American Enterprise Institute

Monday, October 1, 2007

[Edited transcript from audio tapes]

9:15 a.m.	Registration
9:30	Introduction:	Christopher DeMuth, AEI
9:40	Panel I:	The Science on Women and Science: What the Data Say
	Moderator:	Christina Hoff Sommers, AEI
	Panelists:	Rosalind Chaitt Barnett, Brandeis University
		David Geary, University of Missouri
		Richard Haier, University of California–Irvine Medical School
		Elizabeth Spelke, Harvard University
11:50	Break
Noon	Luncheon
	Keynote Speaker:	Simon Baron-Cohen, Cambridge University
1:30 p.m.	Break
1:40	Panel II:	Stereotype Threat: The State of the Research
	Moderator:	Christina Hoff Sommers, AEI
	Panelists:	Joshua Aronson, New York University
		Amy Wax, University of Pennsylvania Law School
3:00	Speaker:	Charles Murray, AEI
3:40	Adjournment

Proceedings:

PANEL I:  The Science on Women and Science:  What the Data Say

Christopher DeMuth:  Good morning and welcome.  My name is Chris DeMuth, I’m President of the American Enterprise Institute and I’m delighted to welcome you all here today for this conference on Women and Science.  This is a conference of AEI’s Brady Program in culture and freedom, and I’d like to take the occasion to thank the W.H. Brady Foundation, and, particularly, Elizabeth Brady Lurie for their wonderful gift that made it possible for us to establish this AEI project, and many conferences such as the one we are holding today. 

The subject of women and science and, in particular, the relatively low rates of participation of women in the quantitative sciences and engineering fields has been a subject of tremendous amount of attention and vigorous controversy in recent years.  Some of these controversies have been a little esoteric for us plain hum-drum Washington politicos, but women in science is now also the subject of a great deal of federal grant making, and when something becomes a subject of grant making, that is something that we in Washington can really understand and get our arms around, so we are now paying attention also. 

Today we will be focusing not so much on the politics of women in science, but to the science of women in science, and I’m very grateful to my colleague, Christina Hoff Sommers, who has conceived and organized this session from the beginning, and who will be our host and moderator throughout the day.  She and I are also grateful to our colleagues, Charles Murray and Jurgen Reinhoudt for helping with many aspects of conference planning over the past several months.  But, most of all, I’m immensely gratified that this conference has attracted so many exceptionally accomplished individuals from academic research and the professional fields who have been paying attention to these subjects and are with us today, both as members of the panels, and of guests in the audience who I know will be full participants in our discussions. 

We have a rich and full agenda, and I know I am certainly looking forward to a vigorous and productive series of discussions.  So I would like to turn things over now for a few initial words, and to moderate our first session on The Science on Women and Science:  What the Data Say, to Christina Hoff Sommers.  Christina?  [Applause]. 

Christina Hoff Sommers:  Thank you, Chris.  Good morning and welcome to the American Enterprise Institute.  I have the agreeable privilege of moderating this intriguing conference.  In the Fall of 2006, the National Academy of Sciences, in cooperation with two other very prestigious organizations, professional organizations, they released a report entitled, Beyond Bias and Barriers, Fulfilling the Potential of Women in Academic Science and Engineering.  The report claimed to find widespread bias against women in every field of science and engineering.  The authors included Donna Shalala, the former Secretary of Health of HHS, and now President of Miami University.  She, along with several others, concluded that “it was not lack of talent, but unintentional and outmoded institutional structures that are hindering the access and advance of women.  The time for action is now.” 

Now, the literature on sex differences is very complex, sometimes contradictory, there are all sorts of disagreements, schools of thought, feuds, researchers don’t seem to agree on many things.  For example, women are very successful in getting PhDs in certain fields, and less in others.  Sixty-five percent of the PhDs in education go to women, 54 percent of degrees in social science, 47 percent in the life sciences, then it goes down to 26 percent for physical sciences and 17 percent for engineering. 

Interestingly enough, in the 1960s, women were about 5 percent of veterinaries, and today they’re approaching 80 percent of enrollments in our schools of veterinary medicine.  I heard one disgruntled farmer complaining that now that women had taken over the field there was a bias in favor of cats.  [Laughter] He had hogs and cattle and he couldn’t find anyone to take, obviously prejudice.  But, anyway, maybe someone today can explain to us why this is happening. 

Fortunately, for us, we have a group of fantastic scholars here today.  Some are sympathetic to the NAS report, others are not, and they’re going to be able to shed light on this debate.  The NAS report calls for workshops for academic personnel to help them overcome hidden bias, it also calls for federal agencies to implement stringent Title IX compliance reviews of math, science and engineering programs.  One federal agency, the National Science Foundation, is already in full swing with initiatives designed to bring justice to women in academic science. 

Now what I set out to do several months ago was to assemble the best researchers I could possibly find in the area of sex differences, and I think I have been wonderfully successful.  Each of our speakers is going to hold forth for about 15-20 minutes this morning, on this morning’s panel.  I’m going to be strict about time limits because I want to allow plenty of time for audience members to ask questions and make statements.  Our speakers have just far too many awards and degrees and books to list them, you’ll find them in your programs, so I’ll just say very generally that our first presenter will be Professor Rosalind Barnett, from Brandeis University.  She’s a senior scientist at the Women’s Study Center at Brandeis, my alma mater, and welcome to AEI. 

We also have Professor Elizabeth Spelke.  She is the Marshall Berkman Professor of Psychology at Harvard University.  She’s a star in the field of cognitive psychology.  She was profiled in The New Yorker.  Very excited to hear her speak.  We will hear from Richard Haier, a Professor of Psychology and Pediatric Neurology at the University of California in Irvine.  And our final speaker will be David Geary.  He’s the Curator Professor at the University of Missouri’s Department of Psychology, the Department of Psychological Sciences, and he has chaired that department for many years.  He specializes in cognitive developmental psychology.  So I think Professor Barnett, we’ll begin. 

Rosalind Chait Barnett:  Thank you very much for inviting me.  The title of my talk, which you’ll find out in a minute, is From Stone Walls to Invisible Walls.  Briefly, the question of where are women in science is two parts, of the women in science where are they, and why aren’t there more women in science, which I’ll touch on briefly, then I’ll share with you my particular focus. 

But, first, I’m going to take a few minutes to talk about what I think are three very over-hyped stories that get too much attention, and then I’m going to take you on a long journey over time from the Renaissance to the present to put this whole question in a broader framework, and then I have some concluding comments.  My particular focus will be on three subjects, women’s access to education in science, women’s opportunities for employment in science, and women’s appropriate recognition for their scientific work.  However, as I mentioned, I’m going to take a few minutes to touch on these three over-hyped stories that suck up all the oxygen from what I think is the real story of women in science. 

The first, women innately don’t have what it takes to succeed in math.  This argument is often made based on the following considerations -- that women have almost never made it into the ranks of most accomplished mathematicians and scientists, and women’s brains, hormones, and motivation are deficient.  These arguments continue to be made in spite of the fact that numerous peer review studies fail to find any evidence of large gender differences in math and science ability, including a major med analysis of four million students, which revealed essentially no gender differences in math aptitude. 

Here you can see this on the slide.  I don’t know if this has a laser pointer.  Oops, it does not, too bad.  The point being that the big story here is the enormous within gender variability, which overwhelms the mean difference, which shows a slight advantage for males, but the big story is down here, and that within gender variability is lost in the war of words about gender differences.  Unless you think this is a dead story, Kathleen Parker did a piece just two weeks ago in The Washington Post Writer’s Group, repeating the same tired truths without any supporting data. 

Second over-hyped story, in my view, males are over-represented at the upper tail of distribution in math aptitude scores, and, therefore, are more highly represented in leadership positions in math and science.  However, studies show only a weak relationship between scoring and the upper tail ability and eventual success in math and science careers.  In fact, of the college educated professional workforce in math, science, and engineering, fewer than one-third of the men had SATM scores above 650, which is the lower end of the threshold typically presumed to be required for success in these fields. 

And in one long-term study, after taking into account relevant experiential and preferential variables, sex accounted for only 1 percent of the variants in science and math career outcomes among students who scored at the extreme tail of the math aptitude test.  Those scores in the top of the math aptitude test alone do not, by themselves, tell the whole story of success in math and science careers. 

The third story is academic success is zero sum game, when girls succeed, boys fail, and vice-versa.  These two graphs are going to show you, really suggest that’s not the case.  Here is a graph of a male and female’s aptitude score, age 13, on the NAEP, which is the Nation’s Report Card.  This is a sample of 172,000 fourth graders, going from 1973 to 2004.  As you can see, the steady increase of both boys and girls in math scores, the gap between them is about stable, has not been much changed, boys and girls are both getting better.  In the next slide, you see the same thing for, oops, where did it go, so these are two, very much the same kind of thing. 

Now I’m going to go back to my own focus, the long-term picture of institutional, cultural and organizational factors that have made it appear that science is a male preserve, and have made it extraordinarily difficult for women to assume leadership roles, or even to be visible in math and science.  To develop my ideas, I’m going back in time to the Renaissance, and then I’m going to jump ahead, finally touching on data from the 19th Century to the present. 

As you will see, much has changed, especially with respect to women’s access to education and employment in the sciences, yet, regrettably, their advances are not often recognized or rewarded, continuing to fuel the belief that science and math are still male preserves.  I believe this journey through time is pertinent to our understanding of the question of this conference, namely, where are the women in science?  As I hope to show, this question has long been asked, and the rationalizations for the “situation of women today” are strikingly similar to those offered in the past.  Perhaps this historical glimpse will help us move past the simple reasons put forth today and lead us to a better and fuller understanding of why we’re asking this question in the way we are asking it. 

Let’s go back -- so many major scientific and mathematical discoveries were made during this particular period, that is widely viewed as the foundation of modern science.  The first thing to notice is that this period was marked by an amazing abundance of male superstars in science.  And, just briefly, Copernicus, Galileo, Fairmount, Kepler, Descartes, Newton, Leibniz and da Vinci. 

Question -- during this extraordinary period, what were the women doing?  Ironically, as they watched the lives and rites of their husbands, sons and brothers expand, their lives contracted.  During the height of the Renaissance, when science was flourishing, respectable women had only two life options, they either entered into a marriage, often arranged, or they went into a convent.  Many women, wealthy or not, chose the convent.  Why?  Because a fear of dying in childbirth.  There was a very high rate of women’s death in childbirth. 

To put these options into context, during the 18th Century only approximately half of the eligible women in Venice were married.  So, what was convent life like?  Walled-off as they were, women had no possibility of participating in the intellectual and scientific life of the times, these wild, wonderfully-rich time.  Convent life thrived throughout Europe, where patrician girls were sent to be educated and kept secure until a good marriage was arranged for them.  They were generally taught poetry, music, embroidery, and other skills useful for managing a household. 

Indulge me for just a minute, just imagine how different the story of science today might have been if half the sons of the ruling classes were, as youngsters, sent away to spend their lives behind stone walls while their sisters were free to pursue their intellectual interests. 

To give you a feel for how prevalent convent life was, consider that during the Italian Renaissance, Venice, a city with a population of 86,000 have 50 convents and 3,000 nuns.  To put these numbers in context, I live in a town with a population of about 10,000, roughly one-tenth the population of 16th Century Venice.  We have one grocery store, one drug store, and one auto mechanic shop.  We would have to have five convents in my little town to have the number of convents proportionate to the number in Venice during the Renaissance. 

Is it any wonder that the pursuit of science was then and has continued to be deemed a male pursuit?  Social role theory teaches us that when occupations are sex-typed, as science was during the Renaissance, it is human nature to infer that there must be something inherent about men that predisposes them toward math and science, something that women do not possess. 

Before moving ahead, I want to share two brief vignettes.  Galileo, arguably the most illustrious of the Renaissance scientists, had three illegitimate children -- two daughters and a son.  Of the three, his eldest, Virginia, was the only one who “mirrored his own brilliance, industry, sensibility and virtue” and was, in his words, a woman of exquisite mind.  But he deemed her unmarriageable because he had not married her mother.  She was illegitimate, as were all the other two children.  At age 13, he placed her, and her 12 year old sister, in a convent, where they lived out their lives in poverty and seclusion.  In contrast, his son was legitimized by Fia, by the Grand Duke of Tuscany, and went off to study law at the university. 

The second vignette has to do with Leonardo da Vinci, who was also an illegitimate son.  He was born to a peasant woman.  Had he been born a girl he would have been deemed unmarriageable, and, surely, been sent off to spend his life behind convent walls. 

Let’s jump ahead to the 17th Century in America.  There were two remarkable characteristics -- development of schools for sons of wealthy families, and strong limitations on education for girls.  A recurring rationalization, limiting girl’s access to education, was that learning would have serious negative effects on them. 

Here are some of the arguments.  As the brain develops, the ovaries shrivel, which was medical wisdom at the time.  Education will undermine their health and that of their future children.  Education will decrease their willingness to do housework and obey their husbands.  Education will lead to their inclusion of men’s activities, as you’re taking over men’s jobs.  Echoes of these concerns can be heard today in the rationalization that women can’t manage both a career and family, and don’t have the physical stamina to pursue demanding work at the highest levels in math and science. 

So, while the stone walls at boys schools lock girls out, the girls open their own schools.  In the 19th Century there were dame schools.  These were informal instruction held in the homes of women who lived nearby where girls were taught basic reading and writing, embroidery, and other feminine skills.  From the mid-1820’s to the present in America, there was a rapid spread of education for women in the U.S., mainly these dame schools, and, later, to women’s academies, which were characterized by having all female faculty -- they were all single women, they could not be teaching if they were married, but, yet, they provided rigorous training in science, certain sciences. 

These schools became a female enclave within the American scientific community.  They were not intended to open new careers for women, but, rather, to make these women better mothers for the American republic to raise moral and patriotic sons.  Despite the odds, women pursue their interests in science. 

The early 19th Century saw a groundswell of popular books and textbooks written for women by men and women on such scientific subjects as botany, chemistry and geology.  There was an enormous audience hungry for such books reflected in this following.  These are the sales figures.  Conversations on Chemistry, which was published in 1806, went through more than 15 editions in the U.S. before 1860.  Familiar Lectures on Botany in 1829 went through at least 17 editions and sold over 272,000 copies by 1872.  Introduction to Botany had at least nine English editions by 1841. 

But, we have women’s employment in science.  Where were they employed?  The women’s colleges had been the largest employers of women scientists, but they did very little research, these women faculty, because they had very heavy teaching loads, which was true until relatively recently, and they had no incentive in terms of advancement.  These women had the best jobs that were available in science at the time.  They rarely had PhDs.  Why?  Because they were not admitted to matriculate in any graduate schools until the 1890’s, and progress was slow and uneven in granting matriculation rights to women graduate students. 

For example, Princeton, Harvard and NYU refused to matriculate women students to their graduate schools until the 1960’s.  As the women’s colleges upgraded, they required PhDs for the new faculty.  Since there weren’t many women faculty, and they wanted to upgrade their images, they started hiring male faculty, married men faculty, and so the number of women who had positions eligible for them were decreased. 

Women retirees were replaced by men as women’s colleges wanted to have a different face than older, single women.  And, so, even as women got more PhDs, employment became a problem.  Administrators also had fears that women were bad risks, again, the stories of being vulnerable, they couldn’t hack it, they didn’t have what it takes. 

To induce male PhDs to teach at women’s colleges, they had to offer them special incentives, including reduced teaching loads, good salaries, research support, and living quarters for their family.  So while women couldn’t marry, men could marry and the schools provided support for them.  So, not surprisingly, men started to produce research results, lending support to the belief that women’s accomplishments in science do not match those of their male counterparts. 

Having finally gained access to higher education in science, women’s next hurdle, then, was to find employment in science.  It was hard to get early data on employment, so I’m going to jump ahead to some recent data.  These are data from 2002.  As you can see, women had many more opportunities of places to work, but only 42 percent of PhDs are working at four-year colleges.  Even though the greatest growth in employment, and some of the most remarkable developments in medicine today have come from the private sector, analyses of gender differences in science are based largely on studies of the Academy, as is the theme today. 

In fact, the number of U.S. life scientists working outside the Academy grew from 83,000 in 1980 to 181,000 in 2000.  While all employment settings provide both obstacles and opportunities for women scientists, arguably the most extreme examples are universities, which epitomize hierarchical organizations, and biotech firms, which epitomize flatter networked organizations. 

Biotech firms have strong ties to universities, they have flatter project-based employment structures, and cutting edge approaches to science.  Here is an example of a networked organization, where every person has many, many colleagues, and they provide better workplaces for women.  Why?  Because these organizations rely on partnerships to be successful, they’re more flexible and transparent than hierarchical organizations.  Moreover, there are fewer chances for sexism to thrive, because advancement is based on input from a wide range of people, rather than a few, as is typical in hierarchical organizations. 

Here is some data based on a study of, I think, 2000, women, I had that a little bit later on, you can see here for male scientists, the odds of promotion to supervisory positions do not differ by the organization, but are very, very different for women.  Women were eight times more likely than their counterparts in hierarchical organizations to supervisory positions.  So organizational context mattered, they affect career outcomes for women scientists.  However, the heavy focus, even today, on the situation of women scientists in the Academy reflects a persistent idea that any PhD worth her salt obtains a university position; other options are considered second best. 

Despite all the odds, there were many outstanding women who were never given an opportunity to make a career in the Academy, and women scientists today who are holding high level leadership positions in government and industry.  Just to give you an idea, Margaret Mead, probably one of the most notable women scientists of the 20th Century, reportedly was never offered a tenure academic position.  She was hired by the American Museum of Natural History, where she worked out of an attic in 1927, and was not promoted to full curator until 1964, by which time she was world famous. 

Rachel Carson, world famous biologist and bestselling author of Silent Spring, never had a faculty position.  Barbara McClintock, 1941, just before the University of Missouri denied her tenure, she moved to Cold Spring Harbor Laboratory where she won numerous prizes, including the 1983 Nobel Prize in Physiology or Medicine, and, notably, she was the first and only woman to receive an unshared Nobel Prize in that category. 

Finally, Dian Fossey did her pioneering work in Rwanda as an independent researcher.  She only got her PhD after the major phase of her work was completed. 

There are a number of women who are almost Nobel Laureates.  Physicist C.S. Wu of Columbia, performed crucial experiments proving the theory that won her colleagues, Lee and Yang, the 1957 Nobel Prize in Physics.  Biochemist Viola Graham, who helped, there is no picture of her on the Internet, but this is her article with James Sumner of Cornell, for which she shared the 1946 Nobel Prize in Chemistry with two other men, not with her. 

Geneticist Esther Lederberg, who helped her then husband, Joshua, with microbial research that won him and two other men the Nobel Prize in 1958, again, not including her.  Marguerite Vogt, the colleague and close collaborator for 20 years on DNA tumor viruses and cell growth.  Renato Dulbecco, who shared the prize with two other men in 1975, again, not including her.  Columnist Anna Schwartz, who co-authored several books with Milton Friedman, and worked for decades on the detailed economic data that formed the basis for the work that won him, alone, the Economics Prize in 1976. 

I won’t go through the rest, and some of them you know, some of them you don’t know.  Ruth Hubbard, the one on the bottom here, she had worked for years on the chemistry vision before marrying her husband, George Wald, in ’58, who won the 1967 Nobel Prize for working the same area, and many assumed that they had always collaborated and that he deserved most of the credit for her earlier independent work as well. 

Apparently, once she married a scientist of greater reputation, a woman’s own independent work, which all too easily be dismissed as a small part of his.  And, today, we have many women scientists in leadership roles outside of the Academy, including the Chairman of Dow Corning, the Chief Executive Officer of [indiscernible], and President of [indiscernible].  We have women, 4 out of the 27 institute directors at NIH are women.  So this list is obviously, not exhaustive, it’s merely meant to illustrate that there are many organizations other than university that provide opportunities for women scientists to achieve prominent positions of leadership. 

Although much still needs to be done, women scientists today need appropriate recognition for their scientific work.  I’m sure you all know the story of the MIT senior female faculty who were marginalized and whose plight was made very well known in the national press.  Compared to male faculty they did not receive equitable salaries, laboratory space, and so forth. 

Why does this bias and recognition persist?  Many reasons, I’m sure, but, just to give you one idea, one of the processes that has just recently been operationalized, two Swedish scientists noted that female scientists applying for prestigious fellowships at the Swedish Medical Research Council during the 1990’s had been less than half as successful as male applicants. 

So the question was, does a peer review system evaluate men and women on an equal basis?  So what they did, the researchers got access to the MRC reviewer subjective scores, and found that they gave, this is the three criteria they rated the applicants on -- scientific competence, relevance of the research proposal, and quality of the research -- and they found that the reviewers gave the female applicants lower average scores than the males on all three parameters, but especially on scientific competence, which is typically related to the number of, and quality of their scientific publications.  So the inference is that women earned lower scores because they were less productive. 

But, were they?  So the researchers developed six objective measures of scientific productivity from the applicant’s CD, which they checked, and these measures included the total number of papers published in high impact journals and the total number of first-authored papers in high impact journals.  Do the men and women with equal scientific productivity receive the same competence ratings?  Absolutely not. 

Here’s the graph that shows that.  As you can see, the most productive group of female scientists are those with 100 total impact points or more was the only group to be judged as competent as men, although only as competent as the least productive group of male applicants, the ones whose numbers had fewer than 20 impact points. 

To tell you this somewhat differently, their analyses found that to be awarded the same competence score as a male colleague, a female scientist would have to produce approximately three extra high impact papers and journals, such as Nature or Science, or 20 extra papers in excellent specialist journals.  Thus, a female applicant had to be 2.5 times more productive than the average male applicant to receive the same competence score.  And this was done in Sweden. 

This study provides direct evidence that a peer review bias system is subject to sex bias.  Clearly, as scientists [indiscernible] and other human beings to the effects of agenda prejudice.  If changes aren’t made to this, according to the researchers, a large pool of promising talent will be wasted.  So, clearly, women scientists have broken through the stone walls.  Much has to happen before the invisible walls come down.  Thank you.  [Applause]. 

Elizabeth Spelke:  Thanks to the American Enterprise Institute, and to Dr. Sommers for organizing this event.  I’m very happy to be here.  I have not spent my life studying the topic of women in science, but I have been thinking about it a lot over the last 2½ years, since then President of Harvard, Lawrence Summers, made three famous suggestions. 

First, he suggested that men may have higher intrinsic aptitude for math and science, both in general and also at the highest levels of ability.  Second, that men may show a profile of motivation that is better suited to high intensity work, long hours of work in the sciences and other technical fields.  And, third, that because of the forces of free market economics, gender discrimination is probably not a major source accounting for the paucity of women in science. 

Now, I think all three of these suggestions are very worth discussing, I hope we discuss all of them today.  I want to focus my 20 minutes on the first two of them, and ask, what is the intrinsic aptitude of males and females for science, and what are the intrinsic differences in their motivational patterns that might be relevant to their success in science? 

To start with questions of intrinsic aptitude, I think first it’s important to note that although science has a long and illustrious history, as we’ve just heard, measured against the human scale of historical time, it has a very short history, measured against the scale of evolutionary time.  There hasn’t been time for the human brain to evolve special purpose capacities expressly for doing symbolic mathematics and science.  Instead, when we learn and practice science and mathematics, we bring to bare core cognitive systems that evolved for other purposes and we harness them for this new function. 

Now, there’s been great progress made over the last 10 or 20 years in understanding what those systems are and what their properties are through converging lines of research of five kinds.  First, research on the cognitive capacities of our close primate relatives, research on the emergence of cognitive capacities in human infants, research on patterns of variability and universality in cognitive capacities across cultures, and especially research on the cognitive capacities that children bring to bare when they first learn mathematics and science, and that adults bring to bare when we engage in mathematical and scientific reasoning. 

And to make 20 years of research, to collapse that into a ridiculously short 5 minutes, I think we see evidence from all of this work for three fundamental systems at the core of human, mathematical and scientific reasoning -- a system for representing and reasoning about objects, one for representing and reasoning about number, and one for representing and reasoning about geometry -- all three of which emerge spontaneously, quite early in human infants, therefore, very likely have, in part, a genetic basis. 

What’s more, over the course of the preschool years, we see children spontaneously using these systems to develop systematic knowledge of object mechanics, to develop an understanding of symbolic number and symbolic arithmetic, and to develop, to master a whole host of representational devices that are crucial to math and science, particularly things like maps and measurement. 

So, with that research as background, I think we can look at Summers’ first suggestion in the form of two questions.  We can ask first, do boys out-perform girls at tasks tapping any of the three core systems?  And, second, do boys have superior abilities to harness these systems, for example, for learning symbolic mathematics, learning to use maps and other symbolic geometrical devices?  Let me see if I can quickly give you a flavor for a very large body of work that does bear on these questions. 

Let’s start with objects.  Well, there’s, for as long as 70 years or more, there have been many studies looking at children’s spontaneous attention to objects, and engagement with objects.  In the late 1970s, Eleanor Mccabe [ph.] and Carolyn Jackson reviewed this literature, asking, do we see any general sex differences in spontaneous attention?  Their answer was no.  Girls and boys are equally interested in objects, equally interested in people. 

But, more recently, over the last 30 years, we’ve gone beyond patterns of spontaneous attention and asked, how able are young children to process the properties of objects, retain them over time, and analyze them?  For example, as in this first case here, how well does a baby who looks at an object, able to hold that object in mind when it moves out of view, how well can they remember it and its properties, or, when babies see two objects, for example, one going inside another, how well do they understand that mechanical and spatial relationship?  How many objects can babies keep track of at once if they see multiple objects disappearing one at a time into a container? 

Now, all of these studies provide evidence for interesting patterns of abilities and limits, and we can look at that evidence and ask, is there evidence for sex differences, systematic differences between male and female infants in these abilities?  The answer, in a word, is no.  There is broadly, highly convergent patterns of performance by the two genders in infancy. 

What about later in childhood?  This system doesn’t go away.  It continues to exist in children and in adults, and one task that’s become very popular for tapping this system of object representation.  In adults, it’s a so-called task of multiple object tracking.  I don’t have time to really describe this, but, just sufficient to say, it’s like a shell game where you’re confronted with an array of objects, you have to keep track of some subset of them, and they all move around independently, and you have to keep your attention on them as they move.  It’s a very demanding task for us as adults.  We tend to fall apart if we have to keep track of more than about four of these things. 

And it’s possible to design child-friendly versions of these tasks, and ask, how good are children at keeping track of multiple objects over time?  We’ve been doing this with children aged 4-8 years so we can go back and ask, are there gender differences?  Here’s average performance of boys and girls, highly equal.  Here’s performance at the high end, the kids who are best at keeping track of the most objects, the most reliable, and what you see in both of them is no tendency for boys to be outperforming girls on object tracking tasks. 

Let me move to number, lots of evidence that starting very early in infancy, perhaps at birth, babies are sensitive to approximate numerical magnitudes.  They can tell the difference between an array of eight objects and an array of 16, though not between eight and nine.  They are sensitive to ordinal relationships between objects, and they can even engage in a form of non-symbolic arithmetic. 

If you show an infant an array of eight objects go into a box, and then a second array of eight objects that go in the box, they know there’s approximately 16 things there, and they react with surprise if you empty the box and reveal only 8 or 32.  So, all these abilities are present at an early age.  Are there sex differences?  No, not in any of them. 

We can go on and ask what happens to these abilities over development.  There’s a very rich literature in cognitive science and cognitive neuroscience providing evidence that this sense of approximate numerical magnitude is critical for our reasoning about symbolic arithmetic.  What’s more, we’ve shown recently that if you test for this sense of magnitude in children just at the start of school, they have it, of course, but variability in their performance on these non-symbolic tests predicts how well they succeed at mathematics at the end of the kindergarten year.  So we can ask, at this age, do we see a difference between males and females?  Again, the answer is no, not on average, and not at the highest levels. 

Well, finally, let me turn to geometry, and I wish I had more time to tell you about two lines of research; one showing that very early in development infants are sensitive to the geometry of the surrounding spatial layout, and they use this sensitivity in their navigation.  I decided I didn’t have time to talk about that.  But, second, infants are also sensitive to the geometry of visual forms, basic geometric relations like distance and angle, and equally sensitive to this geometry in studies of infants.  What happens later in development? 

Let me mention findings from just one study conducted, published last year, that attempted to survey in children aged 6 to 10 years, and in adults, both in our culture and in a remote Amazonian culture, sensitivity to a broad range of geometric properties.  It’s probably hard to see on this slide, but here’s a subset of the things we tested for. 

The task is very simple.  On each individual trial, people see six different geometric displays, five of which share a property, like perpendicularity, the sixth does not, and their task is to indicate which is the outlying figure.  Of course, I’ve indicated the outlying figures here, they’re not indicated for the subjects in the task.  And at cross-trials we test for a variety of Euclidian properties, like angle and distance, and also topological properties like chirality and degree of connectedness.  So, looking across sensitivity to all these different properties, do we see a difference between male and female 6 to 10 year olds?  Answer, no, not on average, and not at the highest levels.  If we look just at the people who are doing best on this task, we do not see a preponderance of males. 

But, I want to spend 1 more minute on this task and show you what the trial-by-trial performance looks like.  Now we tested for somewhat more than 40 different geometrical relationships.  What you see here is trial-by-trial, the performance of girls in green, and of boys in blue.  Notice, first, that there isn’t a single geometric property that is easy for one gender, and hard for the other.  Rather, what you find is highly convergent performance across boys and girls. 

But if you look hard, you also see a couple of properties that trend toward showing a gender difference.  In particular, by a too lax statistical method, we see two properties where females tend to do better than males.  I give an example of one of them there.  And across this whole test, we see exactly one property where males tend to do better than females.  Now that may look familiar to people with any background in discussions on sex differences.  That property, the one property males do better at is what is tested in tests of mental rotation.  So if you think spatial ability depends only on mental rotation, one of the 40 things we tested for, you should conclude that there is a male advantage at space.  If you take a broader view, that advantage seems to go away. 

Well, what about sex differences in the capacity to harness to these abilities to do higher-level things, like symbolic arithmetic, and map reading?  Let me quickly go through the evidence here.  Now, the symbolic system of number is mastered by children roughly over the ages of 2 to 4 or 5.  They learn to count, and they learn to use counting to do elementary, simple arithmetic.  There’s a lot of variability in the speed at which different children acquire these abilities.  And that, too, is predictive of their success in elementary school mathematics.  Are there gender differences?  Answer, no, highly similar patterns of performance over this age range in mastery of the counting system. 

What about map reading?  Beautiful research over the last 20 years shows that it’s at about age 4 that children first start understanding visual representational devices like maps.  So we focused on that age, and presented children with a simple, purely geometrical map.  So, in this task, children are shown a piece of paper, it’s got three elements on it in a particular geometrical relationship, and they’re told that they will be able to find a toy at a location indicated at one of those elements.  Then they turn around, they have to leave the map behind, turn around, face an array of three objects ten times bigger, three-dimensional array, and they have to find the object that corresponds to the position on the map.  This is a purely geometric map-reading task.  And we can ask, are boys better?  And the answer, again, is no. 

So, in summary, we’ve looked at three core systems and see no advantage favoring males, two emerging cognitive skills and also see no advantage.  My conclusion from this work is that the state of the evidence to date does not favor the hypothesis of a male advantage in intrinsic aptitude for math and science. 

What about gender differences in intrinsic motivation?  Are women biologically predisposed not to like math and science as much as men, or not to work in patterns that are most conducive to high levels of achievement in math and science?  Well, in contrast to studies of cognitive abilities, it’s harder to answer this question from studies of motivational patterns because most of the motivational variables that psychologists have looked at systematically don’t have the right kind of properties to be good candidates for a genetically determined difference in motivation. 

Within a single person, most motivational variables change considerably over development and change when you move from one situation to another, or change when you move from one culture to another.  Certainly variables like, what do people want to do, if you ask people what they want to do you find enormous differences across cultures, and within one culture across historical time. 

But there is one motivational variable that does look like a good candidate for a genetically determined factor that could be predictive of success in science and other things, and that’s the variable of self-regulation.  Now, the systematic study of self-regulation began in the early ‘70s with work by the psychologist, Walter Michel, who took 4-year old children and presented them with the following choice.  He showed them two treats, one little treat, like a single marshmallow, another big treat, like a whole pile of marshmallows, and he said to the kids, okay, you’ve got a choice, you can have the single treat any time you want, any time you decide you want that treat you ring this bell and I’ll come into the room and give it to you, or you can have the bigger treat, but, if you want to have the bigger treat you’ve got to wait for me to return.  Okay?  So, ring the bell you get the little one, wait for me to return you get the big one.  And now he leaves the room. 

The kid is alone in the room and he simply measures, how long does the child wait to get the whole dish of marshmallows instead of the single marshmallow?  Now, this simple measure is astonishingly predictive of later achievement.  It predicts how well these kids studied at age 4, this measure predicted how well they would do in high school, it predicted their SAT scores.  In a follow-up, when these people were in their 30s, it predicted their highest level of educational attainment, and in a very recent study focusing on 8th graders, this variable, measured in a more age appropriate way for 8th graders, out-predicted IQ in predicting the child’s ultimate school achievement that year. 

So children were tested toward the beginning of the year in their capacity to self-regulate, ala Michel, and also in IQ, and then at the end of the year they asked, how well did they do in school, who got into the competitive high schools, both variables predicted, but self-regulation predicted more. 

So, we can ask, are males better at self-regulation?  Well, most studies find no sex differences, but there was a meta-analysis recently.  The results of that meta-analysis are that sex differences are extremely small, but they do seem to exist, and they don’t favor males.  There is a slight advantage for females in tasks of self-regulation. 

So, stepping back from this work, I think what we see is evidence that boys and girls are equally endowed with the core cognitive abilities at the root of science and mathematics, they are equally likely to show at least one motivational pattern that does seem to be the only one that I found that actually seems to be predictive of later academic success.  And in light of those findings, we shouldn’t be surprised by the statistics that show that today girls and boys perform equally well in math and science subjects, both in high school and in college.  There is no evidence from any of this work that males are better suited to become scientists. 

So I want to end with a question, which is, why do so many people believe that that’s the case?  I think that there are many potential answers here, but I want to leave you with just one suggestion.  The suggestion is that as we heard from Dr. Barnett, there are many more male than female scientists practicing in academia today, and, at any given time in history, it can be extremely difficult to distinguish what’s typical of the practitioners of a profession from what’s necessary to success in that profession, and it’s easier to see this if you go back to a different historical time. 

The time I want to take you back to is the 1930s, the institution, once again, is Harvard, the person is E.G. Boring, who was essentially the leader of the Psych Department at Harvard for 20 years.  Boring was an extremely tolerant person.  He has claimed that the only thing he was intolerant of was intolerance.  He was opposed to anti-Semitism.  He had many Jewish students.  But when he wrote letters of recommendation, he rarely recommended his Jewish students for the top jobs.  He would say things like, this student has done some really high-quality work, but, unfortunately, he’s not cut out to be a scientist. 

Now, why was he not cut out to be a scientist?  Well, Winston has gone back over the letters written both by Boring and by his colleagues describing some of these students, and they point to the following negative characteristics -- too talkative, too aggressive, too eager, and, my personal favorite, Christina, you should like this one too, gesticulates excitedly.  Now, what’s going on here?  Boring emphasized throughout that every person has to be judged on their merits, but, what’s being judged is who is suitable for science. 

Now, in Boring’s time at Harvard, science was the province not only of men, but of Christian men from genteel backgrounds, who let him finish his sentences, who didn’t get too excited about their hypotheses, didn’t push them too hard, and he came to the not unreasonable conclusion that that kind of dispassionate, calm consideration of the evidence was a central ingredient for science.  Okay? 

Now, a lot of time separates us from the time of Boring.  Fortunately for Christina and me it’s okay to gesticulate a little when you talk, and you can still be a scientist.  Since Boring’s time, the gates of the academic community have opened wide to people from all different religious backgrounds, to people from many, many different cultural backgrounds, so that a far wider range of talent has gotten in, and, not coincidentally, with this opening of the doors, and the increase in scientific talent, the second half of the 20th Century in the United States saw the greatest time of progress and flourishing in science than I think has ever been known in human history. 

What we need to ask now is, are we at the end of this process, or can we do still better in science by opening the gates still wider to the most talented people?  Beyond bias and barriers, the report at the center of the discussion today argues that we can do still better, and I hope that we do.  Thank you.  [Applause]. 

Christina Hoff Sommers:  And now Professor Haier. 

Richard Haier:  Well, good morning.  I can’t see everyone over to my left, if you can’t see me, I was going to say, if you visualize George Clooney you won’t be that disappointed.  [Laughter].  My wife told me that.  She’s on medication.  [Laughter].  Well, I’m glad to be here this morning.  This is an extremely interesting topic.  I have not been a sex difference researcher very long.  I have found some things that differ between men and women in our studies, I’m going to tell you about some of them today.  I have one slide, further on, where I’m going to leave the podium and go over and try to make it work, it’s a little animation, and I can’t do it from here. 

But, I want to start by telling you about Daniel Tammet.  Anybody know Daniel?  Daniel has just written his autobiography called Born on a Blue Day.  Daniel is autistic, so, he’s got those genes, he is also a savant, he’s got those genes, he also is normal to above-average intelligence, which is extremely unusual.  Most of the time when you see a savant they’re mentally retarded, often profoundly, except they can do one mental activity in an extraordinary way.  Daniel is very good with numbers.  He’s very good at learning foreign languages.  He learned Icelandic in a week when they took him, the BBC, I think it was, took him to Iceland and put him with a tutor and a week later he was interviewed on Icelandic television fluently.  Really quite extraordinary! 

He also, for a charity, decided that he would memorize the digits of Pi, you know, 3.14 and then the sequence of numbers goes infinitely.  How many digits do you think you could remember if I gave you like $10,000.00?  Could you remember 500?  No.  Daniel remembered and he recited on television with people checking 22,514 digits of Pi.  22,514, it took him just over 5 hours.  He just kind of read it off his memory.  What is his brain like?  Wouldn’t you just love to know what that brain is doing when he’s memorizing those digits, when those digits are stored and when he’s recalling those digits?  Really extraordinary! 

The brain, of course, is very complicated, it has lots of different pieces to it, each piece does something a little different, and it is an important thing to understand how the brain works and what is the relationship between the brain and cognition.  I think we all understand that cognition happens in the brain, it’s fundamentally a biological process, and it’s extraordinarily complicated. 

This cartoon says, separated at birth, the Mallifert twins meet accidentally, and they are in the patent office with the same invention.  We know from studies of identical twins and identical twins reared apart that scores on IQ tests are very heritable, somewhere between 50 and 80 percent of the variability of IQ scores is probably a genetic, it’s really an important finding because if something is genetic, it means there is some biology underneath it.  If something is biological, it may or may not be genetic, but if something is genetic, genes work through biology. 

So, knowing that there is some component of heredity in intelligence really validates the idea that there is some underlying biology of intelligence.  This can be studied with brain imaging.  This is an image from a technique called positron emission tomography, where you inject radioactive sugar into a person and basically see where it goes in their brain while they are doing some interesting task.  So a PET scan like this, will look different in the same person, depending on what their brain is doing during the procedure.  So if you want to know where is silent reading, you have the person do reading.  If you want to know where math is, you have a person do math. 

This is a slice of the brain just as if I took a knife and sliced right through my head and brain like this, and then looked down.  So here is the front part of the brain, here is the back part of the brain, here is the right, here is the left.  The colors show the amount of sugar activity, the harder a part of the brain is working, the more glucose, that’s sugar, the more glucose it uses, that’s where the energy from neuron firing comes from.  And, you can see the parts of the brain that are most active here in white, reds and oranges. 

And, if you’re ever on Jeopardy and the final category is positron emission tomography, the scale here is micromoles of glucose per 100 grams of brain tissue per minute, which all I mean to tell you about that is this picture is quantified, you can do statistics on it, it’s real science, so you can tell how hard the brain is working. 

Now, back some 20 years ago, we decided to use PET scanning to ask the question, where in the brain is intelligence?  So if you have people working on difficult reasoning problems during this procedure, you can see what part of the brain lights up.  This would be an example of the kind of problem we used.  There are some symbols up here arranged according to a rule or a pattern.  You have to intuit that rule or pattern and then pick from the eight multiple choices down here, the one and only one choice that goes in the lower right-hand corner to complete the pattern. 

Anybody want to yell out the answer?  How many people don’t have a clue?  So we have a few supervisors here from various activity.   If you look at the top row, you see you have four diamonds, two are white, two are black.  Look at the next row, four diamonds, two are white, two are black. Look at it this way, four diamonds, two are white and two are black, and, therefore, the answer is?  Number three because now when you put one black diamond in the corner here you have two black diamonds and two white diamonds, so you have four, the same this way.  So the pattern is maintained. 

And I’m sorry to tell you, but this is actually an easy item.  The actual test we use is considerably more difficult, it’s a timed test, people work on it for 32 minutes while that radioactive sugar is labeling the brain.  And what we found was really quite surprising to us.  No one predicted it. 

We have two brain slices from one person on the right, and the same two brain slices from a different person on the left, all on the same metabolic scale.  The person on the right scored 33 out of 36, the highest score in the sample.  The person on the left only scored 11.  Do you notice anything unusual here?  This person who is not doing too well, that brain is really working hard.  The person over here is doing very well, much lower metabolic rate really throughout the brain. 

This led us to the idea that it’s not how hard your brain works that makes you smart, but how efficiently your brain might work to make it smart.  In 1988 this got a lot of attention.  It was one of the first studies of its kind.  This picture was published in Newsweek.  There were cartoons about it.  It’s probably way too simple a thought to say that intelligence has to do with brain efficiency, but it’s been a pretty good concept and a lot of research on it. 

We next went to a study of the computer game Tetris because we wanted to know what happens when you learn something.  Does your brain get more efficient?  We found people who had never heard of Tetris back in the early ‘90s when it was introduced and we did a PET scan on them before they practiced, here, and then the same person after practice.  This is after 50 days of practice.  50 days, they got extremely good, they were going so fast in this game you could scarcely believe a human being could do this.  What happened?  They are processing more stimuli, it’s faster, it’s harder, and metabolic rate in the brain actually went down.  Moreover, it went down the most in those subjects that had the highest scores on the intelligence test.  The smartest people became most brain efficient. 

Now this is interesting.  And then, I had an insight.  When I was in graduate school in 1971 at Johns Hopkins, that was the year of the now famous study of mathematically precocious youth, started in that year, and I actually worked on that.  I had a long-standing interest in mathematical precocity.  It occurred to me, we can resolve this, definitively, what we’ll do is PET scans in men and women while they’re doing an SAT math test, and we can see, do the people who do the best on the math test, are they the most brain efficient, and then we get to see any sex difference in this.  This would really be cool. 

And, what we found, these are group composite images, 11 people in each group, and, to make a long story short, in men the higher the metabolic rate in these temporal areas the better they did on an SAT math test during the imaging.  In women we couldn’t find any area that was related to how well they did.  And, by the way, in this study, the men and women were matched on SAT scores.  We had men and women matched for having SAT math scores over 700, and then in the average range around 500. 

This is really interesting because, and it didn’t show anything about brain efficiency, by the way, it showed the harder the brain was working here in the men the better they did on math, there was no such relationship in the women, women were just as good at math, how they did it, according to this study, remains a mystery.  This was published back in 1995.  I show it because it illustrates that even when you equate men and women for high-end performance you don’t necessarily get the same brain result.  This was a big hit back in 1995. 

Now I want to talk about a different kind of imaging, it’s called structural imaging.  This is what a typical MRI scan is.  PET scanning is functional.  PET scan changes if you’re reading or doing arithmetic, if you’re awake or asleep, if you’re alive or dead, the PET scan looks different.  Not so with a structural MRI.  You could put a cadaver in the MRI and you’ll get a brain image that looks just like a living person, it’s structural, there is no functional information. 

Interpreting functional imaging in this business is very difficult, it depends on the task and so on.  Structural imaging is actually a little easier.  We’re using a technique called voxel based morphometry, where we have algorithms to segment gray matter and white matter, we can quantify this voxel by voxel in the image.  We did a study where we correlated IQ scores with the amount of gray matter.  The red and yellow areas show where there are statistically significant correlations between the amount of gray matter and IQ.  You can see here that there are areas in the frontal lobe and areas in the left hemisphere and some areas in the right hemisphere, there are areas distributed around the brain, they’re not just in the frontal lobe, and these are areas, this is a sample size of about 47 people, there are areas where the more gray matter you have the higher your IQ score. 

Now, if IQ scores are kind of meaningless, why would they be related to this kind of gray matter structure in the brain?  So, this is interesting, but what really surprised us, and, by the way, in this analysis here, sex was statistically removed to have no effect.  This is a combination of men and women.  When we broke this out separately by sex, we found this.  And if you remember nothing else of my talk today, remember this slide, because we were really shocked at this, because, again, the men and women in this sample were matched almost identically on IQ.  They are exactly the same. 

Yet, what we found in the men was more frontal areas than in the women, although the women had this nice language area that the men didn’t have.  The men had this big area back here in the parietal occipital lobe, kind of a visual spatial area.  There was white matter involved in the women that was not involved in the men.  This was pretty striking because what this means is that there might be more than one brain design to lead to comparable cognitive performance. 

Not all brains work the same way.  Teachers have known this from day one.  Not all brains work the same way.  That’s what I believe these data are telling us.  Here’s the same data just showed from a different viewpoint, so you can really see the frontal lobe differences between men and women.  The parts of the frontal lobe are different in the women than in the men. 

Now, interestingly, those areas that I just showed, the area back here and parts of the frontal lobe, are highly genetic.  This is a study of twins done by the UCLA imaging group.  The red area shows the parts of the cortex that are most heritable.  Not every part of your cortex is equally genetic.  They’re different.  Many of the areas that we see related to IQ are also highly heritable.  That’s interesting. 

Recently we’ve published a review of all the brain imaging studies of intelligence.  There are 37 of them.  What we have concluded is that there is a combination of areas in the parietal lobe, particularly, and in the frontal lobe, with some other areas as well, that these are the areas that turn up most often as related to intelligence in these 37 studies, all of which use different imaging technique, all of which had different kinds of subjects, all of which had different measures of intelligence, yet, these areas came up quite often.  So we believe that the way information flows around the parietal frontal networks is related to intelligence.  I may come to define intelligence someday by some measure of the efficiency of information flow around these specific areas. 

Here’s our P-FIT model down here.  Here are 111 subjects, men and women together, showing where there is a correlation between gray matter and IQ.  We have these frontal areas, we have these language areas, we have these temporal areas back here, but when we take this group and break it down into, I think this is 57 males and the rest females, you see men have a different part of the frontal lobe, and men have this posterior area here that women don’t have.  Again, the men and women are matched on IQ.  There is not a question of men being better, women being better, this is a full-scale, (WAIS-R) Full scale IQ scores. 

I think this is interesting, and I want to point out that we now have another sample, completely independent sample of 100 people, and as we are sitting here today, my assistant is grinding those numbers through to see whether we can replicate this in that other sample of 100.  By the way, if I put these brain areas, the amount of gray matter in these areas into a multiple regression equation to try to predict IQ, the amount of gray matter in just five areas predicts IQ extremely well.  Now it depends on kind of cross-validating this.  So, Einstein’s brain actually was bigger right here and had more glial cells, which are support cells for neurons. 

I’m not going to spend too much time on Einstein’s brain, but, this is the slide I want to just see if I can make it work for a second.  Now watch that.  Now let me tell you what you’re looking at, and then we’ll tap it one more time.  This is a new imaging technology called magneto encephalography -- it measures magnetic fields millisecond by millisecond. 

What you’re watching is what goes on in the brain for a period of 1 second after a person presses a button, one button if a light flashes on the right side of the screen, a different button if the light flashes on the other side of the screen, left side of the screen.  What you’re watching is a single trial, one button pressed, and following that button press, when the light comes on you’re seeing how the brain responds to the light and decides to press the button over 1 second. 

You’ll notice, as we play it again, 100 milliseconds, it starts here, then we get all this here, and now we’re in the front, there’s nothing more back here, and then it fades away, by 900 milliseconds the brain is done.  Now, imagine doing this in Daniel while he’s performing some of his feats.  What you saw in that 1 second of time to a very simple decision gives you some idea of the complexity of what we’re dealing with here.  That was, is the light flash on the right or the left, and look at the brain activity that was going on. 

We know almost nothing about how the brain works.  We know almost nothing about whether sex differences in cognition, even where there are some, and, in most places there aren’t, but even where there are some, we know almost nothing about the brain.  I’m going to conclude by just asking the question, wouldn’t it be nice to have some brain imaging in extremely successful women? 

Here we have Rosalind Franklin who was probably the real discoverer of the double helix and the DNA story.  She did imaging.  She was visual-spatial.  And, of course, we have Einstein himself.  Wouldn’t it be just great to know how these brains work?  I think the only problem I had with the NAS report is it really kind of prematurely excluded the idea that biology has anything at all to do with this.  I think that’s an overstatement and really not correct.  I’ll stop with that.  [Applause]. 

Christina Hoff Sommers:  Mr. Geary. 

David Geary:  Thank you for inviting me today.  It’s been a very interesting group of talks and I’m sure there will be a lot of interesting discussion.  I’m going to move well beyond the Renaissance, or, well before the Renaissance, and start at the beginning.  What I really want to do is to give us a framework for thinking about why there might be biological sex differences, and then, of course, trying to relate those to sex differences in the modern day becomes a bit more complex, but, we have to start somewhere. 

I’m going to tell you a little bit about the evolution of sex differences, why we should expect some sex differences in humans, sex differences in these core knowledge areas that Dr. Spelke talked about a bit as these emerge during development, and then, how do we make these links to math and science, or, can we make these links to math and science? 

So, Darwin was a brilliant guy, it would have been nice to image his brain as well.  This is from one of his books in 1871.  Here we see sex differences across a wide variety of species for the antelope species down there, the females don’t have the horns.  His question was, is there mechanisms, or, are there mechanisms that operate in nature that can explain all of these different sex differences?  He said, yes, there was, and proposed that many of these differences emerge as a result of intrasexual competition, competition for access to mates, or intersexual choice, discriminative choice of mating partners. 

His theory went unnoticed or ridiculed for about 100 years, but now we know that these patterns are ubiquitous in nature in all sexually reproducing species to some extent.  About 30 some years ago these intersexual choice, and intrasexual competition were linked to sex differences in parental investment, the higher investing sex, typically females, but not always, as I’ll show in a bit, are typically engaged in less competition and are more choosy when it comes to mates, and, even more recently, the sex differences in parental investment have been tied to an even more fundamental difference in the potential reproductive rate, and I’ll give an example of that in a bit as well. 

Here I show a bowerbird, and, this is a bower, you’re probably familiar with these.  This is the male, this is the female, and this is his stuff, bower bling, I guess would be the correct term for that, the scientific term for that.  These bower, these male bowers build these huge stick structures, they collect all sorts of stuff.  This is not a nest, this is just to attract the female.  Building these structures is a very complex type of activity, but, nevertheless, has evolved, it’s one of the most complex behavioral forms of competition in nature that we’re aware of, except for, perhaps, humans. 

We see some interesting sex differences, and there are a couple of points I want to make.  One is, females mature at 2 years of age, males physiologically mature at 7.  They begin to reproduce at 10, if they reproduce at all, 15 percent of males sire about 85 percent of the offspring.  Now what’s important here -- two points, one is, we have evolution creating a very long developmental period.  During this developmental period, in males in this species, for humans it’s for both males and females, during this long developmental period we see a lot of practice going on, male bowerbirds watch mature males, they imitate the bower building activities, they practice fighting, they do all sorts of things.  It takes them at least seven years to get good enough to even be competitive in bower building.  This is probably related to prenatal exposure to male hormones, but practice during development is very important. 

Testosterone, post-natal testosterone levels are related to the energetic features of competition, but not to the skill.  So males with a lot of high levels of testosterone gather a lot of sticks and a lot of bling, but their bowers aren’t any good, necessarily, it depends on what they’ve done during the developmental period.  So, there’s a lot of things going on here.  Biology does not mean development and experiences are not important.  In fact, biology can result in the evolution of sensitivity to experience. 

Sexual selection also gives us a way of thinking about within sex variation.  Here we have another example from Darwin.  We have hummingbirds here, the male has this long symmetric tail feathers, and that’s the female there.  Females choose these males because males, the offspring of these males have lower mortality rates than the offspring of males with shorter tails. 

This leads to a situation of directional selection, in this case, the bigger the better.  Strong directional selection, if it differs for males or females, or if it is the same for males or females, leads to two changes, generally, higher genetic variance, in this case, in the males, related to the development of these tail feathers, and reduced canalization of these traits.  What that means is that not only has evolution resulted in more genes for the development of this trait, but also in increased sensitivity to the environment.  So, males of these species that are put in good environments have great tail feathers, so to speak, and those in poor environments suffer the most.  Females are in between.  So we have the evolution of greater sensitivity in males and females, but only for this particular trait.  So, evolution is, there’s a lot going on there. 

A critical test of Darwin’s theory comes from species with sex role reversals.  These are very important.  Here we have pipefish, but we see the same pattern in other species, you get a number of species with sex role reversals.  This is a pregnant male, believe it or not.  If you’ve seen this before, males have this ventral pouch here, females deposit eggs in there, and then the males fertilize them.  The males basically take care of the offspring.  Going back to reproductive rate, this means that females can reproduce faster than males, which is a reversal of the typical pattern.  And, so, the limiting factor in female reproductive success is the number of non-pregnant males that they can convince to accept their eggs.  In this situation, since it takes longer to incubate than to deposit eggs, we predict females are going to be more competitive than males, and that is, in fact, that we find females compete more for males, males are choosier, and here we see a female down here, they’re larger, more colorful, and, behaviorally, more aggressive. 

Now, what about humans?  What can we say about sexual selection in humans?  A lot of arguments that a lot of these things are just so stories, that people are just kind of making up arguments about what they think works based on whatever, but, we can do much better than that.  Comparatively, so across species, larger males, as strongly related to physical male-male competition and polygyny, 18 percent of primates are basically monogamous, and, in those species, there are no physical sex differences in size, or very small differences, and no differences in developmental, pattern of developmental maturation. 

When we get bigger males than females, there’s almost always an evolutionary history of males beating up on one another for access to mates.  Anthropologically, we see one-on-one in kin-based coalitional male-male competition is common in industrial societies, conflict at least once a year, rating [ph], warfare, so forth, occurs in about 90 percent of these societies, and the mortality rate of young males is 25 percent, very, very serious business. 

Population genetic data, Y chromosome, as well as mitochondrial DNA, are very consistent with these patterns here.  We can look at the general principles, we can look at consistencies across species, we can look at what people do in the wild, and say that, yes, I don’t think we can dismiss this as a way of approaching sex differences. 

So, predicted human sex differences, there is many, most of which we cannot get in here, talk about today, but let’s focus on male-male competition.  We’d expect, as with the bowerbirds, that the emergence of these traits during development will be a result of an interaction between gene expression and experience.  For a species with a short developmental period, experience is less important.  We have a huge developmental period, about twice as long as common chimpanzees and bonobos, our closest relatives.  So we are designed to be open to experience, both males and females. 

Children’s early cognitive competencies and interest biases will interact in ways to facilitate the development of sexually selective traits, so traits that are related to these components of sexual selection.  We expect to predict sex differences in activity preferences and sex differences in later interests. 

But, I want to have a caveat to that.  Darwin noted in 1871, there’s a striking parallelism between mammals and birds and their secondary sexual characteristics, namely their weapons for fighting with rival males in their ornamental appendages and in their colors.  In both classes, when the male differs from the female, the young of both sexes almost always resemble each other, and a large majority of classes resemble the adult female.  In both classes, the male assumes the characteristics proper to its sex shortly before the age of reproduction. 

What’s important here, the point here, is that it is not the case that if it’s biology and hormones we find early differences, and if it’s culture and experience we find later differences.  It’s not that simple.  In many species a lot of the evolved sex differences related to sexual selection don’t emerge until the time of maturation, or emerge slowly during the developmental period, as we saw with the bowerbirds.  So, there’s a lot going on there and we have to consider development and the evolution of the developmental period. 

So, of course, it’s a long way from sexual selection to math and science.  We saw a lot of important male scientists during the 14th Century, and so forth, but we didn’t get any data on their reproductive success, so we don’t know how Euclid did, and others, we know that Newton didn’t do so well.  So, as Dr. Spelke said, we have to look at the core evolved biases and see how these interact with experiences during school and during development and, as related in this particular case, to math and science. 

Where to look, I’m going to skip that. 

So, some hypotheses.  I would like us to consider that aspects of male-male competition during human evolutionary history, in particular, tribal rating, tool construction, which is typically, or, almost is very highly male biased, resulted in the elaboration of some physical systems, in particular, travel and novel territory.  This would involve the elaboration of three-dimensional visual spatial systems for navigation.  These would be more complex types of things than the types of items that Dr. Spelke showed us. 

Tool construction, we would predict interest in objects, object manipulation, mechanical reasoning, use of weapons, implicit understanding of object motion and trajectory in three-dimensional space.  These are the primary or core abilities and motivational biases that may contribute to ease of an interest in learning modern day mathematics and sciences, mathematics and some aspects of sciences, but, I actually expect these to be very limited in terms of which sciences they influence. 

In any case, are there sex differences?  Well, early on there’s not much, but, during development, we do begin to see differences.  There are differences in interest in mechanical toys, some forms of construction play that emerge during the preschool years.  This emerges by the end of the preschool years into a very large difference.  This difference is related, at least in part, to prenatal exposure to male hormones. 

One study recently showed that boys have a better intuitive understanding of tool use by 18 months of age, a follow-up study conducted by another group showed that skill development, as related tool use, was related to object oriented play in boys, but not girls.  So, the benefits of experience seem to differ there across the sexes.  Sex differences in three-dimensional spatial abilities and mental retention, we see, and, of course, that’s not the only spatial abilities, but there are differences there. 

This is looking at sex differences, average abilities, and, so, if there are no sex differences, we’d have a cut right here at 50 percent.  Across a number of language domains, 65 percent or so of women do better than the average man, that’s what that means.  In some areas of language production the differences are much larger.  I don’t know if any of you have noticed that a lot of guys have these pauses in their speech.  Have you guys noticed that?  It’s not that they’re dumbfounded, I suspect, at least I hope not, I suspect it is a word retrieval difficulty, and, if we look at those types of things, 9 out of 10 women do better than the average man, that is, they have fewer pauses. 

If we look at three-dimensional spatial abilities, 20 percent of women do better than the average man.  If we look at mechanical abilities, the difference is, 15 percent do better than the average guy.  So there are clearly women out there who do well in these tasks, but not as many as men, and women have advantages in other areas. 

It’s also important to consider not only sex differences, average sex differences, but also what you’re good at.  So, what you decide to do, what you like in school, the occupations you may choose later on, is going to not only depend on what you’re good at relative to other people, but what you’re good at relative to the other skills that you have.  And, so, this is looking at intra-individual differences, and I kind of separate those out, but, looking at verbal, number, this includes quantitative reasoning, and spatial abilities, 23-24 percent of men, that’s their best skill.  Some women, it is their best skill, but, about 6 percent or so; numerical reasoning, 9 percent or so of men, but 2 or 3 percent of women.  And, 20 percent of women, language skills are their best skills. 

This is an amalgam of other types of things that aren’t relevant to what we’re talking about here.  So, intra-individual cognitive competencies are important as well.  Here, we see, some guys are just really good at spatial types of things, and that’s the best skill for them. 

Cognitive profiles of mathematics and science majors, this is a bit dated, but, it makes a point.  400,000 students looking at the 20 percent within their sex, so, high spatial males, a large number of them go into the physical sciences, engineering, fields, many few are women.  I suspect that this has changed, that this has gone down, and this has gone up.  There is certainly a lot of room for improvement.  So a lot of high spatial ability females aren’t going into the physical sciences to the rate they can.  High verbal abilities, we see a lot of men going into the humanities and social sciences, rather than the physical sciences, and women going into humanity social sciences and education. 

Sex differences in variability, we talked about the variability issue earlier, and here we see, I can’t read that, but, I think that says quantitative reasoning, we have more men at the low end, this is 320,000 11-year olds, basically every child in Great Britain, more or less, at that time, this was published just last year.  We have more boys at the low end, more boys at the high end.  Non-verbal reasoning, we get a similar pattern. 

What’s interesting here is, we don’t get this pattern for verbal skills, in fact, we get more guys at the low end, but more women at the high end.  And I actually predicted something like this a few years ago in a book chapter on sexual selection in cognition, arguing that if females used language to manipulate social relationships, and so forth, as part of female-female competition, we might find such a pattern.  But, what’s important here is the variabilities do not extend to everything, but, extend to specific types of things. 

Sex differences on the SAT in this country are amazingly robust, surprisingly robust.  As performance goes down, the gap stays about the same, about one-third of a standard deviation.  If we control for three-dimensional spatial abilities, most, or all of this gap, disappears.  So, the sex differences in complex spatial skills that I talked about later may contribute at least to some aspects of whatever is being measured by the SAT mathematic scale. 

The mathematically gifted, again, this is the extreme of folks, this isn’t the threshold of what it takes to enter these sciences, but, to do very well, Camilla Benbow and David Lubinski have looked at these individuals now for several decades, assessed them when they were 12-14 years old, and those that do well on the SAT mathematics at that age have an enhanced visual spatial working memory, and an enhanced kind of representation, or access to number information, and they do very well professionally, and they’re over-represented in number of PhDs, MDs and so forth in the sciences and engineering.  Again, this is the high end, this is not, kind of, an entry-lever types of things.  There are differences in occupational choices and tradeoffs that they’re finding in this sample for very talented women, they are over-represented in these fields as well, but, they’re less likely to trade off family life and social life for academic achievement, on average. 

We saw Einstein’s brain a little while ago, and, so, I was going to skip this, but I think I’m going to go ahead and read it, since I have 2 minutes anyway.  So, the words of the language, and, this is a description, Hadamard asked a number of mathematicians and scientists kind of descriptions of how they made their discoveries and how they worked, Einstein was among them, and this is a part of his description.  He says, the words of the language, as they are written or spoken, do not seem to play any role in my mechanism of thought.  They’re cyclical entities, which seem to serve as elements in thought, are certain signs, or more or less clear images, which can be voluntarily reproduced and combined. 

There is, of course, a certain connection between those elements and relevant logical concepts.  (My eyes are strained, so I’m trying to read here.)  Very, very interesting description given the enhancement of his parietal region, the visual spatial aspects of his brain, and, of course, the conscious control of thought voluntarily produced, and so forth, it is a good sign of attention control and what’s being measured by IQ. 

In any case, I want to conclude, I want us to at least consider that sexual selection provides an explanation of many sex differences, this has been demonstrated across hundreds of species, the sex role reversal species provide critical, potentially falsifiable tests of these predictions, a hallmark of a good scientific theory, and, in fact, they withhold, the theory stands up. 

Mathematics and the sciences are, of course, dependent on the insights of a few individuals, and the retention of this knowledge across generations, and the teaching to children in each new generation.  Sex differences and this knowledge cannot be the direct result of sexual selection, but may indirectly be reflected through primary or core cognitive systems, and motivational systems, upon which modern day mathematics and sciences are built. 

My hypotheses are that sexual selection have elaborated some folk’s physical competencies, and associated motivational biases more in males than in females, which indirectly contribute to some sex differences in performance and interest in mathematics and physical sciences and engineering.  Now, I want to add here, these are differences that emerge slowly over a long developmental period.  Clearly we don’t know fully the interactions between any kind of core knowledge biases and how these may play out in schools.  We know that both males and females have extended developmental periods, and, therefore, are going to be more plastic or open to lots of different types of cultural-based learning than is the case in most other species in which sexual selection has been demonstrated.  But, nonetheless, I don’t think we can dismiss this. 

Christina Hoff Sommers:  Thank you.  [Applause].  If it’s alright with panel, I’m going to go directly to the audience, who may have questions, and we’ll start with Steven Rhoads , an expert on sex differences.  We can have another panel just drawn from our audience. 

Steven Rhoads:  Should I address it to particular people, or the panel in general?  I guess Professor Spelke and Professor Geary, I’d be interested, in particular, in my reading of this as an outsider, it seems to me the most persuasive view biology has something to do with it, is, comes from the nurturing of women, which I don’t think is only socially constructed. 

In other words, it is related to testosterone, women with more testosterone are less interested in dolls, or their kids, more interested in staying home with the kid, and I just think that that’s, if you look at the recent Pew study, still, after three generations of feminism, I think, in the textbooks, you still have, what, 29 percent of women with kids 15 and under, not 2 and under, 15 and under, something like that, would want to stay, work full-time, 77 percent or so of men want to work full-time.  I just have to think that that’s something biological and women’s attraction to nurturing, which has got to inhibit kind of the single-mindedness that some scientists have about not minding working 70 hours a week and so on. 

I guess the other side of that, I think, is if you look at where the PhDs occur, of course, developmental psychology, women are two-thirds or so of the PhDs, mechanical engineering, not much at all, and I guess I wouldn’t have predicted that the barriers of patriarchy would have been left here.  These guys with the pocket calculators, the stars, and so on, they’re the real patriot, you’re not coming in here, boy, you may take over the law schools with all these gladiators, and you may take over the med schools, you know, what could be more hierarchical than seeing, you know, parade around the doctor and then the resident and intern.  You might have guessed in 1960 that these are going to be the places that are never going to tumble, but, mechanical engineering, I mean, I just.  Okay, so that’s kind of my two-prime question. 

Elizabeth Spelke:  One thing that Dr. Geary and I don’t disagree on is that biology, well, genetics, as well as ontogenetic biological processes are important for development, and one thing that I don’t think anybody can disagree on is that there are biological differences between men and women.  The question is, do those differences play into differential aptitude for science?  Now, you’ve appealed, in part, to differences between what men and women say they want to do, say how they want to spend their time, and it’s, of course, altogether possible that genetic differences will influence those choices. 

But we also know that cultural expectations and norms have an enormous influence on those choices, because, to see that, we only have to look at what people say in answer to that question has changed over the decades.  You pointed to examples with veterinarians.  It’s changed enormously.  What people think of as possible lives for themselves is very influenced by what they see as appropriate, what they see as possible and open to them, and these are very open to change. 

This doesn’t mean that genetics doesn’t play a role, but it means that the task of teasing apart genetic influences from other influences, is an extremely difficult one, and we would be wrong, I think, to conclude, at this point, from what people say today, that what they say is reflecting their underlying genetic makeup. 

I want to also get to the plausibility argument that I think Dr. Geary made for why general principles of sexual selection might favor men in science.  I think the evolutionary work on sexual selection is wonderful and important, and I think it may apply to humans in interesting ways, but I don’t get the implications for science.  It seems to me that if you took a Darwinian perspective and looked at human prehistory, you might wonder that anybody would ever do science at all.  The women should be devoting all of their time to nurturing their precious relatively few numbers of offspring.  The men should be devoting all of their time to fighting each other.  Nobody should be thinking about Fermat’s last theorem. 

The question is, how do, if these differences do condition how girls and boys develop, and how men and women choose to divide their time, how does that play out in the context of modern societies?  Even if it were the case, that men were, in general, more competitive, is there reason to think that greater competitiveness leads to greater science, particularly today, and a time of great collaboration and inter-disciplinary work, and so forth, that I think is less than obvious. 

Even if it were to turn out that women were more focused on family, and, by the way, there is reason to think that the difference between men and women in competitiveness and the difference between men and women in valuing family is more, is not a black and white difference in absolute quantity -- more a difference in the kind of style of evaluation.  It’s not the case that women aren’t competitive, and it’s not the case that men don’t care about their children.  But, in any case, even if there were these general patterns, how does that impact on how people decide to spend their time, the kinds of intellectual problems that they find interesting and rewarding to look at?  That’s where I don’t see the connection to the evolutionary approach at this time. 

David Geary:  A couple things, first, we’re pretty sure there’s a relationship between evolution and reproduction.  That, I think, has been established.  But, a couple things, one, with the Benbow and Lubinski study, some of the tradeoffs we see in these very high ability men and women, women make more of the family, social-oriented tradeoffs than men.  In terms of life satisfaction, there is no sex difference there.  So men are working more, on average, than women, and women are spending more on social relationships, including family. 

But, humans are very interesting.  There’s only about 3 percent of a million species where guys do anything in terms of care of infants, and investment, or, even protecting of offspring.  Humans are one of those species, which makes us very, very different from even chimps or bonobos, or other species. 

So we have male paternal investment.  So the sex difference there is actually smaller than lots of other species.  We expect there to be less differences in humans than in other species.  When males invest in offspring, they become an important resource for females.  Women like guys who are going to invest, help out, and so forth.  We predict female-female competition, so we expect females to be actually more competitive with one another, and, generally, in humans, than we would, perhaps, in other species.  So, there’s a lot of nuances going on there. 

With respect to the interest things, I think what we would predict is that, more generally, I didn’t talk about it today, but there is a pretty robust sex difference in interest in objects, mechanical types of things, at least that emerges during development, and interest in living types of things. 

So, I think what I would predict is that women are going to segregate into more living-oriented sciences, medicine, psychology, vet schools, law schools, well, I guess law is not science, and men are going to go more into the physical sciences.  So, the question is, to get more physicists, do we have to take away from the number of medical doctors or number of vets?  There’s a tradeoff there.  I think science is broadly defined.  I think there’d be smaller differences, as you narrow the definition, I think that’s where you’re going to find more differences. 

Christina Hoff Sommers:  Question here. 

Male Audience Member:  A brief comment, and then a question for Dr. Spelke.  The first comment, tacking on to what Dr. Geary just said, is, given that the science, the largest by far, sciences, of the life sciences and the social sciences, there is no deficit of women overall, whereas in the physical sciences and engineering there is a tremendous difference.  It seems to me it really would be more productive to spend time, and have conferences, on the deficit of women in those areas where there really is a deficit, not just in science broadly. 

But, let me get to my specific question for Dr. Spelke.  I found your anecdote about Dr. Boring very interesting, in which he assumed that certain Jewish characteristics of aggressiveness or being talkative or gesticulating wildly would make one unsuited for a career in science.  I’m curious if you could point to specific characteristics associated with women that might, unfortunately, be assumed to be associated with success in science that we, in an irrelevant way, use in making those kind of ill-formed judgments. 

Elizabeth Spelke:  Thank you, I’d be happy to do that.  Let me give you two examples.  One is the example of spatial ability.  I think Dr. Geary and I would agree that the claim that males are better, in general, at spatial abilities is not quite right.  Rather, you can decompose spatial abilities into different kinds of abilities in the work that we’ve done, sensitivity to different kinds of geometrical relationships, and attention to different ki