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by Vanessa Hamer

This past January, Harvard President Lawrence H. Summers ignited a media firestorm by suggesting that the under-representation of women in high-end science jobs may stem from low innate aptitude among women. His remarks, made at a National Bureau of Labor Research conference on diversifying the science and engineering workforce, have caused a stir about biological evidence, discrimination, and academic freedom. While quite a lot has been written on both sides of the issue, substantive discussion has focused more on his sometimes ill-chosen words than on the scientific arguments surrounding innate aptitude itself

 In His Own Words

It is best to begin with Dr. Summers’ own words in order to examine the evidence and logic brought to bear on his claims. He began his talk with a list of possible reasons why women are underrepresented in high-end science professions:

There are three broad hypotheses…[T]he first is what I call the high-powered job hypothesis. The second is what I would call different availability of aptitude at the high end, and the third is what I would call different socialization and patterns of discrimination in a [faculty hiring] search. And in my own view, their importance probably ranks in exactly the order that I just described.

The Harvard president's first hypothesis was that relatively fewer women are willing to make a commitment to a job that requires “80 hours a week” in order to rise to high-powered positions such as tenured faculty and department head. He buttressed anecdotal examples with expert opinion and broad data on the sex-ratio changes as students begin professional life, indicating interest in raising children as a key factor in professional decisions.  He raised, but did not answer normative questions, such as whether we should live in a society in which familial and professional pressures are locked in direct and ultimate conflict. Notably, Duke instituted a policy in 2003 that offers tenure clock relief for pregnancy, adoption, and other family-related changes. The relief is available regardless of gender.

Summers is certainly entitled to a personal intuition rooted in his extensive experience in higher education, but he provides no justification for his ranking order. After the presentation, an audience member took up this very question by positing an alternative explanation, namely that the first and second reasons – the high-powered job hypothesis and the aptitude hypothesis – might be largely or entirely results of the third.

I should mention briefly that Summers' audience saw red not only because of his ranking system, but also because of the way he used anecdotes such as “I guess my experience with my two and a half year old twin daughters who were not given dolls and who were given trucks, and found themselves saying to each other, look, daddy truck is carrying the baby truck, tells me something.” He used performance to suggest biologically-determined causes in a way that is highly speculative at best. The effect (performance) is many links down the chain from its unknown causes (the causes, after all, are what we are trying to determine), yet he seemed comfortable drawing an easy relationship between the two based on irrelevant evidence.

Summers used only slightly more cautious language in positing that differences in the standard deviation for male and female cognitive traits may provide an explanation for differences in high-powered science jobs. His evidence proceeds from Hedges and Nowell[i], whose meta-study found that males are more likely than females to be found at the tail ends of a given intelligence-test bell curve – again, performance data. As one approaches the ends of the tails, the effect is larger, and Summers offers that at several standard deviations above or below the mean the ratio of individuals with identical scores may be one female for every five males. This type of speculation ignores that standardized test scores are poor predictors of female career choice and success. Additionally, even if the performance data were robust enough to draw such predictions (which they are not), the proportion of women faculty at the top 50 institutions is still far less than the predicted 20%, and for the vast majority of science-related disciplines it is less than half of Summers' rough prediction.[ii] Not to be overlooked are women of color, who are almost completely absent from science faculty at top schools. Finally, the “high or low, but not average” performance exhibited by more boys on aptitude tests fits a model of performance under high pressure. A sports analogy works here: in the big game, we tend to play at our best or worst, but not at our accumulated average.

Oh yeah? (Dis)Prove it!

Summers' broadest arguments are woefully flawed, but might we improve on them?  He is right on at least one point: to adhere to scientific method, we must seriously consider that aptitude differences could be innate. After all, science is the business of skepticism, and proceeds by refutation of hypotheses, not by confirmation. If we want to know whether water availability is the limiting factor in plant growth, our experimental design assumes the opposite. We hold all other variables constant and vary water availability for multiple plants, and the measurements must bear out a statistically significant difference in growth with respect to water availability in order to support our original intuition. The difference must rise out of the background noise in spite of consistency across other variables.

In the social sciences, because controlled experimentation is often impossible (and/or immoral), teasing out the limiting factors is far more difficult. Statistical tools enable us to tease out variables that covary with one another, yet because no control group is available, results are always inconclusive in a way that is distinct from the “hard” sciences. Our theoretical Laplacian demon knows the arrangement of matter in a way that a social science demon does not. Given these limits, we are still curious, so let's improve on Summers by constructing a best possible argument for inhereted aptitude in order that we might then put it to the test.

Performance

With remarkable consistency, boys tend to score higher than girls on certain math-related aptitude tests under certain conditions[iii]. The discrepancy is generally found in performance of specific tasks relating to spatial modeling. Girls, with less consistency, tend to score higher on short-term memory tests[iv].

Two glaring exceptions are worth noting. In Iceland[v] and Japan[vi], girls tend to score higher nearly across the board on math-related tests. Although such statistical outliers are statistically significant, such a conclusion was based on performance testing, which may not be genetically significant.  These two exceptions may be treated as anomalous data points in an otherwise consistent model. Also, Icelandic and Japanese women less commonly pursue math- and science-related careers, so we might just as easily consider that while the female mean is higher, males are still more likely to show aptitude at the tail ends of the curve. In fact, Icelandic and Japanese women both have a higher performance mean and are overrepresented at the high tails of the curve. Additionally, Japanese girls tend to perform far higher than American boys for similar mathematical tests. As Virginia Valian noted, “Maybe Asians are innately better at math. If so, following Summers's reasoning, Harvard should be preferentially hiring Asian women over American men.”[vii]

In any case, adolescent and teen performance on standardized tests cannot control for social environment, and so it cannot distinguish between social and biological factors. The nature-nurture distinction is perilous and misleading, as gene expression is irreducibly based on environment, yet we are still able to isolate genes – such as those for brown eyes – without which, a trait is not observed. Genes have power, but when and how much?

Some researchers have approached the problem using early testing, since children are presumably not exposed to the same sex-specific social cues early on. When spatial-visual ability is further subdivided, visual-spatial working memory[viii] and mental rotation tasks[ix] generally show sex differences whereas spatial perception tasks do not[x]. A meta-study found that, in general, math-related sex differences show up around 8 years of age[xi]

Early social environment is critical to brain development, and while very young girls and boys are nearly identical biologically speaking, they are subject to sex-related social cues from birth. For example, parents handle male babies more than female  babies[xii]. A large pool of literature exists concerning links between participation in spatial activities (such as ball sports) and acquisition of visual-spatial skills, but most experimental designs limit findings to correlation, not causation[xiii].

Discrimination should not be left out of the picture. A recent New York Times article cites a 1983 study in which participants rated a mathematics paper using a five-point scale[xiv]. Both men and women rated papers attributed to “John” rather than “Joan” significantly higher. Men rated the papers written by “John” a full point higher. The same article cites a survey at Princeton in which students were asked to preference job applicants based on background. Preference for female applicants was dramatically lower than for male applicants when the CV was in fact identical in both cases.

The formative years provide ample evidence that social cues influence the decision to pursue math and science courses. From middle school forward, boys and girls who exhibit the same level of classroom achievement tended to take different paths[xv]. In general, girls tend to get better grades in primary schooling, but on average, American women are paid less than men, even when controlling for position, education, and family status.

Awareness that social bias exists and is meaningful, though, does not negate the possibility of underlying physical mechanisms that also affect intelligence. Here, the social sciences must give way to the material sciences in order to take a closer look.

Structure

It seems we must look at the brain itself. A hundred years ago, the relative size of male and female brains was used as evidence of female inferiority, though brain size tends to correlate better with height (and other measures of body size, depending on who is doing the measuring) than intelligence. Today, the tests are more nuanced.  Not surprisingly, male and female brains are highly similar, with only slight dimorphisms. Where do these dimorphisms occur? Sex is determined by the presence or absence of a single chromosome, so study of characteristics linked to that chromosome may put us on the right path. Male and female hormone levels are, on the whole, good indicators of brain development details. The hypothalamus secretes sex hormones, so we expect - and find - differences in the size of specific hypothalamic regions[xvi]. The corpus callosum, the tract which connects the left and right hemispheres, tends to be larger in female humans[xvii]. Interestingly, female rats tend to have smaller corpus callosi.  When injected with male sex hormones at an early age, however, the structure tends to be larger[xviii].

Function

Structure seems indicative, but differences in structures are generally differences in size, and there is some indication that the same anatomical features may be used in different ways when working through a problem. Removal of the hippocampus, a key structure in spatial memory and orientation, results in a decline in visual memory for women but not for men. Male and female rats use different strategies to solve the same spatial navigation problems, and differences in hippocampus size has been implicated as a cause[xix].

An article appearing in the March issue of NeuroImage suggests that, when compared using MRI, the brains of men and women with equivalent IQ scores appear to “work” differently, with women using more white matter and men using more gray matter (these data were scaled to brain size to control for size bias) [xx]. These data have led to the assertion in popular science publications that women's brains are "more efficient," neuron for neuron, than men's[xxi]. This standard of efficiency is highly limited and artificially constructed, so I suggest that such broad normative conclusions are in many ways just as loaded – not to mention just as speculative – as Summers' commentary.

How much of brain structure is determined by genes, and how much by environment? We have returned to the same sticky problem. A limited number of recent studies indicate, for example, that even the volume of gray matter in a brain is affected by learning[xxii][xxiii].

Perhaps we can make evaluations by comparing sex-differences with another well-studied, though less politically-charged, type of difference. Left-handers have been of note historically as particularly susceptible to genius. Any baseball fan will tell you that being a lefty has its perks, and hard data reveal that lefties are found in greater proportions in science, math, and music related professions[xxiv]. Handedness has long been associated with differences in abilities distributions, with left-handers performing higher on spatial and other tasks that are more closely associated with the brain's right hemisphere[xxv]. I must note here that the association is easily and commonly exaggerated and that “handedness” is more of a continuum than a dichotomy, still the general relationship holds. Handedness is related to the functional (rather than the structural) asymmetry of the hemispheres -- an asymmetry that is present at birth[xxvi][xxvii]. However, given that a greater proportion of males than females are left-handed, the implications of handedness on intelligence remains a burning question[xxviii].

What does it all mean?

The relationship is provocative, for if data on handedness are similar to data on sex, what are we to do? Should we act as social engineers, instituting affirmative action for right-handers who want to be scientists? Should we give all lefties a violin at the age of three? Perhaps we should first look to maintain our skepticism of simple causal networks. Even if we can assume (as I think we safely can) that lefties or righties as a group are not subject to a distinct class of social cues that is similar in function to sex-related cues – to put it crudely, that no one tells righty Bobby he will never be a doctor because “only southpaws can count” – all lefties and righties are subject to social pressures based on their sex.

What remains to be seen is whether a cognitive difference in visual-spatial ability is maintained once handedness data are controlled for sex. If the brain of a male lefty works through a complex problem differently than the brain of a male righty, then does the lefty male show a greater aptitude (whether in correctness or speed) for solving the problem? Current findings are inconclusive and contradictory[xxix]. For example, right-handed women are slightly more likely to score highly on certain math-related tests than their female left-handed counterparts, while left-handed men seem to perform similarly in relation to right-handed men[xxx]. Part of the issue may lie in the question itself and in the inherent “complexity” of increasingly difficult test problems. Measurement of high functioning requires tasks of ranging difficulty. Tasks are difficult in part because they are complex, and as complexity increases more types of processing are needed. Available data suggests individuals of differing gender, education type, and experience attack the same discrete problem using different strategies and neural pathways. No studies, however, have been able to tease out each of these variables in terms of the others, and the common use of the SAT and GRE as benchmark has well-documented shortcomings. We have thus far not been able to achieve the resolution we might like in answering reductive “nature” questions.

I would argue that the complex cognitive ability – whether it be visual-spatial, verbal, memory related, or quantitative – related to high achievement is hugely impacted by development and social cues. Virginia Woolf famously invented Judith, an equally-gifted sister for Shakespeare, and asked whether she might have written Lear, Richard III, or Macbeth. A heap of social constraints and cues would undoubtedly have kept her out of the theater and the canon.

Of course, I doubt that Summers would disagree on this point. Still, his commitment to innate biological capabilities as a catch-all cause for differences in distant and complex test performance belies his use of the type of fall-back, baseless assumptions with which he explicitly takes issue in his remarks. Public frustration with the politics of academia is warranted and much needed. He is willing to err on the side of genetics, while many others are willing to err on the side of socialization. I would argue that both are scientifically and ethically lackadasical. In the case of Summers, his ultimate position on the sources of performance differences is unfounded, both by his own evidence and by a broader swath of available evidence. His remarks may have been ill-spoken and ill-supported, but by becoming the plain and public symbol of a mindset so taboo that we might naively question whether it still exists academic circles, Dr. Summers inadvertantly brought to light the way in which political ends can be served by a smokescreen of broad quasi-scientific claims.


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[ii]Summers, Donna J. and Rogers, Diana C. Executive Summary. A National Analysis of Diversity in Science and Engineering Faculties at Research Universities.

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[vii]Valian, Virginia. “Raise your hand if you're a woman in science.” Washington Post. 30 January 2005. B01.

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[xxx]Casey, M. B., & Brabeck, M. M. (1989). Exceptions to the male advantage on a spatial task: Family handedness and college major as a factor identifying women who excel. Neuropsychologia, 27, 689–696.

 

 

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