The gene-environment hypothesis

February 17, 2011 Leave a comment

When it comes to the B-W IQ gap there are three possible types of non-hereditarian explanations: the gap is due to different frequencies of IQ affecting environmental factors (VE-factors) which vary within both populations (e.g. SES or average savings or bad schools), the gap is due to some gene-environment factors which are as yet undetected by heritability estimates, or that gap is due to IQ affecting environmental factors (X-factors) which are unique to one or the other population and are more or less equally distributed across that population.

X-factor explanations were always theoretically implausible. For one, Blacks and Whites more or less live in the same environment unlike, for example, Americans and North Koreans or Americans now and Americans 50 years from now; given this, it’s rather hard to think of factors which could uniquely affect one or the other population and affect that population more or less equally. Additionally, X-factor explanations require a rather unparsimonious dual hypothesis for intellectual differences within the US; accordingly, the causes of differences within populations are completely distinct from the causes of differences between populations.

Given this situation, and given that high within group heritability of intelligence precludes any one VE factor from accounting for the Gap, environmentalists are left with either arguing that numerous VE factors sneak through the cracks of heritability and cause the gap (let’s call this the many causes hypothesis) or maintaining that gene-environment correlations confound heritability estimates (let’s call this the gene-environment hypothesis). Unlike Nisbett (2010), Flynn (2010) concedes that Jensen’s within-group/between group heritability argument makes implausible the many causes hypothesis. Instead, he tries to escape Jensen’s “steal chain of ideas,” as he calls it, by proposing a COVGE model of heritabiltiy. This is a seductive proposition because it offers a single unified explanation for within, between, and across cohort variations in intelligence. Jensen long argued that a between group genetic explanation should be treated as the default explanation as it offers the most parsimonious account of differences within and between groups (X-factors need not apply). With the gene-environment model, Flynn one ups the genetic explanation and offers a single account which can incorporate individual, group and cross cohort differences in IQ.

Let’s look at what Flynn (2010) has to say about intelligence and within group heritability:

Originally, Jensen argued: (1) the heritability of IQ within whites and probably within blacks was 0.80 and between family factors accounted for only 0.12 of IQ variance — with only the latter relevant to group differences; (2) the square root of the percentage of variance explained gives the correlation between between-family environment and IQ, a correlation of about 0.33 (square root of 0.12=0.34); (3) if there is no genetic difference, blacks can be treated as a sample of the white population selected out by environmental inferiority; (4) enter regression to themean — for blacks to be one SD below whites for IQ, they would have to be 3 Sds (3×.33=1) below the white mean for quality of environment; (5) no sane person can believe that — it means the average black cognitive environment is below the bottom 0.2% of white environments; (6) evading this dilemma entails positing a fantastic “factor X”, something that blights the environment of every black to the same degree (and thus does not reduce within-black heritability estimates), while being totally absent among whites (thus having no effect on within-white heritability estimates). [comment: When it comes to X-factors. Flynn is being disingenuous — years before developing his 2001 model with Dickens, he considered the Flynn effect and X-factors, in general, to be theoretically plausible and, presumably, not “fantastic” accounts for the said racial group differences]

I used the Flynn Effect to break this steel chain of ideas: (1) the heritability of IQ both within the present and the last generations may well be 0.80 with factors relevant to group differences at 0.12; (2) the correlation between IQ and relevant environment is 0.33; (3) the present generation is analogous to a sample of the last selected out by a more enriched environment (a proposition I defend by denying a significant role to genetic enhancement); (4) enter regression to the mean — since the Dutch of 1982 scored 1.33 SDs higher than the Dutch of 1952 on Raven’s Progressive Matrices, the latter would have had to have a cognitive environment 4 SDs (4×0.33=1.33) below the average environment of the former; (5) either there was a factor X that separated the generations (which I too dismiss as fantastic) or something was wrong with Jensen’s case. When Dickens and Flynn developed their model, I knew what was wrong: it shows how heritability estimates can be as high as you please without robbing environment of its potency to create huge IQ gains over time.

To sum up: Jensen used the high heritability of IQ to argue that, in absence of X-factors, differences between populations were not likely due to variable environmental factors and therefore most probably had a partial genetic basis. Flynn turns this logic around, using it to construct the IQ paradox and argue that the conventional understanding of heritability must be wrong. Accordingly, given the high heritability of IQ, the rapid secular increase in IQ can not be due to variable environmental factors and so must be due to genetics or X-factors; as the latter two explanations are supposedly implausible, there must be something incorrect about the conventional ( g + e) understanding of heritability.

Flynn concludes that high heritability doesn’t constrict vE explanations for differences (both between cohorts and between populations) because there must be some variable environmental factors which go undetected by heritability studies (i.e. variable environmental factors that are recursively correlated with genotype). That is, heritablility estimates must miss gene x environment correlations (rGE).

From Dickens and Flynn (2001):

“Thanks to industrialization, it is likely that the cognitive complexity of the average person’s job has increased over the last century. There is no doubt that more-demanding educational credentials control access to a wide range of jobs. There are far more people in scientific, managerial, and technical positions than ever before.6 Increased leisure time is another possible trigger for IQ gains, as some activities undertaken during extended“

“Between generations, the mask slips. For it to do its work, the worse environment of the earlier generation would have to be matched by worse genes for IQ; and the better environment of the later generation would have to be matched by better genes for IQ. However, because the two generations are equivalent for genes, there is no matching and therefore no masking. The potency of environmental factors stands out in bold relief.”

Initially, the COV GE model seems reasonable; when it comes to individual differences, it doesn’t seem outlandish to suppose that naturally born intellectuals might increase their verbal IQ through bookish behavior. On the subpopulation level, likewise, it’s doesn’t seem implausible that a propensity for studiousness might lead to cognitive enhancement. Yet, there are a few bumps which preclude a simple GE model for the US subpopultion differences.

When it comes to subpopultion differences in the US, the differences are general intelligence-loaded. As such, a COVGE explanation for these differences would entail a GE explanation for the high heritability of g. Arguing for the necessity of a GE explanation for g by way of the Flynn effect doesn’t work because the cause of g differences are qualitatively different from the cause of the Flynn effect (See: Wicherts, Dolan, Hessen, et al. 2004) and g has shown no secular rise. Flynn’s argument is reduced to a plead for the possibility of a g GE explanation. This is problematic.

When it comes to rGE explanations (the more a posteriori plausible of the two GE possibilities), GE theorists are forced to maintain that g is created from the outside in. (See theoretical diagram). Since g is structurally the same across individuals, cultures, sexes, and subpopulations, not only would the patterns of one’s environment have to construct g, the patterns of everyone’s environment would have to construct the same g.

Additionally, IQ g has numerous endophenotypic correlates, such as the volume of white and grey matter, the mass of the prefrontal lobe, and total brain size [1, 11, 13] and the overlap between IQ (g) and many of these endophenotypes is entirely due to genetic influences [1, 13]. To explain this genetic covariation, rGE theorists must maintain that genetics sets the parameters for environmental selection, which leads to the development of different cognitive phenotypes, which, in turn, molds the endophenotypic differences, thus creating the three way correlation.

Since the Phenotypic/endophenotypic correlations have been found to be a function of differential rates of change during the development process [ 9] and correspondingly since IQ differences are stable after adulthood, rGE theorists must maintain that this environmentally induced endophenotypic molding occurs primarily during the developmental process and starts early on. If we kept in mind what we said above, that the genes that lead to slight genetic IQ differences in infancy are the same genes that lead to large genetic based differences in adulthood, and note that the heritability of many dispositions also increases with age [4], we can readily identify the problem with this conception. Somehow, dispositional differences, which are under heavy environmental influence early on, must set the phenotypic/endophenotypic molding (environmental) parameters in a way that happens to correspond to the genetic driven phenotype that the individual will later express.

None of the above logically precludes a rGE explanation for g; the explanation would just have to be exceedingly complex. That said, there is a growing body of evidence against the active GE model. Since according to the active rGE model, environmentally conditioned phenotypic differences cause endophenotypic differences, the active rGE model predicts that environments will correlate with endophenotyes [13]. This was found to not be the case by Posthuma et al. (2003), De Moor et al. (2008), van Leeuwen et al (2009), and Betjeman (2009), disconfirming the model.

In addition to the above, Shikishima et al. (2009) found evidence of a causal genetic g. This effectively rules out the possibility of a purely active rGE created g:

Accordingly, our findings could furnish an argument against the typical criticisms offered by those who are opposed to the concept of g; in other words, g is an “artifact” (Simon, 1969) of the statistical methods that psychologists apply to the data. Gould (1981) argued that g, as a factor extracted from the factor analysis, is neither a “thing with physical reality” nor a “causal entity”, but is a “mathematical abstraction”, maintaining that “we cannot reify g as a ‘thing’ unless we have convincing, independent information beyond the fact of correlation itself.” Although the present study also draws information from correlations, we were able to depict the structure of human intelligence beyond the fact of phenotypic and genetic correlations with an explicit comparison between the independent pathway and the common pathway model; and as a “causal entity”, as a highly genetically driven entity…

…Several recent reports have shown that g is also correlated with a variety of neural mechanisms, such as glucose metabolism (Haier, 2003), cortical development (Shaw et al., 2006), and biochemical activity (Jung et al., 2005), along with the identification of promising endophenotypes for intelligence such as working memory and processing speed (van Leeuwen, van den Berg, Hoekstra, & Boomsma, 2007). These studies allow us to assume that it is now reasonable to consider g to be a physiological or biological, genetic entity.

Given this, while some GE interactions and correlations might occur for g, they’re unlikely to be substantial causes of differences. When it comes to the Flynn effect, it’s likely that this effect is accounted for my some variation of:

a) across cohort bias (i.e apparent rises in IQ are a measurement artifact). (See: Wicherts, Dolan, Hessen, et al. 2004; Gottfredson, 2009; Kaufman, 2010; Beaujeana and Osterlind, 2008)

b) rises in IQ which do not represent rises in general intelligence. (See: Gottfredson, 2007; Gottfredson, 2009, Jensen, 2011.) When it comes to comparisons across time, Jensen (2011) makes the key point:

The central issue is that methodology by which the dependent variable (viz., secular gains in IQ scores) has been measured, fails to meet the standard of the advanced sciences on an absolutely critical point! Despite the popular inference drawn from all the IQ data collected, this research can neither confirm nor reject the existence of the FE. Doubling the amount of the already massive data (other conditions being unaltered) could not resolve the issue. But whatever the outcome of a proper investigation of the FE, the gentleman– scholar philosopher James Flynn deserves recognition as an important figure in the history of psychometrics. The term Flynn Effect, however, will go down in history as a blind alley in psychometrics, viz., trying to answer a basic, nontrivial factual question using wholly inappropriate data.

Suppose a study were performed on the secular trend in the mean height (measured in either centimeters or inches) of 10- year old school children born and reared in a given locality over the past century. The result per se is not controversial and provides a valid basis for research on its causes. Indeed, such studies are among the least controversial findings in the science of human growth and development. Why? Because ‘height’ can be defined objectively by describing the physical operations used to measure it. The problem with IQ tests and virtually all other scales of mental ability in popular use is that the scores they yield are only ordinal (i.e., rank-order) scales; they lack properties of true ratio scales, which are essential to the interpretation of the obtained measures.

1.1. Minimum requirements for the scientific study of secular changes in psychometric variables

Four conditions are essential for advancing scientific knowledge about intelligence: (a) clearly formulated coherent theory of intelligence; (b) instrumentation for the ratio-scale testing of theory-driven hypotheses; (c) a standard protocol for administering the use of this equipment; and (d) appropriate statistical analysis of the raw data so obtained.

c) heterosis (See: Mingroni, 2007)

d) plan old X-factors working across time (e.g. nutrition). These are hardly implausible since different times represent different environments.

With regards to that last point, Flynn (and Taylor) fallaciously argued that since between population X-factors were theoretically implausible and empirically found not to exist, then between generation X-factors were likewise implausible. The fallacy of this argument is readily shown by the findings of Ang et al., 2009. The authors find no significant difference in the rate of the Flynn effect between ethnic groups, sexes, urbanity, age, family interaction, household income, and income x race. In this data, the Flynn effect indeed behaves like an x-factor.

Of course, even if one did maintain that differences between groups were due to rGE correlations, one would still be left with the basic problem found in the case of X-factor explanations. The rGE factors would have to differentially hit one population living more or less in the same environment — as such they can not be population generic rGE factors like those Flynn argues are behind the secular rise (of IQ). To get around this, Flynn seems to posits average genetic difference between populations!

“The standard model that poses the paradox assumes that environment and genetic endowment are uncorrelated. Applied to basketball, this implies that good coaching, practicing, preoccupation with basketball, and all other environmental factors that influence performance must be unrelated to whether genes contribute to someone being tall, slim, and well coordinated. For this to be true, players must be selected at random for the varsity basketball team and get the benefits of professional coaching and intense practice, without regard to build, quickness, and degree of interest”

So, in short, at minimal Jensen’s dilemma forces Flynn et al. to accept a genetic component to the difference. (What are the cognitive equivalents to well built, quick, attuned, “tall, slim, and well coordinated”?) While this technically might not be a hereditarian account of the gap, it surely is not a 0-genetic account (2).


1. X-factors accounts for groups differences when the groups are living in approximately the same environment are not just theoretically implausible, but, in the US, they have been empirically ruled out. Both Flynn and Nisbett have conceded this. This is substantial given that an X-factor explanation was long held to be the cause of the gap. For example, refer to Sandra Scar’s . In: Race, social class, and individual differences in I.Q.

X-Factors were empirically ruled out by the following studies:

Lubke, et al. 2003. On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model

Rowe, 1994. No more than skin deep. American Psychologist; Rowe and Cleveland, 1996. Academic achievement in Blacks and Whites: Are the developmental processes similar?

Rowe, et. al., Vazsonyi 1994. No more than skin deep: Ethnic and racial similarity in developmental process.

Rowe, et. al., 1995. Ethnic and racial similarity in developmental process: A study of academic achievement.

Rowe, et al. (1994) found that that Blacks, Hispanics, Asians, and Whites have the same relation between background variables and developmental outcomes; Rowe and Cleveland (1994) found that the achievement tests of Black and White siblings and half sibling had the same structure of variances and covariances and that a quantitative genetic model was the best fit explanation. Based on an analysis AFOQT scores; Ree and Carreta (1995) found that the above ethnoracial groups had approximately the same general intelligence loadings — finding effectively ruling out x-factors. X-factors accounts for cohort differences across time are both theoretically plausible and, in many cases, empirically certain (differences in nutrition, etc).

(2) A gene x environment explanation which does not allow one to parse out a specifically genetic components would be non-hereditarian.

(3) There are two types of GE interactions. The first involves non-additive genetics. When it comes to the IQ wars, environmentalists hold out for the possibility of this. With non-additive GE:

What constitutes a good environment for one genotype in terms of the development of the phenotype may constitute a bad environment for some other genotype in terms of the development of the phenotype OR Environmental advantage, through acting in some phenotypic direction for all genotypes may have unequal phenotypic effects on different phenotypes

This is the type of GE that I’m maintaining plays an insignificant role in the heritability of IQ. The second type of GE involves additive genetics. With this type of GE, environmental differences interact with the heritability of a trait, such that poor environments can depress heritability and rich environments can increase heritability. This is the type of GE that is commonly found. What one notices is that with age the h^2 if IQ for impoverished kids nonetheless increases. My guess would be that by adulthood no gene-environment interaction of this sort is found. Whatever the case, this type of GE is irrelevant to the above discussion.

Images from:

Tucker-Drob et al., 2011. Emergence of a Gene× Socioeconomic Status Interaction on Infant Mental Ability Between 10 Months and 2 Years

Harden, 2007. Genotype by environment interaction in adolescents’ cognitive aptitude


Ang et al., 2009. The Flynn Effect within subgroups in the U.S.: Gender, race, income, education, and urbanization differences in the NLSY-Children data

Beaujeana and Osterlind, 2008. Using Item Response Theory to assess the Flynn Effect in the National Longitudinal Study of Youth 79 Children and Young Adults data

Borsboom and Dolan, 2006. Why g is not an adaptation: A comment on Kanazawa (2004).

Dickens, W. T., & Flynn, J. R. (2001). Heritability estimates versus large environmental effects: The IQ paradox resolved.


Gottfredson, 2007. Shattering logic to explain the Flynn Effect.

Gottfredson, 2009. Of what value is intelligence?

“The puzzle of the Flynn Effect remains unsolved in large part because intelligence tests lack ratio-level measurement, that is, a scale that starts at zero quantity and counts in equal-size units from there. Without that capability, we cannot know whether the observed secular rise in average IQ reflects an absolute change in the level of any of the constructs captured by IQ tests, either in g itself or some normally inconsequential source of variance in the composite IQ score. Critics often implicitly assume that IQ scores can register changes in absolute ability level when they criticize intelligence tests. A currently popular critique in this vein is that the Flynn Effect disproves what had come to seem incontrovertibly true, namely, that IQ tests measure a stable, heritable, psychometrically unitary general intelligence. Their argument is that g cannot at once be heritable, unitary, and yet some subtest scores increase dramatically over time. Flynn (2007), for example, argues that the rise has “shattered g.” It obviously has not. The problem is not with our conception or measurement of g, but with our measurement technology not yet allowing us to measure absolute rather than just relative positions along any ability continuum. This is the severest limitation of current IQ tests, yet to be overcome (Jensen, 2006), but it does nothing to invalidate the construct of g or the utility of current IQ tests for many diagnostic, selection, placement, training, and treatment purposes.”

Flynn, 2010. The spectacles through which I see the race and IQ debate

Hulshoff Pol, et al. 2004. Genetic Contributions to Human Brain Morphology and Intelligence

Jensen, 1998. Population Differences In Intelligence: Causal Hypotheses. In: The g Factor: The Science of Mental Ability

From Jensen (1998):

[Given BGH = WGH*(rg(1-rp)/rp(1-rg))] …To accept the preponderance of evidence that WGH > 0 and still insist that BGH = 0 regardless of the magnitude of the WGH, we must attribute the cause of the group difference to either of two sources: (1) the same kinds of environmental factors that influence the level of g but that do so at much greater magnitude between groups than within either group, or (2) empirically identified environmental factors that create variance between groups but do not do so within groups. The “relaxed” default hypothesis allows both of these possibilities. The dual hypothesis, on the other hand, requires either much larger environmental effects between groups than are empirically found, on average, within either group, or the existence of some additional empirically unidentified source of nongenetic variance that causes the difference between groups but does not contribute to individual differences within either group. If the two groups are hypothesized not to differ in WGH or in total phenotypic variance, this hypothesized additional source of nongenetic variance between groups must either have equal but opposite effects within each group, or it must exist only within one group but without producing any additional variance within that group. In 1973, I dubbed this hypothesized additional nongenetic effect Factor X. When groups of blacks and whites who are matched on virtually all of the environmental variables known to be correlated with IQ within either racial population still show a substantial mean difference in IQ, Factor X is the favored explanation in lieu of the hypothesis that genetic factors, though constituting the largest source of variance within groups, are at all involved in the IQ difference between groups. Thus Factor X is an ad hoc hypothesis that violates Occam’s razor, the well-known maxim in science which states that if a phenomenon can he explained without assuming some hypothetical entity, there is no ground for assuming it.

Jensen, 2011. The theory of intelligence and its measurement

Kaufman, 2010. In What Way Are Apples and Oranges Alike?” A Critique of Flynn’s Interpretation of the Flynn Effect

Lenroot, et al., 2007. Differences in genetic and environmental influences on the human cerebral cortex associated with development during childhood and adolescence

Miele, F.: 2002, Intelligence, Race, and Genetics: Conversations with Arthur Jensen.

Mingroni, 2007. Resolving the IQ Paradox: Heterosis as a Cause of the Flynn Effect and

Other Trends

“First, as Dickens and Flynn (2001) pointed out, estimates of the heritability (h2) of IQ are high, at about .75 in adults (Neisser et al., 1996). Without positing genetic change, this would seem to require positing environmental factors that cause large changes over time yet do not vary enough at any single point in time to reduce heritability estimates very much. Dickens and Flynn referred to such an implausible aspect of the environment as a “factor X.” Of note, although Dickens and Flynn carried out their analysis using a value of .75 for h2, they suggest that assuming values as low as .60 would still necessitate positing implausibly large change in those factors that do create environmental variance within generations (i.e., non–factor Xs). The magnitude of IQ heritability estimates, however, is only the first part of the problem. In addition to the magnitude of IQ heritability, the fact that estimates appear to have remained stable over time (Jensen, 1998, pp. 322–323) is also a problem for environmental hypotheses. Some hypotheses, such as nutrition, suggest that IQ-depressing environmental factors kept individuals of the past far below their maximum genetic potential for IQ. However, unless these factors depressed everyone’s IQ by the same amount, their removal from the IQ environment should have also removed an environmental source of variance, thereby causing heritability estimates to rise over time. Conversely, if the trend is due to something like a practice effect that has artificially raised IQ, this should represent the introduction of a new source of environmental variance that should have caused heritability to decline over time, unless, that is, everyone today is practicing the same amount. The consistency of heritability estimates would therefore still pose a problem for environmental hypotheses, even if the estimates were lower, because t would suggest that large evironmental change has occurred, without either the addition or subtraction of any noticeable ource of environmental variance.”

Panizzon et al., 2009. Distinct Genetic Influences on Cortical Surface Area and Cortical Thickness

Ree and Carretta, 1995. Group differences in aptitude factor structure on the ASVAB

Rowe, et al., 2001. Expanding variance and the case of historical changes in IQ means: A critique of Dickens and Flynn

We find a model including a phenotype-environment correlation, like that in Dickens and Flynn’s (2001) Model 2, to be reasonable and plausible. However, features of the model raise three important questions. First, the model implies increasing IQ variance under typical parameter specifications. Yet empirical evidence suggests that IQ variance has not increased, and may even have declined.

Second, the model has not been fit to empirical data. Rather, its performance was evaluated by specifying different (nonoptimized) parameter values and observing the results. Then, they were compared with patterns in the literature. Dickens and Flynn discussed the difficulty of finding appropriate data. Although we are not as pessimistic about data availability as they are, we recognize the problem. However, we also have concern over model fixes and adjustments when those were created to match external empirical patterns, without mechanisms to evaluate their legitimacy. Further, in a related concern, we wonder whether the model can be falsified. What types of patterns would do so? Can we distinguish between incorrectness at a fundamental level, as opposed to problems in some particulars (to which mathematical fixes can be applied)?

Third, we note that more conventional processes—based on more parsimonious models—can account for some IQ gain. Rowe, Jacobson, and Van den Oord (1999) found that genetic and environmental components of IQ variance were moderated by socioeconomic status (SES). In the bottom 20% of the SES distribution, c2 =.40, whereas it was approximately zero in the remainder of the distribution (a result replicated by Thompson, Tiu, & Detterman, 1999). To further elaborate, shared environmental effects were strongest where there was greatest potential of improvement in environmental circumstances. Some part of the Flynn effect may have derived from improved environmental conditions for poor families. As Dickens and Flynn suggested, this effect size may be no more than one third of a standard deviation, too little to be viewed as a complete explanation of the historical change. However, the simplicity of this explanation (and others reviewed earlier) provides a stark contrast to the complexity of Dickens and Flynn’s Model 3.

Rowe and Cleveland, 1996. Academic achievement in Blacks and Whites: Are the developmental processes similar?

Rowe, Vazsonyi, and Flannery, 1994. No more than skin deep: ethnic and racial similarity in developmental process.

This study investigated the similarity of developmental processes in Hispanics, blacks, and whites using correlation matrices. The matrices contained PIAT scores at two time points and a measure of environmental quality specific to each child. All measures were completed by siblings; hence, the correlational structure included all family effects through sibling psychological resemblance and all effects through the HOME measure of family environment. These correlation matrices were statistically equal across Hispanics, blacks, and whites. From this equality of correlation matrices, we concluded that developmental processes that determine variation in PIAT scores were similar across ethnic and racial groups. Statistical power, of course, limits the ability of this study to detect ethnic and racial group differences. Each ethnic or racial group, however, had more than 100 sibling pairs. Small developmental ef- fects may have gone undetected, but certainly larger ones would have appeared as differences in the correlational structures. As a second step, we proposed a specific structural equation model to explain variation in achievement. It postulated an achievement latent trait specific to each child and treated any association between achievement and family environment as noncausal. Other research on the NLSY (Rodgers et al., 1994) found that heritable effects on PIAT subtests were moderate, whereas shared environmental effects were relatively weak. Thus, our model emphasizing genetic effects but minimal family environment effects on PIAT achievement variation is consis tent with these direct behavior genetic analyses of the PIAT subtests in the NLSY. Nonetheless, the family environment (i.e., the HOME score) may also contain some environmental effects on achievement, but ones weaker than the family environment-achievement correlation parameter d (.314), which may contain genetic as well as environmental components. This study’s findings bear upon earlier studies of the construct validity of IQ across ethnic and racial groups. This previous research consisted essentially of showing the equality of 2×2 co variance matrices. In each such matrix, one variable was IQ and another was a theoretically related developmental outcome (e.g., course grades, job performance ratings). In general, such 2×2 matrices were statistically equal for blacks and whites (the groups most frequently studied; Barrett & Depinet, 1991; Cole, 1981; Jensen, 1980). By these statistical criteria, IQ was determined to be an equivalent psychological construct in different ethnic and racial groups. In this study, however, the argument goes considerably further by proposing that the determinants of achievement are identical across ethnic and racial groups. Our explanation for the similarity of developmental processes is that (a) different ethnic and racial groups possess a common gene pool, which can create behavioral similarities, and that (b) among second- generation ethnic and racial groups in the United States, cultural differences are smaller than commonly believed because of the omnipresent force of our mass-market culture, from teleision to fast-food restaurants (see Rowe et al., 1994).

Certainly, a burden of proof must shift to those scholars arguing a cultural difference position. They need to explain how matrices representing developmental processes can be so similar across ethnic and racial groups if major developmental processes exert a minority-specific influence on school achievement. Further research on this topic should consider replacing the more distal categories of ethnicity and race with more proximal cultural variables to measure and identify local cultures. Although local cultures (e.g., a ghetto or barrio culture) may moderate developmental processes, this claim, like the claim about ethnicity and race, remains one that has been widely accepted in the social sciences without strong empirical evidence.

Shaw, 2007. Intelligence and the developing human brain

Taylor, 2006. Heritability and Heterogeneity: The Irrelevance of Heritability in Explaining Differences between Means for Different Human Groups or Generations

Wicherts, Dolan, Hessen, et al. 2004. Are intelligence tests measurement invariant over time? Investigating the nature of the Flynn effect

This clearly contrasts with our current findings on the Flynn effect. It appears therefore that the nature of the Flynn effect is qualitatively different from the nature of B–W differences in the United States. Each comparison of groups should be investigated separately. IQ gaps between cohorts do not teach us anything about IQ gaps between contemporary groups, except that each IQ gap should not be confused with real (i.e., latent) differences in intelligence. Only after a proper analysis of measurement invariance of these IQ gaps is conducted can anything be concluded concerning true differences between groups.

[1] Betjemann, et al., 2009. Genetic Covariation Between Brain Volumes and IQ,

[2] Boomsma, et al., 1998. Genetic influences on childhood IQ in 5- and 7-year-old Dutch twins

[3] Bouchard, 2009. Genetic influence on human intelligence (Spearman’s g): How much?

[4] Gardner, 2007. A meta-analysis of age-related changes in heritability of behavioral phenotypes over adolescence and young adulthood.

[5] Flynn, 2007. What is intelligence? Beyond the Flynn effect.

[6] Haworth, 2009. The heritability of general cognitive ability increases linearly from childhood to young adulthood

[7] Plomin, 1987. Development, genetics, and psychology.

[8] Posthuma, et al., 2003. Brain volumes and the WAIS-III dimensions of verbal comprehension, working memory, perceptual organization, and processing speed.

[9] Shaw et al., 2006. Intellectual ability and cortical development in children and adolescent

[10] Shikishima, et al., 2009. Is g an entity? A Japanese twin study using syllogisms and intelligence tests.

[11] Smit et al., 2010. Endophenotypes in a Dynamically Connected Brain.

[10] van Leeuwen, 2008. A twin-family study of general IQ

[13] van Leeuwen et al., 2009. A genetic analysis of brain volumes and IQ in children.

[14] Jensen, 1973, Educatability and group differences.

Categories: Uncategorized

Spearman’s hypothesis and the Jensen Effect

February 17, 2011 Leave a comment

Group differences and g. There are numerous psychometric tests of intelligence. It has been found that there is a central common factor. This central factor is called general intelligence or g. G is of interest because: 1) it is psychometrically structurally similar across populations, sexes, ages, and cultures, (and several species), 2) it represents a behavior-psychometric manifold with numerous educational, psychological, and sociological correlates, 3) it correlates with the cognitive complexity of activities, 4) it is highly heritable (within populations) and correlates with inbreeding depression scores, and 5) it has numerous neurophysiological correlates such as brain neural conduction velocity, cerebral glucose metabolic rate, the latency and amplitude of evoked electrical brain potentials, the speed and efficiency of brain functioning inferred from reaction time, neural organization, the volume of white and grey matter, the mass of the prefrontal lobe, total brain size, and cranial capacity.

In the history of the debate, the truth of 1-5 were vigorously fought by environmentalists for a reason. If it turned out that g was just a “test artifact” or if it was shown that there was no such physiological manifold — or that groups did not differ in that regards — the hereditarian hypothesis, and its implications, would have been weakened (see: Brand, 2001; Jensen, 2000). Flynn (2007), in fact, recently made the later environmental case, holding that psychometric intelligence is a conglomerate of physiological functions which can “swim free of g,” and, years before (1987), he made the former case, arguing that IQ was only “a correlate with weak causal links to intelligence” (i.e. there’s no central factor binding the various manifestations of intelligence). Steve Rose (1995) articulated one of the environmentalist reasons for opposing the reality of g, worrying that “if intelligence is one thing, it becomes appropriate to seek a single causative agent.” Indeed. As such, Flynn’s (2010) concession, in wake of the mounting evidence, that general intelligence “does have some root in brain physiology” is significant. For a nice summary of the g affair refer to Brand, Constales, and Kane (2003).

The reality of g does two things: it makes the implications of the hereditarian hypothesis, if true, unavoidable, and it substantially strengthens the hereditarian argument. With regards to the former, as Jensen (2000) pointed out, g “lies at the heart of the whole problematic nexus involving the nature of group differences, the merits of meritocratic selection in a diverse society, the legitimacy of using tests, their adverse impact on certain groups, and its redress by group preferences in college admissions and employment.” With regards to the latter, the reality of g loaded differences makes implausible a number of environmental arguments (including virtually all purely sociological ones, such as test bias, motivation, stereotype effect, etc.). Murray (2005) articulates this point well:

A concrete example illustrates how Spearman’s hypothesis works. Two items in the Wechsler and Stanford-Binet IQ tests are known as “forward digit span” and “backward digit span.” In the forward version, the subject repeats a random sequence of one-digit numbers given by the examiner, starting with two digits and adding another with each iteration. The subject’s score is the number of digits that he can repeat without error on two consecutive trials. Digits-backward works exactly the same way except that the digits must be repeated in the opposite order.

Digits-backward is much more g-loaded than digits-forward. Try it yourself and you will see why. Digits-forward is a straightforward matter of short-term memory. Digits-backward makes your brain work much harder.

The black-white difference in digits-backward is about twice as large as the difference in digits-forward.[60] It is a clean example of an effect that resists cultural explanation. It cannot be explained by differential educational attainment, income, or any other socioeconomic factor. Parenting style is irrelevant. Reluctance to “act white” is irrelevant. Motivation is irrelevant. There is no way that any of these variables could systematically encourage black performance in digits-forward while depressing it in digits-backward in the same test at the same time with the same examiner in the same setting.

The g-loadedness of group differences also allows for the argument from Spearman’s hypothesis.

Jensen and Rushton used the method of correlated vectors to show that the B-W gap correlates with more heritable IQ subtests and that the gap is gloaded. They argue that the correlation with heritability implies that there is a genetic etiology to these differences. Additionally, the g-loadedness of the gap supports the hereditarian position. First, given that the environmental hypothesis predicts a relationship between environmentality and group differences and no relationship between heritability and group differences, the correlation with heritability supports the hereditarian hypothesis. Second, environmental explanations or combinations thereof are found wanting when it comes to explaining the said average differences in general intelligence, given that g stands at the nexus of a whole web of psychological, social, and neurophysiological factors; this leaves genetic explanations as the default.

The environmentalists’ reply. Flynn (2006) makes the case that the GQ (general intelligence) gap is closing, which implies that environmental factors are effecting a change; as such, the current lack of coherent environmental explanations does not imply genetic origin to the gap. With regards to the latter argument, Flynn (2010) replies that since g is a proxy for cognitive complexity and since environmental deficits increasing impose disadvantage with complexity, the environmental hypothesis can offer a plausible explanation to the gloaded specificity. As such, a genetic etiology to the differences is not implied. To quote Flynn:

(1) g would be of no interest were it not correlated with cognitive complexity. (2) Given hierarchy of tasks, a worse performing group (whatever the cause of its deficit) will tend to hit a “complexity ceiling” — fall further behind a better group the more complex the task. (3) Heritability of relevant traits will increase the more complex the task. (4) Thus, the fact that group performance gaps correlate with heritability gives no clue to the origin of group differences. (5) When a lower performing group gains on a higher performing one, their gains will tend to diminish the more complex the task. Thus, blacks have gained 5.50 IQ points on whites since 1972 but only 5.13 GQ points. (6) Recent achievement test data confirm these IQ gains but the data as a whole pose problems for the external validity of black IQ. (7) The FE is irrelevant to showing that the racial IQ gap is environmental but it was historically valuable in clarifying the debate.

Both of Flynn’s counters are flawed. With regards to the closing of the GQ gap, the best explanation to date for this was put forth by Murray (2006) and Chay et al. (2009). Accordingly, the g gap was partially closed by health improvements (i.e . environmental influences that had immediate biological impact). This explains the change in the substantially biological g and leaves environmental explanations wanting to explain current g-loaded differences, at least between mid to upper SES members of the said populations. With regards to the g-loadedness of group differences, Flynn’s cognitive explanation does not hold in wake of the vast social, psychological, and neurophysiological manifold that general intelligence represents.

We could use a basketball analogy to capture both positions on this matter. Flynn argues that g is analogous to general basketball ability; it’s important because it correlates with the ability to do complex moves, say like making reverse two-handed dunks. Flynn’s point is that to do a reverse two-handed dunk, one needs to learn all the basic moves. Since environmental disadvantages (poor coaches, limited practicing space, etc.) handicap one when it comes to basic moves, they necessarily handicap one more when it comes to complex basketball moves. Rushton and Jensen argue the g is analogous to a highly heritable athletic quotient; it’s important because it correlates with basic physiology, generalized sports ability, and basic eye-motor coordination. Their point is that it’s implausible that disadvantages in basketball training would lead to across the board disadvantages in all athletic endeavors and, moreover, lead to a larger handicap in general athleticism than to a handicap in basic basketball ability. Rather than disadvantages in basketball training leading to disadvantages in general athletic ability, it’s much more plausible that disadvantages in general athletic ability would lead to a reduced effectiveness of basketball training.

Flynn and other environmentalists can only circumnavigate g by insisting that a web of g affecting environmental circumstances, in effect, constructs g from the outside in. Given that g is psychometrically structurally similar across populations, sexes, ages, and cultures this seems implausible as it would necessitate that either everyone happened to encounter the same patter of g formative environmental circumstances just at different levels of intensity or that environmental circumstances were themselves intercorrelated.

Given the weakness of environmental accounts of GQ differences, the hereditarian hypothesis is a more plausible explanation than the environmental (0-genetic) hypothesis.


Brand, 2001. The g Factor – General Intelligence and its Implications (This is a free, downloadable book)

Brand, Constales, and Kane, 2003. WHY IGNORE THE G FACTOR? — Historical considerations.

Chay, et al., 2009. Birth cohort and the black-white achievement gap: The roles of access and health soon after birth

Dickens and Flynn, 2006. Black Americans reduce the racial IQ gap

Flynn, 2010. The spectacles through which I see the race and IQ debate

Murray, 2005. Inequality taboo.

Murray, 2006. Changes over time in the black-white difference on mental tests: Evidence from the children of the 1979 cohort of the National Longitudinal Survey of Youth

Rushton and Jensen, 2010. Race and IQ: A Theory-Based Review of the Research in Richard Nisbett’s

Categories: Uncategorized

Brain size and correlates with IQ

February 17, 2011 19 comments

When it comes to racial differences in intelligence, the average differences in cranial capacity are an important piece to the puzzle. Whatever their ultimate cause (i.e. whether due to environmental differences or genetic differences that resulted from environmental differences), they establish the deep roots of general mental ability differences. In their recent discussion of race, genes, and IQ, Hunt and Carlson (2007) maintain that differential brain size is a reasonable line of investigation:

Rushton (1995) maintained that one of the reasons for the White–African American disparity in IQ scores is that Whites have larger brain sizes than African Americans. Leaving aside the issue of whether or not one accepts this particular argument, the argument itself illustrates a useful principle. Differences in brain size are associated with intelligence (McDaniel, 2005). Rushton has stated a hypothesis about a biological mechanism, known to influence intelligence, that might explain the difference. Rushton’s claim for a racial disparity in brain sizes was based on exterior skull measures. Further studies, using modern imaging techniques, may provide a more sensitive test of the hypothesis. It would not be appropriate to enter into a detailed discussion here. Our point is simply that discussing this sort of claim is far more likely to increase our understanding of the disparity than is arguing about the percentage of variance associated with biological or environmental variables.

What is the status of this line of investigation?

Obviously, for the brain size explanation to be plausible brain size, cranial capacity, and head circumference need to correlate with differences in intelligence within populations; moreover, brain size, cranial capacity, and head circumference need to be partially heritable. Rushton and Ankney (2009) summarize the findings to date with regards to brain size and intelligence: based on 28 non-clinical published brain imaging samples (N= 1,389) a .40 correlation between IQ and brain size measured by MRI was found; based on 59 published samples (N= 63,405) a .20 correlation between IQ and head circumference was found. These findings are consistent with others. In a meta-analysis McDaniel (2004) found an in vivo brain volume/IQ correlation of 0.33 based on 37 published studies (N= 1535); Reviewing all the data to date (N = 935), Miller and Penke (2007) found a in vivo brain volume/general intelligence (GQ) of .41; the heritability of adult brain volume (N =2494) was found to be .89. Pietschnig, Zeiler, and Voracek, (submitted), found an in vivo brain volume/IQ correlation of .24 based on a meta-analysis of 94 studies published and unpublished.

With regards to the heritability of brain size, based on a review of 14 twin studies using CT to measure brain size, Peper et al. (2007) found a .81 heritablity of global brain measures, .66-97 heritability of brain volume, and .82/.88 heritability of grey/white matter. Moreover, based on a twin study, Posthuma et al. (2002) found a .30 genetic correlation between general intelligence and brain volume, replicating the findings of Pennington et al. (2000), who found a genetic correlation of 0.48 (as cited in Gignac et al, 2003). So, within populations, one can infer a genetic basis to intelligence differences, partially mediated by differences in brain size.

This conclusion is confirmed by the preponderance of the evidence which shows a within family correlation between brain size and IQ (Gignac et al, 2003; Rushton and Jensen, 2010).

In addition to the above two prerequisites, there must be consistent findings of between population differences.

Cranial capacity differences between continental populations

As for cranial capacity differences between regional populations, Beals, et al. (1984) found an average Asian, European, and African cranial capacity of, respectively, 1380, 1362 (sd = 35), and 1276 (sd =85) (N= 20,000); they attributed the selection to cold weather adaptation. (See: Smith and Beals (1990) for population means and standard deviations.) Rushton (2005) summarizes previous global findings: East Asians 1,364, Europeans 1,347, S.Africans 1,267. (See also: Rushton, 1990). Based on a recent study of 699 Nigerians of different ethnicities, Odokuma, et al. (2010) found a mean cranial volume of 1271.

Odokuma, et al. (2010) found a mean cranial volume of 1271; they conclude the following:

The findings in this study are similar to previous studies (Morton, 1839) where the mean cranial volume of the skulls of whites was 1,425 cm³, while that of the Blacks was 1,278 cm³. Based on the measurement of 144 skulls of Native Americans, Morton (1839) reported a figure of 1,344 cm³. Gould (1981) and Rushton (1995) have also showed very similar figures. Tribe had a significant effect on cranial volume at 0.05 levels of significance. Intercultural comparisons demonstrated significant variation as reported by Howells (1989), Froment (1992) and Lahr (1996). While the Ibo’s had an average cranial capacity of 1273.39 cm3 , that of the Urhobo’s was 1255.89 cm3. The Edo’s was 1310.08 cm3. This may be attributable to a . common ancestral origin of the Ibo and Urhobo people or inter marriages which are very common between these cultures with interchange of physical characteristics over the years since these people have been cordial neighbours

There is general acceptance of these findings in the physical anthropology literature. For example, in Chapter 5, Regional Variation and Evolution, of their textbook, Human Lineage, Cartmill and Smith (2009), acknowledge these differences, stating:

They conclude*, as do some others, that the differences are unrelated to IQ, but agree that the differences are genetic, in part. As others argue that the population differences are wholly environmental (e.g., Brody, 2003), it’s important to look at populations that share a common environment.

Cranial capacity differences between racial groups in the US

In the US, it seems that there is indeed a small average difference in cranial circumference between racial groups but more data is needed. (see: Rushton and Ankney, 1999).

Brain mass differences between racial groups in the US

Jensen (1998) summarizes the brain mass findings from the Case-Western Reserve (1980) study (N= 811 W, 450 B). An age matched and height adjusted B-W differences of ~100g (~.78SD) was found, which is commensurate with the findings of Bean (1906), Mall (1909), Pearl (1934), and Vint (1934) as described in Rushton and Ankney (2009). Holloway (2002) found a B-W difference of 63 grams (N = 1,391 W; 615 Black). Similar findings have been found based in imaging studies (see 5). In their study, Isamah, et al. (2010) found that African Americans have 1 SD less total cerebrum volume than European Americans.

Overall, it looks like there’s some consistency among the findings. The results have been numerously replicated, as summarized by Lynn (2006).

Next there must be plausible accounts for selective pressure, relating population differences, brain size, and intelligence. Bailey and Geary (2010) found the following correlations based on the location of 175 archeological sites dating from 1.9 million to 10 thousand years ago.

Cranial capacity positively correlates with latitude and population density both which are positively intercorrelated. It negatively correlates with parasite load and mean temperature.

This conforms with the major evolutionary-hereditarian explanations: Population Density (1), Paleoclimate (2), Geographic Novelty (3), and Disease Burden (4). Ash and Gordon, in Ash and Gordon (2007), give a nice discussion of this perspective:

More recent studies on ASPM and MCPH1, as summarized by Montgomery (2010), bolster this case.

From an environmental standpoint, one would either have to argue that the between population differences in brain size don’t cause between difference in intelligence (a la Cartmill and Smith, 2009) or that the between differences in brain size, while related to differences in cognition, ultimately have an environmental origin. The former requires one to argue that different populations are wired differently, say in the manner of males and females, so that genetic between population differences in size don’t entail between population differences in intelligence; this isn’t supported by within sex studies on brain functioning and structure; and the consistently positive size/IQ correlation within clinal populations (e.g., Sudanese, Guatemalans, African Americans, Asian Indians, Turks, Chileans, etc.) strongly argues against this (see: Rushton and Ankney, 2009). The latter is probably true to some extent; a complete environmental explanation, though, seems very implausible, for several reasons:

1) It requires one to discount the historical evidence which shows a consistent pattern of differences over time and which strongly suggests an adaptive origin to the differences.

2) As the racial population differences in cranial capacity are intercorrelated with numerous other musculoskeletal trait differences, arguing that the cranial capacity differences have a wholly environmental origin require one to maintain that the whole matrix of musculoskeletal trait differences likewise has an environmental origin. Which is implausible. As Rushton and Rushton (2001) note: “[R]ace differences in brain size are correlated with 37 musculoskeletal variables shown in standard evolutionary textbooks to change systematically with increments in brain size. The 37 variables include cranial traits (such as jaw size and shape, tooth size and shape, muscle attachment sites, and orbital bone indentations), and postcranial traits (such as pelvic width, thighbone curvature, and knee joint surface area). Across the three populations, the ‘‘ecological correlations’’ [Jensen, A. R. (1998). The g factor. Westport, CT: Praeger] between brain size and the 37 morphological traits averaged a remarkable r = .94; r = .94. If the races did not differ in brain size, these correlations could not have been found.” See also Rushton and Rushton (2004)

3) Classic studies of geological differences and Cephalic Indexes (CI) contradict a purely environmental hypothesis for differences. For example, Herskovits (1930), found the following association between genealogy an CI:

4) It has been found that population craniometric differences align with genetic differences. As Hubbe et al. (2009) note:

On the other hand, several studies have demonstrated a geographic structure in modern human craniometric diversity on a global level (e.g., Howells, 1973, 1989; Hanihara, 1996). Craniometric data have been found to follow a common geographic pattern with genetic markers, including both classical and microsatellite DNA markers (Relethford, 1994, 2004a, 2009; Manica et al., 2007; Betti et al., 2009). These findings have been interpreted as resulting from an isolation-by-distance model of evolutionary diversification. Furthermore, population relationships inferred from cranial morphology (as reflected both by traditional linear measurements and by 3D geometric morphometric data) have been shown to match those inferred from genetic data (Roseman, 2004; Harvati and Weaver, 2006a,b; Smith, 2009). Taken together, these results suggest that human cranial morphology preserves a relatively strong population history signal, in addition to a climatic and possibly also dietary/masticatory signal (e.g., Relethford, 2004a).

One effectively has to argue that the correlation between craniometric differences and genetic differences is spurious to maintain an environmental case.

Overall, given the reasons above, it seems probable that some of the differences in cranial capacity/brain volume have an evolutionary genetic origin. And, given the genetic correlations between brain volume and IQ, this seems to support a Brain size argument for genotypic IQ differences.


*Cartmill and Smith (2009) cite Deacon (1997). In the cited chapter, “The Size of intelligence: A gross misunderstanding,” Deacon, in fact, does not discuss intrahuman variation in intelligence or brain size. Rather, he argues, as the title of the chapters suggests, that bigger brains did not make humans smarter than other primates. (Cf. Reader (2011), “The evolution of primate general and cultural intelligence; Table 4. The relationship between primate general intelligence and brain volume).

In their discussion, Cartmill and Smith (2009( not only misrepresent Deacon’s chapter but, having brought up the issue, neglect to inform the readers about the copious amount of evidence that has accumulated internationally since the ’90s which establishes that bigger human brains are, indeed, smarter (McDaniel, 2005; Rushton and Ankney, 2009). Moreover, they cite Beals et al.’s thermoregulatory explanation for human variation without making mention that this climatic explanation is complementary with a size-intelligence one (e.g., Ash and Gordon 2007).

(1) Cochran, G., & Harpending, H. (2009). The 10,000-year explosion: How civilization
accelerated human evolution.

(2) Kanazawa, 2008. Temperature and evolutionary novelty as forces behind the evolution of general intelligence. Intelligence

Lynn, 1991. The evolution of race differences in intelligence

Templer and Arikawa, 2006. Temperature, skin color, per capita income, and IQ: An international perspective.

(3) Kanazawa, S. (2004b). General intelligence as a domain-specific adaptation.

(4) Eppig, Fincher, and Thornhil, 2010. Parasite prevalence and the worldwide distribution of cognitive ability

We also propose a complementary hypothesis that may explain some of the effects of infectious disease on intelligence. As we mentioned, it is possible that a conditional developmental pathway exists that invests more energy into the immune system at the expense of brain development. In an environment where there has consistently been a high metabolic cost associated with parasitic infection, selection would not favour the maintenance of a phenotypically plastic trait. That is, the conditional strategy of allocating more energy into brain development during periods of health would be lost, evolutionarily, if periods of health were rare. Peoples living in areas of consistently high prevalence of infectious disease over evolutionary time thus may possess adaptations that favour high obligatory investment in immune function at the expense of other metabolically expensive traits such as intelligence. Data do not currently exist on temporal variation of the severity of infectious disease across the world over human history. For genetically distinct adaptations in intelligence to exist based on this principle, parasite levels must be quite consistent over evolutionary time.

(5) Holloway (2008: The Human Brain Evolving: A Personal Retrospective) has an interesting discussion on the brain controversy:

In the late 1970s and early 1980s, I collected autopsy data from the Pathology department at Columbia’s College of Physicians and Surgeons (now CUMS). I was interested in age, sex, and ethnic effects on brain size changes through time as might be found in cross-sectional data. Roughly 2000 cases were collected, without personal identifications, and all cases of brain pathology were culled out of the data set. The results, unpublished, were roughly the same as found in the Ho et al. (1980, 1981) work on a sample from Milwaukee, which indicated that African American brains were statistically significantly lower in weight than were European American brains, that is, of course referring to the mean values. Ho et al. (1980) concluded that cultural effects were the reason behind the difference. Interestingly, Lieberman (2005) in his review of Rushton’s (2000, 2002) claims regarding ethnic (racial) differences in brain sizes and behaviors ignored this work by Ho et al. Needless to say, Tobias’s oft-cited paper on brain weight collecting methods (Tobias 1970) was cited to claim that autopsy data on brain weights are useless. Unfortunately, however problematic such data are, one tends to forget that autopsies are not done discriminately. Once the body is on the morgue slab, the autopsy is conducted in exactly the same fashion irrespective of the cadaver’s race, and thus comparisons of such data collected by the same anatomist or medical examiner are surely valid, depending on which variables are being compared. Comparing data collected by different examiners may of course be difficult, and perhaps statistical metaanalyses would be in order. To my knowledge, none exists.

Simply put, this research area remains an intensely political and near-suicidal enterprise. (Indeed, one colleague suggested I should incinerate the data; another suggested this kind of study had led to his relatives perishing in the Nazi concentration camps.) The continuing gap in African American and European-descent test scores on various cognitive tests (particularly IQ) throughout the United States and the world (Lynn & Vanhanen 2006) is a source of tremendous concern and acrimonious debate. Indeed, Jon Marks claimed he “outed” me as a “racist” (Marks 2000; see Holloway 2000 for reply) in his biological section of the American Anthropologist Newsletter because I had the temerity to defend Arthur Jensen against Loring Brace’s assertion that Jensen was a bigot. I had read much of this literature (e.g., Jensen 1998) including Jensen’s infamous 1969 piece in the Harvard Law Review and did not find him a racist. I remain appalled at our discipline, which regards him as such and which invented the appellation “Jensenism” to tar and feather him. I remain interested in the possibility that different populations have variation both in their brains and their behavior, but the issue is so politically incorrect that one cannot even approach such a study with anything but trepidation. (For example, the Annual Review article by Freedman & DeBoer 1979 was declared by sociocultural students at Columbia as racist and therefore not to be read!) If one disbelieves there are populational differences in the weight and/or structure of the brain, one should examine the papers by Klekamp and his colleagues, particularly regarding the finding that the primary visual striate cortex of Australian aborigines is significantly larger than in brains from people of European descent (Klekamp et al. 1994). This paper is, to my knowledge, the only paper published since the 1930s that demonstrates a real difference in brain morphology between modern populations (the last compilation of some of these earlier studies on brain morphology differences between different populations can be found in C.J. Connolly’s 1950 book, External Morphology of the Primate Brain, which is a sort of bible for most people working in paleoneurology. See also Kochetkova 1978.) Of course, there is Gould’s Mismeasure of Man, another bible of sorts, which should be read along with Michael’s (1988) Current Anthropology paper, which found that Morton’s rankings were correct and which Gould ignored in his later editions of the same book. There is certainly no evidence that Paul Broca used his elbow on the scales when measuring brains of peoples of European descent! Additional autopsy data sets await my attention, including some 5000 cases from Hong Kong, collected by my colleague Philip Beh, and ∼7500 cases from Singapore, the latter of multiple ethnicities. I hope to get to these data sets when I retire.


Ash and Gordon, 2007. Paleoclimatic Variation and Brain Expansion during Human Evolution

Bailey and Geary, 2010. Hominid Brain Evolution: Testing Climatic, Ecological, and Social Competition Models

Beals, et al., 1984. Brain Size, Cranial Morphology, Climate, and Time Machines

Brody, 2003. Jensen’s genetic interpretation of racial differences in intelligence: Critical evaluation

Gignac et al., 2003. Factors influencing the relationship between brain size and intelligence. In: Nyborg H, Ed. The scientific study of general intelligence: Tribute to Arthur R. Jensen.

Herskovits, 1930. The anthropometry of the American Negro

Hubbe et al., 2009. Climate signatures in the morphological differentiation of worldwide modern human populations

Hunt and Carlson, 2007. Considerations relating to the study of group differences in intelligence

Hwang, et al. 1995. Study on the adult Korean cranial capacity

Isamah, et al., 2010. Variability in Frontotemporal Brain Structure: The Importance of Recruitment of African Americans in Neuroscience Research

Jensen, 1998. The G-Factor

Kanazawa, 2008. Temperature and evolutionary novelty as forces behind the evolution of general intelligence.

Lynn, 2006. Race differences in intelligence.

McDaniels, 2003. Big-brained people are smarter: A meta-analysis of the relationship
between in vivo brain volume and intelligence.

Mekel-Bobrov, 2007. The ongoing adaptive evolution of ASPM and Microcephalin is not explained by increased intelligence and Microcephalin is not explained by increased intelligence

Montgomery, 2010. Brain Evolution: Microcephaly Genes Weigh In.

Peper et al., 2007. Genetic influences on human brain structure: A review of brain imaging studies in twins

Pietschnig, Zeiler, and Voracek, Unpublished. Of valid concerns and invalid effects: Meta-analyzing associations of in-vivo brain volume and IQ.

Posthuma et al., 2002. The association between brain volume and intelligence is of genetic origin.

Odokuma, Igbigbi, Akpuaka, and Esigbenu, 2010. Craniometric patterns of three Nigerian ethnic groups

Rushton, 1990. Race, brain size and intelligence: A rejoinder to Cain and Vanderwolf

Rushton and Ankney, 1999. Size matters: a review and new analyses of racial di􏰀erences in cranial capacity and intelligence that refute Kamin and Omari

Rushton and Rusthon, 2001. Brain size, IQ, and racial-group differences: Evidence from musculoskeletal traits

Rushton and Rusthon, 2004. Progressive changes in brain size and musculo-skeletal traits in seven hominoid populations

Rushton and Ankney, 2009. Whole Brain Size and General Mental Ability: A Review

Rusthon and Jensen, 2010. Race and IQ: A Theory-Based Review of the Research in Richard Nisbett’s Intelligence and How to Get It

Shockely, 1972. Dysgenics, Geneticity, Raceology: A Chalenge to the Intelectual Responsibility of Educators

Smith and Beals, 1990. Cultural correlates with cranial capacity

Categories: Uncategorized

Population Genetics

February 17, 2011 1 comment

McEvoy, 2010. Whole-Genome Genetic Diversity in a Sample of Australians with Deep Aboriginal Ancestry

Bastos-Rodriguez, Pinmenta, Penal, 2006. The Genetic Structure of Human Populations Studied Through Short Insertion-Deletion Polymorphisms.

Li, et al., 2008. Worldwide Human Relationships Inferred from Genome-Wide Patterns of Variation

Jinchuan Xing et al. 2010. Toward a more uniform sampling of human genetic diversity: A survey of worldwide populations by high-density genotyping.

Tishkoff, et. al., 2009. The Genetic Structure and History of Africans and African Americans.

Nelis, 2009. Genetic Structure of Europeans: A View from the North–East.

Heath, 2008. Investigation of the fine structure of European populations with applications to disease association studies.

Coop, Pickrell, and Novembre, 2009. The Role of Geography in Human Adaptation.

Lopez, et al., 2009. Genetic variation and recent positive selection in worldwide human populations: evidence from nearly 1 million SNPs.

Categories: Uncategorized

Achievement gaps on general intelligence loaded tests

February 17, 2011 3 comments

“A second issue is what to do about the achievement gap between children of different ethnicities. This is an extremely difficult and vexing question, one to which many scholars have given a great deal of attention. Our society desperately needs to close this gap.”
— Sternberg, 2008. The Answer Depends On the Question: A Reply To Eric Jensen


Constant at ~1 SD for over 22 years.

In 2009, the SAT gap remained at 1 SD (1).


1. The 2009 SAT reports:
Black averaged scores: 426.67 SD 93.67; White averaged scores: 524.34 SD 100.34

D=(XA-XB)/Sw; Sw=[(NASA^2 + NBSB^2)/(NA + NB)]^1/2 = 1 SD

Categories: Uncategorized

Cognitive Differences and Population Genetics

February 17, 2011 Leave a comment

From Pickrell, Coop, Novembre, et. al., 2009. Signals of recent positive selection in a worldwide sample of Human Populations.

NRG–ERBB4 pathway Among the top selection candidates shown in Figure 1, we noticed that two—ERBB4 and NRG3—are, in fact, binding partners (Zhang et al. 1997). Although these two genes are large, and thus contain a numberof tested windows, they both are outliers with respect to the rest of the genome even after a conservative Bonferroni correction for the number of windows (empirical P = 0.001 and P = 0.006 in the Middle East for ERBB4 and NRG3, respectively). Further inspection of genes in the NRG–ERBB4 pathway (Kanehisa et al. 2008) revealed a striking alignment of selection signals (Fig. 5A). ERBB4 shows extreme extreme iHS signals in all non-African populations (Fig. 5B,C), NRG3 shows extreme iHS signals in West Eurasian populations, and two other binding partners of ERBB4—NRG1 and NRG2—fall well into the 1% tail of iHS scores in East Asia (Fig. 5A). Further, ADAM17, the gene encoding the enzyme that converts NRG1 to its active form (Mei and Xiong 2008), falls in a region that contains some of the most extreme XP-EHH scores in East Asia (maximum value of XP-EHHinthe region of 4.2 at rs2709591, empirical P = 2 3 10 5). The NRG–ERBB4 signaling pathway is well-studied and known to be involved in the development of a number of tissues, including heart, neural, and mammary tissue (Gassmann et al. 1995; Tidcombe et al. 2003). Variants in genes in this pathway have been associated with risk of schizophrenia and various psychiatric phenotypes (Stefansson et al. 2002; Hall et al. 2006; Mei and Xiong 2008). We suggest that an unidentified phenotype affected by this pathway has experienced strong recent selection in non-African populations

As for the NRG-ErB pathway. From: Zhang, 1997. Neuregulin-3 (NRG3): A novel neural tissue-enriched protein that binds and activates ErbB4:

NRG3 is predicted to contain an extracellular domain with an epidermal growth factor (EGF) motif, a transmembrane domain, and a large cytoplasmic domain. We show that the EGF-like domain of NRG3 binds to the extracellular domain of ErbB4 in vitro. Moreover, NRG3 binds to ErbB4 expressed on cells and stimulates tyrosine phosphorylation of this receptor. The expression of NRG3 is highly restricted to the developing and adult nervous system. These data suggest that NRG3 is a novel, neural-enriched ligand for ErbB4.

From: Hernández-Miranda, Parnavelas, and Chiara, 2010. Molecules and mechanisms involved in the generation and migration of cortical interneurons:

NGRs (neuregulins), a family of growth factors encoded by four structurally related genes (NRG-1, NRG-2, NRG-3 and NRG-4), have been related to important events in the developing nervous system (Falls, 2003a; Anton et al., 2004; Xu et al., 2009). They are ligands for receptor tyrosine kinases of the ErbB family and activate a wide spectrum of intracellular signalling cascades, resulting in the induction of cellular responses in different organs (Buonanno and Fischbach, 2001; Falls, 2003a, 2003b; Anton et al., 2004; Britsch, 2007; Birchmeier, 2009). Several lines of evidence suggest that NRG-1 acts as a chemoattractant for interneurons (Yau et al., 2003; Flames et al., 2004). First, ErB4 is expressed in tangentially migrating neurons and co-localises with the interneuron marker DLX2 (Yau et al., 2003). Secondly, soluble NGR1-Ig is expressed in the cortical proliferative zones, and has been hypothesized to attract migrating interneurons to the IZ/SVZ path (Flames et al., 2004; Ghashghaei et al., 2006). Thirdly, secreted NGR1 is a potent chemoattractant for MGE-derived cells in vitro (Flames et al., 2004). Fourthly, loss-of-function assays have demonstrated that the migration of cortical interneurons depends on ErB4 signalling, and their number is significantly decreased in conditional ErB4 mutants (Flames et al., 2004).

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The geography of African IQ

February 17, 2011 Leave a comment

I thought that there might have been a pattern. Guess not.

IQ, gene frequencies, colonial rule.


Wicherts et al., 2009. A systematic literature review of the average IQ of sub-Saharan Africans.

Rindermann et al. 2009. The impact of smart fractions, cognitive ability of politicians and average competence of peoples on social development

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