Sex differences in intelligence
This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these template messages)(Learn how and when to remove this template message)
|This article is one of a series on:|
|Sex differences in humans|
Differences in human intelligence have long been a topic of debate among researchers and scholars. With the advent of the concept of g or general intelligence, many researchers have argued for no significant sex differences in g factor or general intelligence, while others have argued for greater intelligence for males, and others for females. The split view between these researchers depended on the methodology and tests they used for their claims. One study found some advantage for women in later life, while another found that male advantages on some cognitive tests are minimized when controlling for socioeconomic factors.
Some studies have concluded that there is larger variability in male scores compared to female scores, which results in more males than females in the top and bottom of the IQ distribution. Additionally, there are differences in the capacity of males and females in performing certain tasks, such as rotation of objects in space, often categorized as spatial ability.
- 1 Historical perspectives
- 2 Current research on general intelligence
- 3 Brain and intelligence
- 4 Mathematics performance
- 5 Spatial ability
- 6 Cognitive reflection test
- 7 Sex differences in academics
- 8 Self-fulfilling effects of scientific accounts of sex differences
- 9 Dyslexia
- 10 See also
- 11 References
Prior to the 20th century, it was a commonly held view that men were intellectually superior to women. In 1801, Thomas Gisborne said that women were naturally suited to domestic work and not spheres suited to men such as politics, science, or business. He stated that this was because women did not possess the same level of rational thinking that men did and had naturally superior abilities in skills related to family support.
In 1875, Herbert Spencer said that women were incapable of abstract thought and could not understand issues of justice and had only the ability to understand issues of care. In 1925, Sigmund Freud also stated that women were less morally developed in the concept of justice and, unlike men, were more influenced by feeling than rational thought. Early brain studies comparing mass and volumes between the sexes concluded that women were intellectually inferior because they have smaller and lighter brains. Many believed that the size difference caused women to be excitable, emotional, sensitive, and therefore not suited for political participation.
In the nineteenth century, whether men and women had equal intelligence was seen by many as a prerequisite for the granting of suffrage. Leta Hollingworth argued that women were not permitted to realize their full potential, as they were confined to the roles of child-rearing and housekeeping.
During the early twentieth century, the scientific consensus shifted to the view that gender plays no role in intelligence. In his 1916 study of children's IQs, psychologist Lewis Terman concluded that "the intelligence of girls, at least up to 14 years, does not differ materially from that of boys". He did, however, find "rather marked" differences on a minority of tests. For example, he found boys were "decidedly better" in arithmetical reasoning, while girls were "superior" at answering comprehension questions. He also proposed that discrimination, lack of opportunity, women's responsibilities in motherhood, or emotional factors may have accounted for the fact that few women had careers in intellectual fields.
Current research on general intelligence
According to the 1994 report Intelligence: Knowns and Unknowns by the American Psychological Association, "Most standard tests of intelligence have been constructed so that there are no overall score differences between females and males." Thus, there is little difference between the average IQ scores of men and women. Differences have been reported, however, in specific areas such as mathematics and verbal measures. Also, the variability of male scores is greater than that of females, resulting in more males than females in the top and bottom of the IQ distribution.
Researchers in favor of males in g factor
At one time, the overwhelming consensus was that there were no sex differences in g factor or general intelligence. However, researcher Richard Lynn challenged this consensus on two grounds:
- that males have bigger brain size in proportion to their body, and
- that there are little or no sex differences up until the age of 16 because males have slower developmental maturation.
A 2004 meta-analysis by Richard Lynn and Paul Irwing published in 2005 found that the mean IQ of men exceeded that of women by up to 5 points on the Raven's Progressive Matrices test. Lynn's findings were debated in a series of articles for Nature. Jackson and Philipe Rushton found that males aged 17–18 years had an average of 3.63 IQ points in excess of their female equivalents on the Scholastic Assessment Test. Irwing (2012) found a 3-point IQ advantage for males in g from subjects aged 16–89 in the United States on the WAIS III test favoring men on the information, arithmetic, and symbol search, and favoring women on the processing speed. A 2005 study by Ian Deary, Paul Irwing, Geoff Der, and Timothy Bates, focusing on the ASVAB showed a significantly higher variance in male scores, resulting in more than twice as many men as women scoring in the top 2%. The study also found a very small (d' ≈ 0.07, about 1 IQ point) average male advantage in g on the Armed Services Vocational Aptitude Battery test. Another study by researcher Jianghong Liu also found 3 points higher male scores on the WISC, which is the children's version of the WAIS III. He stated that greater male performance on items such as Picture Arrangement, Object Assembly, Picture Completion and Block Design is because these tests measure visual-spatial abilities in which males typically perform better than females.
Researchers in favor of females in g factor
Aside from traditional IQ tests like Raven's and WAIS, researchers have also used other tests that tap more into the Cattell-Horn-Caroll theory of intelligence in relation to gender. For example, a 2008 study by researcher Timothy Z. Keith on 25 subtests of Woodcock-Johnson Tests of Cognitive Abilities, along with a sample of 6,818 adults and children from 6 to 59, found females scoring higher on the latent processing speed (Gs) factor, a small male advantage on the latent comprehension–knowledge (Gc) factor, higher male score on the latent visual–spatial reasoning (Gv) and higher male latent quantitative reasoning (RQ) factor. The study found no difference in latent long-term retrieval (Glr), short-term memory (Gsm), auditory processing (Ga) and fluid intelligence (Gf) factors. However the sex difference in general intelligence (g-factor) was inconsistent in children with small higher female g factor during adolescence, and consistent higher female latent g factor during adulthood. The finding of the study confirmed Lynn's theory that males develop slower, but did not replicate results that males after 16 years old should have higher g factor. Lead researcher Timothy Keith suggests past researchers like Lynn's had used emergent scores to calculate g factor which is not accurate since most intelligent theories define g factor as a latent variable and not an emergent one. Researcher Timothy Z Keith replicated the same results again in the same year when he conducted a study of 3,025 6–18 year old participants with higher female latent g factor at all ages.
A 2015 study published in the journal of Psychology in Schools found no sex differences on standardized testing of achievement except a small persistent female advantage in reading and large female advantage in writing among a nationally representative sample of 1,574 6–21 year old participants.
Researchers in favor of no sex differences or inconclusive consensus
 In 2006, researchers Stephen Camarata and Richard Woodcock also replicated exactly the same results in sample of 4,253 children and adults but found no sex differences in g factor. In 2011, researcher Timothy Z. Keith also found no significant sex differences in latent g factor among participants of 5- to 17-year-olds on a different IQ test known as the Differential Ability Scales.
In 2000, researchers Roberto Colom and Francisco J. Abad conducted a large study of 10,475 adults on five IQ tests taken from the Primary Mental Abilities and found negligible or no significant sex differences. The tests conducted were on vocabulary, spatial rotation, verbal fluency and inductive reasoning. Roberto Colom in 2002 found that males' IQs were 3.16 points higher on the WAIS III test, but that there was no difference on the general intelligence factor (g) and therefore explained the differences as due to non-g factors such as specific group factors and test specificity. Responding to findings by Richard Lynn in 2002, researchers Roberto Colom and Oscar Garcia Lopez proposed that g factor is the variance of correlation among different IQ tests and not the sum of group scores published by Lynn in his studies. So measuring variance of Colom's study of 4,072 high school graduates, they found that females outperform males on the inductive reasoning part of the Primary Mental Abilities test, males outperform females on the Raven's Progressive Matrices and that there is no difference on the Culture-Fair intelligence test and therefore concluded no difference in general intelligence. Using multi-group covariance and mean group structure analysis in 2006, researchers Sophie van der Sluis, Conor V. Dolan and Roberto Colom found that g factor couldn't explain any sex differences on the WAIS III. Later that year in another study, the same researchers concluded that sex differences on WAIS are due to primary factors or broad stratum abilities like working memory and perceptual organization not g factor. A study conducted by Jim Flynn and Lilia Rossi-Case (2011) found that men and women achieved roughly equal IQ scores on Raven's Progressive Matrices after reviewing recent standardization samples in five modernized nations. In 2010, researcher Emily Savage-McGlynn proposed that inconsistent results in sex differences are due to opportunity samples rather than samples that represent the general population. After a study with a nationally representative sample of 926 participants in the UK, no sex difference was found in the Raven's Progressive Matrices test.
In 2007, Johnson and Bouchard (2007) conducted 40–60 mental tests that were not constructed to eliminate sex differences also found no sex differences in general intelligence or g factor except in residual factors such as verbal abilities and mental rotation. Another study published in the Journal of Psychoeductional Assessment also found no sex differences in g factor in a sample size of 744 5–85 year old participants on the Wide Range Intelligence Test. Gerhard Meisenberg, based on data from 5975 males and 5939 females who took the Armed Services Vocational Aptitude Battery, found that men generally score higher than women. However, this advantage has no consistent relationship to the g-loading of the sub-tests. So, it cannot be due to differences in general intelligence.
A 2009 study published in the Archives of Clinical Neuropsychology also found no sex differences in fluid intelligence except female 8 point advantage on writing and male 4 point advantage in math among 22–90 year old participants in a sample of 500 participants.
Debate and division among researchers
The current literature on sex differences produced inconsistent results depending on the type of testing used. Among the researchers who conducted studies on intelligence, many state that there are no sex differences in g (Jensen 1998, Colom, Garcia, Juan-Esponiza & Abad 2000, Colom, Garcia, Juan-Esponiza & Abad 2002, Camarata and Woodcock 2006), some found a difference favoring males (Lynn 1999, Lynn Irwing 2004, 2008) and some found a difference favoring females (Keith, Reynolds, Patel & Ridley 2006 and Reynolds, Keith, Ridley, Patel 2006). The issues remains unresolved if one uses standardized tests as Jensen (1998) and Colom, Garcia (2002) agrees that there might be a small insignificant sex difference in intelligence in general (IQ) but this may not necessarily reflect a sex difference in general intelligence (g factor). The difference between the two concepts is that IQ is a psychometric scoring system measured with standardized testing, while g factor is a latent scientific construct that correlates with all cognitive tests and achievements in life. Although most researchers distinguish between g and IQ, those that argue for greater male intelligence assert that IQ and g are synonymous (Lynn & Irwing 2004) and so the real division comes from defining IQ in relation to g factor. However, in 2008 Lynn and Irwing proposed that since working memory ability correlate highest with g factor, researchers would have no choice but to accept greater male intelligence if differences on working memory tasks are found. As a result, a neuroimaging study published by Schmidt (2009) conducted an investigation into this proposal by measuring sex differences on an n-back working memory task. The results found no sex difference in working memory capacity and thus contradicting the position pushed forward by Lynn and Irwing (2008) and more in line with those arguing for no sex differences in intelligence. The same results in the 2009 n-back working memory study have also been replicated in numerous other studies since.
American Psychological Association
A 2012 review by researchers Richard E. Nisbett, Joshua Aronson, Clancy Blair, William Dickens, James Flynn, Diane F. Halpern and Eric Turkheimer discussed Arthur Jensen's 1998 studies on sex differences in intelligence. Jensen's tests were significantly g loaded but were not set-up to get rid of any sex differences (read differential item functioning). They summarized his conclusions as he quoted, "No evidence was found for sex differences in the mean level of g or in the variability of g. Males, on average, excel on some factors; females on others." Jensen’s results that no overall sex differences existed for g has been reinforced by researchers who analyzed this issue with a battery of 42 mental ability tests and found no overall sex difference.
Although most of the tests showed no difference, there were some that did. For example, they found female subjects performed better on verbal abilities while males performed better on visuospatial abilities. For verbal fluency, females have been specifically found to perform slightly better in vocabulary and reading comprehension but significantly higher in speech production and essay writing. Males have been specifically found to perform better on spatial visualization, spatial perception, and mental rotation. Researchers had then recommended that general models such as fluid and crystallized intelligence be divided into verbal, perceptual and visuospatial domains of g, because when this model is applied then females excel at verbal and perceptual tasks while males on visuospatial tasks, thus evening out the sex differences on IQ tests.
Some studies have identified the degree of IQ variance as a difference between males and females. Males tend to show greater variability on many traits, for example having both highest and lowest scores on tests of cognitive abilities, though this may differ between countries.
"Feingold (1992b) and Hedges and Nowell (1995) have reported that, despite average sex differences being small and relatively stable over time, test score variances of males were generally larger than those of females."(p187) Feingold "found that males were more variable than females on tests of quantitative reasoning, spatial visualisation, spelling, and general knowledge. ... Hedges and Nowell go one step further and demonstrate that, with the exception of performance on tests of reading comprehension, perceptual speed, and associative memory, more males than females were observed among high-scoring individuals."(p187–188)
Psychologist and psychometrician Steve Blinkhorn published a criticism in the journal Nature against Richard Lynn and Paul Irwing on their meta-analysis of sex differences, where he said they were flawed in excluding an intelligence study from Mexico that accounted for almost 45% of the data. He said that had it not been excluded, no sex differences would have been found. He said there was a need for better research designs instead of summing up through meta-analysis.
A 2008 study by Earl Hunt published in the journal Intelligence found that sex differences on tests can result from recruitment factors, so that sex differences are only representative of those that actually participate in the studies. He also contested that not only can there be individual variations that studies consistently recruit, but that these studies wouldn't be representative of the general population especially if the participants only represent college students. A 2009 study by researcher Dominika Dykiert published Intelligence also found that sex differences in mean IQ scores can be partly created by male variability in cognitive abilities and sample restriction. Dykiert highlighted the need for representative samples for studying sex differences in intelligence to eliminate skewed results created by male variance and restriction of sample. Studies have also indicated that tests used to measure sex differences such as Raven's Progressive matrices is not a pure indicator of general intelligence or g factor and only shares 50% of its variance. Tests like WAIS have also been criticized as not directly measuring g factor in a 2002 study published in Intelligence. Lead researcher Roberto Colom has strongly asserted that g factor or general intelligence is the correlation among different test scores and not the summed scores on different tests, such as Raven's or WAIS. Roberto Colom has also pointed out that Raven's Progressive matrices test is biased against females because of the visuospatial nature of the test which tends to favor those with stronger visuospatial skills (i.e. males) instead of g factor. Therefore, he concluded that sex difference research should not be based on a single test but instead on multiple tests loaded with fluid intelligence.
Brain and intelligence
Differences in brain physiology between sexes do not necessarily relate to differences in intellect. Haier et al. found in a 2004 study that "[m]en and women apparently achieve similar IQ results with different brain regions, suggesting that there is no singular underlying neuroanatomical structure to general intelligence and that different types of brain designs may manifest equivalent intellectual performance. For men, the gray matter volume in the frontal and parietal lobes correlates with IQ; for women, the gray matter volume in the frontal lobe and Broca's area (which is used in language processing) correlates with IQ.
Although men have bigger brain size which is partly explained by their bigger bodies, women have greater cortical thickness, cortical complexity and cortical surface area (controlling for body size) which compensates for smaller brain size. Meta-analysis and studies have found that brain size explains 6–12% of variance among individual intelligence and cortical thickness explains 5%.
The study of brain networks of men and women shows that in numerous graph-theoretical parameters, the structural connectome of women are significantly better connected than the connectome of men. Regarding the charge that there is bias in the research, it is argued that women's advantage remains valid if large-brain women and small-brain men are compared, and that this indicates that the graph-theoretical differences are due to sex and not to size differences.
A performance difference in mathematics on the SAT exists in favor of males, though differences in mathematics course performance measures favor females. In 1983, Benbow concluded that the study showed a large sex difference by age 13 and that it was especially pronounced at the high end of the distribution. However, Gallagher and Kaufman criticized Benbow's and others' reports, which found that males were over-represented in the highest percentages, on the grounds that they had not ensured representative sampling.
In a 2008 study paid for by the National Science Foundation in the United States, researchers found that "girls perform as well as boys on standardized math tests. Although 20 years ago, high school boys performed better than girls in math, the researchers found that is no longer the case. The reason, they said, is simple: Girls used to take fewer advanced math courses than boys, but now they are taking just as many." However, the study indicated that, while boys and girls performed similarly on average, boys were over-represented among the very best performers as well as among the very worst. A 2011 meta-analysis with 242 studies from 1990 to 2007 involving 1,286,350 people found no overall sex difference of performance in mathematics. The meta-analysis also found that although there were no overall differences, a small sex difference that favored males in complex problem solving is still present in high school.
Kiefer and Sekaquaptewa proposed that a source of some women's underperformance and lowered perseverance in mathematical fields is these women's underlying "implicit" sex-based stereotypes regarding mathematical ability and association, as well as their identification with their gender. Some psychologists believe that many historical and current sex differences in mathematics performance may be related to boys' higher likelihood of receiving math encouragement than girls. Parents were, and sometimes still are, more likely to consider a son's mathematical achievement as being a natural skill while a daughter's mathematical achievement is more likely to be seen as something she studied hard for. This difference in attitude may contribute to girls and women being discouraged from further involvement in mathematics-related subjects and careers.
Stereotype threat has been shown to affect performance and confidence in mathematics of both males and females. In another experiment conducted on 80 Cornell University undergraduates, participants were taught how to perform a new task in gender salient (male advantage) versus gender-neutral conditions. Participants in the gender salient condition were told, “males on average perform better on such tests.” Even though participants were given identical scores at the end of the task, women in the gender salient condition rated their ability on the task significantly lower than those in the gender-neutral condition, and even reported reduced interest in related occupations. While these two studies highlight the effect of stereotype threat on self-perception, another experiment by Rydell and colleagues found that stereotype threat can actually impair women’s ability to learn new gender-typed material. Women in the stereotype threat condition had difficulty encoding math-related information into memory and, therefore, learned fewer mathematical rules and showed poorer math performance than did controls. Stereotype threat reduced women’s, but not men’s, ability to learn abstract mathematical rules and to transfer these rules to a second, isomorphic task. The researchers concluded that negative stereotypes about women in math reduce their level of math learning, which then leads to poorer performance in negatively stereotyped domains.
Two cross-country comparisons have found great variation in the gender differences regarding the degree of variance in mathematical ability. In most nations males have greater variance. In a few females have greater variance. Hyde and Mertz argue that boys and girls differ in the variance of their ability due to sociocultural factors.
Metastudies show a male advantage in mental rotation and assessing horizontality and verticality and a female advantage in spatial memory. A proposed hypothesis is that men and women evolved different mental abilities to adapt to their different roles in society. This explanation suggests that men may have evolved greater spatial abilities as a result of certain behaviors, such as navigating during a hunt. Similarly, this hypothesis suggests that women may have evolved to devote more mental resources to remembering locations of food sources in relation to objects and other features in order to gather food.
A number of studies have shown that women tend to rely more on visual information than men in a number of spatial tasks related to perceived orientation. However, 'visual dependence' has been found to be task specific and not a general characteristic of spatial processing that differs between the sexes. Here an alternative hypothesis suggests that heightened visual dependence in females does not generalize to all aspects of spatial processing but is probably attributable to task-specific differences in how male and females brains process multisensory spatial information.
Results from studies conducted in the physical environment are not conclusive about sex differences, with various studies on the same task showing no differences. For example, there are studies that show no difference in finding one's way between two places. One study found men more likely to report having a good sense of direction and are more confident about finding their way in a new environment, but evidence does not support men having better map reading skills. Women have been found to use landmarks more often when giving directions and when describing routes. Additionally, a study concludes that women are better at recalling where objects are located in a physical environment. Women show greater proficiency and reliance on distinctive landmarks for navigation while males rely on an overall mental map.
Performance in mental rotation and similar spatial tasks is affected by gender expectations. For example, studies show that being told before the test that men typically perform better, or that the task is linked with jobs like aviation engineering typically associated with men versus jobs like fashion design typically associated with women, will negatively affect female performance on spatial rotation and positively influence it when subjects are told the opposite. Experiences such as playing video games also increase a person's mental rotation ability. A study from the University of Toronto showed that differences in ability get reduced after playing video games requiring complex mental rotation. The experiment showed that playing such games creates larger gains in spatial cognition in females than males. However, male participants still performed superior to female participants both before and after training.
The possibility of testosterone and other androgens as a cause of sex differences in psychology has been a subject of study. Adult women who were exposed to unusually high levels of androgens in the womb due to congenital adrenal hyperplasia score significantly higher on tests of spatial ability. Many studies find positive correlations between testosterone levels in healthy males and measures of spatial ability. However, the relationship is complex.
A study was done to compare the relationship between mental rotation ability and gender difference, specifically in the SAT math section. Cognitive gender differences are apparent and findings of a male advantage in certain mathematical domains have been demonstrated cross-culturally. These gender differences found are largely in geometry and word problems and tend to be in countries with the highest-achieving students and with the largest gender gap in experience. Smaller differences were noted in countries with lower achieving students in mathematics which includes the United States. Moore and Smith say that within the United States, poorly educated female students outperform their male peers, but as the level of education increases, the male advantage in mathematics emerges.
Spatial ability may be responsible in part for facilitating gender differences in math aptitude. In 1995, Casey et al. looked at the relationship of mental rotation ability and the SAT-M among four samples. The four samples were: (1) undergraduates at two liberal arts colleges in the Northeast that were tested on their mental rotation ability in groups of 10–20; (2) a group of mathematically talented preteens participating in a summer math and science training in the Midwest, which included seventh to ninth graders who were either recruited from a national talent search program or statewide teacher selection program; (3) a high-ability group of college-bound students who were enrolled in a middle-income suburban high school in the Northeast and elected to take the SAT; and (4) a low-ability group of college-bound students who were enrolled in a middle-income suburban high school in the Northeast and elected to take the SAT. The data used were SAT math and verbal scores and mental rotation scores. Mental rotation was assessed using the Vandenberg Test of Mental Rotation. Students were asked to match two out of four choices to a standard figure.
The study found that when mental rotation is used as a predictor of Math aptitude for female students, the correlations between mental rotation and SAT-Math scores ranged from 0.35 to 0.38 whereas males showed no consistent pattern. Male correlations ranged from −0.03 to 0.54. However, a finding was that in the three high ability samples, there was a significant gender difference in SAT-Math scores alone. This difference favored males. In the three high ability samples, males scored higher than females in mental rotation ability. For the verbal aptitude test on the SAT, there was a significant difference in verbal ability for the low ability college bound sample favoring girls.
Cognitive reflection test
The ability to apply analytical analysis (as opposed to making intuitive decisions, i.e. System 1 and System 2 thinking) is related to, but different from IQ. On the cognitive reflection test, which consists of "trick questions" where the intuitive answer is wrong, women scored on average 1.03, while men scored 1.47. Bosch-Domènecha and colleagues (2014) further found not only did men outscore women on the CRT, but that greater exposure to prenatal androgens (via a lower 2D:4D ratio) is significantly correlated with a higher score on the cognitive reflection test regardless of sex. Several other studies have replicated these results and have found that men typically score higher than women on the CRT.
Sex differences in academics
A 2014 meta-analysis of sex differences in scholastic achievement published in the journal of Psychological Bulletin found females outperformed males in teacher-assigned school marks throughout elementary, junior/middle, high school and at both undergraduate and graduate university level. The meta-analysis, done by researchers Daniel Voyer and Susan D. Voyer from the University of New Brunswick, drew from 97 years of 502 effect sizes and 369 samples stemming from the year 1914 to 2011. Another 2015 study by researchers Gijsbert Stoet and David C. Geary in Intelligence found that girl's overall education achievement is better in 70 percent of all the 47–75 countries that participated in PISA. The study consisting of 1.5 million 15-year-olds found higher overall female achievement across reading, mathematics, and science literacy and better performance across 70% of participating countries including many with negative gaps in socioeconomic and gender equality, and they fell behind in only 4% of countries. Stoet and Geary concluded that sex differences in educational achievement are not reliably linked to gender equality.
Beyond sex differences in academic ability, recent research has also been focusing on women’s underrepresentation in higher education, especially in the fields of natural science, technology, engineering and mathematics (STEM). Nonetheless, 2011 data on earned doctorates in the United States by the National Science Foundation shows female representation in all of academia varies, not just in STEM subjects. For example, in 2011, women earned 54% of molecular biology PhDs but less than 20% in physics (STEM). In humanities and social sciences, women earned over 70% of psychology PhDs, but only 30% in philosophy. A 2015 study published in the Journal of Women in Science explained these patterns of female under-representation across academic disciplines, using the field-specific ability beliefs hypothesis, which claims that women are underrepresented in fields whose practitioners believe that raw, innate talent is the main requirement for success, because women are stereotyped as not possessing such talent. The study, conducted on 1820 academics from 30 different disciplines, found that the more a particular field values gifted-ness, the fewer the female PhDs in that discipline. According to the researchers, “emphasis on raw aptitude may activate negative stereotypes in women’s own minds, making them vulnerable to stereotype threat”.
Numerous studies have shown that gender stereotyping plays a big role in influencing women’s academic and career aspirations by reducing their interest in male-typed occupations like engineering, leadership and mathematics. In a 2002 study published in the Journal of Personality and Social Psychology, a group of researchers found that gender-stereotypic television commercials can restrain women academically and professionally. Exposure to the stereotypic commercials resulted in women underperforming on a subsequent math test and avoiding math items in favor of verbal items. Furthermore, the women who viewed the stereotypic commercials expressed less interest in quantitative fields and more interest in verbal domains, both academically and professionally.
Self-fulfilling effects of scientific accounts of sex differences
Claims from scientific research on sex differences in intelligence, quite independently of their scientific validity, can have self-fulfilling effects and therefore sustain the very sex differences they seek to explain. Psychologist Barry Schwartz extended the concept of idea technology to convey the point that the ideas and concepts produced by science and technology, just like the tangible products, can affect our lives. This notion is also described as looping effects or feedback effects in cognition and culture, whereby the causal understanding of a particular social group changes the very character of the group, leading to further change in the phenomenon under investigation.
Scientific claims about hardwired sex differences interact with people’s minds as they are communicated to the public in popular books and articles. ‘Brain facts’ have become increasingly popular and persuasive on public attitudes due to the increased fascination with neuroscience. Suggestions made in public domains that sex differences in achievement in science, mathematics or philosophy can be explained by innate brain differences can have important psychological effects. Haslam and colleagues found that emphasis on the biological causes of sex differences in intelligence suggests inevitability and fixedness, creating essentialist beliefs about gender that deepen social divides.
Research suggests that claims about psychological sex differences having biological causes underlie the negative effects of stereotype threat. Two studies in maths performance found that stereotype threat effects were only seen when it was explicitly claimed that the gender gap in performance was a result of genetic factors. Furthermore, believing that males are hardwired to be better at math or any particular field reinforces the idea both that such abilities are "gifted" rather than earned, and that females need to work much harder because they do not possess this innate ability. An intervention study by Columbia and NYU psychologists challenged the ‘fixed’ view of intelligence and eliminated the gender gap in maths performance. Specifically, seventh grade students in the experimental conditions were encouraged either to view intelligence as malleable or to attribute academic difficulties to the novelty of middle school. Results showed that females in experimental conditions achieved significantly higher math standardized test scores than those in the control group.
Beyond academia, the endorsement of biological explanations of sex differences in intelligence has been associated with greater modern day sexism. In their research on the theorization of gender, Morton and colleagues found that people shown scientific claims that males and females are hardwired to be different, compared to those told that such ideas are under debate, expressed more agreement that society treats women fairly. Additionally, male participants became more supportive of sex discrimination at work after reading the scientific facts. Other research indicates that shifting the focus from biological to environmental contributors of behavior can decrease endorsement of sexist hierarchies.
As with any scientific work, claims about sex differences in the brain and their relation to behavior are subject to debate. Future research could be usefully targeted at exploring further the effects of believing in hardwired sex differences on society and how to reverse the negative consequences. Current research shows that believing in such claims increases gender bias in evaluations of others and backlash effects against females who violate gender norms. Such claims could result in parents and teachers perceiving and treating children more stereotypically, given the rising influence of gender specific "brain-based" educational strategies recommended to schools.
Dyslexia is a learning disability that impairs a person’s fluency or comprehension accuracy in being able to read. The cause of this disability is associated with abnormal brain anatomy and function. Gray matter deficits have been demonstrated in dyslexics using structural magnetic resonance imaging. This deficit has been found in specific regions within the left hemisphere involved in language.
There is higher prevalence of dyslexia in males than in females. However, different abnormalities are found in female brains as opposed to male brains. In a study that examined gray matter volume in dyslexic females, it was found that there was less gray matter volume in the right precuneus and paracentral lobule/medial frontal gyrus. In males, there was less gray matter volume in the left inferior parietal cortex. This study shows that dyslexia in females does not involve the left hemisphere regions involved in language as it does in males. Instead, it affects the sensory and motor cortices such as the motor and premotor cortex and primary visual cortex.
- Sex differences in humans
- Sex differences in psychology
- Sex differences in emotional intelligence
- Emotional intelligence
- van der Sluis, Sophie; Posthuma, Danielle; Dolan, Conor V.; de Geus, Eco J. C.; Colom, Roberto; Boomsma, Dorret I. (2006-05-01). "Sex differences on the Dutch WAIS-III". Intelligence. 34 (3): 273–289. doi:10.1016/j.intell.2005.08.002.
- Dolan, Conor V.; Colom, Roberto; Abad, Francisco J.; Wicherts, Jelte M.; Hessen, David J.; van de Sluis, Sophie (2006-03-01). "Multi-group covariance and mean structure modeling of the relationship between the WAIS-III common factors and sex and educational attainment in Spain". Intelligence. 34 (2): 193–210. doi:10.1016/j.intell.2005.09.003.
- Irwing, Paul; Lynn, Richard (2005). "Sex differences in means and variability on the progressive matrices in university students: A meta-analysis". British Journal of Psychology. 96 (4): 505–24. doi:10.1348/000712605X53542. PMID 16248939.
- Keith, Timothy Z.; Reynolds, Matthew R.; Patel, Puja G.; Ridley, Kristen P. (2008). "Sex differences in latent cognitive abilities ages 6 to 59: Evidence from the Woodcock–Johnson III tests of cognitive abilities". Intelligence. 36 (6): 502–525. doi:10.1016/j.intell.2007.11.001.
- Colom, Roberto; Escorial, Sergio; Rebollo, Irene (2004). "Sex differences on the Progressive Matrices are influenced by sex differences on spatial ability". Personality and Individual Differences. 37 (6): 1289–1293. doi:10.1016/j.paid.2003.12.014.
- Keith, Timothy Z.; Reynolds, Matthew R.; Patel, Puja G.; Ridley, Kristen P. (2008). "Sex differences in latent cognitive abilities ages 6 to 59: Evidence from the Woodcock–Johnson III tests of cognitive abilities". Intelligence. 36 (6): 502–25. doi:10.1016/j.intell.2007.11.001.
- Jorm, Anthony F.; Anstey, Kaarin J.; Christensen, Helen; Rodgers, Bryan (2004). "Gender differences in cognitive abilities: The mediating role of health state and health habits". Intelligence. 32: 7–23. doi:10.1016/j.intell.2003.08.001.
- Deary, Ian J.; Irwing, Paul; Der, Geoff; Bates, Timothy C. (2007). "Brother–sister differences in the g factor in intelligence: Analysis of full, opposite-sex siblings from the NLSY1979". Intelligence. 35 (5): 451–6. doi:10.1016/j.intell.2006.09.003.
- Wai, Jonathan; Cacchio, Megan; Putallaz, Martha; Makel, Matthew C. (2010). "Sex differences in the right tail of cognitive abilities: A 30year examination". Intelligence. 38 (4): 412–423. doi:10.1016/j.intell.2010.04.006. ISSN 0160-2896.
- Lips, Hilary M. (1997). Sex & Gender: An Introduction (3rd ed.). Mountain View, Calif.: Mayfield. p. 40. ISBN 978-1559346306.
- Denmark, Florence L.; Paludi, Michele A. (2008). Psychology of Women: A Handbook of Issues and Theories (2nd ed.). Westport, Conn.: Praeger. pp. 7–11. ISBN 978-0275991623.
- Thomas Gisborne, An enquiry into the duties of the female sex, Printed by A. Strahan for T. Cadell jun. and W. Davies, 1801[page needed]
- Judith Worell, Encyclopedia of women and gender: sex similarities and differences and the impact of society on gender, Volume 1, Elsevier, 2001, ISBN 0-12-227246-3, ISBN 978-0-12-227246-2[page needed]
- Fine, Cordelia (2010). Delusions of Gender: How Our Minds, Society, and Neurosexism Create Difference. W. W. Norton. ISBN 978-0-393-06838-2.[page needed]
- Margarete Grandner, Austrian women in the nineteenth and twentieth centuries: cross-disciplinary perspectives, Berghahn Books, 1996, ISBN 1-57181-045-5, ISBN 978-1-57181-045-8
- Burt, C. L.; Moore, R. C. (1912). "The mental differences between the sexes". Journal of Experimental Pedagogy. 1 (273–284): 355–388.
- Terman, Lewis M. (1916). The measurement of intelligence: an explanation of and a complete guide for the use of the Stanford revision and extension of the Binet-Simon intelligence scale. Boston: Houghton Mifflin. pp. 68–72. OCLC 186102.
- Neisser, Ulric; Boodoo, Gwyneth; Bouchard, Thomas J., Jr.; Boykin, A. Wade; Brody, Nathan; Ceci, Stephen J.; Halpern, Diane F.; Loehlin, John C.; Perloff, Robert (1996). "Intelligence: Knowns and Unknowns". American Psychologist. 51 (2): 77–101. doi:10.1037/0003-066X.51.2.77.
- Baumeister, Roy F (2001). Social psychology and human sexuality: essential readings. Psychology Press. ISBN 978-1-84169-019-3.[page needed]
- Baumeister, Roy F. (2010). Is there anything good about men?: how cuflourish by exploiting men. Oxford University Press. ISBN 978-0-19-537410-0.[page needed]
- Hedges, L.; Nowell, A (1995). "Sex differences in mental test scores, variability, and numbers of high-scoring individuals". Science. 269 (5220): 41–5. Bibcode:1995Sci...269...41H. doi:10.1126/science.7604277. PMID 7604277.
- Colom, R; García, LF; Juan-Espinosa, M; Abad, FJ (2002). "Null sex differences in general intelligence: Evidence from the WAIS-III". The Spanish Journal of Psychology. 5 (1): 29–35. doi:10.1017/s1138741600005801. PMID 12025362.
- Nyborg, Helmuth (July 2012). "A conversation with Richard Lynn". Personality and Individual Differences. 53 (2): 79–84. doi:10.1016/j.paid.2011.02.033.
- Lynn, Richard; Irwing, Paul (2004). "Sex differences on the progressive matrices: A meta-analysis". Intelligence. 32 (5): 481–498. doi:10.1016/j.intell.2004.06.008.
- Blinkhorn, Steve (2005). "Intelligence: A gender bender" (PDF). Nature. 438 (7064): 31–2. Bibcode:2005Natur.438...31B. doi:10.1038/438031a. PMID 16267535.
- Irwing, Paul; Lynn, Richard (2006). "Intelligence: Is there a sex difference in IQ scores?". Nature. 442 (7098): E1, discussion E1–2. Bibcode:2006Natur.442E...1I. doi:10.1038/nature04966. PMID 16823409.
- Blinkhorn, Steve (July 2006). "Is there a sex difference in IQ scores? (Reply)". Nature. 442 (7098): E1–E2. doi:10.1038/nature04967.
- Jackson, Douglas N.; Rushton, J. Philippe (2006). "Males have greater g: Sex differences in general mental ability from 100,000 17- to 18-year-olds on the Scholastic Assessment Test". Intelligence. 34 (5): 479–486. doi:10.1016/j.intell.2006.03.005.
- Irwing, Paul (2012). "Sex differences in g: An analysis of the US standardization sample of the WAIS-III". Personality and Individual Differences. 53 (2): 126–31. doi:10.1016/j.paid.2011.05.001.
- Liu, Jianghong; Lynn, Richard (2015-03-01). "Chinese sex differences in intelligence: Some new evidence". Personality and Individual Differences. 75: 90–93. doi:10.1016/j.paid.2014.11.002. PMC 4261186. PMID 25506114.
- Scheiber, Caroline; Reynolds, Matthew R.; Hajovsky, Daniel B.; Kaufman, Alan S. (2015). "Gender Differences in Achievement in a Large Nationally Representative Sample of Children and Adolescents". Psychology in the Schools. 52 (4): 335–348. doi:10.1002/pits.21827.
- Reynolds, Matthew R.; Keith, Timothy Z.; Ridley, Kristen P.; Patel, Puja G. (2008). "Sex differences in latent general and broad cognitive abilities for children and youth: Evidence from higher-order MG-MACS and MIMIC models". Intelligence. 36 (3): 236–260. doi:10.1016/j.intell.2007.06.003.
- Camarata, Stephen; Woodcock, Richard (2006). "Sex differences in processing speed: Developmental effects in males and females". Intelligence. 34 (3): 231–252. doi:10.1016/j.intell.2005.12.001.
- Keith, Timothy Z.; Reynolds, Matthew R.; Roberts, Lisa G.; Winter, Amanda L.; Austin, Cynthia A. (2011-09-01). "Sex differences in latent cognitive abilities ages 5 to 17: Evidence from the Differential Ability Scales—Second Edition". Intelligence. 39 (5): 389–404. doi:10.1016/j.intell.2011.06.008.
- Colom, Roberto; Juan-Espinosa, Manuel; Abad, Francisco; Garcı́a, Luı́s F. (2000). "Negligible Sex Differences in General Intelligence". Intelligence. 28: 57–68. doi:10.1016/S0160-2896(99)00035-5. Retrieved 2016-01-23.
- Colom, Roberto; Garcı́a-López, Oscar (2002-02-01). "Sex differences in fluid intelligence among high school graduates". Personality and Individual Differences. 32 (3): 445–451. doi:10.1016/S0191-8869(01)00040-X.
- Flynn, Jim; Rossi-Casé, Lilia (2011). "Modern women match men on Raven's Progressive Matrices". Personality and Individual Differences. 50 (6): 799–803. doi:10.1016/j.paid.2010.12.035.
- Savage-McGlynn, Emily (2012-07-01). "Sex differences in intelligence in younger and older participants of the Raven's Standard Progressive Matrices Plus". Personality and Individual Differences. Evolution of race and sex differences in intelligence and personality: Tribute to Richard Lynn at eighty. 53 (2): 137–141. doi:10.1016/j.paid.2011.06.013.
- Johnson, Wendy; Bouchard, Thomas J. (2007). "Sex differences in mental ability: A proposed means to link them to brain structure and function". Intelligence. 35 (3): 197–209. doi:10.1016/j.intell.2006.07.003.
- Johnson, Wendy; Bouchard, Thomas J. (2007). "Sex differences in mental abilities: g masks the dimensions on which they lie". Intelligence. 35 (1): 23–39. doi:10.1016/j.intell.2006.03.012.
- Shields, J. (2004). "Validity of the Wide Range Intelligence Test: Differential Effects across Race/Ethnicity, Gender, and Education Level". Journal of Psychoeducational Assessment. 22 (4): 287–303. doi:10.1177/073428290402200401.
- Meisenberg, Gerhard. "Sex Differences in Intelligence: Developmental Origin Yes, Jensen Effect No." Mankind Quarterly 2017 58:1 101-108. https://www.researchgate.net/profile/Gerhard_Meisenberg/publication/319711784_Sex_Differences_in_Intelligence_Developmental_Origin_Yes_Jensen_Effect_No/links/59ba9a62a6fdcc687235782c/Sex-Differences-in-Intelligence-Developmental-Origin-Yes-Jensen-Effect-No.pdf.
- Kaufman, A. S.; Kaufman, J. C.; Liu, X.; Johnson, C. K. (2009). "How do Educational Attainment and Gender Relate to Fluid Intelligence, Crystallized Intelligence, and Academic Skills at Ages 22–90 Years?". Archives of Clinical Neuropsychology. 24 (2): 153–163. doi:10.1093/arclin/acp015. PMID 19185449.
- Chamorro-Premuzic, Tomas; Stumm, Sophie von; Furnham, Adrian (2015-06-22). The Wiley-Blackwell Handbook of Individual Differences. John Wiley & Sons. ISBN 9781119050308.
- Schmidt, Heike; Jogia, Jigar; Fast, Kristina; Christodoulou, Tessa; Haldane, Morgan; Kumari, Veena; Frangou, Sophia (2009-11-01). "No gender differences in brain activation during the N-back task: an fMRI study in healthy individuals". Human Brain Mapping. 30 (11): 3609–3615. doi:10.1002/hbm.20783. ISSN 1097-0193. PMID 19387979.
- J. A., Tende; Tende; A., J. (2012). "Sex differences in the working memory of students in Ahmadu Bello University, Zaria, Nigeria using the N-b". IOSR Journal of Dental and Medical Sciences. 2 (6): 8–11. doi:10.9790/0853-0260811.
- "N -back task to assess sex difference in working memory: A pilot study". ResearchGate. Retrieved 2016-02-02.
- Li, Ting; Luo, Qingming; Gong, Hui (2010-05-01). "Gender-specific hemodynamics in prefrontal cortex during a verbal working memory task by near-infrared spectroscopy". Behavioural Brain Research. 209 (1): 148–153. doi:10.1016/j.bbr.2010.01.033. ISSN 1872-7549. PMID 20117145.
- Robert, Michèle; Savoie, Nada (2006). "Are there gender differences in verbal and visuospatial working-memory resources?". European Journal of Cognitive Psychology. 18 (3): 378–397. doi:10.1080/09541440500234104.
- Harness, Ashley (2008). "Sex differences in working memory". Psychological Reports. 103 (5): 214. doi:10.2466/PR0.103.5.214-218. Retrieved 2016-02-02.
- Goldstein, Jill M.; Jerram, Matthew; Poldrack, Russell; Anagnoson, Robert; Breiter, Hans C.; Makris, Nikos; Goodman, Julie M.; Tsuang, Ming T.; Seidman, Larry J. (2005-07-01). "Sex differences in prefrontal cortical brain activity during fMRI of auditory verbal working memory". Neuropsychology. 19 (4): 509–519. doi:10.1037/0894-4220.127.116.119. ISSN 0894-4105. PMID 16060826.
- Johnson, Wendy; Bouchard Jr., Thomas J. (2005-07-01). "The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized". Intelligence. 33 (4): 393–416. doi:10.1016/j.intell.2004.12.002.
- Nisbet, Richard E (2012). "Intelligence New Findings and Theoretical Developments". American Psychologist. 67 (2): 130–59. doi:10.1037/a0026699. PMID 22233090.
- (us), National Academy of Sciences; (us), National Academy of Engineering; Engineering, and Institute of Medicine (US) Committee on Maximizing the Potential of Women in Academic Science and (2006-01-01). "Women in Science and Mathematics". National Academies Press (US).
- Lehrke, R. (1997). Sex linkage of intelligence: The X-Factor. NY: Praeger.[page needed]
- Lubinski, D.; Benbow, C. P. (2006). "Study of Mathematically Precocious Youth After 35 Years: Uncovering Antecedents for the Development of Math-Science Expertise". Perspectives on Psychological Science. 1 (4): 316–45. doi:10.1111/j.1745-6916.2006.00019.x. JSTOR 40212176. PMID 26151798.
- Hedges, Larry V.; Nowell, Amy (1995). "Sex Differences in Mental Test Scores, Variability, and Numbers of High-Scoring Individuals". Science. 269 (5220): 41–5. Bibcode:1995Sci...269...41H. doi:10.1126/science.7604277. PMID 7604277.
- Hyde, J. S.; Mertz, J. E. (2009). "Gender, culture, and mathematics performance". Proceedings of the National Academy of Sciences. 106 (22): 8801–7. Bibcode:2009PNAS..106.8801H. doi:10.1073/pnas.0901265106. PMC 2689999. PMID 19487665.
- Ali, MS; Suliman, MI; Kareem, A; Iqbal, M (2009). "Comparison of gender performance on an intelligence test among medical students". Journal of Ayub Medical College, Abbottabad. 21 (3): 163–5. PMID 20929039.
- Archer, John; Lloyd, Barbara (2002-07-11). Sex and Gender. Cambridge University Press. ISBN 9780521635332.
- Hunt, Earl; Madhyastha, Tara (2008-11-01). "Recruitment modeling: An analysis and an application to the study of male–female differences in intelligence". Intelligence. 36 (6): 653–663. doi:10.1016/j.intell.2008.03.002.
- Dykiert, Dominika; Gale, Catharine R.; Deary, Ian J. (2009-01-01). "Are apparent sex differences in mean IQ scores created in part by sample restriction and increased male variance?". Intelligence. 37 (1): 42–47. doi:10.1016/j.intell.2008.06.002.
- Gignac, Gilles E. (2015-09-01). "Raven's is not a pure measure of general intelligence: Implications for g factor theory and the brief measurement of g". Intelligence. 52: 71–79. doi:10.1016/j.intell.2015.07.006.
- Colom, Roberto; Abad, Francisco J; Garcı́a, Luis F; Juan-Espinosa, Manuel (2002-09-01). "Education, Wechsler's Full Scale IQ, and g". Intelligence. 30 (5): 449–462. doi:10.1016/S0160-2896(02)00122-8.
- Colom, R. (2002). "Education, Wechsler's Full Scale IQ, and g". Intelligence. 30 (5): 449–462. doi:10.1016/S0160-2896(02)00122-8. Retrieved 2016-01-28.
- Haier, Richard J.; Jung, Rex E.; Yeo, Ronald A.; Head, Kevin; Alkire, Michael T. (2005). "The neuroanatomy of general intelligence: Sex matters". NeuroImage. 25 (1): 320–7. doi:10.1016/j.neuroimage.2004.11.019. PMID 15734366.
- Cosgrove, Kelly P.; Mazure, Carolyn M.; Staley, Julie K. (2007). "Evolving Knowledge of Sex Differences in Brain Structure, Function, and Chemistry". Biological Psychiatry. 62 (8): 847–55. doi:10.1016/j.biopsych.2007.03.001. PMC 2711771. PMID 17544382.
- Sex difference in the human brain, their underpinnings and implications. Elsevier. 2010-12-03. ISBN 9780444536310.
- Pietschnig, Jakob; Penke, Lars; Wicherts, Jelte M.; Zeiler, Michael; Voracek, Martin (2015-10-01). "Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean?". Neuroscience & Biobehavioral Reviews. 57: 411–432. doi:10.1016/j.neubiorev.2015.09.017. PMID 26449760.
- Ritchie, Stuart J.; Booth, Tom; Valdés Hernández, Maria del C.; Corley, Janie; Maniega, Susana Muñoz; Gow, Alan J.; Royle, Natalie A.; Pattie, Alison; Karama, Sherif (2015-01-01). "Beyond a bigger brain: Multivariable structural brain imaging and intelligence". Intelligence. 51: 47–56. doi:10.1016/j.intell.2015.05.001. ISSN 0160-2896. PMC 4518535. PMID 26240470.
- Szalkai, Balazs; et al. (2015). "Graph Theoretical Analysis Reveals: Women's Brains Are Better Connected than Men's". PLoS ONE. 10 (7): e0130045. doi:10.1371/journal.pone.0130045. PMC 4488527. PMID 26132764.
- Szalkai, Balázs; Varga, Bálint; Grolmusz, Vince (2017). "Brain size bias compensated graph-theoretical parameters are also better in women's structural connectomes". Brain Imaging and Behavior. 12 (3): 663–673. doi:10.1007/s11682-017-9720-0. ISSN 1931-7565. PMID 28447246.
- Ann M. Gallagher, James C. Kaufman, Gender differences in mathematics: an integrative psychological approach, Cambridge University Press, 2005, ISBN 0-521-82605-5, ISBN 978-0-521-82605-1[page needed]
- Benbow, C.; Stanley, J. (1983). "Sex differences in mathematical reasoning ability: More facts". Science. 222 (4627): 1029–31. doi:10.1126/science.6648516. PMID 6648516.
- Lewin, Tamar (July 25, 2008)."Math Scores Show No Gap for Girls, Study Finds", The New York Times.
- Hyde, J. S.; Lindberg, S. M.; Linn, M. C.; Ellis, A. B.; Williams, C. C. (2008). "DIVERSITY: Gender Similarities Characterize Math Performance". Science. 321 (5888): 494–5. doi:10.1126/science.1160364. PMID 18653867.
- Winstein, Keith J. (July 25, 2008). "Boys' Math Scores Hit Highs and Lows", The Wall Street Journal (New York).
- Benbow, C. P.; Lubinski, D.; Shea, D. L.; Eftekhari-Sanjani, H. (2000). "Sex Differences in Mathematical Reasoning Ability at Age 13: Their Status 20 Years Later". Psychological Science. 11 (6): 474–80. CiteSeerX 10.1.1.557.7972. doi:10.1111/1467-9280.00291. PMID 11202492.
- Lindberg, Sara M.; Hyde, Janet Shibley; Petersen, Jennifer L.; Linn, Marcia C. (2010-11-01). "New Trends in Gender and Mathematics Performance: A Meta-Analysis". Psychological Bulletin. 136 (6): 1123–1135. doi:10.1037/a0021276. ISSN 0033-2909. PMC 3057475. PMID 21038941.
- Implicit Stereotypes and Gender Identification May Affect Female Math Performance. Science Daily (Jan 24, 2007).
- Wood, Samuel; Wood, Ellen; Boyd Denise (2004). "World of Psychology, The (Fifth Edition)", Allyn & Bacon ISBN 0-205-36137-4
- Correll, S.J. (2004). "Constraints into preferences: Gender, status, and emerging career aspirations". American Sociological Review. 69 (1): 93–113. CiteSeerX 10.1.1.520.8370. doi:10.1177/000312240406900106.
- Rydell, R.J.; Rydell, M.T.; Boucher, K.L. (2010). "The effect of negative performance stereotypes on learning". Journal of Personality & Social Psychology. 99 (6): 883–896. doi:10.1037/a0021139. PMID 20919773.
- Penner, Andrew M. (2008). "Gender Differences in Extreme Mathematical Achievement: An International Perspective on Biological and Social Factors". American Journal of Sociology. 114: S138–S170. doi:10.1086/589252.
- Machin, S.; Pekkarinen, T. (2008). "ASSESSMENT: Global Sex Differences in Test Score Variability". Science. 322 (5906): 1331–2. doi:10.1126/science.1162573. PMID 19039123.
- Chrisler, Joan C; Donald R. McCreary. Handbook of Gender Research in Psychology. Springer, 2010. ISBN 9781441914644.[page needed]
- Halpern, Diane F., Sex differences in cognitive abilities, Psychology Press, 2000, ISBN 0-8058-2792-7, ISBN 978-0-8058-2792-7[page needed]
- Ellis, Lee, Sex differences: summarizing more than a century of scientific research, CRC Press, 2008, ISBN 0-8058-5959-4, ISBN 978-0-8058-5959-1[page needed]
- Eals, Marion, and Irwin Silverman. 1992. Sex differences in spatial abilities: evolutionary theory and data. In The Adapted Mind: Evolutionary Psychology and the Generation of Culture, edited by J. H. Barkow. New York: Oxford University Press.[page needed]
- Jones, C. M; Healy, S. D (2006). "Differences in cue use and spatial memory in men and women". Proceedings of the Royal Society B: Biological Sciences. 273 (1598): 2241–2247. doi:10.1098/rspb.2006.3572. PMC 1635510. PMID 16901845.
- Geary, David C. (1998). Male, female: The evolution of human sex differences. American Psychological Association. ISBN 978-1-55798-527-9.[page needed]
- New, J.; Krasnow, M. M; Truxaw, D.; Gaulin, S. J.C (2007). "Spatial adaptations for plant foraging: Women excel and calories count". Proceedings of the Royal Society B: Biological Sciences. 274 (1626): 2679–2684. doi:10.1098/rspb.2007.0826. PMC 2279214. PMID 17711835.
- Witkin, H. A., Lewis, H. B., Hertzman, M., Machover, K., Meissner, P. B. & Wapner, S. (1954) Personality Through Perception. An Experimental and Clinical Study. Harper, New York.
- Linn, Marcia C.; Petersen, Anne C. (1985). "Emergence and Characterization of Sex Differences in Spatial Ability: A Meta-Analysis". Child Development. 56 (6): 1479–98. doi:10.2307/1130467. JSTOR 1130467. PMID 4075870.
- Barnett-Cowan, M.; Dyde, R. T.; Thompson, C.; Harris, L. R. (2010). "Multisensory determinants of orientation perception: Task-specific sex differences". European Journal of Neuroscience. 31 (10): 1899–907. doi:10.1111/j.1460-9568.2010.07199.x. PMID 20584195.
- Devlin, Ann Sloan, Mind and maze: spatial cognition and environmental behavior, Praeger, 2001, ISBN 0-275-96784-0, ISBN 978-0-275-96784-0[page needed]
- Montello, Daniel R.; Lovelace, Kristin L.; Golledge, Reginald G.; Self, Carole M. (1999). "Sex-Related Differences and Similarities in Geographic and Environmental Spatial Abilities". Annals of the Association of American Geographers. 89 (3): 515–534. CiteSeerX 10.1.1.196.856. doi:10.1111/0004-5608.00160.
- Miller, Leon K.; Santoni, Viana (1986). "Sex differences in spatial abilities: Strategic and experiential correlates". Acta Psychologica. 62 (3): 225–35. doi:10.1016/0001-6918(86)90089-2. PMID 3766198.
- Kimura, Doreen (May 13, 2002). "Sex Differences in the Brain: Men and women display patterns of behavioral and cognitive differences that reflect varying hormonal influences on brain development", Scientific American.
- National Geographic – My Brilliant Brain "Make Me a Genius" http://video.google.com/videoplay?docid=-6378985927858479238#
- Paula J. Caplan, Gender differences in human cognition, Oxford University Press US, 1997, ISBN 0-19-511291-1, ISBN 978-0-19-511291-7[page needed]
- Newcombe, N. S. (2007). Taking Science Seriously: Straight thinking about spatial sex differences. In S. Ceci & W. Williams (eds.), Why aren't more women in science? Top researchers debate the evidence (pp. 69–77). Washington, DC: American Psychological Association.
- Sharps, Matthew J. (1994). "Spatial Congnition and Gender – Instructional and Stimulus Influences on Mental Image Rotation Performance". Psychology of Women Quarterly. 18 (3): 413–425. doi:10.1111/j.1471-6402.1994.tb00464.x.
- McGlone, Matthew S.; Aronson, Joshua (2006). "Stereotype threat, identity salience, and spatial reasoning". Journal of Applied Developmental Psychology. 27 (5): 486–493. doi:10.1016/j.appdev.2006.06.003.
- Hausmann, Markus; Schoofs, Daniela; Rosenthal, Harriet E.S.; Jordan, Kirsten (2009). "Interactive effects of sex hormones and gender stereotypes on cognitive sex differences—A psychobiosocial approach". Psychoneuroendocrinology. 34 (3): 389–401. doi:10.1016/j.psyneuen.2008.09.019. PMID 18992993.
- Cherney, Isabelle D. (2008). "Mom, Let Me Play More Computer Games: They Improve My Mental Rotation Skills". Sex Roles. 59 (11–12): 776–86. doi:10.1007/s11199-008-9498-z.
- Feng, J.; Spence, I.; Pratt, J. (2007). "Playing an Action Video Game Reduces Gender Differences in Spatial Cognition". Psychological Science. 18 (10): 850–5. CiteSeerX 10.1.1.499.1635. doi:10.1111/j.1467-9280.2007.01990.x. PMID 17894600.
- Resnick, Susan M.; Berenbaum, Sheri A.; Gottesman, Irving I.; Bouchard, Thomas J. (1986). "Early hormonal influences on cognitive functioning in congenital adrenal hyperplasia". Developmental Psychology. 22 (2): 191–198. doi:10.1037/0012-1618.104.22.168.
- Janowsky, Jeri S.; Oviatt, Shelia K.; Orwoll, Eric S. (1994). "Testosterone influences spatial cognition in older men". Behavioral Neuroscience. 108 (2): 325–32. doi:10.1037/0735-7044.108.2.325. PMID 8037876.
- Gouchie, C; Kimura, D (1991). "The relationship between testosterone levels and cognitive ability patterns". Psychoneuroendocrinology. 16 (4): 323–34. doi:10.1016/0306-4530(91)90018-O. PMID 1745699.
- Nyborg, H. (1984). "Performance and Intelligence in Hormonally Different Groups". Sex Differences in the Brain - the Relation Between Structure and Function. Progress in Brain Research. 61. pp. 491–508. doi:10.1016/S0079-6123(08)64456-8. ISBN 978-0-444-80532-4. PMID 6396713.
- Casey, M. Beth; Nuttall, Ronald; Pezaris, Elizabeth; Benbow, Camilla Persson (1995). "The influence of spatial ability on gender differences in mathematics college entrance test scores across diverse samples". Developmental Psychology. 31 (4): 697–705. doi:10.1037/0012-1622.214.171.1247.
- Frederick, Shane (2005). "Cognitive Reflection Test and Decision Making" (PDF). Journal of Economic Perspectives. 19 (4): 25–42. doi:10.1257/089533005775196732.
- Bosch-Domènecha, A.; Brañas-Garzab, P.; Espínc, A. M. (2014). "Can exposure to prenatal sex hormones (2D:4D) predict cognitive reflection?". Psychoneuroendocrinology. 43: 1–10. doi:10.1016/j.psyneuen.2014.01.023. PMID 24703165.
- Campitelli, G.; Gerrans, P. (2014). "Does the Cognitive Reflection Test measure cognitive reflection? A mathematical & modeling approach". Memory & Cognition. 42 (3): 434–447. doi:10.3758/s13421-013-0367-9. PMID 24132723.
- Pennycook, G.; Cheyne, J. A.; Koehler, D. J.; Fugelsang, J. A. (2016). "Is the cognitive reflection test a measure of both reflection and intuition?". Behavior Research Methods. 48 (1): 341–8. doi:10.3758/s13428-015-0576-1. PMID 25740762.
- Ring, P.; Neyse, L.; David-Barett, T.; Schmidt, U. (2016). "Gender Differences in Performance Predictions: Evidence from the Cognitive Reflection Test". Frontiers in Psychology. 7: 1680. doi:10.3389/fpsyg.2016.01680. PMC 5089055. PMID 27847487.
- Prima, C.; Morsanyi, K.; Chiesi, F.; Donati, M. A.; Hamilton, J. (2015). "The Development and Testing of a New Version of the Cognitive Reflection Test Applying Item Response Theory (IRT)". Journal of Behavioral Decision Making. 29 (5): 453–69. doi:10.1002/bdm.1883.
- Voyeur, Daniel (2014). "Gender Differences in Scholastic Achievement: A Meta-Analysis" (PDF). Psychological Bulletin. 140 (4): 1174–1204. doi:10.1037/a0036620. PMID 24773502.
- Stoet, Gijsbert; Geary, David C. (2015-01-01). "Sex differences in academic achievement are not related to political, economic, or social equality". Intelligence. 48: 137–151. doi:10.1016/j.intell.2014.11.006.
- S. J. Ceci, W. M. Williams, Why Aren’t More Women in Science? (APA Books, Washington, DC, 2007)
- National Science Foundation, Survey of Earned Doctorates, (2011); [www.nsf.gov/statistics/srvydoctorates/]
- Leslie, S.; Cimpian, A.; Meyer, M.; Freeland, E. (2015). "Expectations of brilliance underlie gender distributions across academic disciplines". Women in Science. 347 (6219): 262–265. doi:10.1126/science.1261375. PMID 25593183.
- Steele, J.R.; Ambady, N. (2006). "Math is hard!" The effect of gender priming on women's attitudes". Journal of Experimental Social Psychology. 42 (4): 428–436. doi:10.1016/j.jesp.2005.06.003.
- Davies, P.G.; Spencer, S.J.; Quinn, D.M.; Gerhardstein, R. (2002). "Consuming images: How television commercials that elicit stereotype threat can restrain women academically and professionally". Personality & Social Psychology Bulletin. 28 (12): 1615–1628. doi:10.1177/014616702237644.
- Davies, P.G.; Spencer, S.J.; Steele, C.M. (2005). "Clearing the air: Identity safety moderates the effects of stereotype threat on women's leadership aspirations". Journal of Personality & Social Psychology. 88 (2): 276–287. doi:10.1037/0022-35126.96.36.1996.
- Fine, C (2011). "Explaining, or Sustaining, the Status Quo? The Potentially Self-Fulfilling Effects of 'Hardwired' Accounts of Sex Differences". Neuroethics. 5 (3): 285–294. doi:10.1007/s12152-011-9118-4.
- Schwartz, B (1997). "Psychology, idea technology, and ideology". Psychological Science. 8 (1): 21–27. doi:10.1111/j.1467-9280.1997.tb00539.x.
- Hacking, I. (1996). The looping effects of human kinds. A Multidisciplinary Debate Causal Cognition, 351–383
- Choudhury, S.; Nagel, S.K.; Slaby, J. (2009). "Critical neuroscience: Linking neuroscience and society through critical practice". BioSocieties. 4: 61–77. doi:10.1017/s1745855209006437.
- McCabe, D.P.; Castel, A.D. (2008). "Seeing is believing: The effect of brain images on judgments of scientific reasoning". Cognition. 107 (1): 343–352. doi:10.1016/j.cognition.2007.07.017. PMID 17803985.
- Weisberg, D.S.; Keil, F.C.; Goodstein, J.; Rawson, E.; Gray, J.R. (2008). "The seductive allure of neuroscience explanations". Journal of Cognitive Neuroscience. 20 (3): 470–477. doi:10.1162/jocn.2008.20040. PMC 2778755. PMID 18004955.
- Racine, E.; Waldman, S.; Rosenberg, J.; Illes, J. (2010). "Contemporary neuroscience in the media". Social Science & Medicine. 71 (4): 725–733. doi:10.1016/j.socscimed.2010.05.017. PMC 2925284. PMID 20609506.
- Haslam, N.; Rothschild, L.; Ernst, D. (2000). "Essentialist beliefs about social categories". British Journal of Social Psychology. 39 (1): 113–127. doi:10.1348/014466600164363.
- Haslam, N (2011). "Genetic essentialism, neuroessentialism, and stigma: Commentary on Dar-Nimrod and Heine (2011)". Psychological Bulletin. 137 (5): 819–82. doi:10.1037/a0022386. PMID 21859181.
- Dar-Nimrod, I.; Heine, S.J. (2006). "Exposure to scientific theories affects women's math performance". Science. 314 (5798): 435. doi:10.1126/science.1131100. PMID 17053140.
- Thoman, D.B.; White, P.H.; Yamawaki, N.; Koishi, H. (2008). "Variations of gender-math stereotype content affect women's vulnerability to stereotype threat". Sex Roles. 58 (9/10): 702–712. doi:10.1007/s11199-008-9390-x.
- Dweck, C. S. (n.d.). Is Math a Gift? Beliefs That Put Females at Risk. Why Aren't More Women in Science?: Top Researchers Debate the Evidence, 47–55
- Good, C.; Aronson, J.; Inzlicht, M. (2003). "Improving adolescents' standardized test performance: An intervention to reduce the effects of stereotype threat". Journal of Applied Developmental Psychology. 24 (6): 645–662. doi:10.1016/j.appdev.2003.09.002.
- Keller, J (2005). "In genes we trust: The biological component of psychological essentialism and its relationship to mechanisms of motivated social cognition". Journal of Personality & Social Psychology. 88 (4): 686–702. doi:10.1037/0022-35188.8.131.526. PMID 15796668.
- Lamke, L.; Bem, S. L. (1993). "The Lenses of Gender: Transforming the Debate on Sexual Inequality". Journal of Marriage and the Family. 55 (4): 1052. doi:10.2307/352790. JSTOR 352790.
- Morton, T.A.; Haslam, S.A.; Hornsey, M.J. (2009). "Theorizing gender in the face of social change: Is there anything essential about essentialism?". Journal of Personality & Social Psychology. 96 (3): 653–664. doi:10.1037/a0012966.
- Dambrun, M.; Kamiejski, R.; Haddadi, N.; Duarte, S. (2009). "Why does social dominance orientation decrease with university exposure to the social sciences? The impact of institutional socialization and the mediating role of "geneticism"". European Journal of Social Psychology. 39 (1): 88–100. doi:10.1002/ejsp.498.
- Eagly, A. H.; Makhijani, M. G.; Klonsky, B. G. (1992). "Gender and the evaluation of leaders: A meta-analysis". Psychological Bulletin. 111 (1): 3–22. doi:10.1037/0033-2909.111.1.3.
- Rudman, L. A.; Glick, P. (2001). "Prescriptive Gender Stereotypes and Backlash Toward Agentic Women". Journal of Social Issues. 57 (4): 743–762. doi:10.1111/0022-4537.00239.
- Gurian, M; Stevens, K (2004). "With boys and girls in mind". Educational Leadership. 62 (3): 21–26.
- Sax, L. 2006. Why gender matters: What parents and teachers need to know about the emerging science of sex differences. New York: Broadway Books
- "Dyslexia Information Page". Archived from the original on 2016-07-27.
- Sun, Ying-Fang; Lee, Jeun-Shenn; Kirby, Ralph (2010). "Brain Imaging Findings in Dyslexia". Pediatrics & Neonatology. 51 (2): 89–96. doi:10.1016/S1875-9572(10)60017-4. PMID 20417459.
- Evans, Tanya M.; Flowers, D. Lynn; Napoliello, Eileen M.; Eden, Guinevere F. (2013). "Sex-specific gray matter volume differences in females with developmental dyslexia". Brain Structure and Function. 219 (3): 1041–1054. doi:10.1007/s00429-013-0552-4. PMC 3775969. PMID 23625146.