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Here is this Rushton paper, cleaned up considerably and now
readable, but still needing a lot of work (especially CLOSE
proofing, as there are many, many typos from the scanning).

A couple of glaring problems:

1) note that the paragraph beginning thus: "Ankney reexamined
autopsy data..." trails off and I cannot find where it picks up.
Here is the end of the paragraph: "...heavier than that of women
(Figure 3), whereas the average dif" That's it. End of paragraph.
No "ferences" elsewhere.

2) the bibliograpy cuts off abruptly in the "S" section.
(A citation by Sorokin, for example, does not appear in the bib).

3) the appendices are utterly unintelligible

There are probably other problems, but those are a few that
caught my eye.

-- Alan


We are grateful to Professor Rushton for providing the following
crucial document which was scanned but not completely corrected
for scanning errors. To be sure to avoid any scanning
errors, please get a copy of the original document. Please
contact the webmaster at [email protected] for any
questions or to suggest corrections. This html page is
provided for reference only.


Psychonomic Bulletin & Review, 1996, 3 (l). 21-36

Brain size and cognitive ability: Correlations with age, sex,
social class, and race


University of Western Ontario, London, Ontario, Canada

We thank E. Hunt, D. N. Jackson. A. R. Jensen, S. Johnson, P.
Kayllonen, H. L. Roediger III, E. Tulving, J. Wicken. L-
Willennan, and several anonymous reviewers for valuable comments
and suggestions. This review draws on what we have previously
published individually and jointly elsewhere. Correspondence
should be addressed to J. P. Rushton. Department of Psychology,
University of Western Ontario, London, ON. Canada N6A 5C2 (e-mail;
[email protected]).


ABSTRACT: Using data from magnetic resonance imaging (MRI),
autopsy, endocranial measurements, and other techniques, we show
that (1) brain size is correlated with cognitive ability about .44
using MRI; (2) brain size varies by age, sex, social class, and
race; and (3) cognitive ability varies by age, sex, social class,
and race. Brain size and cognitive ability show a curvilinear
relation with age, increasing to young adulthood and then
decreasing; increasing from women to men; increasing with
socio-economic status, and increasing from Africans to Europeans
to Asians. Although only further research can determine if such
correlations represent cause and effect, it is clear that the
direction of the brain-size/cognitive-ability relationships
described by Paul Broca (1824-1880), Francis Galton (1822-1911),
and other nineteenth-century visionaries is true, and that the
null hypothesis of no relation, strongly advocated over the last
half century, is false.


With new technologies increasingly available for scanning the
brain, and renewed interest in the evolutionary basis of behavior,
remarkable discoveries are being made that confirm relationships
first established over 100 years ago. Four main procedures have
been used to estimate brain size. In the past, these included
weighing wet brains at autopsy, measuring the volume of empty
skulls using filler, and measuring external head sizes and
estimating volume. Recently, more sophisticated techniques have
been added to the arsenal, including computer assisted tomography
(CAT) and magnetic resonance imaging (MRI) to create, in vrvo,
three-dimensional images of the brain. Data derived from
independent procedures enhance probability of finding truth.

Paul Broca (1824-1880), the renowned French neurologist, made
major contributions to refining early techniques for estimating
brain size. He concluded that variation in brain size was related
to intellectual achievement: mature adults had larger brains than
did either children or the very elderly; skilled workers had
larger brains than did unskilled workers; eminent individuals had
larger brains than did those less eminent; men had larger brains
than did women; and Europeans had larger brains than did Africans.
Such conclusions were widely accepted in the nineteenth century
(e.g., Broca, 1861; Darwin, 1871; Morton, 1849; Topinard, 1878).

Following World War II (1939-1945) and the revulsion toward
Hitler's racial policies, however, craniometry became associated
with extreme forms of racial prejudice. After the U.S. civil
rights movement became prominent in the 1960s, research on brain
size and intelligence, and group differences therein, virtually
ceased and the literature underwent vigorous critiques, notably
from Philip V. Tobias (1970), Leon Kamin (1974), and Stephen Jay
Gould (1978, 1981). In particular, Gould reanalyzed Morton's
(1849) work and alleged "unconscious... finagling" and "Juggling"
(1978, p. 503). In his widely cited Mismeasure of Man, which has
become a standard reference guide to this literature, Gould (1981,
p. 65) suggested how biases could be introduced into such data:
Plausible scenarios are easy to construct. Morton, measuring by
seed, picks up a threateningly large black skull, fills it lightly
and gives it a few desultory shakes. Next, he takes a
distressingly small Caucasian skull, shakes hard and pushes
mightily at the foramen magnum with his thumb. It is easily done,
without conscious motivation; expectation is a powerful guide to

In a book whose title clearly stated their opinion, Not in Our
Genes, Lewontin, Rose, and Kamin (1984) went even further,
implying that the self-deluded prejudice was intentional (p. 52).

The systematic distortion of the evidence by nineteenth-century
anatomists and anthropologists in attempts to prove that the
differences in brain size between male and female brains were
biologically meaningful, or that blacks have smaller brains than
whites has been devastatingly exposed in a detailed reevaluation
by Stephen J. Gould.

However, Gould's (1978, 1981) charge that Morion (1849) doctored
his results to show Caucasian racial superiority has been called
into question. A random sample of the Morton collection was
remeasured by Michael (1988), who found that very few errors had
been made and that these were not in the direction that Gould had
asserted. Instead, errors were found in Gould's own work.

Michael (1988, p. 353) concluded that Morton's research "was
conducted with integrity... [while] Gould is mistaken." As we
shall show, "politically correct" and "egalitarian" conclusions in
favor of the null hypothesis do not hold. Modern studies confirm
many of Broca's (1861) and Morion's (1849) observations.

We emphasize at the outset that enormous variability exists within
each of the populations to be discussed. Because group
distributions overlap substantially on the variables in question,
with average differences amounting to between 4% and 34%, it is
highly problematic to generalize from group averages to
individuals. Nonetheless, as we show, significant among-group
variation in brain size and cognitive ability does exist. This is
not to say, as some readers might implicitly assume, that
brain-size/cognitive-ability differences are due entirely to
genetic factors. Individual brain size (and cognitive ability) can
be affected by nutrition and early experience (Eysenck, 199 la,
1991b; Lynn, 1993b). We later describe a twin study of Whites and
Blacks, boys and girls, estimating that only about 50% of cranial
size variation is due to genetic factors (Rushton & Osbome, 1995).

We also emphasize that nearly all relationships reported in this
paper are correlational. Although we report on parallel
relationships between brain size and cognitive ability across age,
sex, socioeconomic, and racial groups, causal relationships cannot
be demonstrated without longitudinal analysis of individuals.
Moreover, it is important to note that we primarily report on
those menial abilities measured by intelligence tests, although
occasionally we use grades and educational or occupational level.
"Practical" and "social" intelligence (Stemberg, 1988), or
"knowledge," separate from fluid or general (g) intelligence, are
typically not included in our discussion.

Herein, we use the terms East Asian, European, and African to
denote people either from or derived from these geographic areas,
that is, to denote people from the three major geographic races of
humankind. Sometimes the literature refers to these populations as
Orientals, Whites, and Blacks (also as Mongoloids, Caucasoids, and
Negroids; Stringer & Andrews, 1988). Further, we sometimes use
modifiers (e.g., European Americans, White Canadians) and
sometimes national ethnic group names (e.g., Irish, Guatemalan
Indian) to describe some samples more precisely.

Galton (1888) was one of the first to quantify the
brain-size/cognitive-ability relationship in humans. Galton's
subjects were 1,095 Cambridge undergraduate men divided into those
who had achieved first-class honors degrees and those who had not.
Gallon computed head volume by multiplying head length by breadth
by height and plotting the results against age (19 to 25 years)
and class of degree (A, B, C). He reported that (1) cranial
capacity continued to grow after age 19, and (2) men who obtained
high honors degrees had a brain size from 2% to 5% greater than
those who did not. Pearson (1906) reexamined Gallon's data using
his newly developed correlation coefficient and found a small
positive relationship between head size and university grade, This
has remained the general observation, with correlations typically
ranging from .10 to .40 (Jensen & Sinha, 1993; Van Valen, 1974;
Wickett, Vemon, & Lee, 1994).

The Appendix summarizes results from 46 samples of the relation
between head-size/brain-size and cognitive ability. Clinical
samples have been excluded except where clearly identified in the
section on imaging techniques.

The most representative or average correlation has been reported
from those studies providing multiple correlations (e.g., by age
and sex or by adjusting for body size). Corrections for body size
typically were not included because many studies did not report
this statistic (also see below), although age effects were often
controlled, Note that we are simply asking whether, within a
sample, head size and IQ are correlated. We are not asking what
causes head size variations. Double entries were eliminated,
particularly those emanating from the U.S. National Collaborative
Perinatal Project (Broman, Nichols, Shaughnessy, & Kennedy, 1987).
Not included in the Appendix are typological studies showing that
gifted children have larger heads than average (Fisch, Bilek,
Horrobin, & Chang, 1976; Terman, 1926/1959), and mentally
defective children have smaller heads than average (Broman et al.,
1987; Hack et al., 1991).

The 46 samples are categorized into four sections. Section A shows
results of 17 studies that took external head measurements from a
total of 45,056 children and adolescents and correlated these with
estimates of mental ability from ratings, grades, and standardized
tests. Correlations ranged from .08 to .35, with an unweighted
mean of .21 (when weighted by sample size, .20). Section B shows
results from 15 smdies of adult head size/cognitive ability (total
A' = 6,437 people). Correlations ranged from -02 to .39, with an
unweighted mean of .15 (when weighted by sample size, also .15).
Note that the head-size/IQ relation has been found in both sexes
and in East Asians, East Indians, Europeans, Africans, and
Amerindians. Section C shows the results of 7 clinical samples
using a total of 312 adults with brain size estimated by CAT and
MRI and cognitive ability estimated by educational achievement or
by standardized tests. Correlations ranged from .07 to .38, with
an unweighted mean of .24 (when weighted by sample size, .22).
Section D shows the results of 8 nonclinical samples with a total
of 381 adults with brain size estimated by CAT and MRI and
cognitive ability estimated by educational and occupational
achievement or by standardized tests. Correlations ranged from .33
to .69, with an unweighted mean of .44 (when weighted by sample
size, .42).

We obtained the exact p values of all correlations in the Appendix
and, using Fisher's (1970, pp. 99-101) method for combining
independent probabilities, calculated the overall p value, which
was less than 10"] �. However, as Stott (1983, p- 286) noted in
his critical review of the literature on brain size and
intelligence, it is possible that "when correlations are small and
on the borderline of significance, as was the case involving
intelligence and head size, there is no means of ascertaining how
many studies producing results below the level of significance
have been allowed to lie unreported." Most, although not quite
all, of the correlations in the Appendix reached significance, and
none were in the opposite direction. Only one negative report, a
null finding, has come to our attention. Teasdale and Pakkenberg
(1988) estimated brain volume from autopsy data in 26
institutionalized schizophrenic patients in Sweden (14 men and 12
women with a mean age of 73 years at death) and rated their
intelligence on a 3-point scale based on hospital records. After
adjusting for effects of age and sex, Teasdale and Pakkenberg
reported a correlation between brain size and cognitive ability of
-- .05 (n.s.). The sample size was small and the measure of
intelligence questionable, so without additional information to
the contrary, the correlation between head size/brain size and
cognitive ability must be considered an established fact.

Two reviewers suggested that our presentation in the Appendix
rests on the inappropriate assumption that different studies are
equally valid in terms of sample size and nature, measurement of
head or brain, and test of mental ability, and suggested that we
should report only those studies providing the "best evidence."
Because schizophrenics tend to be tall with small heads
(Kretschmer, 1936), and because shrinkage of the brain occurs in
some patient populations, it is possible to further criticize our
inclusion of studies of cognitive ability among schizophrenics and
of those with medically unconfirmable neurologic symptoms (e.g.,
Yeo, Turkheimer, Raz, & Bigler, 1987; DeMyer et al., 1988). Our
view, however, is that it is preferable to show readers all known
data sets concerning brain size and cognitive ability. We are
impressed by the replication of the correlations between head
size/brain size and cognitive ability over such a long period of
time and over such a wide range of subjects and testing
situations. If we took the reviewers' suggestions and examined
only those studies using MRI and other imaging techniques, the
correlation would be considerably higher than from most of the
other estimates in the Appendix. This makes sense, if one assumes
that there is less error variance in imaging techniques, so that
the reliability of the measure is higher and therefore the
correlation between brain size and IQ is greater. Moreover, the
eight imaging samples in Section D, totaling 381 normals, yielded
a mean rot .44 (when weighted by sample size, .42), whereas the
seven samples in Section C, totaling 312 patients, yielded a
correlation ofr = .24 (.22 when weighted for sample size). Thus we
consider our procedures conservative.

A functional relation between head size and cognitive ability has
been implied in two studies showing that the relation exists
within families as well as among them. A tendency for a sibling
with a larger head to have a higher IQ than a sibling with a
smaller head is of special interest, because it controls for many
of the sources of variance that distinguish families such as
cultural background and socioeconomic status. Jensen (1994)
examined 82 pairs of monozygotic and 61 pairs ofdizygotic
adolescent twins and extracted the general factor, or psychometric
g, from their IQ tests and found that it correlated with head size
across individuals (r == .30), within twin pairs (r = .25), and
between twin pairs (r == .32). Jensen and Johnson (1994) examined
the head-size/IQ relation in some 14,000 pairs of siblings from
the Collaborative Perinatal Project (Broman et al., 1987) almost
evenly divided by race (White/Black) and sex, for whom data,
including test data, were obtained at ages 4 and 7 years. Within
each race by sex group, IQ showed low but significant correlations
with head circumference after age and body size were partialed
out. For White boys and White girls, and Black boys and Black
girls, respectively, at age 4, rs = .14, .16, .07, and .07, and at
age 7, rs = .21, .21, .14, and. 15; all correlations significant
at^<.001, twotailed. At age 7 (although not at age 4) the
significant correlation existed within families (r = .11) as well
as between families (r == .20).

It is reasonable to expect that brain size and cognitive ability
are related because Haug (1987, p. 135) showed a correlation ofr =
.479 (n == 81,^ < .001) between number of cortical neurons (based
on a partial count of representative areas of the brain) and brain
size, including both men and women in the sample. The regression
equating the two was given as (# of cortical neurons [in billions]
== 5.583 + 0.006 [cm3 brain volume]). This means that a person
with a brain size of 1,400 cm3 has, on average, 600 million fewer
cortical neurons than an individual with a brain size of 1,500
cm3. The difference between the low end of normal (1,000 cm3) and
the high end (1,700 cm3) works out to be 4.200 billion neurons (a
difference of 27% more neurons from a 41% increase in brain size).

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). Even storing information at the low
average rate of one bit per synapse, which would require two
levels ofsynaptic activity (high and low), the structure as a
whole would generate I014 bits. Contemporary supercomputers, by
comparison, command a memory of about 109 bits of information.

It is also predictable, however, that correlations between IQ and
overall brain size will be modest. First, much of the brain is not
involved in producing what we call intelligence; thus, variation
in size/mass of that tissue will lower the magnitude of the
correlation. Second, IQ, of course, is not a perfect measure of
intelligence and, thus, variation in IQ scores is an imperfect
measure of variation in intelligence.

Although brain size accounts for only a small percentage of
variation in cognitive ability, it is important to note, following
Rosenthal (1984) and Hunter and Schmidt (1990), that small
correlations can have large effects.

For example, although the MRI-established brain-size/ IQ
correlation is only about .40, when squared, it shows that 16% of
the variance is explained, and it also shows that, from regression
predictions, for every 1 standard deviation increase in brain
size, IQ will increase, on average, by 0.40 standard deviations.

Brain size is correlated positively to body size. For example,
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 (1980a, 1980b) of 644
men and 617 women, suggest a correlation of about .20 between
brain mass (grams) and stature and body mass. Similarly, MRI
studies yield an average correlation of about .20 (Pearlson el
al., 1989; Wickett et al., 1994). The brain-size/ body-size
relationship is higher (.30-.40) with measures of the skull (cm3),
either estimated from endocranial volume or from external head
measures. For example, in a stratified random sample of 6,325 U.S.
servicemen, cranial capacity correlated, on average, .38 with
height and .41 with mass in 2,803 women and 3,522 men (Rushton,

There is, however, disagreement about whether or not brain size
should be corrected for body size before brainsize/IQ correlations
are examined (Jensen & Sinha, 1993; Rushton & Ankney, 1995). As
noted by Rushton and Ankney, 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" (see Egan, Wickett. & Vemon, 1995).

Controlling for body size can be regarded to some degree as an
overcorrection because head size itself is part of stature and
body weight.


--- 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 more quickly
decreases in old age (Dekaban & Sadowsky, 1978; Hoetal., 1980a,
1980b; Pakkenberg & Voigt, 1964; Voigt & Pakkenberg, 1983). Ho et
al.'s (1980a, 1980b) data, 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 I. All brains were weighed
on the same balance at the Institute of Pathology at Case Western
Reserve University after those brains with lesions or other
abnormalities were excluded. 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 the age of 25
years. The mass declines slowly from age 26 to 80 years, an
average of 2 g peryear (Ns at 30,40,50,60,70, 80, and 90 = 48, 98,
225,365, 365, 211, and 79, respectively). The decrease after age
80 years is much steeper, the loss being 5 g per year. As shown in
Figure I, although ra-e of mass decrease varies slightly, it is
essentially similar for various subgroups, From birth through
childhood, brain mass at autopsy is correlated with head perimeter
at between 0.80 to 0.98 (Brandt, 1978; Bray, Shields, Wolcott, &
Madsen, 1969; Cooke, Lucas, Yudkin, & Pryse-Davies, 1977). The
correlation between brain mass and head perimeter in adults,
however, is unknown and may be as low as .50 (Van Valen, 1974).
Head perimeter and cranial capacity, like brain mass at autopsy,
also increase with age. The Collaborative Perinatal Project (Table
1), mentioned earlier, followed nearly 40,000 children from
conception to age 7 years. For these children, head perimeter was
measured at birth, 4 months, I year, 4 years, and 7 years (and the
Bayley Mental Scale was given at 8 months, the StanfordBinet at 4
years, and the Wechsier at 7 years). Head perimeters increased
with age and showed individual consistency. For White children,
head perimeter at birth correlated 0.47 with that at 7 years, and
for Black children the correlation was .39. (And, for both races
combined, Bayley IQ scores at 8 months and the Binet IQ scores at
age 4 correlated .25 and ,62, respectively, with Wechsier IQ
scores at age 7. For both White and Black children, head perimeter
at all ages predicted test scores at age 7.

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., point at age 6 years represents average for cases 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 "Analysis of Brain Weight: I. Adult Brain
Weight in Relation to Sex, Race, and Age," by K. C. Ho, U.
Roessmann, J. V. Straumfjord, and G. Monroe, 1980, Archives of
Pathology and Laboratory Medicine, 104. p. 636. Copyright 1980 by
the American Medical Association. Reprinted with permission.

Cranial capacity estimated from external head measures also
increases from age 7 to as late as age 25 as originally shown by
Gallon (1888). The cranial capacities of 4,012 White Australian
boys were tabulated by Jensen and Sinha (1993; from a study by
Miller, 1926) and shown to increase from 1,255 cm3 at age 7 to
1,440 cm3 at age 17. Head size data on 236 pairs of adolescent
twins (472 individuals, Blacks and Whites, boys and giris) were
analyzed by Rushton and Osbome(1995). Collapsing across sex and
race, cranial capacity increased from 1,233 cm3 at age 13 to 1,279
cm3 at age 17. We have calculated cranial capacities from
head-size data for a core longitudinal sample of 748 middle-class
White and Black children provided by Krogman (1970), and analyzed
for sex and race differences by Lynn (1993a), and found that
cranial capacity increased from 1,160 cm3 at age 7 to 1,340 cm3 at
age 15.

Table 1
Head Circumference by Age and Race and Correlations With 1Q at 7 Years
Whites Blacks
Sample Circumference
Age Size (cm) SD r+
Sample Circumference
Size (cm) SD r+
Birth 16.877 34.0 1.5 .13 18.883 33.4 1.7 .12
4 months* 15,905 40.9 1.4 .19 17,793 40.4 1.6 .16
1 year 14,724 45.8 1.5 .20 16,786 45.6 1.5 .15
4 years 12.454 50.1 1.5 .21 14,630 49.9 1.6 .16
7 years 16,949 51.5 1.5 .24 18,644 51.2 1.6 .18

Note -- Data have been calculated from Broman, Nichois,
Shaughnessy, & Kennedy (1987, p. 104, Table 6-10; p. 220,
Table 9-28; p. 226, Table 9-34; p, 233, Table 9-41; p. 247. Table 9-54).

From Race. Evolution and Behavior (p. 40). by J. P. Rushton, 1995,
New Brunswick, NJ: Transaction. Copyright 1995 by Transaction
Publishers. Reprinted with permission, "Contains up to 2% of
children with damage to central nervous system, ^p < .00001.

MRI investigations also show a curvilinear pattern of growth and
change, with an overall decrease in brain volume following the
late teens as gray matter is replaced with cerebrospinal fluid
(range of rs = -- .32 to -.71; Guretal., 1991; Jemigan et al.,
1991; Pfefferbaum et al., 1994; Resnick, 1995). Pfefferbaum et al.
(1994) demarcated cell growth, myelination, pruning, and atrophy.
With a sample of 88 male and female subjects aged 3 months to 30
years, cortical gray matter volume (mainly cell bodies) peaked at
around age 4 years and then declined steadily throughout the life
span; cortical white matter volume (myelin sheath) increased
steadily until about age 20 years and appeared stable thereafter;
and the volume of cortical cerebrospinal fluid remained stable
from 3 months to 20 years. In a sample of 73 male subjects aged 21
to 71 years, cerebrospinal fluid increased exponentially over the
five decades of adulthood studied. Ventricular enlargement from
age 20 to 30 years suggested a possible marker for the onset of
atrophy, whether due to cell loss or cell shrinkage. Other data,
reviewed by Miller (1994), suggest that myelin effectiveness
decreases with aging. Incidentally, these modem data on
age-related brain atrophy confirm Broca's (1861) original data
from the nineteenth century (as reanalyzed by Schreider, 1966).

--- Cognitive Ability

Typically, mental ability measures increase during childhood and
adolescence, decrease slowly between age 25 and 45, and decrease
more quickly after age 45. It once was claimed that this
age-related decline in IQ was spurious because early longitudinal
studies contradicted findings from cross-sectional studies; thus,
the crosssectional observations were derogated as a generation or
"cohort" effect, perhaps due to "more favorable" environments for
younger cohorts (Schaie & Strother, 1968).

However, subsequent longitudinal studies, reviewed by Brody
(1992), have corroborated results from cross-sectional studies.
Brody (1992, p. 238) concluded: "Declines in fluid ability over
the life span up to age 80 might well average 2 standard


--- Brain Size

An absolute difference in brain size between men and women has not
been disputed since at least the time of Broca (1861). It is often
claimed, however, that the sex difference disappears when
corrections are made for body size or age of people sampled
(Gould, 1981; Lewontin et al., 1984). Nevertheless, a recent study
by Ankney (1992) demonstrated that the sex difference in brain
size remains after correction for body size in a sample of
similarly aged men and women (following tentative results by
Dekaban & Sadowsky, 1978; Gur et al., 1991; Hofiman & Swaab,
199I;Holloway, 1980; Swaab& Hofman, 1984; Willerman, Schultz,
Rutledge, & Bigler, 1991).

Ankney (1992) argued 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 do
smaller women, as do larger men compared to smaller men.
Therefore, because the average-sized man is larger than the
average-sized woman, their brain-mass to body-size ratios are
similar (Figure 2). 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).

Ankney reexamined autopsy data on 1,261 American adults (Ho et
al., 1980a, 1980b) and found that at any given body surface area
or height, brains of White men are heavier than those of White
women, as are brains of Black men compared to those of Black
women. For example, among 168-cm (5' 7") tall Whites (the
approximately 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 dif

H 800"
-- o
� 700-1

0 1.5 1.6 1.7 1.8 1.9
Body Surface Area (m)

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. (I980b,
Table 3): men, brain mass = 1,077 g (�56) + 173 (�31) x body
surface area (r= .27,p<.01); women, brain mass == 949 g (�52) +
188 (�32) x body surface area (r = .24, p < M). From "Sex
Differences in Relative Brain Size: The Mismeasure of Woman, Too?"
by C. D. Ankney, 1992, Intelligence, 36, p. 331. Copyright 1992 by
Ablex Publishing Corporation. Reprinted with permisafter
adjustments for body size, differences favoring men of 127 cm3 in
East Asian countries, 163 cm3 in European countries, and 193 cm3
in African countries. (Before adjustments for body size, the
figures are, respectively, 190, 223, and 256 cm3). Andreasen
(1993) corroborated the sex difference in adult brain size using
MRI (see also Gur et al. 1991; Harvey, Persaud, Ron, Baker, &
Murray, 1994; Resnick, 1995; Willerman et al., 1991).

From birth through early months, we found the sex difference held
in several autopsy studies when, following Ankney's (1992)
procedure (Figure 3), we compared brain masses of boys and girls
after matching them for stature (Dekaban & Sadowsky, 1978;
Pakkenberg & Voigt, 1964; Voigt & Pakkenberg, 1983). In children
from 4 to 7 years of age, sex differences are found with brain
size inferred from external head measurements.

After adjustments for body size and race, sex differences in head
perimeter are about 0.40 SD (Jensen & Johnson, 1994). From 7 to 17
years, sex differences in cranial capacity are in the range of 60
to 100 cm3 (Lynn, 1993a; Rushton & Osborne, 1995).

--- Cognitive Ability

These results present a paradox. Women have proportionately
smaller brains than do men, but apparently have the same
intelligence test scores. According to Kimura (1992), however,
women excel in verbal ability, perceptual speed, and motor
coordination within personal space, ference in brain mass,
uncorrected for body size, was 140 g. Thus, only about 30% of the
sex difference in brain size is due to differences in body size.

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 adjustment, via analysis
ofcovariance, 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, done to rule out any body-size effect (see also
Rushlon, 1992a; pp. 406-408). Moreover, the difference was
replicated across samples of Asians, Whites, and Blacks, as well
as across officers and enlisted personnel. Parenthetically, in the
Army data, 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; Hofman, 1991).

n 1250

0 140 150 160 170 180 190 200 210
Height (cm)

Figure 3. The relation between brain mass and body height in
White men and women. Lines drawn from equations in Ho et al.
(I980b, Table I): men, brain mass ^ 920 g (�113) + 2.70 (�.65)
x body height (r = .20, p < -- .01); women, brain mass = 748 g
(� 104) + 3.10 (�.64) x body height (r = .24, p < .01). From "Sex
Differences in Relative Brain Size: The Mismeasure of Woman,
Too?" by C. D. Ankney, 1992, Intelligence, 16, p. 333. Copyright
1992 by Ablex Publishing Corporation. Reprinted with pennis

Analysis Number

Figure 4. Cranial capacity for a stratified random sample of
6325 U.S. Army personnel. The data, grouped into six sex-byrace
categories; are collapsed across military rank. (Asian men, closed
circles; White men, closed squares; Black men, closed triangles;
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 Asians averaged larger than did Whites or
Blacks. Analysis 1 presents the data unadjusted for body size
showing no difference for Asian and European men. Adapted from
"Cranial Capacity Related to Sex, Rank, and Race in a Stratified
Random Sample of 6.325 U.S. Military Personnel," by J. P. Rushton,
1992, Intelligence, 16, p. 408.

Copyright 1992 by Ablex Publishing Corporation. Adapted with

Other studies have confirmed the sex difference. Rushton (1994)
calculated cranial sizes from data on tens of thousands of men and
women aged 25 to 45 collated by the International Labour Office in
Geneva and found, whereas men do better on various spatial tests
and on tests of mathematical reasoning. Although controversy
exists about the magnitude of the sex difference in spatial
ability under various testing conditions, reviews by Pool (1994)
and Voyer, Voyer, and Bryden (1995) have shown that on the
"purest" spatial measures, such as rotating an imaginary object,
or shooting at a moving rather than a stationary target, the sex
difference approaches 1 standard deviation. Thus, Ankney (1992,
1995) hypothesized, the sex difference in brain size relates to
those intellectual abilities at which men excel, that is, spatial
and mathematical abilities require more "brain power."
Analogously, whereas increasing word-processing power in a
computer requires some extra capacity, increasing 3-dimensional
processing, as in graphics, requires a major increase in capacity.

The nineteenth century proposition that men average slightly
higher in general intelligence than do women (e.g., Broca, 1861,
p. 153) has also been reactivated. Lynn's (1994) resolution of the
paradox of the sex difference in brain size was to contradict
(with evidence) the consensus view that there is no difference in
general intelligence. He reviewed data from Britain, Greece,
China, Israel, the Netherlands, Norway, Sweden, and Indonesia, as
well as the United States, to show that men averaged about 4 IQ
points higher than did women on a number of published intelligence
tests. Independently, Jackson (1993) reported a 12 percentile
point advantage to men in a general factor of ability extracted
from data from 180,000 German medical school applicants and, from
the same data set, Stumpfand Jackson (1994) reported a
half-standard deviation advantage to men in reasoning ability.

Subsequently, Jackson (1995) showed an 8 percentile advantage for
men on a general cognitive ability factor extracted from the U.S.
Scholastic Aptitude Test {N = 112,516 individuals).


--- 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 did those in lower ones. For example, Galton
collected head measurements, and information on educational and
occupational background, from thousands of individuals at his
laboratory in 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/educational groups. Nearly a century later. Gallon's
data were analyzed by Johnson, McCIeam, Yuen, Nagoshi, Ahem, and
Cole (1985), who found that professional and semiprofessional
groups averaged significantly larger head sizes (both length and
width) than did unskilled groups. The results were striking for
men but less clear-cut for women.

We have calculated cranial capacities from Johnson et al.'s (1985)
summary of Gallon's head-size data and found that cranial capacity
increased from unskilled to professional classes from 1,324 to
1,468 cm3 in men but only from 1,256 to 1,264 cm3 in women. These
figures are uncorrected for body size. A relationship between head
size and occupational status has also been found after correction
for body size.

Reviewing much of the literature, Jensen and Sinha (1993) drew an
important distinction between a person's SES of origin, which is
the SES attained by the person's parents, and attained SES, which
is the level of SES attained by the person in adulthood.
Correlations of IQ, head size, and other variables are always
smaller when derived from "SES of origin" than when derived from
"attained SES."

The largest set of data on head circumference, from a report by
Broman, Nichols, and Kennedy (1975) on approximately 10,000 White
and 12,000 Black 4-year-old children, was analyzed by Jensen and
Sinha (1993) and showed a small but significant correlation with
social class of origin within both White and Black populations,
after height was controlled (r = .10). Jensen and Sinha (1993)
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 .25, independent of
body size.

Although these correlations are small, they are lower bound
estimates uncorrected for unreliability of measurement and sex
differences in brain size. Pearlson et al. (1989) and Andreasen
etal. (1990) used brain imaging techniques and found significant
main effects of brain size on occupational status and/or
educational level; higher status subjects had, on average, a
larger brain than did lower status subjects. Rushton (1992a) used
externally measured cranial size of 6,325 U.S. servicemen and
found that, both before and after adjusting for effects of
stature, weight, race, and sex, officers averaged significantly
larger cranial capacities than did enlisted personnel (1,384 vs.
1,374 cm3 before adjustments; 1,393 vs. 1,375 cm3 after
adjustments). Further, in each of six separate sex (men, women) by
race (Asian, White, Black) comparisons, officers had a
significantly greater cranial capacity than did enlisted

--- Cognitive Ability

The socioeconomic hierarchies of modem societies in Europe, North
America, and Japan are significantly correlated with scores on
standard IQ tests (Gottfredson, 1986; Hermstein & Murray, 1994;
Jensen, l993a). The basic finding is that there is a difference of
nearly 3 standard deviations (45 IQ points) between average
members of professional and unskilled classes. These are groupmean
differences with considerable overlap of distributions.
Nonetheless, the overall correlation between an individual's IQ
and his or her SES of origin is between .30 and .40, and the
correlation between IQ and attained SES, or occupational level, is
about 0.50 (Hermstein & Murray, 1994; Jensen, 1980). In studies of
intergenerational social mobility, Mascie-Taylor and Gibson (1978)
and Waller (1971) obtained IQ scores of fathers and their adult
sons. They found that, on average, children with lower test scores
than their fathers had gone down in social class as adults, but
those with higher test scores had gone up.


--- Brain Size

In the following review, we conclude that a gradient exists in
brain size from East Asians to Europeans to Africans. As such, we
disagree with the prevailing view that the racial differences in
brain size established in the nineteenth century disappear when
corrections are made for body size and other variables such as
"bias." Because ofinelegancies in many of the studies, however,
only tentative conclusions are warranted, pending more definitive
research. Among the problems we encountered in conducting our
review were the following; (1) What groups should be included in a
racial category? (2) How should we interpret group differences
uncorrected for body size? and (3) How should we interpret
differences in magnitude of only 1% to 3% between races? We
decided to (1) focus primarily on East Asians, Europeans, and
Africans, so we excluded Amerindians, Australian Aboriginees, and
East Indians; (2) correct for body size whenever possible, as we
did earlier in the section on sex differences; and (3) assume that
because a 1% difference of 14 cm3 in brain size translates into
millions of neurons and hundreds of millions of synapses (Haug,
1987), they are not as "miniscule" as they might appear.

In an analysis highly critical of the early literature on wet
brain mass measured at autopsy, Tobias (1970) held that all
interracial comparisons were "invalid" "misleading," and
"meaningless" because 14 crucial variables had been ieft
uncontrolled. In one study or another, these included "sex, body
size, age of death, nutritional state in early life, source of the
sample, occupational group, cause of death, lapse of time after
death, temperature after death, anatomical level of severance,
presence or absence ofcerebrospinal fluid, ofmeninges, and of
blood vessels" (p. 3). Tobias pointed out that each of these
variables alone could increase or decrease brain mass by 10% to
20%, an amount equivalent to or greater than any purported race
differences. He also opposed conclusions of race differences in
structural variables such as cortical thickness, size of frontal
lobe, or complexity of the brain's convolutions.

Rushton (1988a), however, countered that aggregating across
studies typically cancels measurement error, at least
nonsystematic measurement error. Calculating the midpoints of the
range of scores provided by Tobias (1970, p. 6, Table 2), he found
that a "Mongoloid Series" (Tobias's term) averaged 1,368 g,
Caucasoids 1,378 g, and Negroids, 1,316 g. Rushton (I988b) also
averaged a related measure that took body size into account, that
is, the "millions of excess nerve cells" estimated by Tobias for
eight subgroups and nationalities (1970, p. 9, Table 3).

These were the number of neurons available for general adaptive
purposes over and above that necessary for maintaining bodily
functioning and were derivable from equations based on
brain-/body-weight relationships (Jerison, 1963, 1973). Tobias was
skeptical of the value of his "exercise" and provided few details.
Nonetheless, Rushton (I988b) found, in millions of excess neurons,
Mongoloids = 8,990, Caucasoids ^ 8,650, and Negroids = 8,550. As
we shall show, modem studies confirm racial differences in
autopsied brain size.

Many more studies have estimated brain size from cranial capacity
for, as Baker (1974, p. 429) remarked, "Skulls are many, freshly
removed brains are few." The cranial capacity literature, however,
has also undergone serious critiques, as in Gould's (1978, 1981)
reanalysis of Morton's (1849) data described in our Introduction.

Rushton (1988a) showed that Morton's data, even as reassessed by
Gould (1978, p. 508, Table 6), indicated that in cubic inches,
Mongoloids = 85-5, Caucasoids = 84.5, andNegroids = 83.0, which
convert to 1,401,1,385, and 1,360 cm3, respectively. Rushton
(1995, p. 115, Table 6.1) also showed that the same racial
differences held after a subsequent tabulation by Gould (1981),
following an admission by Gould (1981, p. 66) of his own
"embarrassing" error in calculating his 1978 figures. In both his
1978 and 1981 writings, Gould dismissed the differences as
"trivial." But, as noted, differences of 1 cubic inch (16 cm3) in
brain size are not trivial in that they contain literally millions
of neurons and hundreds of millions of synapses.

Modern studies have confirmed earlier findings. Analyzing data on
brain mass at autopsy for 1,261 American subjects aged 25 to 80,
after excluding obviously damaged brains. Ho et al. (1980a)
reported that brain mass averaged 1,392 g in 416 White men (SD =
130) and 1,286 g in 228 Black men (SD = 138), a difference of 106
g. Similarly, brain mass averaged 1,252 g in 395 White women (SD
== 125) and 1,158 g in 222 Black women (SD = 119), a difference of
94 g. Although Ho et al. (1980a) did not provide values corrected
for age or body size, the race differences in absolute brain mass
cannot be explained by those variables: Black men and women in the
sample were, on average, virtually identical in age and size to
their White counterparts.

Analyzing the world database of about 20,000 skulls, uncorrected
for body size, Beals, Smith, and Dodd (1984, p. 307, Table 5)
found that the size of sex-combined brain cases differed by
continental area. Excluding Caucasoid areas of Asia (e.g.,
India) and Africa (e.g., Egypt), 19 Asian populations averaged
1,415 cm3 (SD = 51), 10 European groups averaged 1,362 cm3 (SD =
35), and 9 African groups averaged 1,268 cm3 (SD ^ 85). Using

MRI to measure brain volume in a combined sample of 108 normal and
clinical subjects in Britain aged 18 to 48 years, Harvey et al.
(1994) found that 41 non-Caucasians (Africans and West Indians)
had a smaller brain volume (p = .007) than did 67 Caucasians,
although Harvey et al. (1994) provided little information on
ethnicity and no details on how, or if, the samples were matched
for age, sex, or body size.

Several studies of cranial capacity calculated from external head
measurements were conducted by Rushton, who found, after
corrections were made for body size, that East Asians consistently
averaged larger crania than did Europeans or Africans. For
example, Rushton (1992a) examined a stratified random sample of
6,325 U.S. Army personnel and calculated that for Asians, Whites,
and Blacks, cranial capacities corrected for body size averaged
1,416, 1,380, and 1,359 cm3, respectively (Figure 4). In an
examination of averaged measurements from tens of thousands of men
and women, aged 25 to 45, collated by the International Labour
Office in Geneva, Rushton (1994) calculated that East Asians,
Europeans, and Africans averaged body-size corrected cranial
capacities of 1,308 (SD = 37), 1,297 (SD = 38), and 1,241 cm3 (SD
= 38), respectively.

No exact solution is possible, of course, to the problem of how
large the racial differences are in brain size. There is much
variability from sample to sample, with a clear overlap of
distributions. Nonetheless, the consistency of results found even
with the use of different procedures is noteworthy. Rushton (1995)
reviewed the world database from (1) autopsies, (2) endocranial
volume, (3) cranial capacities estimated from head measurements,
and (4) cranial capacities estimated from head measurements and
also corrected for body size, and found, respectively, in cm3 or
equivalents: East Asians and their descendants = 1,351, 1,415,
1,335,1,356 (mean = 1,364); Europeans and their descendants ==
1,356, 1,362, 1,341, 1,329 (mean == 1,347); and Africans and their
descendants = 1,223, 1,268, 1,284, 1,294 (mean ^ 1,267).

The overall mean Asian/European difference favoring Asians was 17
cm3, and the overall mean European/ African difference favoring
Europeans was 80 cm3. Within-race differences, due to method of
estimation, averaged 31 cm3.

Racial differences in head size appear early in life. As shown in
Table 1, head circumference of White children (uncorrected for
body size) is greater than that of Black children in each age
category by a mean of 0.36 cm or approximately 0.2 SD. The greater
head size of White children, however, is not a function of greater
body size because Black children are taller than White children at
both 4 and 7 years (Broman et al., 1987, Tables 7-8, 819). From 7
to 17 years, the White advantage in cranial capacity is 16 cm3
(Lynn, 1993a; Rushton & Osbome, 1995). With these adolescent data,
however, there is a Striking race X sex interaction, with the
White/Black difference present only for males. On the basis of the
age x sex x race data, Rushton and Osbome (1995) suggested this
was due to maturational differences, with girls maturing earlier
than boys and Blacks maturing earlier than Whites, resulting in
young Black girls being especially larger in body size relative to
their counterparts.

Because this section may be contentious for some readers, it is
worth detailing the concerns of one reviewer who found it "very
misleading." He separated and reexamined published data and
concluded that race differences in brain size were very small. For
example, he noted that cranial capacities of Blacks in the U.S.
Army sampled by Rushton (1992a) fell within the range of Europeans
from the International Labour Office sampled by Rushton (1994),
and he noted that the U.S. Asian/ White difference showed a race X
sex interaction such that a larger difference existed for Asian
women relative to European women than for Asian men relative to
European men. (In Figure 4, for example, Asian men average smaller
brains than White men until body size corrections are made.) The
reviewer also re-examined the International Labour Office data
presented by Rushton (1994). He/she added to the analyses samples
from North and South India that had been explicitly excluded by
Rushton (1994, pp. 288-289, along with Latin American, North
African, and Southeast Asian samples, so as to produce the
"clearest" test of the racial gradient) and thereby reduced the
White/Black difference to non-significance.

We do not doubt that sampling problems occur due to differences in
locating populations, measuring heads, calculating cranial
capacities, and controlling for body size. Mean differences within
races and overlap among races are to be expected. For example,
Rushton (1992a) showed that, in the U.S. Army data. Black officers
averaged significantly larger crania than Black enlisted personnel
(1,369 vs. 1,355 cm3) and (nonsignificantly) larger crania than
White enlisted personnel (1.369 vs. 1,366cm3)Almost any confirmed
hypothesis can be made null if one selects subsets of data. We do
not believe such an approach is useful for making progress in
science. Identifying potential problems in particular studies
should lead to calls for additional research, not trenchant
acceptance of the ni*-!] hypothesis. As we have reviewed,
deconstructing data has led to the erroneous dismissal of
fascinating brain-behavior relationships for six decades. We think
that the onus is on critics to gather new data, using modem
techniques, if they wish to support their null hypothesis that
Asians = Whites == Blacks.

--- Cognitive Ability

Overall, racial differences in measured intelligence parallel
those found in brain size. Although not shown in Table 1, the
three tests of mental ability in the Collaborative Perinatal
Project (Bayley at 8 months, StanfordBinet at 4 years, and
Wechsler at 7 years) all favored White children. The global
literature on cognitive ability was reviewed by Lynn (1991) and
Rushton (1995). East Asians, measured in North America and in
Pacific Rim countries, typically average IQs in the range of 101
to 111. Caucasoid populations in North America, Europe, and
Australasia typically average IQs of from 85 to 115 with an
overall mean of 100. African populations living south of the
Sahara, in North America, in the Caribbean, and in Britain
typically have mean IQs of from 70 to 90.

Questions remain about the validity of using tests for racial
comparisons. Because the tests show similar patterns of internal
item consistency and predictive validity for all groups, and
because the same differences are found on relatively culture-free
tests, many psychometricians think the tests are valid measures of
racial differences, at least among people sharing the culture of
the authors of the test (Hermstein & Murray, 1994; Jensen, 1980;
Snyderman & Rothman, 1987, 1988; Wigdor & Garner, 1982). Speed of
decision making (especially the more complex "odd-man-ouf test;
Jensen, 1993b) typically shows the same three-way racial pattern
as do test scores. Investigations have been done on 9- to
12-yearolds from six countries. Children were asked to decide
which of several lights stands out from others and move a hand to
press a button. All children can perform the task in less than 1
sec, but children with higher IQ scores performed faster (after
controlling for movement time) than did those with lower scores.
Lynn (1991) found that Asian children from Hong Kong and Japan
were faster than were European children from Britain and Ireland,
who in turn were faster than African children from South Africa
(see also Lynn & Shigehisa, 1991). With similar tasks, as well as
those involving retrieval of well-learned facts from long-term
memory, this pattern of racial differences was also found in
California (Jensen, 1993b; Jensen & Whang, 1993,1994).

Additional analyses have shown that differences in African and
European brain size are correlated with differences in mental
ability. In a sample of 286 White and Black adolescents, Jensen
(1994) found that the greater the difference between White and
Black children on 17 cognitive tests, the higher was that tests'
correlation with head size (r ^ .533, p < .05; with unreliability
of measurement controlled, r ^ .715,p < .01). In a study of 4and
7-year-olds, the White and Black samples differed by about 1
standard deviation in IQ and significantly (p < .001) also in head
size (White > Black), even with age, height, and weight
statistically controlled (Jensen & Johnson, 1994). It is
noteworthy that there was no difference in average head size
between White and Black children who were matched on IQ scores
(and on age, height, and weight).


Because of a three-fold increase in relative size of the hominid
brain over the last 3 million years in which australopithecenes
averaged 500 cm3 (the size of a chimpanzee brain), Homo erectus
about 1,000 cm3, and Homo sapiens about 1,300 cm3, it is
reasonable to hypothesize that bigger brains evolved via natural
selection for increased intelligence (Jerison, 1973).
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 (Armstrong, 1990). Across
species, large brains are related to other life history traits,
such as a longer gestation, a slower rate of maturation, a higher
rate of offspring survival, a lower reproductive output, and a
longer life (Hofman, 1993; Pagel & Harvey, 1988). From an
adaptationist perspective, unless large brains had contributed
substantially to evolutionary fitness (defined as increased
survival of genes through successive generations), they would not
have evolved.

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 (Lynn, 1994). Race differences in cranial
capacity may have originated from evolutionary pressures in colder
climates for greater intelligence (Rushton, 1995).

The brain size of individuals, of course, is also affected by
nutrition and experience, most obviously through illness and
trauma. Despite such selection, cranial size, and by inference
brain size, retains moderate heritability in modem humans. Rushton
and Osbome (1995) studied genetic and environmental contributions
to cranial size among 236 pairs of adolescent twins (472
individuals) aged 13to 17 years. Cranial sizes were calculated for
187 boys and 285 girls, 222 Whites and 250 Blacks; age, sex, and
race differences have been reported in relevant sections above.

The genetic contribution to cranial size for the total sample
ranged from 38% to 51%, depending on particular adjustments made
for body size. Environmental effects common to both twins, such as
parental socioeconomic status, ranged from 6% to 20% and
environmental effects unique to each twin, such as illness and
trauma, ranged from 42% to 52%. Heritability estimates did not
vary significantly by sex or race, although there was a trend for
heritabilities to be lower in Blacks than in Whites.

Eysenck (1991 a) and Lynn (1993b) have applied a nutrient
deficiency hypothesis to explain some of the race differences. The
heritabiiity of cognitive ability is now well established from
numerous adoption, twin, and family studies (Bouchard & McGue,
1981). Particularly noteworthy are the heritabilities of around
80% found in adult twins reared apart (Bouchard, Lykken, McGue,
Segal, & Tellegen, 1990; Pedersen, Plomin, Nesselroade, & McCleam,
1992). Moderate to substantial genetic influence on IQ has also
been found in studies of non-Whites, including African Americans
(Osborne, 1980; Scarr, Weinberg, & Waldman, 1993) and Japanese
(Lynn & Hattori, 1990).

Transracial adoption studies suggest a genetic contribution to the
between-group differences. Studies of Korean and Vietnamese
children adopted into White American and White Belgian homes have
shown that, although as babies many had been hospitalized for
malnutrition, they grew to excel in academic ability with IQs 10
points or more higher than their adoptive national norms (dark &
Hanisee, 1982; Frydman & Lynn, 1989; Winick, Meyer, & Harris,
1975). By contrast, Weinberg, Scarr, and Waldman (1992) found that
at age 17, Black and mixed-race children adopted into White
middleclass families performed at a lower level than the White
siblings with whom they had been raised. Regardless, the
importance of genetics for explaining among-group differences in
intelligence remains much more controversial than for explaining
within-group differences (Brody, 1992; Rushton, 1995; Waldman,
Weinberg, & Scarr, 1994).


Differences in cognitive abilities are correlated with differences
in brain size, and both brain size and cognitive ability are
correlated with age, sex, social class, and race. As noted
earlier, correlation does not prove cause and effect, but, just as
zero correlations provide no support for a hypothesis of cause and
effect, non-zero correlations do provide support. We are convinced
that the brain-size/cognitive-ability correlations that we have
reported are, in fact, due to cause and effect. This is because we
are unaware of any variable, other than the brain, that can
directly mediate cognitive ability.

Some have suggested that perhaps increased intellectual activity
and/or improved nutrition cause higher cognitive ability. But,
just as physical activity and/or better nutrition can only
increase physical strength via their effects on muscles, increased
cognitive "strength" can occur only via increased brain function.
Of course, brain size is not the only mediator of brain function;
Miller's (1994) review suggests that amount of brain myelination
is related to IQ (as in work by Schultz, 1991; Willerman, Schultz,
Rutledge, & Bigler, 1994). Nonetheless, we believe that the
important research questions are as follows: (1) What is
responsible for these group differences in brain size; that is,
are they genetically and/or environmentally caused? and (2) Why
does variation in brain size correlate with differences in
cognitive ability? Numerous problem areas remain to be researched.
For example, it is not known whether women have fewer neurons than
do men; there may be greater cortical packing density in women,
and thus, it is myelin thickness or some other variable that is
responsible for the sex differences in brain size (Haug, 1987). In
a postmortem study of brain tissue from the temporal lobes of 5
women and 4 men, Witelson, Glezer, and Kigar (1995) supported the
hypothesis that women's neurons are packed more tightly.

It is unknown, however, whether tightly packed neurons are more or
less efficient than are those that are more widely spaced; the
latter may allow a greater number of synaptic connections. Serious
paradoxes also require resolution. For example. White women have
brain sizes equal to or smaller than those of Black men, but
nonetheless score higher than do Black men in general cognitive
ability. Additional research with magnetic resonance imaging or
behavior genetic techniques is certain to enrich knowledge of
these important relationships. MRI may identify features of the
brain that correlate even more highly with IQ than does volume
(some possibilities are neuronal density, white/gray contrast,
ventricle/brain ratio, and various specific brain regions). More
generally, as Broca and other nineteenth-century scientists
conjectured so long ago, it may be the complexities of the
convolutions of the brain, and the varieties and efficiencies of
its commissures, rather than its actual size, that is related to
intellectual ability and that differentiates populations.


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---- APPENDIX (Continued)

Source Sample Measurement Correlation

Head'TBrain Test

Osborne(l992) 224 European American children (106 boys, 118 girls)
aged 13 to 17; controls for height and weight capacity basic .29
252 African American children (84 boys, 168 girls) aged 13 to 17;
controls for height and weight capacity basic .28
Lynn&Jindal(1993) 200 East Indian 9-year-olds from Kurukshetra in
northern perimeter matrices ,15 India (100 boys, 100 girls) Swan &
Miszkiewicz (undated, cited in Jensen& Sinha, 1993) 843 European
American children in Grades K.-12. with age controlled perimeter
IQ tests .08 Summary' of A Number of samples: 17 Total N: 45.056
Range of r:-08--35Unweighted mean r: .21 ^-weighted mean r: .20 B.
Adults by External Head Measurements Pearson(1906) 1,011 British
male university students length grades .11 Pearl (1906) 935
Bavarian male soldiers perimeter officers' ratings .14
Reid&Mulligan(1923) 449 Scottish male medical students capacity
grades .08 Sommerville(1924) 105 European American .male
university students capacity Thorndike .08 Wrzosek(1931, cited in
Henneberg, Budnik, Pezacka,&Puch(1985) 160 Polish male medical
students capacity Baley's Polish language IQ lest ,14
Schreider(1968) SO Otomi Amerindians from Mexico ofunreported sex
perimeter form board .39 158 French peasants ofunreported sex
perimeter matrices .23 Passingham(l979) 415 English villagers (212
men, 203 women) aged 18 to 75 capacity WA1S .13 Susanne(1979)
2,071 Belgian male conscripts perimeter matrices .19 Henneberg
etal.(1985) 302 Polish medical students (151 men, 151 women) aged
18 to 30 years capacity Baley's Polish language IQ test .14 Bogaen
&. Rushton (1989) 216 While Canadian mate and female university
students, adjusted for sex perimeter MAB .14 Rushlon(1992b) 73
Asian Canadian male and female university students perimeter MAB
.14 211 While Canadian male and female university students
perimeter MAB .21 Reed &Jensen(1993) 2i 1 European American male
college students capacity various .02 Wickeneial-(l994) 40 While
Canadian female university students perimeter MAB .11 Summary ofB
Number of samples: 15 Total N: 6,437 Range off: .02-.39 Unweighted
mean r: .15 ^/-weighted mean r:. 15 C. Adult Clinical Samples by
Imaging Techniques Yeoet al. (1987) 41 European Americans (14 men,
27 women, mean age =38) with medically unconfirmabie neurologic
symptoms DcMycretat-0988) 45 schizophrenic patients and controls
matched for age, race. and sex but not education, with a mean age
of 29 years CAT brain area of 8 or 9 contiguous slices
encompassing 53% of brain MR1, average of 4 slices WAIS WAIS
verbal .07 .21

---- APPENDIX (Continued)

Measurement Source Sample Head/Brain Test Correlation Andreasen
etal.(1990) Flaunietal.(1994) Harvey etal.f 1994) Harvey el al.
(1994) Haieretal.(1995) Summary ofC D. Adult Nonclinical Samples
by Pearlsonelal.(1989) Andreasen et al. (1990) Willermanetal.0991)
Andreasen el al. (1993) Razeial.(1993) Harvey et al. (1994)
Wickelteial.(1994) Eganetal.(1994) Summary' ofD 54 mainly (99%)
European American MRI frontal lobe educational level .31
schizophrenics (36 men, 18 women) with a mean age of 34 years 72
schizophrenic patients (52 men and MRI volume WAIS-R .11 22 women)
with height controlled 26 British bipolar patients (62% women, MRI
volume NART .38 65% Caucasian) aged 21-49 48 British schizophrenic
patients MRI volume NART .24 (59% women, 65% Caucasian) aged 19-61
26 mixed mild mental retardation. MRI volume WAIS-R -36 Down
syndrome, and college student controls (38% controls, 54% males)
with a mean age of 28. Corrected for extended IQ range Number of
samples: 7 Total ?312 Range of r: .07-.38 Unweighted mean r. .24
^-weighted mean r: .22 Imaging Techniques 84 normal Americans of
racially CAT area of occupational stalus .35* heterogeneous
background a single slice (63% White, 35% men. with a mean age of
40) used as a control group for a study of schizophrenics 47
European Americans MRI frontal lobe educational level .33 (28 men,
19 women) with a mean age of 35 years 40 European American
university MRI volume WA1S .35 students (20 men, 20 women).

Corrected for sex, body size, and the extended IQ range 67
European American adults MR! volume WA1S -38 (37 men, 30 women)
with a mean age of 38 29 European American adults MRI volume CFIT
.43 (17 men, 12 women) aged 18 to 78 34 normal British controls
MRI volume NART .69 (45% women, 62% Caucasian) aged 19 to 49 years
40 White Canadian women aged MRI volume MAB -541 20-30 years;
height and weight partialed out and corrected for restriction of
range 40 British military (unreported MRI volume WAIS-R -48+ race
and sex breakdown) aged 23 years. Corrected for height, weight,
and restricted range Number of samples: 8 Total A': 381 Range of
r: .33-.69 Unweighted mean r: .44 // -- weighted mean r. .42 __
___________________ Note -- CAT, Computer Assisted Tomography;
CFIT, Culture Free Intelligence Test; MAB, Multidimensional
Aptitude Battery; MRI. Magnetic Resonance Imaging; NART, New Adult
Reading Test; PMAT, Primary Mental Abilities Test; WAIS-R,
Wechsler Adult Intelligence Scale-Revised: W1SC. Wechsler
Intelligence Scale for Children. *Eta con-elation calculated from
/"ratio (Schultz, 1991), +As corrected by Egan el a]. (1995).

(Manuscript received April 24, 1994;

revision accepted for publication May 17, 1995.)