Effect size (d): –0.05
Correlation with GGGI: –.18 (ns)
Relative-strength scores
Effect size (d): –0.55
Correlation with GGGI: –.04 (ns)
Subject: Science literacy
Mean scores
Effect size (d): +0.01
Correlation with GGGI: –.23 (ns)
Relative-strength scores
Effect size (d): –0.42
Correlation with GGGI: –.42 (.001)
Subject: Reading comprehension
Mean scores
Effect size (d): +0.32
Correlation with GGGI: –.06 (ns)
Relative-strength scores
Effect size (d): +0.78
Correlation with GGGI: +.30 (.017)
Source: Stoet and Geary (2018): Table S2. The unit of analysis is the country. A negative score indicates a higher male mean. The sample is limited to countries with GGGI (Global Gender Gap Index) scores (n = 62).
As I described in chapter 3, there’s hardly any sex difference in mathematics and science mean scores—and yet there’s a big sex difference favoring males on relative-strength scores. There’s a noticeable sex difference favoring females on reading comprehension mean scores—but a bigger one for relative-strength scores. In other words, we could expect a significant male-female disparity toward STEM even when the mean test scores are the same just because of the sex difference in relative strengths. Two other quick points:
The global consistency in relative-strength scores is nearly perfect. On the relative-strength score for reading, the effect size favored girls in all of the countries. For math, the relative-strength effect size favored boys in all of the countries. For science, the relative-strength effect size favored boys in 61 out of 62 countries.59
The more gender-egalitarian the country, the greater the boys’ relative strength in science (r = –.42, p < .001). To illustrate, the bottom five countries on the Global Gender Gap Index were Jordan, Lebanon, Turkey, Algeria, and Tunisia. The mean relative-strength effect size for sex differences in science literacy was –0.18.60 The top five countries on the Global Gender Gap Index were Iceland, Finland, Norway, Sweden, and Ireland. The mean relative-strength effect size was –0.55.
The more gender-egalitarian the country, the greater the girls’ relative strength in reading (r = +.30, p = .017). The difference in relative-strength mean effect size for the bottom five countries on the Global Gender Gap Index was +0.69; for the top five countries, it was +0.83.
If you hold to a social-construct theory of sex differences in test scores, it is hard to explain these results. In contrast, it is easy to explain them if you postulate inborn sex differences that influence academic ability. It’s a version of the Matthew effect—the rich get richer and the poor get poorer (Matthew 25:29).61 In the case of education, the Matthew effect takes the form of widening test score differences when good students and poor students are both exposed to improved education. The test scores of the poor students may rise, but those of the good students usually rise more.[62]
A third important finding is not part of the data in the table. Stoet and Geary also calculated the correlation between the percentage of girls with a relative strength in science or math and the Global Gender Gap Index. The result was a correlation of –.41 (p < .003).63 The implication is that the more gender-equal the country, the fewer of the women who are capable of successful STEM careers choose to go into them.
Why? Stoet and Geary point out that perhaps it reflects the greater freedom of talented women in advanced countries. In poorer countries where economic insecurity is high—the ones that tend to be toward the bottom of the Global Gender Gap Index—jobs in STEM fields are among the most secure and well paid. A girl from such a country whose relative strength is verbal skills but who also has high math and science skills has a strong economic incentive to override her preferences and go into a STEM career. In a country near the top of the gender equality ladder such as Norway or Finland, economic security is assured through the welfare state, and good jobs in non-STEM fields are abundant even though they may not pay as well as many STEM jobs. If one postulates an inborn female tendency to be drawn toward People-oriented fields, it is to be expected that as national affluence and economic security increase, more women will choose fields that correspond to their interests rather than STEM fields that offer higher job security and income.[64]
Recapitulation
In the words of Proposition #3, “On average, women worldwide are more attracted to vocations centered on people and men to vocations centered on things.” The proposition is true with regard to the general population and to the gifted men and women of SMPY. It is established by scores on tests of vocational interests and by the revealed preferences of the jobs that people take. It applies to those with advanced educations and those with high school educations. It has persisted over a half century of second-wave feminism and has not diminished in the last three decades.
The subtext of this chapter has been that it’s not plausible to explain the entire difference in educational and vocational interests as artifacts of gender roles and socialization. If that were the case, the world shouldn’t look the way it does. In contrast, a mixed model—it’s partly culture, partly innate preferences—works just fine. In this narrative, females really were artificially deterred from STEM educations and occupations through the 1950s and into the 1960s. One of the effects of the feminist revolution was that new opportunities opened up for women and women took advantage of them. The changes in women’s choices of college majors dramatically reflect that, along with their movement into professions such as medicine, the law, and business. But something has to explain how quickly those changes settled into a new equilibrium of educational and occupational choices that has lasted for 30 to 40 years now.
Is the patriarchy still to blame? One can try to defend that position, but it has to be done with data. I hope the nationwide, enduring patterns of educational and vocational choices across women of different interests and levels of ability have shown how hard it is to make that case.
5
Sex Differences in the Brain
Proposition #4: Many sex differences in the brain are coordinate with sex differences in personality, abilities, and social behavior.
Twenty years ago, this chapter would have been able to discuss many sex differences in rodent brains but not in human brains.1 Then, doing that kind of research on humans could be a career-killer. “Be careful, it’s the third rail,” a senior colleague told neurobiologist Larry Cahill in 2000. “Fortunately, times are changing,” Cahill wrote in the introduction to a special double issue of the Journal of Neuroscience Research in 2017 devoted to sex differences in the brain:
The past 15 to 20 years in particular witnessed an explosion of research (despite the prevailing biases against the topic) documenting sex influences at all levels of brain function. So overpowering is the wave of research that the standard ways of dismissing sex influences (e.g., “They are all small and unreliable,” “They are all due to circulating hormones,” “They are all due to human culture,” and “They don’t exist on the molecular level”) have all been swept away, at least for those cognizant of the research.2
This chapter is in no sense a survey of the state of knowledge about sex differences in the brain. There’s too much going on, it’s far too complex, and too much still consists of tentative findings for anything resembling a comprehensive discussion. My first goal for this chapter is to present some of the most important known sex differences in the brain. My second goal is to give you a sense of the exciting progress that is being made in linking specific differences in the brain to specific behavioral sex differences. Proposition #4 is accordingly modest. Stated informally, the proposition amounts to, “What we see in observable differences between males and females on the People-Things dimension hangs together with things that are being learned about differences in male and female brains. The connections are
still approximate, with many unknowns remaining, but they are becoming clearer.”
Second Interlude: Things About Genetic Sex Differences and the Brain That You Need to Know to Read the Rest of the Book
Genotype and Phenotype
These words pop up continually from here on out. Genotype refers to the genetic makeup of an individual. Phenotype refers to the observable characteristics of the organism produced by a combination of the genotype and the environment.
The Basics of Genetic Sex Differences
The human genome is organized into 23 pairs of chromosomes. Twenty-two out of the 23 pairs are called autosomal, meaning that they have nothing to do with whether an embryo develops into a male or a female. The remaining pair are the sex chromosomes.
In women, both chromosomes are labeled X. Men have an X chromosome with the same genes as the X chromosome in women. The other chromosome, labeled the Y chromosome, exists only in men. It has about 58 million base pairs and over 200 genes. One specific gene on the Y chromosome, designated Sry, initiates sexual differentiation of the gonads, which in turn produces a cascade of specific forms of sexual differentiation.
In females, one of the two X chromosomes is usually inactivated. Currently, it is thought that “usually” means in 80–88 percent of the cells.3 In the others, both X chromosomes continue to have effects on the phenotype. The inactivation is primarily done by an RNA gene labeled Xist. The process has regularly been described as random, though recent work indicates that it is more subtle and interesting than that.4 This inactivation process means that the cells in a female’s body constitute a mosaic. Two adjacent cells can have different activated X chromosomes.
Basics of Brain Structure
The architecture and functioning of the human brain are dauntingly complex. Here are the indispensable basics:
Brain stem, cerebellum, and cerebrum. All of the dozens of regions in the brain fit into one of these three structures.
The brain stem, at the bottom of the brain and the top of the neck, is the smallest of the three, and structurally continuous with the spinal cord. It is the primal brain and has primal tasks such as respiration and cardiac function. Recent research indicates that the locus coeruleus in the brain stem might have a role in mediating sex differences in the brain.5
The human cerebellum, behind and partly above the brain stem, is larger than the brain stem but smaller than the cerebrum. Its functions include emotional processing and other higher cognitive functions, but the cerebellum is most closely associated with motor control. It coordinates and executes signals from other parts of the brain and from the spinal cord.6
The cerebrum has by far the largest volume of the three structures. The familiar word cerebral comes from cerebrum, which indicates its role as the center of intellectual activity. It also has a significant role in emotional processing. The cerebrum and its component regions will be the focus of the discussion in this chapter.
Gray matter, white matter, neuron, and axon. The tissue in the brain consists of a combination of gray matter and white matter. Gray matter is composed primarily of neurons—the billions of cells that process information—though it also includes some axons, blood vessels, and connective tissue.7 White matter is composed largely of axons—projections of the neurons that transmit information to other neurons. It is called “white” because axons are often sheathed with myelin, a fatty white substance that plays an important role in determining the efficiency and speed of the transmission.
Hemispheres. The cerebrum is divided into hemispheres, left and right, separated by a fissure that runs from the front of the cerebrum to the back. The main connection between the two hemispheres is the corpus callosum, a broad, flat bundle of white matter about four inches long, also running from front to back. Most of the regions of the brain are represented in both hemispheres—there is a “left amygdala” and a “right amygdala,” for example.
The hemispheres are the origin of phrases you have probably heard, “left brain” and “right brain.” The meaning of these labels is often oversimplified—both of the brain’s hemispheres are involved to some degree in almost all kinds of processing. The two chief differences relevant to this chapter are that the right hemisphere is dominant for visuospatial activity and the left hemisphere is especially important with regard to language and is thought to have nearly exclusive responsibility for language production.8
Each hemisphere is also associated with control of one half of the body but reversed. The left hemisphere interacts primarily with the right side of the body, and the right hemisphere with the left.
Cerebral cortex. The cerebrum contains dozens of specific regions. Of these, the largest in humans is the cerebral cortex. It is only about 2–3 millimeters thick, but it is the outer layer of the cerebrum covering both hemispheres. It is divided into four lobes—frontal, parietal, temporal, and occipital. It is involved in all the higher forms of cognition, with some specialization within lobes and within layers of the lobes.
Subcortical regions. Neuroscientists have identified many regions beneath the cerebral cortex—hence the word subcortical to describe them. The ones I will be mentioning most often are the amygdala, hippocampus, thalamus, and hypothalamus.
A Word About Brain Scans
Presenting the evidence that male and female brains function in different ways will often call upon the results of brain scans—the method that produces those pictures you have probably seen of different parts of the brain lighting up in bright colors. Neuroscientists use several methods to produce those images. The most powerful are positron emission tomography (PET), magnetic resonance spectroscopy (MRS), magnetoencephalography (MEG), diffusor tensor imaging (DTI), and functional magnetic resonance imaging (fMRI).
None of these techniques directly detect what neurons are doing. Rather, they rely on indirect indicators of activity such as increased blood flow to areas that are active, the diffusion rates of water to measure connectivity across regions of the brain, or the concentrations of chemicals in different parts of the brain. Indirect measures can be statistically reliable—there is no doubt that neuronal activity really does cause increased blood flow, for example—but it’s not nearly as simple as a binary “yes, these neurons are active, and no, those other neurons are not.”
Furthermore, these dramatic images are not snapshots of an actual, individual brain taken at a moment in time. Rather, millions of bits of data from many scans have been analyzed and combined into a single image. The colors are a method of communicating results, chosen to be visually efficient, with more intense colors indicating higher levels of activity, but they are arbitrary (your brain tissue doesn’t actually turn bright orange when you’re thinking hard).
The ability to create dramatic images has led to some shoddy work. Many researchers have been guilty of overinterpreting small samples, data mining, failure to use proper controls, or simply not understanding the subtleties of a demanding methodology.9 But, properly done, brain imaging has produced valuable and replicated findings.10
The Argument About Dimorphism in Male and Female Brains
The study of sex differences in the brain proceeds bit by bit, identifying evidence of discrete distinctions. Some of these distinctions remain discrete, with isolated effects (as far as anyone knows now). Sometimes the distinctions add up, forming a pattern that has greater effects than the sum of the parts. But in all cases, it remains true that much brain function and the products of that brain function are shared by males and females.
This truth can be obscured by headlines and titles that are far too catchy—“Men Are from Mars, Women Are from Venus” comes to mind. Recently, some researchers have gone to the opposite extreme. The minimal-differences argument was made most famously in an article titled “Sex Beyond the Genitalia: The Human Brain Mosaic.” The first author among 14 was psychologist Daphna Joel. The Joel study argued, “We should shift from thinking of brains as falling into two classes, one typical of males and the other typical of f
emales, to appreciating the variability of the human brain mosaic.”11
The article contained an empirical assertion: “The lack of internal consistency in human brain and gender characteristics undermines the dimorphic view of human brain and behavior.”12 Specifically, the authors analyzed the 10 regions of the brain that showed the largest sex differences. For each region, the authors classified a subject as being at the “male-end” or “female-end,” defining the “male-end” and “female-end” zones as the scores of the 33 percent most extreme males and females, respectively.13 It was on the basis of these classifications that the authors found that “35 percent of brains showed substantial variability, and only 6 percent of brains were internally consistent.”14 But the definition of “internally consistent” required that all 10 regions show values at the male-end for a man and all 10 at the female-end for a woman. Psychologist David Schmitt summarized his problem with this definition: “That is, for a sex difference in the brain to ‘really’ exist, men must have relatively masculine brains in each and every respect, and women must have relatively feminine brains in each and every respect. Otherwise, no sex difference. Really?”15 Larry Cahill was blunter, going on record in Scientific American with his view that the methodology in the Joel article was rigged and “the paper is ideology masquerading as science.”16
A year later, the same journal published technical rejoinders.17 The details of the counterarguments were varied, but their broad theme was the one I presented in chapter 1 with regard to personality: Small differences on individual measures routinely constitute large and important differences in the aggregate.
What can be said about where the debate over the “female brain” versus the “male brain” stands? If the question is whether neuroscientists, given data on a wide variety of brain parameters, can accurately identify a specific brain as belonging to a male or a female, the answer is yes. Their accuracy is increasing as more is learned. One response to the Joel study, using the same data as the article had used, classified 69–77 percent of the brains accurately.18 Another classified 80 percent correctly.19 A third, using a different dataset, classified 90–95 percent accurately.20 In 2018, a new method based on multivariate quantification of gray matter correctly classified 93 percent of participants in two separate samples.21 But it remains true that most of the individual sex differences in the brain (though not all) involve a great deal of overlap.
Human Diversity Page 12
