of Personality and Social Psychology, 77(3):642–655. ↲
Kalma A (1991). Hierarchisation and dominance assessment at first glance. European Journal of Social Psychology, 21(2):165–181. ↲
Bailey DH et al. (2012) Sex differences in in-group cooperation vary dynamically with competitive conditions and outcomes. Evolutionary Psychology: An International Journal of Evolutionary Approaches to Psychology and Behavior, 10(1):102–119. ↲
Benenson JF, et al. (2014) Rank influences human sex differences in dyadic cooperation. Current Biology, 24(5):R190–R191. ↲
Benenson JF, et al. (2014). Human males appear more prepared than females to resolve conflicts with same-sex peers. Human Nature, 25(2):251–268. ↲
Benenson JF, et al. (2012) Boys affiliate more than girls with a familiar same-sex peer. Journal of Experimental Child Psychology, 113(4):587–593. ↲
Benenson JF & Alavi K (2004) Sex differences in children’s investment in same-sex peers. Evolution and Human Behavior, 25(4):258–266. ↲
Benenson JF & Christakos A (2003). The greater fragility of females’ versus males’ closest same-sex friendships. Child Development, 74(4):1123–1129. ↲
Tezer E & Demir A (2001). Conflict behaviors toward same-sex and opposite-sex peers among male and female late adolescents. Adolescence, 36(143):525–533. ↲
Halari R & Kumari V. (2005) Comparable cortical activation with inferior performance in women during a novel cognitive inhibition task. Behavioural Brain Research, 158(1):167–173. ↲
Nyborg H, 2003. ↲
Neisser U, et al. (1996) Intelligence: Knowns and unknowns. American Psychologist, 51:77–101. ↲
Jackson DN. (2002) Evaluating g in the SAT: Implications for the sex differences and interpretations of verbal and quantitative aptitude. Paper presented at the International Society for Intelligence Research, Nashville, TN. ↲
Lynn R, et al. (2011) Intelligence in Taiwan: Progressive Matrices means and sex differences in means and variances for 6- to 17-year-olds. Journal of Biosocial Science, 43(4):469–474. ↲
Charlton BG. (2008) Pioneering studies of IQ by G.H. Thomson and J.F. Duff: An example of established knowledge subsequently “hidden in plain sight.” Medical Hypotheses, 71(5):625–628. ↲
Deary I (2003). Population sex differences in IQ at age 11: the Scottish mental survey 1932. Intelligence, 31(6):533–542. ↲
Hedges LV & Nowell A. (1995) Sex differences in mental test scores, variability, and numbers of high-scoring individuals. Science, 269(5220):41–45. ↲
Lynn R & Mulhern G. (1991) A comparison of sex differences on the Scottish and American standardisation samples of the WISC-R. Personality and Individual Differences, 12(11):1179–1182. ↲
Arden R & Plomin R. (2006) Sex differences in variance of intelligence across childhood. Personality and Individual Differences, 41(1):39–48. ↲
Deary I, et al. (2007) Brother–sister differences in the g factor in intelligence: Analysis of full, opposite-sex siblings from the NLSY1979. Intelligence, 35(5):451–456. ↲
Lehre AC, et al. (2009) Greater intrasex phenotype variability in males than in females is a fundamental aspect of the gender differences in humans. Developmental Psychobiology, 51(2):198–206. ↲
Feingold A. (1995) The additive effects of differences in central tendency and variability are important in comparisons between groups. American Psychologist, 50(1):5–13. ↲
Giedd JN, 2012. ↲
Lenroot RK, et al, 2007. ↲