Introduction

Throughout the nineteenth and early twentieth centuries, the relation between whole brain size and GMA was almost universally accepted (Broca, 1861; Darwin, 1871; Morton, 1849; Topinard, 1878). The renowned French neurologist Paul Broca (1824–1880) measured external and internal skull dimensions and weighed wet brains at autopsy and observed that mature adults averaged a larger brain than either children or the very elderly, skilled workers averaged a larger brain than the unskilled, and eminent individuals averaged a larger brain than the less eminent. Charles Darwin (1871) cited Broca's studies in The Descent of Man to support his theory of evolution:

No one, I presume, doubts that the large size of the brain in man, relatively to his body, in comparison with that of the gorilla or orang, is closely connected with his higher mental powers. We meet the closely analogous facts with insects, in which the cerebral ganglia are of extraordinary dimensions in ants; these ganglia in all the Hymenoptera being many times larger than in the less intelligent orders, such as beetles…

The belief that there exists in man some close relation between the size of the brain and the development of the intellectual faculties is supported by the comparison of the skulls of savage and civilized races, of ancient and modern people, and by analogy of the whole vertebrate series.

Darwin's cousin, Sir Francis Galton (1888), was the first to quantify the relation between brain size and GMA in living people. He multiplied head length by breadth by height and plotted the results against class of degree in more than 1,000 male undergraduates at Cambridge University. He reported that men who obtained high honors degrees had a brain size 2%–5% greater than those who did not. Years later, Karl Pearson (1906) reanalyzed Galton's data using the correlation coefficient he had invented for this type of analysis; he found r = 0.11.

Galton (1869) was also the first to formally propose the scientific concept of GMA. Subsequently, Spearman (1904, 1927) found the various GMA items and subtests correlated positively, indicating they all measured, in different degrees, one underlying dimension, which he dubbed “g,” the general factor of intelligence. Thus, a “spatial” test may be relatively high on g (mental rotation) or low (perceptual speed), a “verbal” test may be relatively high (reasoning) or low (fluency), as might a “memory” test be high (repeating a series in reverse order) or low (repeating a series in presented order). Scores on the g factor give the highest correlations with school grades, job performance, and other criteria such as 0.70–0.80 with academic achievement, 0.70 with military training, 0.30–0.60 with work performance, 0.30–0.40 with income, and 0.20 with conscientiousness, law abidingness, and longevity (Jensen, 1998; Lubinski, 2004).

Following World War II (1939–1945) and the revulsion evoked by Hitler's racial policies, craniometry became associated with extreme forms of racial prejudice. Research on brain size and intelligence virtually ceased, and the literature underwent vigorous critiques (Gould, 1978, 1981; Kamin, 1974; Tobias, 1970). However, as we shall show, modern studies confirm many of the earliest observations.

Anthropologist Van Valen (1974) rekindled discussion of brain size/GMA relations in Homo sapiens by reviewing a handful of studies using external head size which, when corrected for attenuation of measurement, gave an estimate of r = 0.30. He pointed out, it was predictable that correlations between IQ and overall brain size would be modest. First, much of the brain is not involved in producing GMA; thus, variation in size or mass of that tissue will lower the correlation. Second, the measures of GMA are imperfect.

In the appendix, we update our previous reviews of the human brain size/GMA literature (see also Gignac, Vernon, & Wickett, 2003; McDaniel, 2005). To be included, the samples had to be nonclinical; the published reference had to report an actual correlation; and the studies were not to be based on personal communications, unpublished papers, or works merely cited. We report the average or most representative correlation from those studies providing multiple correlations. Double entries were eliminated, particularly those emanating from the U.S. National Collaborative Perinatal Project (Broman et al., 1975, 1987). When possible, data were coded separately by sex. Corrections for body size typically were not included because many studies did not report this statistic, although age effects often were controlled for.

Appendix 1 shows the results of 28 studies that used brain imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) in a total of 1,389 normal subjects. The correlations with GMA range from 0.04 to 0.69, with an unweighted mean of 0.40 (when weighted by sample size, 0.38). Appendix 2 shows the results of 59 studies that recorded external head measurements in a total of 63,405 children, adolescents, and adults. The correlations range from 0.02 to 0.55 with an unweighted mean of 0.21 (when weighted by sample size, 0.20). We obtained the exact p values of all correlations in both Appendix 1 and Appendix 2 using Fisher's (1970, pp. 99–101) method for combining independent probabilities, and calculated the overall p values, which are less than 10⁻¹⁰ in both cases.

Six studies that used Jensen's (1998) method of correlated vectors to distill g from the subtests of an IQ test found the correlation with brain size is even higher (r = 0.63). The procedure consists of calculating the association between a column of quantified elements (such as g loadings) and any parallel column of independently derived scores (such as the correlation between GMA subtests and brain size). For example, Jensen (1994) found a simple correlation of 0.19 between head circumference and g on 17 cognitive tests among 286 adolescents became 0.64 using the method of correlated vectors. Wickett, Vernon, and Lee (2000) correlated brain volume by means of MRI in 68 adults and found r = 0.38 with g extracted from a battery of cognitive tests, but 0.59 when using correlated vectors. Wickett et al.'s head perimeter measure similarly went from 0.19 to 0.34. Schoenemann et al. (2000) obtained a simple correlation of 0.45 between brain volume and g, which Jensen (1998, p. 147) found to be 0.51 using the method of correlated vectors. Jensen (personal communication, August 8, 2002) carried out a vector analysis of the MRI study by MacLullich et al. (2002) and raised the correlation between g and cognitive ability from 0.42 to 0.78. Finally, Colom et al. (2006a) reanalyzed published data on 47 adults and found a correlation of 0.89 between g loadings and number of gray matter clusters, which was higher than the baseline correlations of 0.28–0.51 (Haier, Jung, Yeo, Head, & Alkire, 2004; Wilke, Sohn, Byars, & Holland, 2003).

Additional findings in Appendix 1 are of interest. For example, the brain volume–GMA correlation is equally strong in males and females (e.g., Andreasen et al., 1993; Wickett, Vernon, & Lee, 1994, 2000). It is also found in people of East Asian, East Indian, European, Turkish, African, South American, and Amerindian descent (e.g., Andreasen et al., 1993; Ivanovic et al., 2004; Tan et al., 1999). Age, although it plays a role in brain size and GMA, does not confound the results. Studies using a narrow age range of younger or older samples show the same magnitude of correlation (e.g., Deary et al., 2007; Egan et al., 1994).

Several studies have examined whether different regions of the brain would show differential correlations with GMA. Many appear to show that the size effects are manifest throughout the brain and not specific to any particular region (Andreasen et al., 1993; Reiss et al., 1996). However, other studies show GMA centered in the frontal brain regions (Jung & Haier, 2007). Colom et al. (2006a, 2006b) used the method of correlated vectors and found evidence for both positions—the more g-loaded subtests were distributed throughout the brain but concentrated most in the frontal lobes.

A unique study that did not fit the appendix is Witelson et al.'s (Witelson, Beresh, & Kigar, 2006) prospective study of 100 cancer patients (58 women and 42 men) who completed the Wechsler Adult Intelligence Scale and agreed to a postmortem examination. The subjects were Caucasian 25- to 83-year-olds (mean = 56 years). Although the subjects were ambulatory and well functioning, they were all on medication, which may have affected their performance. Also, some patients survived for several years after completing the tests, resulting in a delay before brain measurement. Nonetheless, the average correlation between brain weight and GMA for males was r = 0.26, and for females it was r = 0.31.

A functional relation between brain size and cognitive ability has been found in four studies showing that the correlation between brain size and GMA holds true within families as well as between families (Bergvall et al., 2006; Gignac et al., 2003; Jensen, 1994; Jensen & Johnson, 1994; although one study failed to do so: Schoenemann et al., 2000). The within-family finding is of special interest because it controls for most of the sources of variance that distinguish families, such as social class, styles of child rearing, and general nutrition, that differ between families. The largest of these is a population-based study that measured head size at birth and GMA at age 18 at the time of conscription in the Swedish military (Bergvall et al., 2006). Analyses were made of data on 96,189 males who had at least one full brother similarly measured. The between-family analysis showed that a small head circumference for gestational age (>1 SD below the mean) was associated with a 10% increase in risk of low intellectual performance (the lowest 11% of scores); the within-family analysis showed a 5% increase in risk.

The human brain may contain up to 100 billion (10¹¹) nerve cells classifiable into 10,000 types resulting in 100,000 billion synapses (Kandel, 1991). The number of neurons available to process information may mediate the correlation between brain size and GMA. Haug (1987, p. 135) showed a correlation of r = 0.48 (N = 81, p < 0.001) between number of cortical neurons (based on a partial count of representative areas of the brain) and brain size. The regression equation was number of cortical neurons (in billions) = 5.583 + 0.006 (cm³ brain volume). The difference between the low end of normal (1,000 cm³) and the high end (1,700 cm³) worked out to be 4.283 billion neurons. Subsequently, Pakkenberg and Gundersen (1997) found a correlation of r = 0.56 between brain size and number of neurons.

Brain size is also correlated with body size. Results from autopsy studies such as the one by Dekaban and Sadowsky (1978) of 2,773 men and 1,963 women, as well as the one by Ho, Roessmann, Straumfjord, and Monroe (1980) of 644 men and 617 women, suggest a correlation of about 0.20 between brain mass (grams) and stature and body mass. Witelson et al.'s (2006) study of 100 cases found body size accounted for from 1% to 4% of the variation in cerebral volume in each sex. Similarly, MRI studies find an average correlation of about 0.20 (Pearlson et al., 1989; Wickett et al., 1994). The relationship is higher (0.30–0.40) with measures of the skull (cm³), estimated either from endocranial volume or from external head measures. In a stratified random sample of 6,325 U.S. servicemen, cranial capacity correlated, on average, 0.38 with height and 0.41 with mass in 2,803 women and 3,522 men (Rushton, 1992a).

Body size is also correlated with GMA about 0.20–0.25. A study of 10,424 children born in Aberdeen (Scotland) between 1950 and 1956 found correlations of r = 0.20–0.25 between body weight at birth and GMA at age 7 years and between height at age 7 and GMA at 11 years (Lawlor et al., 2005). A nation-wide study of 950,000 Swedish men, with analyses made of full-brother-pairs to control for the effects of shared family environment, found a significant relation between heights at age 18 years and completed education at 27 years (Magnusson, Rasmussen, & Gyllensten, 2006).

There is disagreement about whether or not brain size should be corrected for body size when examining brain size/GMA correlations. Controlling for body size changes the question from “Is IQ correlated with absolute brain size?” to “Is IQ correlated with relative brain size?” Although these are quite different questions, evidence shows that the answer to both is yes.

The brain size/GMA relation is also found in other species. For example, Hamilton (1935) found that rats selected for 12 generations to be either “maze-bright” or “maze-dull” differed by 2.5 standard deviations in brain weight; within unselected control rats, there was a correlation of r = 0.25 between maze ability and brain weight. Anderson (1993) found a correlation of r = 0.48 in rats between brain weight at autopsy and GMA extracted from several cognitive tasks. Madden (2001) found that species of bowerbirds that build more complex bowers have larger brains than species that build less complex ones. Schultz, Bradbury, Evans, Gregory, and Blackburn (2005) found that bird species with bigger brains (such as blue tits and magpies) adapted better to the changing British farm and urban environment and were reproductively more successful than populations of birds with smaller brains (like gray partridges and corn buntings). Dunbar and Shultz (2007) found among 24 primate species that the size of the neocortex (relative to body size) correlated r = 0.67 with the size of the social group. Also across 24 primate species, Deaner et al. (2007) found that whole brain size correlates r = 0.65 with domain general learning ability.

Brain Size

Autopsy studies show that brain mass increases during childhood and adolescence and then, beginning as early as 20 years, slowly decreases through middle adulthood, and finally decreases more quickly in old age (Dekaban & Sadowsky, 1978; Ho et al., 1980; Pakkenberg & Voigt, 1964; Voigt & Pakkenberg, 1983). Broca first showed these relationships in the nineteenth century (see reanalysis by Schreider, 1966). The data of Ho et al. (1980), collated for 2,037 subjects from autopsy records, for various subgroups, 1,261 of them between the ages of 25 and 80, are shown in Figure 1. All brains were weighed on the same balance at the Institute of Pathology at Case Western Reserve University after excluding those brains with lesions or other abnormalities. The average mass of the brain increases from 397 g at birth to 1,180 g at 6 years. Growth then slows, and brain mass peaks at about 1,450 g before age 25 years. The mass declines slowly from age 26 to 80 at an average of 2 g per year. The decrease after age 80 years is much steeper, the loss being 5 g per year. As shown in Figure 1, although the rate of decrease varies slightly, it is essentially similar for various subgroups.

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Figure 1

Mean brain weight for 4-year age periods in various subgroups. Brain weight is plotted at midpoint of each age period (e.g., the point at age 6 years represents the average for subjects between 4 and 8 years; White men, open triangles; Black men, solid triangles; White women, open squares; Black women, solid squares). Differences in brain weights among various groups become apparent at age 6 years. (From Ho et al., 1980, p. 636, Figure 2.)

In a cross-sectional stereological investigation, which covered the age range from 20 years to 90 years, Pakkenberg and Gundersen (1997) found that approximately 10% of all neocortical neurons were lost over the life span in both sexes. MRI investigations also show a curvilinear pattern of growth and change, with an overall decrease in brain volume following the late teens as brain tissue is replaced with cerebrospinal fluid (CSF). For example, Pfefferbaum et al. (1994) demarcated cell growth, myelination, pruning, and atrophy, finding that from 3 months to 30 years, cortical gray matter volume (mainly cell bodies) peaked at around age 4 years and then declined steadily throughout the lifespan; cortical white matter volume (myelin sheath) increased steadily until about age 20 years and appeared stable thereafter; and cortical CSF remained stable from 3 months to 20 years and then increased exponentially from 21 to 71 years. Good et al. (2001) studied 465 men and women aged 17–75 years with no history of risk factors and found a decline of global gray matter volume with age of r = 0.49. In the Baltimore Longitudinal Study of Aging, 92 participants aged 59–85 years (at baseline) showed significant annual increases of 1,526 mm³ in ventricular volume (Resnick, Pham, Kraut, Zonderman, & Davatzikos 2003). In that study, volume loss was not uniform, and some areas of the brain reduced in size, while the volume of others was preserved. Staff, Murray, Deary, and Whalley (2006) found that the loss of brain volume in 79–80 year olds was associated with general declines in GMA rather than to any specific abilities.

Brain Size

Nineteenth- and early twentieth-century data from Broca (1861) and others (Hooton, 1939; Sorokin, 1927; Topinard, 1878) suggested that people in higher-status occupations averaged a larger brain or head size than those in lower ones. Galton (1883) collected head size measures and information on the educational and occupational background of thousands of individual visitors to the South Kensington Natural Science Museum in London. However, he had no statistical method for testing the significance of the differences in head size between various occupational or educational groups. Nearly a century later, Galton's data were analyzed by Johnson et al. (1985), who found that professional and semiprofessional groups averaged significantly larger head sizes (in both length and width) than unskilled occupational groups. Subsequently, Rushton and Ankney (1996) calculated cranial capacities from the summary by Johnson et al. (1985) of Galton's head size data and found that cranial capacity increased from unskilled to professional classes from 1,324 to 1,468 cm³ in men and from 1,256 to 1,264 cm³ in women. These figures are uncorrected for body size.

The relationship between head size and occupational status has also been found after correcting for body size. Jensen and Sinha (1993) reviewed much of the literature. They drew an important distinction between a person's socioeconomic position (SEP) of origin (the SEP attained by the person's parents) and the individual's attained SEP (the SEP attained by the person in adulthood). Correlations of IQ, head size, and other variables are always smaller when derived from the SEP of origin than when derived from attained SEP. Thus, Jensen and Sinha analyzed the head circumference data from the National Collaborative Perinatal Project (Broman et al., 1975) of approximately 10,000 White and 12,000 Black 4-year-old children and found a small but significant correlation with social class of origin within both the White and Black populations, after height was controlled for (r = 0.10). Jensen and Sinha also reanalyzed autopsy data reported by Passingham (1979) on 734 men and 305 women and found an overall correlation between brain mass and achieved occupational level of about 0.25, independent of body size.

Studies using brain imaging techniques have also reported significant main effects of brain size on occupational status and education level; higher-status subjects had, on average, a larger brain than lower-status subjects (Andreasen et al., 1990; Pearlson et al., 1989). Rushton (1992a) used the externally measured cranial size of 6,325 U.S. servicemen and found that officers averaged significantly larger cranial capacities than enlisted personnel either before or after adjusting for the effects of stature, weight, race, and sex (1,384 vs. 1,374 cm³ before adjustments; 1,393 vs. 1,375 cm³ after adjustments). The differences between officers and enlisted personnel were found for both men and women, as well as for East Asians, Whites, and Blacks.

Brain Size

An absolute difference in average brain size between men and women has not been disputed since at least the time of Broca (1861). It is often claimed, however, that this difference disappears when corrections are made for body size or age of people sampled (Gould, 1981, 1996). However, Ankney (1992) demonstrated that the sex difference in brain size remains after corrections for body size in a sample of similarly aged men and women (following tentative results by Dekaban & Sadowsky, 1978; Gur et al., 1991; Hofman & Holloway, 1980; Swaab, 1991; Swaab & Hofman, 1984; Willerman et al., 1991).

Ankney (1992) suggested that the large sex difference in brain size went unnoticed for so long because earlier studies used improper statistical techniques to correct for sex differences in body size and thus incorrectly made a large difference “disappear.” The serious methodological error was the use of brain mass/body size ratios instead of analysis of covariance (see Packard & Boardman, 1988). Ankney (1992) illustrated why this is erroneous by showing that, in both men and women, the ratio of brain mass to body size declines as body size increases. Thus, as can be seen in Figure 2, larger women have a lower ratio than smaller women, and the same holds for larger men compared with smaller men. Therefore, because the average-sized man is larger than the average-sized woman, their brain mass to body size ratios are similar. Consequently, the only meaningful comparison is that of brain mass to body size ratios of men and women of equal size. Such comparisons show that at any given size, the ratio of brain mass to body size is much higher in men than in women (Figure 2).

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Figure 2

The relation between the ratio of brain mass/body surface area and body surface area in White men and women. Ankney (1992) calculated the ratios by estimating brain mass at a given body surface area using the equations in Ho et al. (1980, Table 3): men, brain mass = 1,077 g (±56) +173 (±31) × body surface area (r = +0.27, p < 0.01); women, brain mass = 949 g (±52) +188 (±32) × body surface area (r = +0.24, p < 0.01). (From Ankney, 1992, p. 331, Figure 1. Copyright 1992 by Ablex Publishing Corp. Reprinted with permission.).

Ankney (1992) reexamined autopsy data on 1,261 American adults (Ho et al., 1980) and found that at any given body surface area or height, brains of White men are heavier than those of White women, and brains of Black men are heavier than those of Black women. For example, among Whites 168 cm (5′7″) tall (the approximate overall mean height for men and women combined), brain mass of men averages about 100 g heavier than that of women (Figure 3), whereas the average difference in brain mass, uncorrected for body size, is 140 g. Thus, only about 30% of the sex difference in brain size is due to differences in body size.

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Figure 3

The relation between brain mass and body height in White men and women. Lines drawn from equations in Ho et al. (1980, Table 1): men, brain mass = 920 g (±113) + 2.70 (±0.65) × body height (r = 0.20, p < 0.01); women, brain mass = 748 g (±104) + 3.10 (±0.64) × body height (r = +0.24, p < 0.01). (From Ankney, 1992, p. 333, Figure 4. Copyright 1992 by Ablex Publishing Corp. Reprinted with permission.).

Ankney's results were confirmed in a study of cranial capacity in a stratified random sample of 6,325 U.S. Army personnel (Rushton, 1992a). After adjusting, by means of analysis of covariance for effects of age, stature, weight, military rank, and race, men averaged 1,442 cm³ and women 1,332 cm³. This difference was found in all of the 20 or more separate analyses (shown in Figure 4) conducted to rule out any body size effect. Moreover, the difference was replicated across samples of East Asians, Whites, and Blacks, as well as across officers and enlisted personnel. Parenthetically, in the army data, East Asian women constituted the smallest sample (N = 132), and it is probable that this caused the “instability” in estimates of their cranial size when some corrections were made for body size (Figure 4). The sex difference of 110 cm³ found by Rushton, from analysis of external head measurements, is remarkably similar to that (100 g) obtained by Ankney, from analysis of brain mass (1 cm³ = 1.036 g; Hoffman, 1991).

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Figure 4

Cranial capacity for a stratified random sample of 6,325 U.S. Army personnel. The data, grouped into six sex-by-race categories, are collapsed across military rank. (East Asian men, closed circles; White men, closed squares; Black men, closed triangles; East Asian women, open circles; White women, open squares; Black women, open triangles). They show that, across the 19 different analyses controlling for body size, men averaged larger cranial capacities than did women, and East Asians averaged larger than did Whites or Blacks. Analysis 1 presents the data unadjusted for body size showing no difference for East Asian and White men. (From Rushton, 1992a, p. 408, Figure 1. Copyright 1992 by Ablex Publishing Corp. Reprinted with permission.).

Studies using MRI have also confirmed the sex difference in adult brain size (Good et al., 2001; Harvey et al., 1994; Reiss et al., 1996; Willerman et al., 1991). Thus, Ivanovic et al. (2004) carried out a study that controlled for body size in 96 18-year-old male and female high school graduates in Chile and found that the males averaged 1,480 cm³ (SD = 125) before body size adjustments and 1,470 cm³ (SD = 40) after adjustments, while the females averaged 1,394 cm³ (SD = 89) before and 1,404 cm³ (SD = 37) after adjustments.

A stereological investigation by Pakkenberg and Gundersen (1997) found that men had about 4 billion more cortical neurons than did women, and this was not accounted for by differences in height. The average number of neocortical neurons was 19 billion in female brains and 23 billion in male brains, a 16% difference. In their study, which covered the age range from 20 years to 90 years, approximately 10% of all neocortical neurons were lost over the life span in both sexes. Sex and age were the main determinants of the total number of neurons in the human cortex, whereas body size per se had no influence on neuron number.

From birth through the early months, Rushton and Ankney (1966) found the sex difference held across several autopsy studies when, following Ankney's (1992) procedure (see Figure 3), brain masses of boys and girls were compared after matching them for stature (Dekaban & Sadowsky, 1978; Pakkenberg & Voigt, 1964; Voigt & Pakkenberg, 1983). At birth, boys averaged a cranial capacity 5 cm³ larger than girls, a difference that increased to 40 cm³ by 4 months and 50 cm³ by age 1 year, and then remained stable through to age 7 years (Rushton, 1997; controlling for body size). From 7 to 17 years, sex differences in cranial capacity were in the range of 60–100 cm³ (Lynn, 1993; Rushton & Osborne, 1995).

Brain Size

Even before birth, population group differences in average brain size are found from the ninth week of intrauterine life. Schultz (1922) found that 455 White fetuses averaged larger brain cases and smaller faces than did 168 Black fetuses, with the differences becoming more prominent over the course of fetal development. Other traits were found, with Black fetuses averaging a longer forearm to upper arm ratio, a characteristic of Black adults, and a longer fourth to second finger ratio, also characteristic of Black adults (Manning, 2002).

Rushton (1997) analyzed population group differences from birth to age 7 years using measurements of head circumference and GMA gathered on 40,000 children by the U.S. Collaborative Perinatal Project (Broman et al., 1987). The results showed that at birth, 4 months, 1 year, and 7 years, the East Asian children averaged larger cranial volumes than White children who averaged larger cranial volumes than Black children (Figure 5). Within each group, children with larger head sizes obtained higher IQ scores. Moreover, the East Asian children, who averaged the largest craniums, were the shortest in stature and the lightest in weight, whereas the Black children, who averaged the smallest craniums, were the tallest in stature and the heaviest in weight; the differences in brain size were not due to body size.

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Figure 5

Mean cranial capacity (cm³) for African Americans, European Americans, and East Asian Americans from birth through adulthood. Data for birth through age 7 years from the U.S. Perinatal Project; data for adults from the U.S. Army data in Figure 4. (From Rushton, 1997, p. 15, Figure 2. Copyright 1997 by Ablex Publishing Corp. Reprinted with permission.).

Dozens of studies from the 1840s to the 1990s, using four different methods of measuring brain size—MRI, endocranial volume measured from empty skulls, wet brain weight at autopsy, and external head size measurements—all yield similar results. Using MRI, for example, Harvey et al. (1994) found that 41 Africans and West Indians in the United Kingdom had a smaller average brain volume than 67 Caucasians, although Harvey et al. provided no details on how, or if, the samples were matched for age, sex, or body size. In another British study, Jones et al. (1994) found a (not significant) trend for Whites to average a 30 cm³ larger intracranial volume and smaller ventricles than Afro-Caribbeans.

Measuring endocranial volume, the American anthropologist Samuel George Morton (1849) filled over 1,000 skulls with packing material and found that Blacks averaged about 5 cubic inches less cranial capacity than Whites. These results have stood the test of time (Gordon, 1934; Simmons, 1942; Todd, 1923). The largest study of race differences in endocranial volume was by Beals et al. (1984) with measurements of up to 20,000 skulls from around the world. They found that East Asians, Europeans, and Africans averaged cranial volumes of 1,415, 1,362, and 1,268 cm³, respectively.

Weighing brains at autopsy, Broca (1873) found that Whites averaged heavier brains than Blacks and had more complex convolutions and larger frontal lobes. Subsequent studies have found an average Black–White difference of about 100 g (Bean, 1906; Mall, 1909; Pearl, 1934; Vint, 1934). Some studies have found that the more White admixture (judged independently from skin color), the greater the average brain weight in Blacks (Bean, 1906; Pearl, 1934). In a study of 1,261 American adults, Ho et al. (1980) found that 811 White Americans averaged 1,323 g and 450 Black Americans averaged 1,223 g (Figure 1). Since the Blacks and Whites were similar in body size, differences in body size cannot explain away the differences in brain weight.

The largest autopsy study, as yet unpublished, is by anthropologist Ralph Holloway at Columbia University Medical School (personal communications, March 16, 2002, August 26, 2004). He found that in both men and women aged 18–65 years, 615 Blacks, 153 Hispanics, and 1,391 Whites averaged brain weights of 1,222, 1,253, and 1,285 g, respectively. The population groups were all of similar body size. There were also a large number (N = 5,731) of brain weights from 15- to 50-year-old Chinese from Hong Kong and Singapore that averaged 1,290 g.

Cranial volume has also been estimated from external head size measurements (length, width, height). For example, Rushton (1991) examined head size measures in 24 international military samples collated by the U.S. National Aeronautics and Space Administration (NASA) and, after adjusting for the effects of body height, weight, and surface area, found cranial capacity for East Asians was 1,460 cm³ and for Europeans, 1,446 cm³. Rushton (1992a) also calculated average cranial capacities for East Asians, Whites, and Blacks from a stratified random sample of 6,325 U.S. Army personnel and found an average of 1,416, 1,380, and 1,359 cm³, respectively. This study allowed precise adjustments for all kinds of body size measures. Yet adjusting for these did not erase the differences in cranial capacity.

Evolution

It is reasonable to hypothesize that bigger brains have evolved based on natural selection for increased intelligence (Darwin, 1871; Geary, 2005; Jerison, 1973). Over the last 575 million years of evolutionary history, neural complexity and brain size have increased in vertebrates and invertebrates alike (Figure 6), little of which can be explained by body size increases. Russell (1983) calculated encephalization quotients, or EQs, a measure of actual brain size to expected brain size for an animal of that body weight [following Jerison, 1973; EQ = Cranial capacity (cm³)/(0.12)(body weight in grams)^0.67]. Russell (1983) found that the mean EQ was only about 0.30 for mammals living 65 million years ago compared to the average of 1.00 today. EQs for molluscs varied between 0.043 and 0.31, and for insects between 0.008 and 0.045, with the less encephalized species resembling forms that appeared early in the geologic record and the more encephalized species resembling those that appeared later.

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Figure 6

Average encephalization quotient (EQ; natural log), a measure of neural tissue corrected by body size, plotted against elapsed geologic time in millions of years. (After Russell, 1983).

Metabolically, the human brain is an expensive organ. Representing only 2% of body mass, the brain uses about 5% of basal metabolic rate in rats, cats, and dogs, about 10% in rhesus monkeys and other primates, and about 20% in humans (Peters et al., 2004). Moreover, as large brains evolved, they required more prolonged and complex life histories to sustain them. For example, across 234 mammalian species Rushton (2004) found that brain weight correlated with longevity (r = 0.70), gestation time (0.72), birth weight (0.44), litter size (−0.43), age at first mating (0.63), duration of lactation (0.62), body weight (0.44), and body length (0.54). Even after controlling for body weight and body length, brain size continued to predict the other variables (r = 0.59). From an adaptationist perspective, unless large brains substantially contributed to evolutionary fitness (defined as increased survival of genes through successive generations), they would not have evolved. In the evolutionary competition to find and fill new niches, there is always “room at the top” for larger brain size and greater behavioral complexity.

The sexual dimorphism in cranial size and cognitive ability likely originated partly through evolutionary selection of men's hunting ability (Ankney, 1992; Kolakowski & Malina, 1974) and partly through the reproductive success socially dominant men have traditionally enjoyed (Geary, 2005; Lynn, 1994). Population–group differences may have originated from evolutionary pressures presented in colder climates (Rushton, 1995; Templer & Arikawa, 2006). Of course, brain size and intellectual performance are also affected by nutrition and experience (Flynn, 2007; Sternberg, 2004).