Is there, or is there not, a gender gap in mathematics performance? And if there is, is it biological or cultural?

Although the presence of a gender gap in the U.S. tends to be regarded as an obvious truth, evidence is rather more equivocal. One meta-analysis of studies published between 1990 and 2007, for example, found no gender differences in mean performance and nearly equal variability within each gender. Another meta-analysis, using 30 years of SAT and ACT scores, found a very large 13:1 ratio of middle school boys to girls at the highest levels of performance in the early 1980s, which declined to around 4:1 by 1991, where it has remained. A large longitudinal study found that males were doing better in math, across all socioeconomic classes, by the 3^rd grade, with the ratio of boys to girls in the top 5% rising to 3:1 by 5^th grade.

Regardless of the extent of any gender differences in the U.S., the more fundamental question is whether such differences are biological or cultural. The historical changes mentioned above certainly point to a large cultural component. Happily, because so many more countries now participate in the Trends in International Mathematics and Science Study (TIMSS) and the Programme in International Student Assessment (PISA), much better data is now available to answer this question. In 2007, for example, 4^th graders from 38 countries and 8^th graders from 52 countries participated in TIMSS. In 2009, 65 countries participated in PISA.

So what does all this new data reveal about the gender gap? Overall, there was no significant gender gap in the 2003 and 2007 TIMSS, with the exception of the 2007 8^th graders, where girls outperformed boys.

There were, of course, significant gender gaps on a country basis. Researchers looked at several theories for what might underlie these.

Contradicting one theory, gender gaps did not correlate reliably with gender equity. In fact, both boys and girls tended to do better in math when raised in countries where females have better equality. The primary contributor to this appears to be women’s income and rates of participation in the work force. This is in keeping with the idea that maternal education and employment opportunities have benefits for their children’s learning regardless of gender.

The researchers also looked at the more specific hypothesis put forward by Steven Levitt, that gender inequity doesn’t hurt girls' math performance in Muslim countries, where most students attend single-sex schools. This theory was not borne out by the evidence. There was no consistent link between school type and math performance across countries.

However, math performance in the 29 wealthier countries could be predicted to a very high degree by three factors: economic participation and opportunity; GDP per capita; membership of one of three clusters — Middle Eastern (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia); East Asian (Hong Kong, Japan, South Korea, Singapore, Taiwan); rest (Russia, Hungary, Czech Republic, England, Canada, US, Australia, Sweden, Norway, Scotland, Cyprus, Italy, Malta, Israel, Spain, Lithuania, Malaysia, Slovenia, Dubai). The Middle Eastern cluster scored lowest (note the exception of Dubai), and the East Asian the highest. While there are many cultural factors differentiating these clusters, it’s interesting to note that countries’ average performance tended to be higher when students attribute less importance to mastering math.

The investigators also looked at the male variability hypothesis — the idea that males are more variable in their performance, and their predominance at the top is balanced by their predominance at the bottom. The study found however that greater male variation in math achievement varies widely across countries, and is not found at all in some countries.

In sum, the cross-country variability in performance in regard to gender indicates that the most likely cause of any differences lies in country-specific social factors. These could include perception of abilities as fixed vs malleable, attitude toward math, gender beliefs.

Stereotype threat

A popular theory of women’s underachievement in math concerns stereotype threat (first proposed by Spencer, Steele, and Quinn in a 1999 paper). I have reported on this on several occasions. However, a recent review of this research claims that many of the studies were flawed in their methodology and statistical analysis.

Of the 141 studies that cited the original article and related to mathematics, only 23 met the criteria needed (in the reviewers’ opinion) to replicate the original study:

• Both genders tested
• Math test used
• Subjects recruited regardless of preexisting beliefs about gender stereotypes
• Subjects randomly assigned to experimental conditions

Of these 23, three involved younger participants (< 18 years) and were excluded. Of the remaining 20 studies, only 11 (55%) replicated the original effect (a significant interaction between gender and stereotype threat, and women performing significantly worse in the threat condition than in the threat condition compared to men).

Moreover, half the studies confounded the results by statistically adjusting preexisting math scores. That is, the researchers tried to adjust for any preexisting differences in math performance by using a previous math assessment measure such as SAT score to ‘tweak’ the baseline score. This practice has been the subject of some debate, and the reviewers come out firmly against it, arguing that “an important assumption of a covariate analysis is that the groups do not differ on the covariate. But that group difference is exactly what stereotype threat theory tries to explain!” Note, too, that the original study didn’t make such an adjustment.

So what happens if we exclude those studies that confounded the results? That leaves ten studies, of which only three found an effect (and one of these found the effect only in a subset of the math test). In other words, overwhelmingly, it was the studies that adjusted the scores that found an effect (8/10), while those that didn’t adjust them didn’t find the effect (7/10).

The power of the adjustment in producing the effect was confirmed in a meta-analysis.

Now these researchers aren’t saying that stereotype threat doesn’t exist, or that it doesn’t have an effect on women in this domain. Their point is that the size of the effect, and the evidence for the effect, has come to be regarded as greater and more robust than the research warrants.

At a practical level, this may have led to too much emphasis on tackling this problem at the expense of investigating other possible causes and designing other useful interventions.

Research has shown that younger adults are better decision makers than older adults — a curious result. A new study tried to capture more ‘real-world’ decision-making, by requiring participants to evaluate each result in order to strategize the next choice.

This time (whew!), the older adults did better.

In the first experiment, groups of older (60-early 80s) and younger (college-age) adults received points each time they chose from one of four options and tried to maximize the points they earned.  For this task, the younger adults were more efficient at selecting the options that yielded more points.

In the second experiment, the rewards received depended on the choices made previously.  The “decreasing option” gave a larger number of points on each trial, but caused rewards on future trials to be lower. The “increasing option” gave a smaller reward on each trial but caused rewards on future trials to increase.  In one version of the test, the increasing option led to more points earned over the course of the experiment; in another, chasing the increasing option couldn’t make up for the points that could be accrued grabbing the bigger bite on each trial.

The older adults did better on every permutation.

Understanding more complex scenarios is where experience tells. The difference in performance also may reflect the different ways younger and older adults use their brains. Decision-making can involve two different reward learning systems, according to recent thinking. In the model-based system, a cognitive model is constructed that shows how various actions and their rewards are connected to each other. Decisions are made by simulating how one decision will affect future decisions. In the model-free system, on the other hand, only values associated with each choice are considered.

These systems are rooted in different parts of the brain. The model-based system uses the intraparietal sulcus and lateral prefrontal cortex, while the model-free system uses the ventral striatum. There is some evidence that younger adults use the ventral striatum (involved in habitual, reflexive learning and immediate reward) for decision-making more than older adults, and older adults use the dorsolateral prefrontal cortex (involved in more rational, deliberative thinking) more than younger adults.

Brain imaging data from 103 healthy people aged 5-32, each of whom was scanned at least twice, has demonstrated that wiring to the frontal lobe continues to develop after adolescence.

The brain scans focused on 10 major white matter tracts. Significant changes in white matter tracts occurred in the vast majority of children and early adolescents, and these changes were mostly complete by late adolescence for projection and commissural tracts (projection tracts project from the cortex to non-cortical areas, such as the senses and the muscles, or from the thalamus to the cortex; commissural tracts cross from one hemisphere to the other). But association tracts (which connect regions within the same hemisphere) kept developing after adolescence.

This was particularly so for the inferior and superior longitudinal and fronto-occipital fascicule (the inferior longitudinal fasciculus connects the temporal and occipital lobes; the superior longitudinal fasciculus connects the frontal lobe to the occipital lobe and parts of the temporal and parietal lobes). These frontal connections are needed for complex cognitive tasks such as inhibition, executive functioning, and attention.

The researchers speculated that this continuing development may be due to the many life experiences in young adulthood, such as pursing post-secondary education, starting a career, independence and developing new social and family relationships.

But this continuing development wasn’t seen in everyone. Indeed, in some people, there was evidence of reductions, rather than growth, in white matter integrity. It may be that this is connected with the development of psychiatric disorders that typically develop in adolescence or young adulthood — perhaps directly, or because such degradation increases vulnerability to other factors (e.g., to drug use). This is speculative at the moment, but it opens up a new avenue to research.

Following animal research indicating that binge drinking damages the hippocampus, and other research showing that this learning and memory center is still developing during adolescence, a new study has investigated the effects of binge drinking on learning in university students. The study, involving 122 Spanish university students (aged 18-20), of whom half engaged in binge drinking, found a clear association between binge drinking and a lower ability to learn new verbal information.

Specifically, binge drinkers were more affected by interference in the Rey Auditory Verbal Learning Test, and remembered fewer words; they also performed worse on the Weschler Memory Scale-3rd ed. (WMS-III) Logical Memory subtest, both on immediate and delayed recall. However, there were no differences between the two groups on the WMS-III Family Pictures subtest (measuring visual declarative memory).

These results persisted even after controlling for other possible confounding variables such as intellectual levels, history of neurological or psychopathological disorders, other drug use, or family history of alcoholism.

The genders were evenly represented in both groups. Interestingly, and in contradiction of some other research, women were not found to be more vulnerable to the neurotoxic effects of binge drinking.

A study involving 125 younger (average age 19) and older (average age 69) adults has revealed that while younger adults showed better explicit learning, older adults were better at implicit learning. Implicit memory is our unconscious memory, which influences behavior without our awareness.

In the study, participants pressed buttons in response to the colors of words and random letter strings — only the colors were relevant, not the words themselves. They then completed word fragments. In one condition, they were told to use words from the earlier color task to complete the fragments (a test of explicit memory); in the other, this task wasn’t mentioned (a test of implicit memory).

Older adults showed better implicit than explicit memory and better implicit memory than the younger, while the reverse was true for the younger adults. However, on a further test which required younger participants to engage in a number task simultaneously with the color task, younger adults behaved like older ones.

The findings indicate that shallower and less focused processing goes on during multitasking, and (but not inevitably!) with age. The fact that younger adults behaved like older ones when distracted points to the problem, for which we now have quite a body of evidence: with age, we tend to become more easily distracted.