By Greg Downey and Daniel Lende

Neuroanthropology places the brain and nervous system at the center of discussions about human nature, recognizing that much of what makes us distinctive inheres in the size, specialization, and dynamic openness of the human nervous system. By starting with neural physiology and its variability, neuroanthropology situates itself from the beginning in the interaction of nature and culture, the inextricable interweaving of developmental unfolding and evolutionary endowment.

Our brain and nervous system are our cultural organs. While virtually all parts of the human body—skeleton, muscles, joints, guts—bear the stamp of our behavioral variety, our nervous system is especially immature at birth, our brain disproportionately small in relation to its adult size and disproportionately susceptible to cultural sculpting. Compared to other mammals, our first year of life finds our brain developing as if in utero, immersed in language, social interaction, and the material world when other species are still shielded by their mother’s body from this outside world. This immersion means that our ideas about ourselves and how we want to raise our children affect the environmental niche in which our nervous system unfolds, influencing gene expression and developmental processes to the cellular level.

Increasingly, neuroscientists are finding evidence of functional differences in brain activity and architecture between cultural groups, occupations, and individuals with different skill sets. The implication for neuroanthropology is obvious: forms of enculturation, social norms, training regimens, ritual, and patterns of experience shape how our brains work and are structured. But the predominant reason that culture becomes embodied, even though many anthropologists overlook it, is that neuroanatomy inherently makes experience material. Without material change in the brain, learning, memory, maturation, and even trauma could not happen. Neural systems adapt through long-term refinement and remodeling, which leads to deep enculturation. Through systematic change in the nervous system, the human body learns to orchestrate itself as well as it eventually does. Cultural concepts and meanings become anatomy.

Although every animal’s nervous system is open to the world, the human nervous system is especially adept at projecting mental constructs onto the world, transforming the environment into a sociocognitive niche that scaffolds and extends the brain’s abilities. This niche is constructed through social relationships, physical environments, ritual patterns, and symbolic constructs that shape behavior and ideas, create divisions, and pattern lives. Thus, our brains become encultured through reciprocal processes of externalization and internalization, where we use the material world to think and act even as that world shapes our cognitive capacities, sensory systems, and response patterns.

Our ability to learn and remember, our sophisticated skills, our facility with symbolic systems, and our robust self control all mean that the capacity for culture is, in large part, bought with neurological coin. This dynamic infolding of an encultured nervous system happens over developmental time, through the capacity of individuals to internalize both experience and community-generated tools, and then to share thoughts, meanings and accomplishments. Thus, a central principle of neuroanthropology is that it is a mistake to designate a single cause or to apportion credit for specialized skills (individual or species-wide) to one factor for what is actually a complex set of processes.

Most academic research implicitly or explicitly utilizes a reductive cause-effect approach; in popular understandings of the brain, the tendency to single out causal factors is even more prevalent. Rather than one set of genes or an overarching system of meaning, humans’ capacity for abstract thought emerges equally from social and individual sources, built of public symbol, evolutionary endowment, social scaffolding, and private neurological achievements. In neuroanthropology, the goal is not simply to juxtapose a simplistic critique against a one-side initial account, but to attempt a much more holistic, synthetic exploration of how various elements in these dynamic relations interact to produce cognitive functions.

Neuroanthropology: Areas of Application

Neuroanthropology has four clear roles: (1) understanding the interaction of brain and culture and its implication for our understanding of mind, behavior, and self; (2) examining the role of the nervous system in the creation of social structures; (3) providing empirical and critical inquiry into the interplay of neuroscience and ideologies about the brain; and (4) using neuroanthropology to provide novel syntheses and advances in human science theory.

The interaction of brain and culture is neuroanthropology’s core dynamic, exploring the synthesis of nature and nurture and cutting through idealized views of biological mechanisms and cultural symbols. Using social and cultural neuroscience in combination with psychological anthropology and cultural psychology, neuroanthropology builds in-depth analyses of mind, behavior and self based on an understanding of both neurological function and ethnographic reality. This research creates robust analyses of specific neural-cultural phenomena, recognizing that each may demonstrate a distinctive dynamic; for example, neuroanthropological investigation reworks our understanding of human capacities like balance (often assumed to be something innate), studies how practices like meditation shape and piggyback upon neural functioning, and examines the interactive nature of pathologies like addiction and autism.

Neuroanthropology has profound implications for our understanding of how societies become socially structured. Inequality works through the brain and body, involving mechanisms like stress, learning environments, the loss of neuroplasticity, the impact of toxins, educational opportunities (or their absence) and other factors that negatively shape development. Neuroanthropology can play a fundamental role in documenting these effects and in linking them to the social, political and cultural factors that negatively impact on the brain. At the same time, technological and pharmacological interventions are playing an increasing role in managing behavioral disorders, often with great profit for companies, while cognitive enhancement drugs, brain-computer interfaces, and neuro-engineering will surely be used in ways that create new separations between haves and have-nots. Finally, societal appeals to “hard-wired” differences remain a standard approach by people in positions of power to maintain racial, gender, sexual and other inequalities; a deeper understanding of the complex origins and unfolding of key neural and physiological differences undermines accounts that assume these distinctions are inescapable. At the same time, neuroanthropology points to new ways to think about how people become talented and ways to understand intelligence, resiliency, social relations and other factors that shape success in life.

In societies across the globe, the brain now acts as a central metaphor, a substitute for self, a way to explain mental health, a short-hand for why people are different. In reaction, critical approaches have looked at the interpretation and use of brain imagery, psychoactive pharmaceuticals, public presentations of neuroscience research, and related social phenomena. Meanwhile, the pace of neuroscience research, and innovations in associated technologies, has been breathtaking. One aim for neuroanthropology is to make sense of these three related but often conflicting factors in ways that provide grounded research and critical insight into what the realities of brain and self actually are. Neuroanthropology will play a central role in mediating between the claims of different sides with the expertise gained from empiricism as well as the theoretical and critical framework gained from the combination of neuroscience and anthropology. This aspect of neuroanthropology is an absolute necessity given the convergence of these three recent historical phenomena – accelerating research, social reworkings, and intellectual interrogation of both.

Neuroanthropology makes direct contributions to theory development. At the most basic level, it provides a broad umbrella to integrate concepts across academic fields. Embodiment, for example, is an idea explored from basic neuroscience, psychology and cognitive linguistics to anthropology and philosophy. Neuroanthropology provides the conceptual and methodological tools to work through what we mean by such a broad-ranging idea.

Neuroanthropology also has direct implications for anthropology and neuroscience. It demonstrates the necessity of theorizing culture and human experience in ways that are not ignorant of or wholly inconsistent with discoveries about human cognition from brain sciences. Rather than broad-based concepts like habitus or cognitive structure, neuroanthropology focuses on how social and cultural phenomena actually achieve the impact they have on people in material terms. Rather than assuming structural inequality is basic to all societies, neuroanthropologists ask how inequality differentiates people and what we might do about that.

Similarly, on the neurological side, the principal theories of brain development, neural architecture and function remain tied to a biological view of proximate mechanisms and evolutionary origins. Yet it is abundantly clear that many neurological capacities, such as language or skills, do not appear without immersion in culture. Neuroanthropology highlights how that immersion matters to the brain’s construction and function. For example, neuroanthropology can take a basic idea like Hebbian learning — “what fires together, wires together” — and examine how social and cultural processes shape the timing, exposure, and strength of activity, such that the coordinated action of brain systems emerges through cultural dynamics. Neuroanthropology opens up a vibrant new space for thinking about how and why brains work the ways they do.

Neuroscientists and Anthropologists as Partners

By placing the focus on the individual’s nervous system and its relation to the world, neuroanthropology asks challenging questions of scale and depth for both neuroscientists and anthropologists, demanding both groups stretch beyond accustomed frames. For neuroscientists, seriously considering human diversity may require changes in research methods, in such basic processes as averaging and amalgamating imaging data, removing outlying data points (some of the most interesting individuals), and in finding test subjects. It can help cultural neuroimaging researchers to develop a much more sophisticated understanding about what results of comparative brain scan of Asians and Western Europeans might mean and why seeing doesn’t always translate into cultural believing. Thus, neuroanthropology offers to neuroscientists more sophisticated ways of thinking about neural environment, based upon over a century of debate about the nature of cultural variation and how to conceptualize patterns of behavior.

The same thought and subtlety that goes into understanding the relations among parts of the brain and body can be extended to consider how elements of the cultural and social environment are tied into specific brain functions, illuminating some of the specific ways that mind can become extended through cultural leveraging. That is, simply adding ‘culture’ as a single population variable fails to really illuminate the dynamic, inconsistent processes through which neurological potential is channeled by specific cultural institutions or practices. Because the nervous system is embedded within the world, shot through with the environment down to its cellular structure, integrative models of its development must include interacting elements from both inside and outside of the skin.

Although brain scientists have reached out to other interlocutors, we believe that anthropology is an especially strong potential partner. The influence of culture, social interaction and behavior patterns are immediate and susceptible to direct research, often more so than evolutionary theories about brain architecture origin. In addition, ethnographic research offers concrete evidence of how social and cultural dimensions of the environment might affect cognitive function, and illustrates the range of neuroplasticity in developmental outcomes well beyond what most experimental protocols consider. Anthropologists explore naturally-occurring experiments in which the nervous system is developed over a lifetime in diverging directions.

For anthropologists, neuroanthropology entails a return to integrative research after decades in which many biological and cultural anthropologists have seen each other as the primary opposition. The anthropological study of the nervous system calls on anthropologists to make good on our promises of holism. Psychological anthropologists have called for a greater focus on elements of neuroanthropology — affect, memory, neural-based models of cognition, biocultural integration — but a wholesale shift requires anthropologists to maintain a simultaneous consideration of what may have previously been apportioned to different specialties in the field. The nervous system inherently spans boundaries between specialized knowledge of such areas as evolution, child development, physiology, perception, phenomenology, behavioral research, biology and culture. Although some researchers might pull back from considering biology out of a fear of reductionism, the nervous system resists obstinately any simplistic explanation, throwing up counter-examples such as varying degrees of mental modularity, cognitive heterogeneity, and complex mixtures of neuroplasticity and innate endowments shaped by evolution.

With rare exceptions, anthropologists have not participated extensively in the growing movement toward cultural neuroscience. The time is ripe for this engagement: brain scientists are no longer content to just treat cultural difference as a demographic variable, and anthropologists are no longer so afraid of ‘universalizing’ or ‘psychologizing’ that they cannot get involved in this expanding area of research. Anthropologists offer to brain scientists more robust accounts of enculturation to explain observable differences in brain function, a range of resources for extending neurological accounts beyond the individual human organism. Neuroscience research offers to anthropology a more nuanced way of linking universal human tendencies and cultural particularity, and in grounding one foot of the holistic study of human subjects firmly in biology.

Neuroanthropology is a sustained effort, not to mine brain sciences opportunistically, but to engage continually in interrogating the brain sciences to enrich holistic anthropology, while also contributing to the unfolding of cultural neuroscience. Neuroanthropologists will have to keep abreast of new research techniques and findings, and to be willing to modify, expand, or shed outright our theories if they are unsupported by data. Anthropology has tended to be a theoretically heterodox field, producing more than its fair share of paradigms for understanding human social life, so neuroanthropologists should have abundant resources on which to draw, as long as we are willing to range far and wide for our intellectual frameworks, including into the past paradigms of relevant fields.

Unlike some people working in this area, the organizers of this conference do not believe that only one research method will contribute to neuroanthropology, nor that this emerging field of thought will become dominated by a single account of how the brain functions. The brain itself is baroque, fashioned over evolutionary time out of a host of modules and functional units that are still incompletely integrated. Every type of neurological activity does not obey the same rules, nor are they equally susceptible (or immune) to self-reflection and conscious thought. Some cognitive capacities are characterized by deeply-ingrained stereotypical species-general responses; other functions are remarkably plastic, even susceptible to substantial revision and conscious redirection. No one simple theory can explain how every system works so we should recognize that enculturation will vary even among the regions and networks within the brain. If an account of one system remains consistent with its functioning while defying expectations arising from other systems, this is as likely to be a product of the brain’s heterogeneity as it is a reflection of differences in research methods or approaches.

Enough over-arching theories have foundered on human neural heterogeneity to offer ample warning: neuroanthropological theory will have to be partial and incremental rather than overly generalizing and prematurely sweeping. That is, no single enculturation process affects all brain areas equally, so no single account of the relation between brain and culture is likely to prove compelling in all cases. We propose an evidence-based theoretical eclecticism, recognizing that some of our disagreements are likely to arise from the fact that we theorize from different case studies in neural acculturation.

We also see neuroanthropology’s role as a constructive contributor to integrative brain science, not just policing its borders or offering constant critical scrutiny. Certainly, critique has its place, but without helping to produce better paradigms or suggestions for improvement, critique simply leaves conscientious researchers without positive alternatives to the practices that warrant criticism. Full engagement must include constructive proposals for improving both brain science and anthropological research.

Thinking through Human Problems

Neuroanthropology stakes out a new space for research. In examining the interaction of biology and culture, neuroanthropology considers how activities, contexts, and experiences are crucial to forming what it means to be human and how humans are similar and different around the world. Rather than conceiving of subjectivity as a text to be interpreted and the brain as composed of hard-wired circuits or innate modules beholden to selfish genes and evolutionary algorithms, neuroanthropology posits that subjectivity and the brain meet in the things that people do and say and the ways we interact with one another and the environment. Thus, it does not limit itself to psychology, which has a predominant focus on internal states, often separate from the body, physical activity, and the specifics of interaction with cultural environments. Moreover, neuroanthropology does not limit itself to Western notions of mind, self or consciousness, which can dominate discussions in some academic settings.

The inherent variety among different brain systems means that conscious reflection and experience-based accounts have a crucial relation to many of the phenomena we study. Experience-based ethnographic descriptions can offer valuable insights into brain functioning. At times these descriptions can help illuminate the influence of context and experience; at other times, neuroanthropological accounts may highlight the limits of conscious awareness and demonstrate the self-deceptions inherent in some kinds of neurological functioning. For this reason, neuroanthropology brings an ethnographic sensibility to brain research, including a willingness to take into consideration native theories of thought and individuals’ accounts of their own experience. Thus, careful ethnographic research, in-depth interviews, and the analysis of indigenous worldviews will always be central to the neuroanthropological synthesis

At the same, researchers must explore automization, endocrinology, emotion, perception, and other neural systems that contribute to patterns of variation but are not entirely susceptible to reflection. For example, practices of child rearing and early formative experiences are clearly influenced by cultural ideologies about how children should be nurtured, but many of the organic mechanisms through which these ideologies take hold of individuals and affect their long-term development may be unknown, even invisible to the participants.

For a long time, anthropologists have focused on culture as a system of symbolic associations, public signs, or shared meanings. But from the perspective of the nervous system, patterns of variation among different groups may include significant non-conscious, non-symbolic traits, such as patterns of behavior, automatized response, skills, and perceptual biases. This neuroanthropological framing opens more space for considering why all types of cognition may not operate in identical fashion, and how non-cognitive forms of neural enculturation might influence thought and action. Given this type of functioning, neuroanthropologists will have to return to an older notion of ‘culture,’ one that considers capabilities, habits and other forms of collective action (and not just meaning). While it can prove useful to speak principally of ‘culture’ as shared representations, we also must recognize that ‘cultural variation’ will include other sorts of patterned, shared conditionings of the nervous system.

For this reason subjects’-eye-view accounts are critical to neuroanthropology in a way that they might not be to other cognitive theorists. First, we recognize that theories about how the mind works or what it needs are themselves part of the developmental environment in which the brain is formed. Even if these ideas don’t accurately represent actual neural function, they do influence the brain-culture system, and can have an impact on the way the brain works even if that is in a way utterly unintended by those who hold the ideas. That is, whether indigenous theories of thought are accurate, they are part of the ecology of brain conditioning.

Second, consciousness itself is part of complex neural systems, adding degrees of self-regulation, restraint, learning, monitoring, cuing, and a host of other capacities. How people understand and experience their own thought is part and parcel of neural activities, although not necessarily an all-encompassing awareness or even the most important part of that function. Yet most of our cultural and neural functioning is submerged, only accessible to consciousness with extraordinary effort and special techniques, if it is accessible at all. Thus, research techniques should focus on capturing both our conscious awareness of why we do what we do and the inherent processes that shape the flow and outcome of that doing.

Third, we would point out that cognitive science itself is a hybrid, composed of researchers working in a range of fields from philosophy and psychology to neurophysiology, artificial intelligence and robotics. Different types of neurological functioning are susceptible to different types of research and demand varying degrees of analytical flexibility, including modeling and simulation. Although neuroimaging has made remarkable strides in recent decades, even its practitioners recognize that it must combine with other sorts of fields and data in order to draw robust conclusions beyond the narrow confines of experimental protocols.

Fourth, cultural resources like subtle differences in language may support distinctive phenomenological insights into the human nervous system. That is, other cultures may notice things about the human nervous system that our own communities have not observed, thematized, or codified. For example, the cognitive neuroscience of highly skilled communities or specialists who refine certain brain functions, such as meditation, perceptual skills, or high performance cognitive abilities in areas like mental calculation, recall or spatial navigation, have demonstrated marked empirical differences in brain function in imaging studies. But something similar might happen as well in indigenous folk theories of thinking or other neural functions, and we lose a vital resource if we do not ask ourselves how ethnographic communities come to their own ideas about the mind and experience.

When anthropologists and other ethnographers have engaged with cognitive science, they have made remarkable contributions. Neuroscientists with anthropological inclinations have made similar important advances. But overall the traffic has been too little in both directions, and the contributions made have been piece-meal rather than systemic or sustained. The brain sciences need the research and insights that anthropologists have developed in order to seriously explore the wide variation in human cognitive and neural functioning. Anthropology must move beyond critique and engage with these fields in a constructive mode in order to answer basic questions about culture, inequality, and human difference. Together, we can help construct the frameworks that allow the best of diverse research on the brain and human nature to be shared across disciplinary lines.

The potential gains are enormous: a robust account of brains in the wild, an understanding of how we come to possess our distinctive capacities and the degree to which these might be malleable across our entire species. The applications of this sort of research are myriad in diverse areas such as education, cross-cultural communication, developmental psychology, design, therapy, and information technology, to name just a few. But the first step is the one taken here – by coming together, we can achieve significant advances in understanding how our very humanity relies on the intricate interplay of brain and culture.

Greg Downey is senior lecturer in anthropology at Macquarie University. Daniel Lende is assistant professor of anthropology at the University of Notre Dame.

This essay on Why Neuroanthropology? Why Now? is the conference statement for The Encultured Brain: Building Interdisciplinary Collaborations for the Future of Neuroanthropology.

The role of race in human genetics and biomedical research is among the most contested issues in science. Much debate centers on the relative importance of genetic versus sociocultural factors in explaining racial inequalities in health. However, few studies integrate genetic and sociocultural data to test competing explanations directly.

Note how that fits so well into the points just made in Nature/Nurture: Slash to the Rescue. But Gravlee, Non and Mulligan don’t just say we need to overcome the nature vs. nurture dichotomy, they do research that bridges it and even better, test ideas on both sides: “We draw on ethnographic, epidemiologic, and genetic data collected in southeastern Puerto Rico to isolate two distinct variables for which race is often used as a proxy: genetic ancestry versus social classification.”

This type of collaborative research can be crucial to getting the data to answer complicated questions. Connie Mulligan and Lance Gravlee deserve credit for taking the time to discuss how to bring together their respective approaches before going out to do research. In this case, the data come down more on the nurture (or social) side. As they write:

Our preliminary results provide the most direct evidence to date that previously reported associations between genetic ancestry and health may be attributable to sociocultural factors related to race and racism, rather than to functional genetic differences between racially defined groups.

Before someone gets all hot and bothered, Lance has also shown how to bring nurture back to nature. In Gravlee’s recent paper, How Race Becomes Biology: Embodiment of Social Inequality (pdf), he gives us following: “Drawing on recent developments in neighboring disciplines, I present a model for explaining how racial inequality becomes embodied – literally – in the biological well-being of racialized groups and individuals. This model requires a shift in the way we articulate the critique of race as bad biology.”

In the PLoS paper, Lance, Amy and Connie are aiming squarely at the use of race in medicine, where it has become common in some circles to use racial classification as a proxy for genetics. Basically this research destroys the proxy notion, since social classification turns out to be a better predictor of blood pressure than genetic ancestry.

Yet the research also highlights that genetics does play a role, just not in the broad way we normally think (nature as cause). Specifically the data revealed an association between systolic blood pressure and a specific polymorphism, α2C adrenergic receptor deletion, only when social classification and socioeconomic status were included in the analysis.

This research also reveals social complexity. As the figure from the PLoS paper above indicates, there are interactions between racial classification, socioeconomic status, and systolic blood pressure in Puerto Rico. The basic conclusion is the opposite of what many of us might expect – those perceived as darker (negro) have higher blood pressure when in a higher social class. Conversely, those with lighter skin have higher blood pressure with lower SES. These results can be related to complex social dynamics. Darker colored individuals likely face more racial discrimination when in a higher SES because Puerto Rico is still a racially divided country, with wealth and status running lighter to darker. Here is the PLoS paper:

The pattern we observe is consistent with the hypothesis that social classification based on color entails differential exposure to social stressors related to blood pressure. In particular, there is ethnographic evidence that Puerto Ricans perceived as negro, as compared to trigueño or blanco, may encounter more frequent frustrating interactions in high-SES settings due to institutional and interpersonal discrimination.

Put in a broader sense, this paper points to the need to actively consider social inequality and discrimination as causes of health problem, something the “race as genetics” idea completely fails to do. Along with colleagues, Gravlee has made this point forcefully in a previous paper, Race and Ethnicity in Public Health Research: Models to Explain Health Disparities.

At the end of the PLoS paper Lance, Amy and Connie highlight an important direction for future research: “Although our measure of social classification improves on existing approaches, further research is needed to assess how well it approximates the ascription of color in everyday social interaction. Future research could build on our measurement approach by testing whether non-biological markers of social status (e.g., hair style, dress, speech) influence social classification.”

I’d also encourage Lance and his colleagues to look more closely at perceived discrimination, that this is also a crucial mediator of how race ends up driving biology. It’s not just consensus about racial classification, but how an individual person reacts to that. This point is made broadly by Robert Sampson when he discusses perceptions of disorder as an important force behind disparity. Building an ethnographically informed measure of subjective discrimination could add an important link in the pathway from social inequality to changes in blood pressure.

But this paper also challenged me. What is particularly good is that Lance builds on previous research that established how social classification according to “color” trumps actual skin pigmentation in establishing race and in impacting health. Now he and Connie have taken that a step further to get the data and test both biological and cultural ideas.

So this morning I am thinking more seriously how I could better examine the nature/nurture debate around addiction (quite similar in form to the race and health debate – biology does it; no, it’s inequality). How can studying sin become a closer look at how people get engaged in destructive behaviors, and which factors (working together, I’d say) are most important? Because right now the biologists are going to say, well it’s dopamine (or glutamate or whatever neurotransmitter is the flavor of the day) and the anthropologists are going to say, well it’s meaning. I’m still stuck at saying “holistic interactionism” (as Pinker would put it) rather than showing more concretely how the two come together and then relating both to genetics and to symbolism.

Lance Gravlee, Amy Non and Connie Mulligan have already taken that next concrete step. Kudos!

For more on Lance Gravlee’s work, please visit his website. For more on Connie Mulligan’s work, here’s her UF website.

For those looking for coverage of some of the paper’s highlights, you can check out the University of Florida’s press release, Socio-cultural, genetic data work together to reveal health disparities.

Gene Expression also provides a useful summary with Hypertension, Race, Class and Puerto Rico, including a comment by Lance clarifying a couple points.

And here’s the link for the PLoS full text of Genetic Ancestry, Social Classification, and Racial Inequalities in Blood Pressure in Southeastern Puerto Rico.

Poor people have I.Q.’s significantly lower than those of rich people, and the awkward conventional wisdom has been that this is in large part a function of genetics. After all, a series of studies seemed to indicate that I.Q. is largely inherited. Identical twins raised apart, for example, have I.Q.’s that are remarkably similar. They are even closer on average than those of fraternal twins who grow up together.

If intelligence were deeply encoded in our genes, that would lead to the depressing conclusion that neither schooling nor antipoverty programs can accomplish much. Yet while this view of I.Q. as overwhelmingly inherited has been widely held, the evidence is growing that it is, at a practical level, profoundly wrong.

Kristof cites Richard Nisbett’s new book Intelligence and How to Get It: Why Schools and Cultures Count. I covered some of Nisbett’s work in the post IQ, Environment and Anthropology, and Jim Holt gave a strong review of the book recently in the NY Times. The publisher’s home page simply says that this book is a “bold refutation of the belief that genes determine intelligence.”

From the damning research of The Bell Curve to the more recent controversy surrounding geneticist James Watson’s statements, one factor has been consistently left out of the equation: culture…

World-class social psychologist Richard E. Nisbett takes on the idea of intelligence as something that is biologically determined and impervious to culture— with vast implications for the role of education as it relates to social and economic development. Intelligence and How to Get It asserts that intellect is not primarily genetic but is principally determined by societal influences.

Well, not quite. As Kristof notes, “While I.Q. doesn’t measure pure intellect — we’re not certain exactly what it does measure — differences do matter, and a higher I.Q. correlates to greater success in life. Intelligence does seem to be highly inherited in middle-class households, and that’s the reason for the findings of the twins studies: very few impoverished kids were included in those studies. But Eric Turkheimer of the University of Virginia has conducted further research demonstrating that in poor and chaotic households, I.Q. is minimally the result of genetics — because everybody is held back. ‘Bad environments suppress children’s I.Q.’s,’ Professor Turkheimer said.”

First, for those interested in understanding IQ measures, I strongly recommend Greg’s posts Girls Closing Math Gap? Troubles with Intelligence 1 and The Flynn Effect: Troubles with Intelligence 2. In the first post, Greg takes on the idea of “natural” differences in male/female math ability, discusses problems with how IQ gets measured, and discusses how the “changing status of women seems to correlate pretty strongly with the math gap.” In the second post, Greg discusses James Flynn’s work on the steadily rising IQ scores seen around the world, what intelligence actually means, and how best to measure it.

Turning to the inequality side, Kristof’s point is that on a level-playing field genetics can become a primary factor in IQ scores. But just like low-quality nutritional environments can lead to stunting of physical growth, so too can unequal environments stunt the growth of brain function and intellectual growth, as we’ve written about before in Poverty Poisons the Brain and Poverty and the Brain: Becoming Critical.

Eric Turkheimer has a recent paper with K. Paige Harden and John Loelin entitled, Genotype by Environment Interaction in Adolescents’ Cognitive Aptitude (pdf). Using 839 twin pairs from a range of socioeconomic backgrounds, the paper shows that “Shared environmental influences were stronger for adolescents from poorer homes, while genetic influences were stronger for adolescents from more affluent homes.” In an accompanying press article, Turkheimer says “[This research] suggests that if you’re going to work with people’s environment to try and increase IQ, then the place to invest your money is in taking people in really bad environments and making them OK, rather than taking people in pretty good environments and making it better.”

Better outcomes are also a concern for Kristof. He notes that Nisbett “strongly advocates intensive early childhood education because of its proven ability to raise I.Q. and improve long-term outcomes.” The Milwaukee Project showed that in a randomly assigned study, “By age 5, the children in the program averaged an I.Q. of 110, compared with 83 for children in the control group. Even years later in adolescence, those children were still 10 points ahead in I.Q.”

Nisbett also pushes a simple idea: “tell junior-high-school students that I.Q. is expandable, and that their intelligence is something they can help shape. Students exposed to that idea work harder and get better grades. That’s particularly true of girls and math, apparently because some girls assume that they are genetically disadvantaged at numbers; deprived of an excuse for failure, they excel.”

For more on these types of interventions, see Nisbett’s recent op-ed Education Is All In Your Mind. The one thing I would add is that motivation needs to work hand-in-hand with opportunity. Working harder to no effect, with little sense that one’s effort will lead to a better outcome, is pernicious.

Kristof has addressed education and intelligence in other columns, which I also recommend. He wrote about DC schools and the reform efforts of Michelle Rhee in Education’s Ground Zero. Earlier he argued for education as our number one national priority, and a needed focus for both stimulus money and for making the US globally competitive. And in Raising the World’s IQ he discussed the environmental side of generating change, in this case the importance of iodized salt. I’d add lead to that as well, which even at low levels is linked to lower IQ scores.

Gary Evans and Michelle Shamberg recently published a PNAS paper, Childhood Poverty, Chronic Stress and Working Memory (pdf). Here’s the abstract:

The income–achievement gap is a formidable societal problem, but little is known about either neurocognitive or biological mechanisms that might account for income-related deficits in academic achievement. We show that childhood poverty is inversely related to working memory in young adults. Furthermore, this prospective relationship is mediated by elevated chronic stress during childhood. Chronic stress is measured by allostatic load, a biological marker of cumulative wear and tear on the body that is caused by the mobilization of multiple physiological systems in response to chronic environmental demands.

The Evans and Shamberg paper has gotten prominent media attention. Over at Wired, Poverty Goes Straight to the Brain got an enormous number of diggs. Brandon Keim’s opening lines are, “Growing up poor isn’t merely hard on kids. It might also be bad for their brains. A long-term study of cognitive development in lower- and middle-class students found strong links between childhood poverty, physiological stress and adult memory.”

Jonah Lehrer wrote Stress, Poverty, Working Memory which includes this effective summary, “The scientists uncovered a statistically significant link: the longer children had been poor, the worse their working memory. Furthermore, levels of chronic stress seemed to be the causal factor.”

The Economist also covered the PNAS paper, and wrote about how stress does its damage. “Stress also suppresses the generation of new nerve cells in the brain, and causes the ‘remodelling’ of existing ones. Most significantly of all, it shrinks the volume of the prefrontal cortex and the hippocampus. These are the parts of the brain most closely associated with working memory. Children with stressed lives, then, find it harder to learn.”

Lehrer, in his 2006 Seed article Reinvention of the Self about the work of primatologist/psychologist Elizabeth Gould, presents us with one of the main “take home” messages of work that links stress, poverty and development:

The social implications of this research are staggering. If boring environments, stressful noises, and the primate’s particular slot in the dominance hierarchy all shape the architecture of the brain—and Gould’s team has shown that they do—then the playing field isn’t level. Poverty and stress aren’t just an idea: they are an anatomy. Some brains never even have a chance.

This work by Evans and Shamberg is important, another step forward in showing that inequality matters and that it works through specific processes that directly shape individual development and function. But this line of work also has some limitations because it lacks a critical side – do we really need 500+ diggs to know that poverty is bad?

One piece that raises important critical questions is Michelle Chen’s The Impoverished Mind over at RaceWire. She writes, “Put simply, if your childhood is consumed by a constant struggle to survive day-to-day, your brain is less likely to develop the abilities you need to succeed tomorrow, compared to your economically better-off peers. This is empirical evidence that nature-versus-nurture is not an either or, but that social factors interplay with the brain’s biology throughout life.”

Then Chen goes further:

Empirical research on the connection between poverty and intellectual development can cut both ways—leading some to write off poverty as biological destiny, and others to look deeper into missed opportunities to lift youth over economic barriers… The policy implications for the growing body of achievement-gap research are [also] fraught with the same tensions straining other civil rights issues: how do you emphasize systemic impediments without pathologizing communities and cultures? How do you make the case for structural inequalities without fueling reactionary accusations of victimology?

Some of my own concerns focus on the biology side. Take a section from Lehrer’s Seed piece, “From the brain’s perspective, stress is primarily signaled by an increase in the bloodstream of a class of steroid called glucocorticoids, which put the body on a heightened state of alert. But glucocorticoids can have one nasty side-effect: They are toxic for the brain.”

Here stress “from the brain’s perspective” is taken to be entirely physiological. In general, stress is often made out to be psychobiological, an internal and individual state largely shaped by “fight-or-flight” ideas about activation of the stress system. As I’ve argued before, stress is actively social and intimately meaningful. These are not outside the perspective of the brain – they become part of how the brain functions. In this way stress becomes a process and not simply a state, either of mind or body.

But the more problematic area is that the brain becomes a fetish: Oh, see the neuroanatomical changes there, this stuff about poverty being bad for you must be true…

Recall the opening of the Wired piece, “Growing up poor isn’t merely hard on kids. It might also be bad for their brains.” Here children become a mere token, placed to one side in favor of our new marker of individual self, the brain.

While I advocate for the role that brain processes can play in social theory, the sword cuts both ways. Referencing the brain as central mediator of poverty hides the truth, and distorts our understanding. To take a more extreme example to illustrate the same point, it’s like saying that slavery is both harmful to people and morally wrong because it impacts brains.

The brain becomes rather like property in this approach, something a person possesses and that poverty – somehow separate from the person, a naturalized thing that causes stress – negatively impacts. But that approach avoids the radical implications here on both sides.

First, that poverty literally can be anatomy, which means we need to fundamentally rethink the token brain metaphor and actual functioning of the brain. Second, that taking the neuroscience results seriously means that social environments, in all their complexity, become as important as any brain function. Indeed, many would argue that in this case, social inequality is more important than brain function, since it is what is driving the system.

To bring a critical approach into better view, I have found it useful at times to watch the following video of Paul Gilroy speaking on slavery, ignorance, and property. Rather than just working memory, Gilroy brings the larger picture into focus: “[We often] think that ignorance is a kind of vacuum into which truth can get stuffed at the right moment. And I think that we need a better account of the politics and the meaning of ignorance in our time than that. We need to think about ignorance in a different way. We need to think about ignorance as a systematic product.”

By excessively focusing only on the brain, we miss doing what Gilroy advocates for truly understanding ignorance and inequality — engaging in “critique of life as property, of humanity as property, of history as property.” We need to move beyond seeing the brain as a physical thing, similar to a book we can reference to say poverty is bad or a kind of currency that we carry around and barter to show off we’re smart and current. Or worse, something to manipulate through pharmaceuticals and computers and emerging types of neuroengineering. As Gilroy says, we need to become more critical while also realizing the dignity and meaning of people’s lives.

In his paper Sampson is basically taking on the Broken Windows approach to disorder, that visible and quite real signs of disorder encourage people to engage in criminal and other deviant acts. In one sense, Sampson wants to bring Durkheim back into the picture, that anomie – or a spirit or sense of disorder – is also vital to sociology.

As he says, “My general thesis is that perceptions of disorder constitute a fundamental dimension of social inequality at the neighborhood level and perhaps beyond… I argue that the grounds on which perceptions of disorder are formed are contextually shaped by social conditions that go well beyond the usual suspects of observed disorder and poverty, a process that in turn molds reputations, reinforces stigma and influences the future trajectory of an area (6).”

Sampson brings an intriguing mix of photoethnography, historical and theoretical analysis, and quantitative data from Chicago. His main thrust is to say that “because the link between cues of disorder and perception is socially mediated, it is malleable and thus subject to change.” He wants to get away from a mono-causal view of disorder to an understanding of disorder as something more complex and interactive, as these two contrasting figures from his paper show.

Sampson is interested in what might drive that malleability between disorder and perception and highlights the role of social diversity. Here he uses his data to show that diversity and immigration are important in re-energizing cities and in reducing crime. “I found that increasing diversity and immigration have their greatest influence in what were formally racially segregated areas and historically the areas of greatest exclusion by the State (25).” Here’s one figure on linguistic diversity and violence that highlights some of his paper’s data:

He ends with a call for sociologists to focus on how diversity can lead to social order, not just disorder, once again coming back to Durkheim and his collective representations and social solidarity. Sampson writes, “if heterogeneity ultimately serves to reduce disparities in the city through the blurring of boundaries and the slow dissolving of categorical distinctions that to date have been so pervasive, perhaps theorists of urban disorder can help lead the way through efforts such as the present to elucidate what is in fact the social order of the increasingly diverse city, along with the irreducibly social bases of shared perceptions of disorder in the first place (27).”

The Sampson article is great, because there are a range of responses from well-respected sociologists in the BJS itself, as well as this video, A Brief History of Disorder, where editor Richard Wright engages Rob Sampson and Richard Sennett in a discussion about disorder, diversity and the social mediation of perception.

Paul Gilroy provides the first response, illuminating, literary and critical. Here’s one line that sums up much of his view: “How that graffiti became part of the change of signification that connotes decline, racial abjection and disorder is a story that cannot be divorced from the history of post-Black Power forms of cultural and political resistance, from migration stories and transnational interculture which changed the value and meaning of the South Bronx (36).”

Other responses include Per-Olof Wikstrom’s Questions of perception and reality and Diane Davis’ Taking place and space seriously. There are still more responses available in full text from BJS.

Intelligence scales are culturally embedded artifacts designed to meet the idiosyncratic needs of postindustrial western societies, and reflect the equally idiosyncratic assumptions found in the west – such as our habit of referring to someone as “brainy” when we mean “intelligent”, and the widely held assumption that brains got bigger during human evolution because of selection pressure for “intelligence” (and/or language: e.g. Deacon 1992). The idea that human intelligence is the ultimate pinnacle of biological evolution may be little more than colonialist propaganda, suggesting that “scientific” societies are the ultimate pinnacle of cultural evolution – and hence morally entitled to dominate others who formerly managed perfectly well without the blessings of “modernity”.

Sir Francis Galton devised the first intelligence test in the late 19th century and this was followed by the scale developed by Alfred Binet and Théophile Simon between 1905 and 1911 (Atkinson et al., 1993: 457-8). As early as 1884 Galton examined more than 9,000 visitors to the London exhibition and found to his chagrin that eminent British scientists could not be distinguished from ordinary citizens on the basis of head size (ibid: 458). From that point on the kind of assumptions made by Galton have continued to pervade scientific thinking with little or no empirical encouragement.

A curious more recent example is the spate of papers attempting to correlate brain size with “intelligence” as assessed by (notably) the Wechsler Adult Intelligence Scale (Andreason et al., 1993 ; Egan et al., 1994, 1995; Peters, 1995; Flashman et al., 1998; Rushton & Ankney, 1995, 1996, 2000; Vernon et al., 2000; Thompson et al., 2001; MacLullich et al., 2002; Staff, 2002; Drachman, 2002). Some of this research has provoked controversy over issues of anthropological concern, including ethnocentrism, sexism, and racism (Peters, 1995; Rushton & Ankney, 1995, 1996). Researchers did indeed find a positive correlation and this has been acclaimed as a vindication of the studies and the underlying ideology of “big-brained people are smarter” (McDaniel, 2005). In point of fact, a meta-analysis of 37 studies, involving 1,530 people, yielded a best estimate for the population correlation (r) of 0.33 (McDaniel, 2005), suggesting that the intelligence factors measured are associated with around 11% (r2) of brain volume, and cannot account for the bulk of brain expansion during the last 2.5 million years.

Why the scientists concerned should feel they have achieved something useful becomes all the more mysterious when you realize that many components of the scales used assess culturally acquired skills which were invented in historic times – particularly numeracy and written language (other questions address institutionalized factors such as money and banking, and none can be claimed with confidence to be free from cultural conditioning). Many preliterate societies even today lack numbers higher than two, and others no higher than five. Numeracy and literacy originated with the bureaucratic needs of the first civilizations along the river valleys of the Nile, Tigris, Euphrates, Indus, Ganges, and the Yellow River in China. These of course post-date the agricultural revolution (around 10,000 years ago).

An analysis of 217 fossil crania (De Miguel & Henneberg, 2001) – the largest sample we have to date – suggests that average human cranial capacity just prior to the agricultural revolution was around 1,500 cm3, which is about 12% larger than the average human capacity today (1,340 cm3). In other words, intelligence scales measure abilities that developed at a time when brains were most probably getting smaller, and any correlation between such abilities and brain size is less than informative (since it serves only to reinforce current biases in cognitive science). All these studies would seem to be a prodigal waste of research funding and resources – a waste that could easily have been avoided with a little anthropological input.

Currently the dominant theory of brain expansion in primates is the social or Machiavellian intelligence hypothesis, which holds that social intelligence makes greater cognitive demands than object intelligence. So why did all these researchers choose individualistic instruments such as the Wechsler scale (1939) rather than, say, Gardner’s (1983) measures of “six intelligences”? Gardner argued that social, musical, artistic, and “bodily-kinaesthetic” (including dance and sports) skills have been important since the “dawn of civilization” whereas logical scientific thought only came to the fore after the European Renaissance (Atkinson et al., 1993: 476). But the very term we use to define our species – Homo sapiens – presupposes an evolutionary trajectory ultimately directed towards the production of scientists.

The idea of a “general” (as opposed to social) intelligence is at best dubious. The point can be illustrated by a brain scanning study which contrasted “theory of mind” (ToM) with “non-ToM” stories and cartoons (Gallagher et al., 2000). The investigators assumed that brain structures activated by non-ToM stories and cartoons were “general reasoning” areas, and only those uniquely activated by ToM stories and cartoons were “ToM” (i.e. social reasoning) areas. They concluded that ToM involves a rather small area in ventromedial prefrontal cortex. However, the “general reasoning” areas were much more strongly activated during ToM than non-Tom tasks. It would seem more reasonable to infer that “general reasoning” involves a subset of social reasoning areas, and that “general intelligence” is a spin-off benefit of social intelligence. Animals which score most highly in laboratory studies of intelligence and language are invariably highly social – such as chimpanzees, dolphins, and Congo grey parrots.

The discovery of the Flynn effect should have alerted us by now to the culturally conditioned limitations of western intelligence scales. These tests may predict academic performance in western institutions, but they cannot provide reliable information about innate functions of the human brain. More plausible views of the social brain and human brain expansion (in my opinion, of course) can be found at http://www.socialmirrors.org. The Human Evolution page is not yet up, but relevant papers are referenced on the Social Brain page and my own papers can be downloaded from here and from my CV (see About Charles Whitehead: Publications).

References

Andreasen, N. C., Flaum, M., Swayze II, V., O’Leary, D. S., Alliger, R., Cohen, G., et al. (1993) ‘Intelligence and brain structure in normal individuals’, American Journal of Psychiatry, 150, 130–4.

Atkinson, R.L., Atkinson, R.C., Smith, E.E., Bem, D.J. (1993) Introduction to Psychology (Fort Worth: Harcourt Brace).

Deacon, T.W. (1992) ‘The human brain’, in Jones, S., Martin, R., Pilbeam, D., eds., The Cambridge Encyclopedia of Human Evolution (Cambridge: Cambridge University Press) pp. 115-23.

De Miguel, C. & Henneberg, M. (2001) ‘Variation in hominid brain size: How much is due to method?’, Homo, 52 (1), pp. 3-58.

Drachman, D.A. (2002) ‘Hat size, brain size, intelligence, and dementia’, Neurology, 59, 156-157.

Egan, V., Chiswick, A., Santosh, C., Naidu, K., Rimmington, J. E., & Best, J. J. K. (1994) ‘Size isn’t everything: A study of brain volume, intelligence and auditory evoked potentials’, Personality and Individual Differences, 17, 357–367.

Egan, V., Wickett, J. C., & Vernon, P. A. (1995). Brain size and intelligence: Erratum, addendum, and correction. Personality and Individual Differences, 19, 113–115.

Flashman, L. A., Andreasen, N. C., Flaum, M., & Swayze, V. W. (1998) ‘Intelligence and regional brain volumes in normal controls’, Intelligence, 25, 149–160.

Gallagher, H.L., Happé, F., Brunswick, N., Fletcher, P.C., Frith, U., Frith, C.D. (2000) `Reading the mind in cartoons and stories: an fMRI study of “theory of mind” in verbal and nonverbal tasks’, Neuropsychologia 38, 11–21.

MacLullich, A. M. J., Ferguson, K. L., Deary, I. J., Seckl, J. R., Starr, J. M., & Wardlaw, J. M. (2002), ‘Intracranial capacity and brain volumes are associated with cognition in elderly men’, Neurology, 59, 169–174.

McDaniel, M.A. (2005) ‘Big-brained people are smarter: A meta-analysis of the relationship
between in vivo brain volume and intelligence’, Intelligence, 33, 337-46.
Michael A. McDaniel

Peters, M. (1995) ‘Does brain size matter? A reply to Rushton and Ankney’, Canadian Journal of Experimental Psychology; 49 (4).

Rushton, J.P. and Ankney, C.D. (1995) ‘Brain size matters: A reply to Peters’, Canadian Journal of Experimental Psychology; 49 (4).

Rushton, J. P., & Ankney, C. D. (1996) ‘Brain size and cognitive ability: Correlations with age, sex, race, social class, and race’, Psychonomic Bulletin and Review, 3, 21–36.

Rushton, J. P., & Ankney, C. D. (2000) ‘Size matters: A review and new analyses of racial differences in cranial capacity and intelligence that refute Kamin and Omari’, Personality and Individual Differences, 29, 591–620.

Staff, 2002 Staff, R. T. (2002). Personal communication to Michael A. McDaniel on November 11, 2002.

Thompson, P. M., Cannon, T. D., Narr, K. L., Erp, T. V., Poutanen, V. -P., Huttunen, M., et al. (2001) ‘Genetic influences on brain structure’, Nature Neuroscience, 4 (12), 1253–1258.

Vernon, P. A., Wickett, J. C., Bazana, P. G., & Stelmack, R. M. (2000) ‘The neuropsychology and psychophysiology of human intelligence’, in R. J. Sternberg (Ed.), Handbook of intelligence (New York: Cambridge University Press) pp. 245-64., brain

James R. Flynn

Flynn gathered tests from Europe, North America and Asia, around thirty countries in all, and discovered that, for as far back as we had data in any case, average IQ test scores had risen about 3 points per decade and in some cases more. Only recently, in some Scandanavian countries, to the gains appear to be levelling off (see, for example, Sundet 2004; Teasdale and Owen 2005).

We’ve been down this road before at Neuroanthropology before, delving into the murky depths of group averages and tests scores. Back in December 2007, Agustín offered neuroanthropology and race- getting it straight, following up on a discussion sparked by Daniel’s post, IQ, Environment & Anthropology. I put in my two cents, and caught an ear-full, for Girls closing math gap?: Troubles with intelligence #1 (the first ‘part’ of this post). I’ve been wanting to re-enter this particular body of hot water since I read a story on Science Daily, Plastic Brain Outsmarts Experts: Training Can Increase Fluid Intelligence, Once Thought To Be Fixed At Birth, so against my better instincts, my shoes are off and I’m poking my toes in.

Ironically, in spite of the fact that children spend longer on average in school than in previous decades, the Flynn Effect does not show up on the parts of standardized tests that measure school-related subjects. That is, tests of vocabulary, arithmetic, or general knowledge (such as the sorts of facts one learns in school) have showed little increase, but scores have increased markedly on tests thought to measure ‘general intelligence’ (or ‘g’), such as Raven’s Progressive Matrices which require mental manipulation of objects, logical inference, or other abstract reasoning.

The Flynn Effect has been one of the most powerful refutations to those who think that intelligence is ‘innate’ or ‘genetically determined’ because it’s very hard to argue that the genetic pool of a population can improve over time; if genes determine intelligence, how can the average in a population be increasing? Theoreticians called ‘IQ fundamentalists’ by some commentators have put forward a number of arguments about innate intelligence, virtually always pessimistic about minority groups, and often seeking to decrease funding for programs like Head Start that seek to ameliorate inequalities in academic achievement (see for example, Malcolm Gladwell’s discussion of ‘IQ fundamentalists’ in his review of Flynn’s What Is Intelligence? in The New Yorker from around this time last year.

Jon S. Twing at TrueScores pointed out in a post, IQ and the Flynn Effect, that, when he was working on the Third Edition of the Wechsler Intelligence Scale for Children (WISC-III), he was fascinated with a process called ‘continuous norming.’ Twing writes that continuous norming was a process developed by Prof. Richard Gorsuch and applied by Dr. Gale Roid ‘to improve the precision of empirical norms.’ In other words, people directly involved in the design and evaluation of these tests realized that these scores had to be adjusted, but Flynn noticed these changes and drew more widespread attention as well as some fascinating theoretical conclusions from this trend.

A number of explanations have been put forward for the Flynn Effect, including improved nutrition, smaller families which provide more adult interaction for children, greater familiarity with standardized tests, better schooling, and more complicated cognitive environments. Flynn has offered his own explanations in recent publications, which we will return to in a moment because they offer us a nice entry point into the neuroanthropology of ‘intelligence’.

The Flynn Effect runs contrary to our usual pessimism about the intelligence of ‘kids these days.’ As Steven Johnson wrote in Wired, ‘Despite concerns about the dumbing-down of society – the failing schools, the garbage on TV, the decline of reading – the overall population was getting smarter.’ Asked point blank about the alleged intellectual degradation of contemporary life, Flynn has been very abrupt in pushing back against this (see, for example, Matt Nippert’s story on Flynn from the New Zealand Listener). Flynn’s less cynical about IQ testing (although he recognizes its limitations) than he is interested in the underlying cognitive changes that these scores might reflect.

Four paradoxes of the Flynn Effect

In his book, What Is Intelligence?, Flynn identifies four paradoxes that arise from the steady increase in average IQ test scores given the predominant understanding of ‘intelligence.’

The factor analysis paradox: Prior research suggested that a single factor, ‘general intelligence’ or ‘g,’ underlies IQ. The Flynn Effect, however, does not affect all sections of the WISC and other intelligence tests to the same degree; that is, if we’re getting smarter, some parts of our intelligence are getting smarter faster, undermining our confidence in ‘g.’

The intelligence paradox: The Flynn Effect suggests that we are getting smarter relatively quickly, but it’s not obvious (and some would say flies in the face of certain evidence) that kids today are so much smarter than their parents or grandparents (except perhaps when it comes to home electronics). As Flynn writes:

If huge IQ gains are intelligence gains, why are we not stuck by the extraordinary subtlety of our children’s conversation? Why do we not have to make allowances for the limitations of our parents? A difference of some 18 points in the average IQ over two generations ought to be highly visible.

The mental retardation paradox: If the rate of change in IQ is extrapolated backwards, it suggests that people in 1900 had a mean IQ score somewhere between 50 and 70 judged by today’s standards. An IQ level of 75 is typically considered ‘mentally retarded.’ Flynn puts this one nicely, too: ‘Either today’s children are so bright that they should run circles around us, or their grandparents were so dull that it is surprising that they could keep a modern society ticking over.’

The identical twins paradox: Twins raised apart tend to have very similar IQ scores, typically considered strong evidence for a genetic basis for differences in IQ. The Flynn Effect instead suggests that intelligence, if it is being measured by IQ, is more malleable and subject to environmental effects.

Unraveling the paradoxes

According to Flynn, one possible set of explanations (certainly not the only one) is the following:

Functional independence of intelligences: The appearance of a general intelligence, ‘g,’ underlying all the subsections and different cognitive skills on a test like the WISC, is an effect of the test in a static social context. Over time, because shifts occur in the emphasis placed on different aspects of intelligence, different cognitive skills, the relative strengths of the various dimensions can shift. In his talk at the University of Cambridge, Flynn uses the example of an ‘athletic g’ for all the events of the decathlon to discuss how traits that are functionally interdependent might appear to correlate (see also Dickens and Flynn 2001).

Non-uniform intelligence gains: As Flynn writes: ‘The 20th century has seen some cognitive skills make great gains, while others have been in the doldrums.’ Whether or not our scores are increasing, and how much, depends very much upon which cognitive skill we’re testing. He offers a very interesting discussion of the variation in rates of improvement (or even decrease) here. As Flynn explains:

IQ gains over time describe a dynamic situation in which social priorities shift in a multitude of ways. No better maths teaching, more leisure but with the extra leisure devoted to visual rather then verbal pursuits, the spread of the scientific ethos, and a host of other things all occurring together. The average on Similarities rises but the average on Arithmetic and Vocabulary does not.

Increasing skills in abstract thought: Flynn points out the obvious – our ancestors were not mentally retarded. But they also were adept at different sorts of mental skills, an issue I’ll return to a bit later. Flynn suggests that we have become more adept at certain kinds of formal, hypothetical, and logical reasoning, liberating our problem solving ability from concrete reference (something that can be a detriment in certain sorts of problem-solving, as well).

Twins actually generating shared environment: Part of the reason genetic factors appear dominant in some traits is that some of these genetic traits attract certain environmental factors that compound and reinforce the genetic differences. The example given in the Wired article on the Flynn effect is the way that a genetically-based advantage in height might lead two identical twins, separated at birth, to wind up in environments where they both get pretty good at basketball; see the discussion here, too or in Dickens and Flynn 2001). As Flynn writes: ‘genetic advantages that may have been quite modest at birth have a huge effect on eventual basketball skills by getting matched with better environments — and genes thereby get credit for the potency of powerful environmental factors, such as more practice, team play, professional coaching.’

Flynn, together with William T. Dickens (2001), provided a more rigorous mathematical model of the ways in which reciprocal causation and social multipliers could confound the contribution of genetics and environment to IQ, but also found that many factors that influenced childhood IQ scores might not affect adult IQ without change in adult environment to reinforce changes. Because a person’s genotype affects their environment, a potentially small difference in the genetic contribution to intelligence might, through feedback effects, get compounded into a large difference in performance on a standardized test. In addition, there would be social effects if those around you would be developing more sophisticated cognitive abilities; you would both be influenced by them and expected to live up to the new norm in ability.

So do we give up on IQ tests?

Flynn doesn’t think gains in IQ scores are either trivial or cause for discounting standardized testing; the tests are not fatally flawed, although our understanding of the results may be. Flynn argues that we really need to understand what these changes are measuring. People are not growing ‘smarter,’ they are growing better at very specific cognitive skills:

This solution to our paradox does not imply that massive IQ gains over time are trivial. Aside from the escalation in lateral thinking, they represent nothing less than a liberation of the human mind. The scientific world-view, with its vocabulary, taxonomies, and detachment of logic and the hypothetical from concrete referents, has begun to permeate the minds of post-industrial people. This has paved the way for mass education on the university level and the emergence of an intellectual cadre without whom our present civilization would be inconceivable. (from this page in the Cambridge talk)

Flynn surveys a number of studies that suggest that the ability to engage in formal or abstract reasoning strongly correlates, not with intelligence, but with the level of schooling; since, in the West, people stay in school longer if they are more intelligent, it makes sense that the correlation between intelligence and the ability to engage in abstract reasoning would be especially strong in industrialized countries. That is, if schooling affects intellectual abilities measured by these tests, at the same time that these tests provide greater access to formal educational opportunities, then the tests would measure intelligence really well, but not just because they transparently reflect some underlying intelligence. Rather, it would be because the tests themselves are tied up in a self-reinforcing cycle of talent (or its lack) being compounded by opportunities, confidence, and motivation (or their opposite).

Innovations in thinking

Flynn argues that certain shorthand abstractions (SHA), formulae for thinking critically, have entered the cognitive repertoire of educated people, expanding their intellectual capabilities. Ironically, none of these SHAs shows up on a standardized IQ test like WISC or WAIS. If we wanted to measure people’s critical thinking abilities, we would have to come up with a new sort of test.

In a brief form, the concepts (along with the date of their creation and intellectual discipline) include: ‘market’ (1776, economics), ‘percentage’ (1860, statistics), ‘natural selection (1864, biology), ‘control group’ (1875, social science), ‘random sample’ (1877, social science), ‘naturalistic fallacy’ (1903, moral philosophy), ‘charisma effect’ (1922, social science), ‘placebo’ (1938, medicine), ‘falsifiable/tautology’ (1959, philosophy of science), and ‘tolerance school fallacy’ (2000, moral philosophy) (okay… the last one is Flynn’s, but it really builds upon some concepts close to our anthropological hearts, like cultural relativism). I won’t go into any detail (you can read about them here), but the idea is that some concepts that become shared representations – although by no means universal – facilitate forms of thought that are otherwise unlikely, or even impossible.

Flynn also offers a short, incomplete list of some of the concepts that resemble shorthand abstractions, but are actually opposed to scientific thought; among the examples he offers are the idea that some human traits are ‘contrary to nature’ (usually something the speaker want so condemn), ‘intelligent design’, ‘gender science’, and ‘reality is a text’, a nice mix of targets guaranteed to irritate a majority of readers: as Flynn writes, they are ‘evenly divided between the contributions of obscurantist churches and contemporary academics.’

Whether or not we agree about any one of these SHAs (or anti-SHAs, call them ‘shorthand errors’ or SHEs), Flynn’s account is a powerful discussion of how cognition might be affected by social invention, cognitive sharing, and even historical development of thought. Rather than getting ‘smarter’ in some vague way, Flynn offers a much more concrete account of specific thinking tools that individuals may or may not successfully integrate into their repertoire. They clearly have blinders as well as lenses, obscuring certain things or making it harder to have some kinds of thought, as well as facilitating others (like the ways of thinking tested on the WISC and other IQ tests). Although I may quibble with this or that SHA, I admire the degree to which he pins them down, even locating their origin and birth. It’s a kind of intellectual history that’s a bit alien to me as an anthropologist, but I think it’s very persuasive.

I’ll come back later and argue about whether ‘modern’ is such a good term to call it, but given this sophisticated, historical, contingent account of the very specific concepts that the term refers to in Flynn’s theory, I think it works. Perhaps it’s not fortuitous as sloppy thinkers are not going to read the details but possibly assume a very old fashioned ‘the West and the Rest’ division of the world into ‘modern thinkers’ and ‘non-modern thinkers,’ but I can’t accuse Flynn of doing that.

Flynn wrapped up his talk at the University of Cambridge with a discussion of wisdom, whether or not the industrial revolution that produced gains in IQ makes us so materialistic that wisdom will not be able to curb destructive desires. He wonders openly about the future:

The 20th century has been the century of rising IQ, the spread of the language and categories of science, the liberation of reason from the concrete, and the enhancement of on-the-spot problem solving. The 21st century will be the battleground of armies for and against the SHAs. It just might culminate in the triumph of critical thinking if universities hold fast to what they are supposed to be all about. Whether we can hope for anything more, I cannot predict.

Now, I still haven’t gotten to that article on Science Daily on the training of fluid intelligence that started me down this path, but don’t worry, I’m just getting warmed up. I’ve got more to come on training intelligence and whether or not rural people are dumber than city-dwellers… Yeah, it’s just getting good.

Acknowledgments

Thanks to Sue Sheridan who hipped me to the Science Daily piece that made me make my first notes for this posting — and our readers should check out her blog, Life of Wiley, home of the Daily Skeleton Action figure.

Bibliography

Dickens, William T., and James R. Flynn. 2001. Heritability Estimates Versus Large Environmental Effects: The IQ Paradox Resolved. Psychological Review 108(2): 346-369. doi:10.1037//0033-295X. 108.2.346

Flynn, James R. 1984. The mean IQ of Americans: Massive gains 1932 to 1978. Psychological Bulletin 95: 29-51.
_____. 1987. Massive IQ gains in 14 nations: What IQ tests really measure. Psychological Bulletin 101: 171-191.
_____. 1998. IQ gains over time: Toward finding the causes. In U. Neisser, ed., The rising curve: Long-term gains in IQ and related measures. Pp. 25 – 66. Washington, DC: American Psychological Association.
_____. 2000. IQ gains, WISC subtests, and fluid g: g theory and the relevance of Spearman’s hypothesis to race (with Discussion). In G. R. Bock, J. A. Goode, & K. Webb, eds., The nature of intelligence. Pp. 222-223. Novartis Foundation Symposium 233. New York: Wiley.
_____. 2007. What Is Intelligence?: Beyond the Flynn Effect. Cambridge: Cambridge University Press.

Gladwell, Malcolm. 2007. None of the Above: What IQ Doesn’t Tell You About Race. The New Yorker (December 17, 2007).

National Science Foundation. 2008. (June 6). Plastic Brain Outsmarts Exp
erts: Training Can Increase Fluid Intelligence, Once Thought To Be Fixed At Birth. ScienceDaily. Retrieved June 7, 2008, from http://www.sciencedaily.com /releases/2008/06/080605163804.htm

Sundet, Jon Martin. 2004. The end of the Flynn Effect. A study of secular trends in mean intelligence scores of Norwegian conscripts during half a century. Intelligence 32: 349. doi:10.1016/j.intell.2004.06.004.

Teasdale, T. W., and D. R. Owen. 2005. A long-term rise and recent decline in intelligence test performance: The Flynn Effect in reverse. Personality and Individual Differences 39(4): 837–843. doi:10.1016/j.paid.2005.01.029

University of Michigan. 2008. (May 6). Brain-training To Improve Memory Boosts Fluid Intelligence. ScienceDaily. Retrieved June 7, 2008, from http://www.sciencedaily.com /releases/2008/05/080505075642.htm

According to the original story, we learn that in 2002, Pamela Latrimore underwent an MRI that, in the eyes of some, imaged the Virgin Mary where most of us have a cerebellum (although, that would explain if she was having some motor control problems…). The original story, Do you see the Virgin Mary in this brain scan?, appeared in the TCPalm, Florida’s Treasure Coast and Palm Beaches’ news leader.

As the story reports:

Latrimore, a 42-year-old wife and mother without insurance, hadn’t ever really looked at the results of a 2002 MRI scan of her brain. So she didn’t know what her Catholic sister-in-law was talking about a few weeks ago when she said, “Oh my gosh, Pam, you have Mother Mary in your head.”

This story would be unmitigated fun, a chance to spin out all sorts of jokes about which parts of the brain ‘light up’ when we see a pattern of the Holy Mary in our brain images, except for the fact that, if you read a bit further in the TCPalm, you learn why Ms. Latrimore was getting brain scans in the first place, and perhaps why she and her relatives are searching for signs of any divine intervention.

She [Latrimore] prays for strength for her 23-year-old daughter and health for her family, many of whom — like her — are sick with a variety of serious ailments that seem to stem from a childhood in Jacksonville, Ark., a place where Agent Orange was manufactured and which has been investigated by the U.S. Department of Health and Human Services. Some Jacksonville residents were exposed to dioxin, a toxin that can cause a variety of illnesses, including cancer, asthma and liver problems.

Latrimore has had cervical cancer. She suffers from fibroidmyalgia, asthma, seizures, liver problems, ulcers and a variety of other ailments. She feels she is dying.

Latrimore does not have insurance, she was denied disability, and her husband is out of work. The medical bills have piled up — according to the story, she doesn’t know the total but suspects that they are now more than $100,000 that she does not have. And she’s apparently been diagnosed with an additional lung problem.

So this is a health care system without a safety net: take your brain scan, which was probably done looking for the cause of your seizures, and, if you’re lucky, it won’t just be used to diagnose your problem, but might get you a bit of money on eBay to pay for your mounting bills. After all, a toasted cheese sandwich with the Virgin Mary took in $28,000 a few years ago.

The listing on eBay for the Mary MRI can be found here. Reading the listing is heart-breaking, not only because of the woman’s own suffering, but also because of her account of how the manufacture of dioxin and Agent Orange has affected health in her community. She writes that she is putting the image up for auction, not only to raise money for her healthcare, but also to attract greater attention to the problem of environmental poisoning in her area of Florida.

A bit of background research quickly pulled up a story in The Nation, ‘Agent Orange’s Forgotten Victims’ from 1988, and other information on the manufacture, storage and dumping of toxic chemicals in Jacksonville, Arkansas, an area that eventually was declared Vertac/Hercules Environmental Protection Agency (EPA) Superfund site. As Peter Montague writes online at Rachel’s Hazardous Waste News #311: ‘The Vertac site was used for manufacture of DDT, aldrin, dieldrin, toxaphene and the chemical warfare defoliants 2,4-D, Silvex, 2,4,5-T, and Agent Orange.’

In 1986, Vertac declared bankruptcy and left the site to the people of Arkansas to sort out. As The New York Times wrote (here for a more recent story on Jacksonville’s Vertac-generated woes):

Vertac abandoned the plant leaving behind roughly 30,000 barrels of chemical wastes, along with acres of contaminated soil, tanks filled with toxic materials, and miles of poisonous piping. The EPA [U.S. Environmental Protection Agency] considers the site one of the country’s worst hazardous waste sites, not only because of [the] extent of the contamination but also because the plant is only a few blocks from a day care center, a hospital, and hundreds of houses. (this passage quoted in Montague)

I won’t go into all the grim details, the political wrangling and community outrage around an incineration project, except to say that, well, they details are damn grim even though some researchers dispute the data on the human toxicity of dioxin (I wonder who funds those scientists?). The National Toxicology Program’s most recent discussion of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) is pretty damning (For a pdf of the section on dioxin in the 2005 Report on Carcinogens, Eleventh edition, click here). For more specific information on the Vertac Superfund site, you can check out Scorecard’s site on it.

In other words, I hope that some good bids come into eBay for the image of Our Lady of the Cerebellum. If you’re in the market, I’ll do what I can at Neuroanthropology to make sure it’s the most famous MRI ever taken…

h/t: Graça ao Vaughn for bringing us the lead on this one.

As I pointed out in the previous post, however, many commentators suggest that it is not the gap in average test scores that really matters; rather, these critics argue that the different variance in boys’ and girls’ scores explains the disproportionate number of boys who produce exceptional scores (as well as exceptionally bad scores), and thus the marked gap of men and women in PhD math programs, in prestigious prizes for physics and related subjects, and in related fields like engineering. In the earlier post, I argued that even if this greater variance showed up reliably across all testing populations, what exactly was being illuminated was still not clear; that is, many other explanations–other than that men had better ‘math modules’ in their brains, or greater ‘innate’ mathematics ability, or something like that–could explain even very stable differences in math performance. At the time I suggested a number of other possibilities, such as sex differences in stress response during testing, as other possible explanations for even a universal ‘math gap’ (which still had to contend with studies like the two in Science which severely undermined the assertion of universality).

As if on cue, I stumbled upon a video and accompanying article in Science Daily on differences in stress responses among men and women: Neuroscientists Find That Men And Women Respond Differently To Stress (but don’t click on that link — keep reading!). Stress is a good candidate to explain a test-taking gap because the observable physiological processes offer abundant evidence that men and women don’t respond to stress in exactly the same way (although there are underlying commonalities). For example, stress causes different diseases in men and women, and some long-term psychological disorders that demonstrate sex-linked disparities seem to emerge from stress.

Unlike the ‘black box’ explanation that boys and simply better at math or evidence greater variability in innate ability, with no observable neural correlate or plausible explanatory mechanism, in variation in stress response we have a clear candidate for male-female difference that plausibly affects their performance and even physiology (for example, in different stress-related diseases).

Ironically, when I tracked down the original article that the Science Daily piece was likely based on (there’s no citation, so I can’t be certain), I had to delete all the quotes from the Science Daily article from the draft I was writing for this post (that’s why you shouldn’t link to it).

I give science writers a bit of stick from time to time, but in this case, the explanation of the research was not merely misunderstood, it was simply wrong, not even consistent with the abstract from the article I think the popular piece is based on (Wang et al. 2007). So even though the erroneous Science Daily article put me onto this thread, I’m only going to work from the piece published by Jiongjiong Wang and colleagues at the end of 2007 in the journal, Social, Cognitive, and Affective Neuroscience.

Stress responses in men and women to arithmetic tasks

In the abstract, Wang and colleagues explain that they tested 32 subjects using both fMRI and endocrine salivary screening. In the experiment:

Psychological stress was elicited using mental arithmetic tasks under varying pressure. Stress in men was associated with CBF [cerebral blood flow] increase in the right prefrontal cortex (RPFC) and CBF reduction in the left orbitofrontal cortex (LOrF), a robust response that persisted beyond the stress task period. In contrast, stress in women primarily activated the limbic system, including the ventral striatum, putamen, insula and cingulate cortex.

The researchers also found that the men tended to have more intense responses in the hypothalamic-pituitary-adrenal (HPA) system (e.g., the release of cortisol). According to the researchers, the increased activity in the men’s right prefrontal cortex (RPFC) under the acute stress condition was the most significant finding of their experiment, forming a biomarker for a distinctly masculine acute stress response.

What to make of all this? The researchers use a general description of these two different stress responses, first proposed by Taylor et al. (2000), as ‘flight-or-fight’ in men and ‘tend-and-befriend’ in women. At first read, I just groaned, and I’m still opposed to the essentialist and evolutionary mythology being touted as explaining an observable difference in performance. I’m not even going to start down the well-trod path I’ve beaten criticizing ‘evolutionary psychologists’ for naturalizing observed differences between men and women, simultaneously conjuring away the problem of explaining these differences by assuming that they are ‘inherent’ and attributing them to some dramatic fantasy of evolutionary selection — we’ve been here before (say at Girls gone guilty: Evolutionary psych on sex #2 or Chicks dig jerks?: Evolutionary psych on sex #1).

In fact, their data is much more interesting. For example, Wang and colleagues point out that the difference in brain activation patterns might result from different stress coping strategies or from different response to high- and low-stress situations (see Wang et al. 2007: 237-8). Ironically, as Wang and colleagues discuss, the difference in men’s and women’s stress responses depended upon the stimulus being used to produce stress: some experiments used social rejection; others, like the Wang-led team, use arithmetic problems to create stress. Likely, different sorts of stressful situations produce subtle distinctions in stress responses, some evoking more social anxiety, for example, and others creating a greater sense of physical peril. The group offers a path for future research, suggesting that neuroimaging studies of stress responses on different sorts of cognitive tasks might help sort out what’s specific to mathematical problems or might be more general difference between men and women: ‘Given the sensitivity of stress responses to specific context and intensity, we are cautious to generalize the current finding to different types of stress’ (ibid.: 238).

These different stress responses likely affect other mental activities in a variety of ways; we know that not all responses, in parallel fashion, affect health or cortisol production or other physiological correlates of stress. In addition, it’s quite likely that men and women don’t read the situations as equally stressful, either for innate reasons or for encultured ones–the two would be very difficult to disentangle in adults as the physiological effects would be identical. For example, girls and boys might interpret a testing situation in different ways because of peer, family, social, or other dynamics.

But I’m just going to hold my nose about the ‘ev psych’ part of this and plow onward (my objection being the ontogenetic simplifications of how a trait might emerge rather than a phylogenetic objection to saying that men and women might have been subjected to different evolutionary pressures — someday I’m going to have to do a post on this). So, onward with nose held…

Differing stress responses: ‘fight-or-flight’ or ‘tend-and-befriend’

The ‘fight-or-flight’ response ‘invokes resources that increase focus, alertness and fear, while inhibiting appetitive goals to cope with the threat or challenge’ (Wang et al. 2007:236). This pattern shows up in the increasingly active RPFC, associated with vigilance and negative emotion, and the suppression of activity in the LOrF, linked to hedonic behaviour and positive feedback. In other words, in the ‘fight-or-flight’ response, according to this interpretation, an individual becomes very alert to potential dangers and anticipating dire consequences, much less capable of focusing on pleasure-seeking.

Female response, in contrast, ‘primarily involves the limbic system including ventral striatum, putamen, insula and cingulate cortex’ (ibid.). This pattern was labeled by Taylor and colleagues ‘tend-and-befriend,’ and included parts of the brain receptive to oxytocin, vasopressin, dopamine and endorphin, systems that have been linked in previous research to social relations, attachment, and maternal behaviour. The researchers suggest that this social rewards system may blunt the acute stress response, leading women to respond in similar fashion to both high- and low-stress situations.

Wang and colleagues do point out that there are a lot of parallels between male and female responses, including very similar endocrine response, in spite of the predictions of the ‘fight-or-flight’/'tend-and-befriend’ contrast. The point being that, as in many human traits, male and female differences tend to appear in some lights as oppositions, but upon closer examination often reveal instead a largely common, underlying pattern (although the RPFC response was distinctive of their male subjects). Even the differences that do exist, such as a divergence in cortisol feedback due to the effects of reproductive hormones, may or may not be linked to observable differences, such as patterns of ‘ruminative thinking,’ as the researchers discuss. And the study itself didn’t turn up some patterns of activation that the researchers expected, such as a more prominent role for the amygdala (see, for example, Paul Mason’s discussion of the Role of Emotions in Brain Function).

Stress and cognitive function

Turning away from the more general effects of stress, focusing instead on the parts of the stress response that might affect must profoundly cognitive processing, we find that:

Activation of RPFC and right parietal regions [the pattern more pronounced in men] has been associated with various cognitive control tasks, including working memory, response selection and task switching, as well as inhibitory functions …. Ventral striatum along with several limbic regions [both pronounced in women under stress] have also been involved in learning in addition to tasks related to reward, motivation and emotion…. The different computational roles subserved by these brain regions may contribute to the observed gender differences in central stress responses. Although somewhat controlled in the regression analyses, this possibility (e.g. inhibiting incorrect responses in males and updating task strategies in females) cannot be completely ruled out, especially in the direct comparison of average stress responses between men and women.

Wang and colleagues specifically indicate recent research on mathematics and science ability (Hyde and Linn 2006) and suggest that the variation in stress response might underwrite pronounced differences in test results.

The RPFC, for example, is especially associated with executive functions (such as inhibiting emotional responses) and with strategic thinking, which might suggest a particular pattern of responding to stress. If women were more likely to focus on updating the possibility of reward, their own emotional states, and their motives while under stress, this might lead to lower scores when they were stressed by a time-constrained testing format. I’m still not persuaded that this is innately male or inherently impossible for women to achieve. If this is a pattern of brain activity especially likely to lead to certain test scores, and if some women are able to achieve extremely high test scores, than perhaps some women are able to learn this cognitive strategy. After all, it’s not like women don’t have these same parts in their brains. Likewise, I suspect that you could train patterns of response to stress in boys; certainly, the people I work with in sports training are convinced this is the case.

Implications for test-taking

First off, the experiment was ideal for exploring a possible stress-related contributor to the ‘math gap’ because it actually used mental arithmetic as the stimulus. Although not a perfect fit for a test-taking environment (where you don’t do the arithmetic in your head while holding still in a giant fMRI scanner), I doubt we’re going to get much better than this until the imaging technologies make some major jumps.

Second, in general, women after puberty have a lower threshold for perceived stress. The irony is that perceiving that one is stressed can often exacerbate one’s stress response. If you think you are anxious, you can make yourself more anxious. One can easily see how this might affect women’s performance on standardized tests. I never recall being all that stressed out on things like the SAT or GRE (I think I fell asleep during the GRE when I finished one section early), but for some people, this significantly influences their performance. I need to point out that Wang and colleagues specifically controlled for this effect in their research, so it’s not such a factor in their data, but it’s not hard to imagine that, especially given the length of a standardized test and the possibility that difficult questions might heighten stress during the course of an exam, this might become a factor in test score differences.

But the bottom line is that, if boys’ and girls’ brains respond differently to stress, this might disproportionately affect tests exploring various subjects. Female stress response might make their mathematics problem solving drop off more than men’s in timed tests (of course, we still have the much-less-discussed ‘reading gap’ to deal with, too). This sort of pattern does seem to show up in the gap between women’s performance on standardized testing in relation to men’s, and their across-the-board higher averages in marks on university courses (since broader admission of women into universities).

But stress might also lead to a narrowing of girls’ variance on standardized tests. It might diminish ability to perform at an extremely high level, but it also might lift the scores of the lowest scoring, least-motivated individuals. Lack of stress on a standardized math test — the situation boys might be more likely to find themselves in — might improve some young men’s scores, but it also might lead low performers to be even more blasé about their situation. Even the high RPFC activation in men might show up in boys as a clear-headed strategizing about the irrelevance of doing well on a standardized test if they know that they are not high performers in mathematics.

In other words, even a different pattern of brain activation (elevated activity in the RPFC) may simply provide the emotional environment in s stressful environment in which a boy might perform especially well, or calmly assess that the exercise was pointless given what he already knew about his ability. The result would be indistinguishable from an innatist argument that ‘boys have higher variance in innate math ability,’ but the underlying causal mechanism would be subtly different (and thus require different remedial projects if someone tried to address the variation).

One indicator of the possibility of these sorts of subtle mechanism that a Scientific American article by Halpern and colleagues cites is the fact that preschool children score similarly on cognitive tests of quantitative thinking and geometrical reasoning. The start to diverge when the children get to school. Innatist explanations suggest that the ‘true nature’ of boys and girls emerges when they enter school, but it’s just as likely that peer dynamics, including strong sex-stereotyping among kids, starts to really kick in when they are exposed to school. My point is not to argue that an innatist position is untenable, only that the pattern we see is equally consistent with other ways of thinking about how differences might arrive.

More on higher variance arguments

The Scientific American article also looks at the ‘higher variance’ of math ability argument that a number of proponents of innate ability gaps put forward (which I discuss at length in the previous post on math tests). The gap is profound, but the trend in that gap is also interesting. The authors reflect on data that was first assembled in the early 1980s on SAT scores:

There were twice as many boys as girls with math scores of 500 or higher (out of a possible score of 800), four times as many boys with scores of at least 600, and 13 times as many boys with scores of at least 700 (putting these test takers in the top 0.01 percent of 12- to 14-year-olds nationwide).

Although it has drawn little media coverage, dramatic changes have been occurring among these junior math wizards: the relative number of girls among them has been soaring. The ratio of boys to girls, first observed at 13 to 1 in the 1980s, has been dropping steadily and is now only about 3 to 1. During the same period the number of women in a few other scientific fields has surged. In the U.S., women now make up half of new medical school graduates and 75 percent of recent veterinary school graduates. We cannot identify any single cause for the increase in the number of women entering these formerly male-dominated fields, because multiple changes have occurred in society over the past several decades.

Although 3 to 1 is still a very large gap, it’s also startling to see that the gap can close from 13 to 1 to as little as 3 to 1 in a bit more than two decades. There may be an innate gap in math ability, but with all the change in these figures, it seems a bit premature to suggest that we know for certain that we’ve ascertained it and cannot affect change in the performance gap any further.

In addition, specialized courses designed to remedy the women’s specific deficits in visuospatial skills at the Michigan Technological University led to marked improvement among women in this area, one of the abilities considered to be strongly sex-linked, and to higher retention of women in university science and math programs. (I don’t have specifics on this intervention yet, but I will post more information when I get it.)

Again, this doesn’t prove that there are not innate differences between men and women: the origin of the gap in visuospatial skills is not at all clear. Because I’m more of a developmental systems theorist than an innatist, I would tend to look in the developmental trajectory of boys and girls for the difference rather than assume math ability springs from a gene or hormone. Thinking of the child as a developmental system, the gap may arise in an odd, indirect way; for example, boys relatively lower verbal abilities might lead them to compensate by developing visuospatial skills, or girls play patterns — whether due to innate tendencies or socialization — may give them less experience with visuospatial manipulation. Because the gap is mutable and the skills deficits at least partially remediable, I’d say that the burden of proof starts to fall on the innatists; show us where the innate visuospatial ability actually lives in the brain and how it comes into the world pre-destined if a whole host of studies are showing gaps are mutable.

In the end, I suspect that there are biological differences in girls’ and boys’ brains that contribute to differences in test score variance, but these differences may not be where we expect them. For example, they may have more to do with something that indirectly affects math testing like stress response or motivational structure in education. Innatist thinking is too easy, too inconsistent with the actual way that brains and cognitive abilities develop in an unfolding of the human organism in relation to a social and learning environment. There’s a lot of ‘mights’ in my account, but the fact that there are other plausible explanations for something like the math gap — even if it is universal (which the Science papers question) — shows overly glib assertions of innate difference to be a sloppy way out of what are really a whole set of interesting theoretical and empirical questions.

Stumble It!

References
Halpern, Diane F., Camilla P. Benbow, David C. Geary, Ruben C. Gur, Janet Shibley Hyde and Morton Ann Gernsbacher. 2007 (November). Sex, Math and Scientific Achievement: Why do men dominate the fields of science, engineering and mathematics?. Scientific American (available online here)

Hyde, Janet Shibley, and Marcia C. Linn. 2006. Gender similarities in mathematics and science. Science 314 (5799): 599–600. (pdf available here)

Phipps, Alison. 2008. Women in Science, Engineering and Technology: Three Decades of UK Initiatives. Trentham Books.

Taylor, Shelley E., Laura Cousino Klein, Brian P. Lewis, Tara L. Gruenewald, Regan A. R. Gurung, and John A. Updegraff. 2000. Biobehavioral responses to stress in females: tend-and-befriend, not fight-or-flight. Psychological Review 107(3): 411–29. (abstract on Pub Med, pdf available here)

Wang, Jiongjiong, Marc Korczykowski, Hengyi Rao, Yong Fan, John Pluta, Ruben C. Gur, Bruce S. McEwen and John A. Detre. 2007. Gender difference in neural response to psychological stress. Social Cognitive and Affective Neuroscience 2(3):227-239. doi:10.1093/scan/nsm018

A recent report in Science by Janet S. Hyde and colleagues, ‘Gender Similarities Characterize Math Performance,’ used a mass of standardized testing data generated under the No Child Left Behind program to compare male and female performance and found that the scores were more similar than different. The gap in average performance on math tests has shrunk significantly since the 1970s, disappearing in most states and grades for which the research team could get good data. According to Marcia C. Linn of the University of California, Berkeley, one of the co-authors of the study: ‘Now that enrollment in advanced math courses is equalized, we don’t see gender differences in test performance. But people are surprised by these findings, which suggests to me that the stereotypes are still there.’

From the way that this report has been discussed, it seems clear that the data has not settled this question in many people’s minds. Tamar Lewin of The New York Time covered the story in (‘Math Scores Show No Gap for Girls, Study Finds‘) provoking comments on a wide range of websites, including some who insisted that the team led by Hyde missed entirely the point being made by Summers or that Lewin had misread the study (some accusing her of feminist bias). In contrast, Keith J. Winstein of The Wall Street Journal focused not on the average scores, but on the results at the top end of the bell curve, writing, Boys’ Math Scores Hit Highs and Lows, which highlights the discussion of variance in boys’ scores.

Although I briefly want to go over the study and the way its being interpreted, I’m more interested in the shift in test scores over time because I think that the movements in these numbers, including gaps that disappear over time (or don’t), point to a basic problem in the tests themselves. Well, not a problem in the tests—they’re very sophisticated instruments for assessing certain kinds of performance on selected tasks—but rather with the common assumption about what these tests actually reveal and the nature of ‘math ability.’ For me, this larger point is more important for neuroanthropology because it applies to far more than just the ‘math gap.’

The gap in test scores: sample bias?

The gap between boys and girls in standardized math tests in the 1970s and 1980s seemed to open wider as children grew older. From a statistical dead heat early in grade school, a pronounced inequality developed by high school that only grew worse at successive stages, up to a near total male dominance of PhDs, math contests, and high status faculty positions in fields like engineering, physics, and mathematics. A number of people point to the results of things like mathematics olympiads or major prizes for theoretical physics to show that, at the upper end of ability, male dominance is complete.

Although some explained the ‘math gap’ as the result of brain differences (such as differences in spatial sense or abstract reasoning) in boys or girls that affected math ability, others insisted that the inequality was caused by social forces, stereotypes, or other factors. One explanation was that the ‘math gap’ was a self-fulfilling prejudice; the assumption that boys would do better both encouraged boys’ performance and discouraged girls from developing ability in mathematics. Another argument was that girls were discouraged from publicly demonstrating academic gifts in general as they brought stigma (as a geeky young man, I find it hard to believe that my female peers were more severely stereotyped, but that’s a different story). Some critics pointed to the way that changing patterns of enrollment in advanced math classes steadily seemed to be eating away at the ‘math gap’; they felt that if girls pursued the same educational opportunities, they would have similar results.

But another explanation for the ‘math gap’ in performance was a sample bias problem that Hyde and colleagues sought to address in this study. The research team pointed out that only college-bound students took the SAT (and ACT) traditionally , and more girls than boys took the test (100,000 or so more every year for the SAT). The lower average for girls’ test scores might have arisen from the fact that the larger number of girls taking the tests meant that their scores reflected a deeper dip into the talent pool, with a larger percentage of female students being scored. The additional students — perhaps not the most intellectually gifted girls — were pulling down the girls’ average score.

Changing policies for administering standardized tests, especially making them mandatory, might provide a better sample, less prone the bias of one group participating at a higher frequency. For example, in 2002, Colorado and Illinois mandated that every graduating high school senior take the ACT; the gender gap between boys and girls disappeared when girls were no longer over-represented in the test. In fact, girls demonstrated a slightly higher average math score than boys. As the research team writes: ‘These findings support the conclusion that the male advantage on the SAT mathematics test is largely an artifact of sampling.’ In other words, boys scored better because fewer took the test.

Research results from the Hyde team on US scores

The No Child Left Behind program forced states to administer standardized tests broadly. The team led by Hyde, drawn from faculty at the University of Wisconsin and the University of California, Berkeley, contacted all 50 states to try to get access to these scores, but only 10 gave them enough information to make their samples useful. Based on test scores for 7 million students, the research team found no difference in the average math scores; this seemed to be the culmination of a trend of girls steadily gaining ground in math scores, first among younger age groups and recently through adolescence. The following chart shows the gaps at different grade levels for the ten states:

This chart originally appeared in Science magazine with the article. From the UC Berkeley News website, we have the following explanation:

Each square represents a grade level in one of 10 U.S. states. At the center of the chart (the 0 mark), the two genders performed equally in math, with increasing differences between boys and girls toward the left (where girls outperformed boys) and right (where boys outperformed girls). When researchers averaged the results, they found no difference between the two genders in their math proficiency. The 10 states were New Mexico (olive), Kentucky (fuchsia), Wyoming (tan), Minnesota (blue), Missouri (red), West Virginia (lavender), Connecticut (green), California (yellow), Indiana (aqua), and New Jersey (purple).

Hyde’s earlier work (e.g., Hyde 2005) also tested the ‘innate sex differences in math ability hypothesis,’ but the meta-analysis of extant studies is just never going to get the traction that 7 million test scores will. The current study seems to show convincingly that there is no inherent difference between boys and girls on average in mathematics proficiency. This hardly demonstrates that boys and girls have identical brains or thought processes, nor does it really refute the kind of argument that Harvard’s president Summers was making, but it does seem to suggest that the stereotype of girls being unable to do maths is widely inaccurate (not just in a few exceptional cases).

Math and reading gaps and gender equality globally

In fact, the story of the change in the ‘math gap’ in the United States seems to mirror a pattern that is also seen across cultures: changing status of women seems to correlate pretty strongly with the math gap. When women are treated more equally, it shows up in girls’ math scores. Studies of students in different societies show that the difference between boys’ and girls’ averages is not constant, and can be reversed. The Economist recently ran a story on gender achievement gaps in different places based on another Science report by Luigi Guiso of the European University Institute in Florence and colleagues: Diversity: Culture, Gender, and Math.

Guiso’s team used results from the Programme for International Student Assessment (PISA) run by the Organization for Economic Cooperation and Development (OECD). A total of more than 275,000 15-year-olds took the PISA exam in 40 countries. The gap between girls and boys on math was, on average, 2 percent, although the results varied, and the size of the gap correlated with measures of gender inequality. In addition, girls scored on average 7 percent higher than boys on reading, with average boys’ reading scores matching girls’ in not a single country.

The diagram shows a pattern that continued across the pool (see the supplementary data to the original report for the averages on all of the countries participating in the PISA testing).

Interestingly, the gap between boys and girls in geometry was immune to the effects of social equality: boys enjoyed a consistent advantage in the subject. And the female ‘reading gap’ actually grew with greater equality for women, suggesting that greater equality led to better education for girls, and better performance, across subjects (not a real surprise). As the conclusion to Guiso’s paper makes clear:

This evidence suggests that intra-gender performance differences in reading versus mathematics and in arithmetic versus geometry are not eliminated in a more gender-equal culture. By contrast, girls’ underperformance in math relative to boys is eliminated in more gender-equal cultures. In more gender-equal societies, girls perform as well as boys in mathematics and much better than them in reading.

If the math gap explains male dominance in physics and math, and academic hiring was relatively unbiased, then why isn’t there an even more pronounced preference for admitting women into PhD programs and hiring them as faculty in fields like English literature, law, and history?

The gap between men and women in reading scores also suggests another explanation for the math gap: if choice of career is based on comparative advantage (rather than just absolute fitness), then women would likely choose a career where there much greater advantage in literary skill might produce a clearer superiority. That is, even if their math scores go up, their reading scores go up too, so they still enjoy a greater comparative advantage over men in fields that might be considered ‘traditionally female.’

Performance at the extremes

Alex Tabarrok at Marginal Revolution, in a post titled, Summers Vindicated (again), highlights the crucial issue of variance in test scores. That is, for understanding the highest achievers, the averages don’t really matter; we’re talking about the extremes, the geniuses, the Nobel Prize-winning physicists. If the variation in boys’ performance is larger than that of girls, more boys will likely wind up in the ‘upper tail’ of performance, posting the highest scores (and lowest), while the averages could be dead even (or for that matter, male averages could even be lower).

Among white students, Hyde and the team from the University of Wisconsin and the University of California, Berkeley, found that the ratio of girls to boys in the top percentile was 1 to 2 ; twice as many exceptional scores (99 percentile scores) were turned in by white boys as white girls. Among Asian students, however, more girls than boys reached the top percentile (but only slightly more, statistically). Not enough Latino or African-American turned in scores in the top percentile to provide a ratio. (Another interesting wrinkle was that white boys significantly outperformed Asian and Pacific Islanders, both boys and girls, running against stereotypes in some parts of the United States, but that’s for another day…)

The Wisconsin-Berkeley team go on to suggest that the gap in achievement, although it might explain part of the disparity between men and women in certain occupations, doesn’t explain the severe gap in some fields. For example…

If a particular specialty required mathematical skills at the 99th percentile, and the gender ratio is 2.0, we would expect 67% men in the occupation and 33% women. Yet today, for example, Ph.D. programs in engineering average only about 15% women.

I don’t find this a particularly compelling argument — if the specialty required mathematical skill at a slightly higher proficiency, then there’s no inherent reason why 15% would not be the ‘right’ ratio of women to men in a program, but we’ll come back to that…

Some commentators point to this gap between (white) boys and girls in the top percentile to argue that, in fact, Summers’ tempest-provoking speech was inadvertently supported by the report in Science. Whereas the NYTimes report says boys and girls are equal, Heather MacDonald of the Manhattan Institute begs to differ:

Actually, the study, summarized in the July 25 issue of Science, shows something quite different: while boys’ and girls’ average scores are similar, boys outnumber girls among students in both the highest and the lowest score ranges. Either the Times is deliberately concealing the results of the study or its reporter cannot understand the most basic science reporting.

According to MacDonald’s reading, ‘Science’s analysis of math test scores only confirms the hypothesis that cost Summers his Harvard post: that boys are found more often than girls at the outer reaches of the bell curve of abstract reasoning ability.’ Or, as Alex Tabarrok puts it more directly, ‘we can expect that there will be more math geniuses and more dullards, among males than among females.’

MacDonald and others, like Luboš Motl at The Reference Frame, argue that, even this ‘upper tail’ effect underestimates the impact of higher male variance because these tests are simply too simple, too easy, to illuminate the differences between men and women’s brains at the extreme of performance. In my day, if you scored over about a 700 on the SAT math test (I believe — please don’t quote me on that one, it’s been more than a couple decades), you were in the 99th percentile. In fact, a lot of variety was masked by the ‘same’ percentile score. This lack of resolution makes it difficult to study the really extraordinary performers from these tests.

Moreover, the Wisconsin-Berkeley researchers found that most standardized state math tests did not include the most difficult sorts of problems, those demanding complex reasoning or problem solving ability, so it is unlikely that the test even could differentiate among elite levels of performance. In addition, the simple problems are a sad commentary on the state of American secondary education and the stultifying effect of the No Child Left Behind testing regimen, which demands students be tested but encouraged dilution of the tests to make sure that states met goals for funding through a set of perverse incentives.

Luboš Motl, who writes as a ‘conservative physicist,’ takes Hyde and her colleagues to task for not really focusing on measures of extraordinary mathematical talent (Motl’s pretty negative on the research, alleging that the authors have been ‘writing similar cargo cult scientific papers for quite some time.’). Motl, in Janet Hyde: boys = girls in math? Not really, points to the extreme gender gap in winners in the US Mathematical Olympiads, major math prizes, and other more elite forums for demonstrating math ability than the SAT. As he puts it near the end of his post, ‘the more selective your math-related tests become, the lower percentage of females you will obtain (the boys have a greater variance of the distribution).’ Some of the disparities are startling; you can’t even call some ‘disparities’ as there is simply no female representation in the history of certain prizes, and this dominance persists in spite of social change, changes in education, and a host of other factors.

Motl serves up a fair amount of vitriol for ‘political correctness’ and references a study showing that certain fields in the humanities and social sciences have higher numbers of faculty espousing ‘politically correct’ views (see ‘Defining Political Correctness and Its Non-Impact’ on Inside Higher Ed). Although Motl’s attacks on ‘political correctness’ may be heavy handed, I found it fascinating and ironic that one of the key views that the reserachers on ‘political correctness’ used as a diagnostic was specifically about the ‘math gap.’ That is, one of the key questions used to identify which professors were ‘politically correct’ was whether the professor believed that ‘gender gaps in math and science fields are largely due to discrimination.’

What to make of this research and the critiques?

In the first place, the research by Hyde and colleagues tends to poke a gaping hole in the argument that there is an unvarying difference between all girls and all boys in mathematics ability (please note before you start writing hate mail: I know that this is not what most of the bloggers critical of the studies are arguing for…). Although the argument for sex differences may have shifted so that the current debate is focused on variance and extreme performances, let’s not forget that for a very long time, and still in a lot of people’s minds, this basic argument—boys are better at math than girls, period.—was taken for granted because of the ‘math gap’ in the average scores. That gap is now gone.

The Science article makes this case using a new data set (from the NCLB program) to overcome a sample bias in tests like the SAT. On this front, the case is pretty compelling, as is the Guiso-led research on European scores. I would hope that people supporting Summers’ perspective with reference to the data on extreme male performance in Hyde’s study (such as Winstein in The Wall Street Journal) will be equally passionate about denying this very common ‘boys-are-better-than-girls-in-math’ argument as they are about the alleged higher variance of boys.

Summer’s defenders have argued that his comments (and their defense) focus only on extreme performance, only on math geniuses; although the Hyde study discusses the 2 to 1 gap in achieving scores in the 99th percentile among white students, the authors argue that this gap does not explain an even greater disparity in women’s participation in PhD programs and faculty positions in mathematics-related fields. As I suggested above, I don’t find this part of the Wisconsin-Berkeley team’s argument terribly compelling. The 99th percentile cut-off is arbitrary; if it required an even rarer level of mathematics achievement, then, following from this logic, the disparity would be justified. The 2 to 1 gap in performance with such a large pool of (white) students is statistically significant.

But maybe because I work at a university, I don’t think university academic hiring is always a process of finding geniuses (god, I wish it were). Even if there are more male math geniuses, I’m still not sure that explains disparities in hiring or PhD programs; I’d want to see some proof that university professors are reliably geniuses or some concrete studies of social conditions within departments in these fields before I’d rule out the possibility that the gender gaps are at least partially ‘due to discrimination’ (Does that make me PC, or would I need to be more one-sided?). That is, even if women were participating at higher rates, it seems to me that studies of hiring and promotion are more convincing in demonstrating discrimination (or its absence) than statistical arguments about distribution in the field. In fact, there is statistical evidence to argue both ways (see, for example, studies of promotion and hiring compared to the percentage of female candidates in the pools).

The people with the highest math scores don’t necessarily wind up with careers in mathematics. Back in 2006, Jake Young at Pure Pedantry wrote an excellent post on the problems with the ‘upper tail’ hypothesis, Debunking the Upper Tail: More on the Gender Disparity, arguing that it depends upon two key assumptions that don’t hold: a) that you have to perform in the upper tail to go on to a career in these professions, and b) people with high scores in tests of mathematical ‘aptitude’ wind up in math, physics, and engineering. Check out his post for an excellent discussion (see the comments as well).

In the case of certain sorts of awards for creativity in scientific theory (like the Nobel Prize in physics), ability to do mathematics at the highest level may not (may not) be the key intellectual quality that produces the performance. That is, theoretical creativity may be a constellation of factors, including some personality traits as well as abilities considered classically ‘intellectual,’ that trumps pure mathematical problem solving ability. Ironically, male dominance in these awards may not be due to an advantage in mathematical ability (although this advantage might still exist) but because of other characteristics, which may or may not be innate or attributable to being male. In order to know this, we’d have to actually study Nobel Prize winners (we run into a similar issue with study of elite athletes being treated as representative of gender or ethnic groups when we don’t really have a clear sense of why these athletes are winning and whether there’s a consistent source of superiority).

Testing and changes in group ability over time

The bigger point for me, however, is that I’m still not convinced that math tests are testing innate ability. The whole shift in the US ‘math gap’ over time, like the shifts in European countries linked to greater gender equality, is a powerful demonstration that intellectual qualities of an entire group, as measured by standardized tests, can shift over time, even on a scale so enormous that one might reasonably expect environmental factors to be swamped. The fact that the math gap between boys and girls shrank so markedly between the 1970s and 2003, where the clear causes are educational and social, puts the burden of proof squarely on those who want to argue that mathematics abilities are genetic. Even the Flynn Effect, the fact that IQ scores tend to rise over time (discussed here and here at Neuroanthropology), strongly suggests that ‘intelligence’ is a moving target, likely a synergy of different abilities and traits, some of which are plastic.

The terribly consistent geometry gap has the hallmark of some sort of innate difference, but even this might be deceptive. Science Daily recent ran a story, Plastic Brain Outsmarts Experts: Training Can Increase Fluid Intelligence, Once Thought To Be Fixed At Birth, detailing how one dimension of intelligence thought to be relatively fixed, in fact, could be molded by training (and not terribly demanding training at that, unlike a really good mathematics education).

Even if girls and boys had identical mathematics scores, that wouldn’t mean that we’ve proven their brains are indistinguishable (they’re not), nor even that they both have the ‘same’ mathematical ability (they may be achieving comparable scores in different ways). The Hyde-led research team tried to determine whether boys and girls were doing better or worse than each other on different types of questions, but the tests were too remedial to even discern this kind of effect because they had no demanding test questions (although the Guiso-led team’s information on a ‘geometry gap’ being resistant to change is suggestive).

In this sense, I agree strongly with Luboš Motl: the big story here is not the scores, but the fact that the tests are so watered down, likely reflecting the disintegration of high expectations and rigorous mathematics training. I have a suspicion that, no matter how bad the overall education, you’re likely to still get some exceptional performers, but the effect on the vast majority of children will be tragic. Without rigorous math training, one of the opportunities we have to encourage students to develop a whole range of neurological capacities is being squandered.

One neuroanthropological perspective on ‘intelligence’ (or ‘mathematic ability’)

As a neuroanthropologist, I have no problem acknowledging differences between men and women; I think it’s simply a statistical fact that men and women are performing differently, as Motl points out. Nor would I deny that there might be significant cognitive differences between groups of people, including men and women, or that these differences might be grounded in biological differences in the brain. But there seems to be a very quick rush to believing that these tests demonstrate some constant and innate difference between boys and girls; the changes highlighted in both studies published in Science should, at the very least, slow the quick jump from a disparity in scores to assumptions about permanent sex differences. The gap in math scores (and reading scores for that matter) moves, shrinks, changes, grows, or even disappears over time; one would think that this might give those who think ‘intelligence’ is innate reason for pause.

My greater objection in some of the discussion is the lack of a more fine-grained analysis of how any particular difference (such as on a test) arises—it’s often simply attributed to a kind of ‘boy-ness’ or ‘girl-ness.’ Yes, a difference in elite performance might arise because there’s more ‘math power’ in a boy’s brain, but that still leaves us begging a whole lot of questions: what is ‘math power’? how did they get it? why don’t all boys have it? can we give it to girls, or to more boys? do the exceptional girls, however rare, have the same mental ability? if the girls do, is it specifically ‘male’ in some sense, or is it just more likely to occur in boys’ brains?.

For example, there’s often an assumption that greater variance in boys is either genetic or hormonal, and although that could be the case, there are other potential explanations, including mixed explanations that partially, but don’t wholly rely on genetic or hormonal explanations.

We could offer an explanation that tips towards social psychology; for example, just as the boy math geek may be less stigmatized than the girl math geek (as some people argue), the boy math tragedy may be more de-motivated than a girl performing badly. Boys’ exit from education may be supported by male peers or even influenced by innate non-mathematical factors, like having a stronger temper, more pronounced need to defend the sense of self, or more well developed ‘screw-school’ independence. In the ‘math prodigy’ boy, the same temper, desire to defend the self, and independence could boost achievement, especially mathematical creativity.

We could offer an explanation that relies more on the fitness for the testing dynamic itself rather than brain ‘math power’; for example, the high pressure and tight time constraints of very challenging tests (the kinds in math contests) could reward certain kinds of behavioural patterns (high risk, ‘sloppy’ but quick problem solving) or a specific constellation of traits in addition to mathematics ability, so that they are testing other behavioural traits or intellectual abilities in addition to problem solving. Boys’ bodies might respond differently to stress than girls’, producing a better mental state in which to take tests. These other traits might even be ‘innate’ differences between boys and girls (they might not be), but they would be recorded on the test as ‘mathematical ability.’

That is, showing greater variance, even showing that it’s rooted in inherent male-female differences, still doesn’t demonstrate that male brains are inherently better at specific math functions. The effect of other sorts of differences may be indistinguishable on a test, but the implications are profound for how you might redress inequality, test ability, or even how you might teach mathematics to the boys who don’t do well.

In addition, although someone’s liable to criticize this line taken out of context, I’ll write it: there’s an assumption that math tests are testing ‘math ability’ when they might be testing something else in addition to ‘math ability’ (like behavioral responses to stress, test taking strategy, or a number of other things). That is, there’s an assumption that a math test tests a trait, ‘math ability,’ and that it’s distinguishable from other traits, like ‘reading ability,’ in some meaningful sense. I think that it’s more likely that math tests test the ability to take math tests; not only does this include other traits, but even ‘math ability’ itself may be composed of a cluster of functions and abilities, which are themselves diverse in terms of how a psychologist might describe them (‘fixed’ or ‘fluid’ intelligence, ‘procedural’ and ‘propositional’ memory, etc.). Likely, not everyone who does well on a math test is doing the same thing. When I won a math test without a calculator, and the guy who finished second used one, I’m pretty sure that we were engaging in different calculating practices and techniques as we moved through the question.

Although some people may be uninterested in these differences between ways that people achieve solutions in mathematics (perhaps because they’re more interested about differences between boys and girls), from a neuroanthropological perspective, this variation is crucial. Because we are interested in cognitive variation, and in how different practices (like variations in mathematics pedagogy) shape distinct forms of competency, identical scores may actually mask significant cultural-cognitive (or gender-cognitive, for that matter) differences. It may be as important to analyze the apparent parities as to mind the gaps….

I’m going to do another post on ‘intelligence’ soon. This one has taken me too much time to finish, and it’s gotten too long. If you’re in the mood for more, however…

Additional resources
Read more at No Gender Differences In Math Performance and at CNN: Study: Girls equal to boys in math skills. On the blog Ars Technica, Math gender gap gone in grade school, persists in college by Yun Xie (interesting thoughts in that one).

P. Z. Myers at Pharyngula also has a post on a closely related debate (on science, as well as mathematics, and gender disparity): Motivating students (and motivating women) to pursue science careers. Myers does a nifty rhetorical judo take-down on those who use test scores to argue for innate differences; if test scores demonstrate unchangeable ability, then why don’t Americans stop spending money on math and science education for US students and import more Asian kids?

I would never want to be accused of not giving opposing perspectives adequate space: along those lines, there’s a discussion of bias in the sciences over at the ‘Tierney Lab’ at The New York Times website: Intellectual Dishonesty on Sex Bias?

Stumble It!

Still haven’t had enough? There’s an update: Women on tests update: response to stress.

Credits:
Chart 1: Univ. of Wisconsin and UC Berkeley; published in Science magazine; downloaded from http://www.berkeley.edu/news/media/releases/2008/07/24_math.shtml on 31 July 2008.

Chart 2: The Economist, Education and Sex: Vital Statistics.

Photo from a University of Michigan news release from 2003.

References

Guiso, Luigi, Ferdinando Monte, Paola Sapienza, and Luigi Zingales. 2008. Diversity: Culture, Gender, and Math. Science 320 (5880): 1164-1165. doi 10.1126/science.1154094

Hyde, Janet Shibley. 2005. The Gender Similarities Hypothesis. American Psychologist 60(6): 581-592. doi 10.1037/0003-066X.60.6.581.

Hyde, Janet S., Sara M. Lindberg, Marcia C. Linn, Amy B. Ellis, and Caroline C. Williams. 2008. Gender Similarities Characterize Math Performance. Science 321 (5888): 494-495. doi 10.1126/science.1160364