IntelligenceNew Findings and Theoretical DevelopmentsRic.docx

Intelligence
New Findings and Theoretical Developments
Richard E. Nisbett University of Michigan
Joshua Aronson and Clancy Blair New York University
William Dickens Northeastern University
James Flynn University of Otago
Diane F. Halpern Claremont McKenna College
Eric Turkheimer University of Virginia
We review new findings and new theoretical developments
in the field of intelligence. New findings include the follow-
ing: (a) Heritability of IQ varies significantly by social
class. (b) Almost no genetic polymorphisms have been
discovered that are consistently associated with variation
in IQ in the normal range. (c) Much has been learned
about the biological underpinnings of intelligence. (d)
“Crystallized” and “fluid” IQ are quite different aspects of
intelligence at both the behavioral and biological levels.
(e) The importance of the environment for IQ is established
by the 12-point to 18-point increase in IQ when children
are adopted from working-class to middle-class homes. (f)
Even when improvements in IQ produced by the most
effective early childhood interventions fail to persist, there
can be very marked effects on academic achievement and
life outcomes. (g) In most developed countries studied,
gains on IQ tests have continued, and they are beginning in
the developing world. (h) Sex differences in aspects of
intelligence are due partly to identifiable biological factors
and partly to socialization factors. (i) The IQ gap between

Blacks and Whites has been reduced by 0.33 SD in recent
years. We report theorizing concerning (a) the relationship
between working memory and intelligence, (b) the appar-
ent contradiction between strong heritability effects on IQ
and strong secular effects on IQ, (c) whether a general
intelligence factor could arise from initially largely inde-
pendent cognitive skills, (d) the relation between self-reg-
ulation and cognitive skills, and (e) the effects of stress on
intelligence.
Keywords: intelligence, fluid and crystallized intelligence,
environmental and genetic influences, heritability, race and
sex differences
In 1994, a controversial book about intelligence byRichard
Herrnstein and Charles Murray called The BellCurve was
published. The book argued that IQ tests are
an accurate measure of intelligence; that IQ is a strong
predictor of school and career achievement; that IQ is
highly heritable; that IQ is little influenced by environmen-
tal factors; that racial differences in IQ are likely due at
least in part, and perhaps in large part, to genetics; that
environmental effects of all kinds have only a modest effect
on IQ; and that educational and other interventions have
little impact on IQ and little effect on racial differences in
IQ. The authors were skeptical about the ability of public
policy initiatives to have much impact on IQ or IQ-related
outcomes.
The Bell Curve sold more than 300,000 copies and
was given enormous attention by the press, which was
largely uncritical of the methods and conclusions of the
book. The Science Directorate of the American Psycholog-
ical Association felt it was important to present the con-
sensus of intelligence experts on the issues raised by the

book, and to that end a group of experts representing a wide
range of views was commissioned to produce a summary of
facts that were widely agreed upon in the field and a survey
of what the experts felt were important questions requiring
further research. The leader of the group was Ulrich Neis-
ser, and the article that was produced was critical of The
Bell Curve in some important respects (Neisser et al.,
1996). The article was also an excellent summary of what
the great majority of experts believed to be the facts about
intelligence at the time and important future directions for
research.
Fifteen years after publication of the review by Neis-
ser and colleagues (1996), a great many important new
facts about intelligence have been discovered. It is our
intent in this review to update the Neisser et al. article
(which remains in many ways a good summary of the field
This article was published Online First January 2, 2012.
Richard E. Nisbett, Institute for Social Research, University of
Mich-
igan; Joshua Aronson and Clancy Blair, Department of Applied
Psychol-
ogy, New York University; William Dickens, Department of
Economics,
Northeastern University; James Flynn, Department of
Psychology, Uni-
versity of Otago, Dunedin, New Zealand; Diane F. Halpern,
Department
of Psychology, Claremont McKenna College; Eric Turkheimer,
Depart-
ment of Psychology, University of Virginia.
The writing of this article and much of the research that went
into it

were supported by a generous grant from the Russell Sage
Foundation, by
National Institute on Aging Grant 1 R01 AG029509-01A2, and
by Na-
tional Science Foundation Grant 2007: BCS 0717982. The views
pre-
sented here are not necessarily those of the National Science
Foundation.
We thank Angela Duckworth, Richard Haier, Susanne Jaeggi,
John
Jonides, Scott Kaufman, John Protzko, Carl Shulman, Robert
Sternberg,
and Oscar Ybarra for their critiques of an earlier version of this
article.
Correspondence concerning this article should be addressed to
Rich-
ard E. Nisbett, Institute for Social Research, 3229 East Hall,
University of
Michigan, Ann Arbor, MI 48109. E-mail: [email protected]
THIS ARTICLE HAS BEEN CORRECTED. SEE LAST PAGE
130 February–March 2012 ● American Psychologist
© 2012 American Psychological Association 0003-
066X/12/$12.00
Vol. 67, No. 2, 130 –159 DOI: 10.1037/a0026699
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of intelligence). There are three chief respects in which our
review differs importantly from that of Neisser and col-
leagues: (a) Due in part to imaging techniques, a great deal
is now known about the biology of intelligence. (b) Much
more is known about the effects of environment on intel-
ligence, and a great deal of that knowledge points toward
assigning a larger role to the environment than did Neisser
and colleagues and toward a more optimistic attitude about
intervention possibilities. (c) More is now known about the
effects of genes on intelligence and on the interaction of
genes and the environment. Our article also presents a wide
range of new theoretical questions and reviews some at-
tempted solutions to those questions. We do not claim to
represent the full range of views about intelligence. We do
maintain, however, that few of the findings we report have
been widely contradicted. Where we are aware of contro-
versy, we provide sources where readers can be exposed to
alternative views. We acknowledge that the theoretical
questions we raise might not be the ones that every expert
would agree are the most important ones, and we recognize
that not every expert will agree with our views on these
questions. We have referenced alternative views where we
are aware that such exist.
The article is organized under the rubrics of genes and
the environment, new knowledge about the effects of the
environment, new knowledge about interventions, the bi-
ology of intelligence, group differences in IQ, and impor-
tant unresolved issues. Our working definition of intelli-

gence is essentially that offered by Linda Gottfredson
(1997):
[Intelligence] . . . involves the ability to reason, plan, solve
prob-
lems, think abstractly, comprehend complex ideas, learn quickly
and learn from experience. It is not merely book learning, a
narrow academic skill, or test-taking smarts. Rather it reflects a
broader and deeper capability for comprehending our surround-
ings—“catching on,” “making sense” of things, or “figuring
out”
what to do. (p. 13)
The measurement of intelligence is one of psycholo-
gy’s greatest achievements and one of its most controver-
sial. Critics complain that no single test can capture the
complexity of human intelligence, all measurement is im-
perfect, no single measure is completely free from cultural
bias, and there is the potential for misuse of scores on tests
of intelligence. There is some merit to all these criticisms.
But we would counter that the measurement of intelli-
gence—which has been done primarily by IQ tests— has
utilitarian value because it is a reasonably good predictor of
grades at school, performance at work, and many other
aspects of success in life (Gottfredson, 2004; Herrnstein &
Murray, 1994). For example, students who score high on
tests such as the SAT and the ACT, which correlate highly
with IQ measures (Detterman & Daniel, 1989), tend to
perform better in school than those who score lower (Coyle
& Pillow, 2008). Similarly, people in professional careers,
such as attorneys, accountants, and physicians, tend to have
high IQs. Even within very narrowly defined jobs and on
very narrowly defined tasks, those with higher IQs outper-
form those with lower IQs on average, with the effects of
IQ being largest for those occupations and tasks that are

most demanding of cognitive skills (F. L. Schmidt &
Hunter, 1998, 2004). It is important to remain vigilant for
misuse of scores on tests of intelligence or any other
psychological assessment and to look for possible biases in
any measure, but intelligence test scores remain useful
when applied in a thoughtful and transparent manner.
IQ is also important because some group differences
are large and predictive of performance in many domains.
Much evidence indicates that it would be difficult to over-
come racial disadvantage if IQ differences could not be
ameliorated. IQ tests help us to track the changes in intel-
ligence of different groups and of entire nations and to
measure the impact of interventions intended to improve
intelligence.
Types of intelligence other than the analytic kind
examined by IQ tests certainly have a reality. Robert Stern-
berg and his colleagues (Sternberg, 1999, 2006) have stud-
ied practical intelligence, which they define as the ability
to solve concrete problems in real life that require search-
ing for information not necessarily contained in a problem
statement, and for which many solutions are possible, as
well as creativity, or the ability to come up with novel
solutions to problems and to originate interesting questions.
Sternberg and his colleagues maintain that both practical
intelligence and creativity can be measured, that they cor-
relate only moderately with analytic intelligence as mea-
sured by IQ tests, and that they can predict significant
amounts of variance in academic and occupational achieve-
ment over and above what can be predicted by IQ measures
alone. Early claims by Sternberg were disputed by other
intelligence researchers (Brody, 2003; Gottfredson, 2003a,
2003b). Subsequent work by Sternberg (2006, 2007) im-
proved on the original evidence base and showed that
measuring nonanalytic aspects of intelligence could signif-

icantly improve the predictive power of intelligence tests.
See also Hunt (2011) and Willis, Dumont and Kaufman
(2011).
The chief measure that we focus on in this article is
IQ, because it is for that measure that the bulk of evidence
pertinent to intelligence exists. When relevant, we distin-
guish between IQ and g— often identified as the first factor
extracted in a factor analysis of IQ subtests and with which
all IQ subtests correlate. Many intelligence investigators
place great importance on the difference between g and IQ
on the grounds that g tends to predict some academic and
life outcomes and group differences better than does IQ and
because it correlates with some biological measures better
than does IQ (Jensen, 1998). We do not in general share the
view that g is importantly different from IQ, and we do not
interpret correlations between g and life outcomes, group
differences, and biological measures as having the same
import as do many other investigators. Our differences with
those scientists will be noted at several points in this article.
An important distinction commonly made in the liter-
ature is between crystallized intelligence, g(C), or the in-
dividual’s store of knowledge about the nature of the world
and learned operations such as arithmetical ones which can
be drawn on in solving problems, and fluid intelligence,
131February–March 2012 ● American Psychologist
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g(F), which is the ability to solve novel problems that
depend relatively little on stored knowledge as well as the
ability to learn. A test that is often considered the best
available measure of g(F) is Raven’s Progressive Matrices.
This test requires examination of a matrix of geometric
figures that differ from one another according to a rule to be
identified by the individual being tested. This rule is then
used to generate an answer to a question about what new
geometrical figure would satisfy the rule. Some of the
recent advances in intelligence research, particularly those
in the area of the neurobiology of intelligence, have tended
to strongly support the distinction between g(F) and g(C)
(Blair, 2006; Horn & McArdle, 2007). Although much
research on intelligence continues to emphasize a single
dimension, whether Full Scale IQ or factor analytically
derived g, research on the neural basis for intelligence and
also on the development of intelligence suggests that IQ is
best represented by at least two dimensions rather than
one.1 We refer the reader to a volume edited by Sternberg
and Grigorenko (2002) on the topic of g in which propo-
nents and opponents debated the existence and utility of the
g construct.
Genes and the Environment
When the Neisser et al. (1996) article appeared, the con-
troversy over whether genes influence intelligence was
mainly in the past. That controversy has faded still further
in the intervening years, as scientists have learned that not

only intelligence but practically every aspect of behavior
on which human beings differ is heritable to some extent.
Several strands of evidence, however, suggest that the
effects of genes on intelligence, though undeniable, are not
nearly as determinative as hereditarians might have hoped
or as environmentalists feared 25 years ago.
Heritability is the proportion of variability in a phe-
notype that is “accounted for” (in the usual regression
sense) by variation in genotype. Most studies estimate that
the heritability of IQ is somewhere between .4 and .8 (and
generally less for children), but it really makes no sense to
talk about a single value for the heritability of intelligence.
The heritability of a trait depends on the relative variances
of the predictors, in this case genotype and environment.
The concept of heritability has its origins in animal breed-
ing, where variation in genotype and environment is under
the control of the experimenter, and under these conditions
the concept has some real-world applications. In free-
ranging humans, however, variability is uncontrolled, there
is no “true” degree of variation to estimate, and heritability
can take practically any value for any trait depending on the
relative variability of genetic endowment and environment
in the population being studied. In any naturally occurring
population, the heritability of intelligence is not zero (if
genotype varies at all, it will be reflected in IQ scores) and
it is not one (if environment varies at all, it will be reflected
in IQ scores). That the heritability of intelligence is be-
tween zero and one has one important consequence: With-
out additional evidence, correlations between biologically
related parents and children cannot be unambiguously in-
terpreted as either genetic or (as is more frequently at-
tempted) environmental.
Social Class and Heritability of Cognitive

Ability
One example of the population dependence of heritability
is the claim that the heritability of intelligence test scores
differs as a function of age, with heritability increasing
over the course of development (Plomin, DeFries, & Mc-
Clearn, 1990). Another example of population dependence
is that the heritability of intelligence test scores is appar-
ently not constant across different races or socioeconomic
classes. Sandra Scarr (Scarr-Salapatek, 1971) published a
report of twins in the Philadelphia school system showing
that the heritability of aptitude and achievement test scores
was higher for White children than Black children and for
twins raised in relatively richer homes than for twins raised
in poorer ones. This report, although it received some
positive attention at the time, also faced serious criticism
(Eaves & Jinks, 1972).
Aside from a partial replication conducted by Scarr
(1981) and a report from Sweden by Fischbein (1980), the
hypothesis of group differences in heritability lay mostly
fallow for a 20-year period encompassing the time when
the Neisser et al. (1996) article was written. Interest in the
phenomenon was rekindled in 1999 when Rowe, Jacobson,
and Van den Oord (1999) analyzed data from the National
Longitudinal Study of Adolescent Health, a large, repre-
sentative sample of American youth, then in early adoles-
cence, who were administered a version of the Peabody
Picture Vocabulary Test. Rowe et al.’s analysis showed
that most of the variance in families with poorly educated
mothers was explained by the shared environment. (Shared
environment constitutes the environment that is shared by
siblings in the same family and that differs from one family
to another, as opposed to aspects of the environment that
can differ among siblings, such as being a firstborn versus
a later-born child.) Most of the variance for children from
well-educated families was explained by genes.

Turkheimer, Haley, Waldron, D’Onofrio, and Gottes-
man (2003) conducted an analysis of socioeconomic status
(SES) by heritability interactions in the National Collabor-
ative Perinatal Project (NCPP). The NCPP is particularly
well-suited for this purpose because it comprised a repre-
sentative sample of twins, many of them raised in poverty.
A well-validated measure of SES with good psychometric
properties is available in the NCPP dataset (Myrianthopou-
los & French, 1968). Structural equation modeling demon-
strated large statistically significant interactions for Full
Scale and Performance IQ (PIQ) but not for Verbal IQ
(VIQ), although the effects for VIQ were in the same
direction. For families at the lowest levels of SES, shared
environment accounted for almost all of the variation in IQ,
with genes accounting for practically none. As SES in-
1 Horn (1989) discussed several other dimensions of
intelligence at
the same level as g(F) and g(C). We do not doubt the reality of
these other
dimensions, but they have not been sufficiently well researched
to justify
considering them at length.
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creased, the contribution of shared environment diminished
and the contribution of genes increased, crossing in lower
middle-class families. Finally, in the most socioeconomi-
cally advantaged families (who were not wealthy), practi-
cally all of the variation in IQ was accounted for by genes,
and almost none was accounted for by shared environment.
There have been several attempted replications of the
SES by heritability interaction since the Turkheimer et al.
(2003) study was published, with mixed results. (A study
by Nagoshi and Johnson [2005] has been cited as a failure
to replicate the SES interaction [Rushton & Jensen, 2010],
but it is not. Nagoshi and Johnson used the Hawaii Family
Study to demonstrate that there is no change in parent–
child correlations for intelligence test scores as a function
of family income. Parent– child correlations, however, are a
combination of genetic and shared environmental factors.
Every instance of the interaction that has been reported to
date has shown that genetic and shared environmental
components change in opposite directions as a function of
SES, so one would expect them to cancel each other out in
parent– child correlations.)
Harden, Turkheimer, and Loehlin (2007) reanalyzed
the Loehlin and Nichols (1976) National Merit Scholar
Qualifying Test (NMSQT) twin data. This sample of 839
twin pairs was drawn from the population of nearly
600,000 adolescents who completed the NMSQT in 1962.
Around 40% of the variance was accounted for by both
additive genetics and the shared environment in the fami-
lies with the lowest incomes and levels of education,

whereas in the richer and better-educated families, slightly
more than 50% of the variance was accounted for by
genetics and about 30% was accounted for by the shared
environment. A puzzling aspect of this study is that few of
the children in the sample were actually poor, as they had
been selected for participation in the NMSQT.
A recent study of 750 pairs of twins in the Early
Childhood Longitudinal Study, Birth Cohort (Tucker-
Drob, Rhemtulla, Harden, Turkheimer, & Fask, 2011)
identified a gene by SES interaction in the emergence of
genetic variance in early childhood. For the Bayley Scales
of Infant Development administered at 10 months, there
was no heritable variation at any level of SES. But when
the test was repeated at two years of age, significant genetic
variance emerged, with more genetic variance at higher
levels of parental SES.
There are mixed reports about SES by heritability
interactions for adults. Kremen, Jacobson, and Xian (2005)
identified a significant interaction between parental educa-
tion and word recognition scores on the Wide Range
Achievement Test among 690 adult twins in the Vietnam
Era Twin Registry. Descriptive analyses showed that for
the twins with the least educated parents, additive genetics
and shared environment each accounted for 36% of the
variability in reading scores. For the twins with the best
educated parents, additive genetics accounted for 56% of
the variability and shared environment for 12%. The effect
appeared to depend solely on decreases in shared environ-
mental variation as a function of parental education. The
Kremen et al. study is the only one to date that has docu-
mented the SES by heritability interaction in adults. In
contrast, a study of the cognitive ability test given at the
time of military induction to a Vietnam Era Twin Registry

sample that was part of a larger sample of 3,203 pairs
(which included the pairs included in Kremen et al., 2005)
showed no interaction with parental education (Grant et al.,
2010).
There are also mixed results for European studies. As
noted earlier, Fischbein (1980) found the typical SES by
heritability interaction with 12-year-olds in Sweden. More
recently, Asbury, Wachs, and Plomin (2005) studied a
sample of 4,446 four-year-old British twins from the Twins
Early Development Study in London. The only interaction
they found was an interaction in the opposite direction (i.e.,
children in high-risk impoverished environments showed
higher heritabilities). However, their measure of intelli-
gence was based on a child ability scale administered to
parents over the telephone. A second study (Docherty,
Kovas, & Plomin, 2011), conducted using 1,800 of the
children from the Asbury et al. study at age 10, examined
interactions between environmental variables and a set of
10 single nucleotide polymorphisms (SNPs) identified in
earlier studies in the prediction of mathematics ability
measured over the Internet. The set of SNPs had a main
effect on mathematics ability, accounting for 2.7% of the
variance, and showed a variety of small interactions with
environmental measures, mostly in the direction of larger
genetic effects in negative and chaotic environments. A
new analysis of the same dataset extending the analysis
through age 14 (Hanscombe et al., 2012) has found signif-
icant interactions in the original direction for the environ-
mental term. The investigators concluded that shared envi-
ronmental experiences have greater impact on intelligence
in low-SES families.
Van der Sluis, Willemsen, de Geus, Boomsma, and
Posthuma (2008) examined Gene � Environment interac-
tions for cognitive ability in 548 adult twins and 207 of

their siblings from older and younger cohorts of the Neth-
erlands Twin Registry. Mostly small and nonsignificant
interactions were observed between biometric components
of IQ and a variety of environmental variables, including
neighborhood, income, and parental and spousal education
levels. There was a substantial interaction between parental
education and shared environmental effects, in the opposite
direction from the original findings: Older males from more
highly educated families showed larger shared environ-
mental effects.
A recent study suggests that the most commonly
found interaction may operate on the level of the cerebral
cortex. Chiang et al. (2011) assessed cortical white matter
integrity with diffusion tensor imaging in 705 twins and
their siblings. They demonstrated that white matter integ-
rity was highly heritable and, moreover, that there were
significant Gene � Environment interactions, such that
heritabilities were higher among twins with higher SES and
higher IQ scores.
In summary, it appears reasonable to conclude that the
heritability of cognitive ability is attenuated among impov-
erished children and young adults in the United States.
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There is mixed evidence as to whether the effect occurs in
other countries, and there is contradictory evidence about
whether the effect persists into adulthood. One can only
speculate about why these differences among studies might
occur. It appears, for example, that socioeconomic differ-
ences in intelligence are not as pronounced in modern
Europe as they are in the United States. In the Turkheimer
et al. (2003) study, the correlation of SES with IQ was
�.70; in Asbury et al. (2005), it was about �.2. In van der
Sluis et al. (2008), there was only a 5-point IQ difference
between the groups of twins with the most and least edu-
cated parents. Studies of adults must contend with the low
magnitude of shared environmental components overall,
and it may be difficult to detect interactions when there are
low shared environment effects to work with.
One interpretation of the finding that heritability of IQ
is very low for lower SES individuals is that children in
poverty do not get to develop their full genetic potential. If
true, there is room for interventions with that group to have
large effects on IQ. That this interpretation of the finding is
correct is indicated by an actual intervention study
(Turkheimer, Blair, Sojourner, Protzko, & Horn, 2012).
The Infant Health and Development Program (IHDP) was
a broad-based intervention program designed to improve
the cognitive function and school performance of approx-
imately 1,000 low birth weight infants. There were 95 pairs
of identical and fraternal twins who were incidentally in-
cluded in the program. About a third of the pairs were
randomly assigned to the experimental treatment group.
Their families were assigned to receive weekly home visits

from a clinically trained staff person who delivered a
curriculum of child development and parenting education,
a program of mental health counseling and support, and
referral to social services available in the community. From
ages 1 to 3, the treatment-group twin pairs were eligible to
attend a free, full-day, high-quality child development cen-
ter established and run by IHDP staff. The other twin pairs
did not receive home visits or access to the child develop-
ment centers. At 96 months, the children were administered
a battery of standardized tests of cognitive ability and
school achievement, including the Wechsler Intelligence
Scale for Children, the Woodcock-Johnson Achievement
Scales, and Raven’s Progressive Matrices. On the basis of
studies of natural variation in SES, it was hypothesized that
the heritability of intelligence would be higher in the group
randomly selected for exposure to the enriched environ-
ment. Heritabilities were significantly greater than 0 in the
intervention group on seven of eight tests and were higher
in the intervention group than in the control group on all
seven.
The underrepresentation of low-SES individuals in
behavioral genetic studies causes a problem for studies of
heritability. The estimates of heritability just discussed are
based on studies of twins. Another way to estimate the
relative importance of genes and environment is to com-
pare the correlation between adopted children’s IQ and that
of their birth parents with the correlation between adopted
children’s IQ and that of their adoptive parents. The former
correlation is generally higher than the latter, sometimes
much higher. Many have concluded on the basis of such
findings that environments are relatively unimportant in
determining IQ, since variations in the environments of
adoptive families are not very highly associated with vari-
ation in children’s IQ. But work by Stoolmiller (1999)

shows that estimates of the relative contributions of genes
and environment may be very sensitive to the inclusion of
disadvantaged populations in a given study. Adoption stud-
ies may tend to underestimate the role of environment and
overstate the role of genetics due to the restricted social
class range of adoptive homes. Adoptive families are gen-
erally of relatively high SES. Moreover, observation of
family settings by the HOME technique (Home Observa-
tion for Measurement of the Environment; Bradley et al.,
1993; Phillips, Brooks-Gunn, Duncan, Klebanov, & Crane,
1998) shows that the environments of adoptive families are
much more supportive of intellectual growth than are those
of nonadoptive families. The restriction of range (as much
as 70% in some studies; Stoolmiller, 1999) means that the
possible magnitude of correlations between adoptive par-
ents’ IQ and that of their children is curtailed.
Stoolmiller’s (1999) conclusions have been ques-
tioned by Loehlin and Horn (2000) and McGue et al.
(2007). Loehlin and Horn pointed to a number of implica-
tions of Stoolmiller’s analysis that are inconsistent with
facts Stoolmiller did not consider, but they provided no
formal tests of the significance of these inconsistencies, so
it is impossible to know how important these inconsisten-
cies are. McGue et al. showed that regressions of IQ on a
few characteristics that were restricted in range in their data
show those variables to have no effect. But the variables
they examined are certainly not the only ones with notably
restricted range relative to the whole population, many of
which are not represented in their data. Perhaps more
important, McGue and colleagues were looking at the
effects of restriction of range on a sample whose range was
already restricted with respect to the population from which
it was drawn. Their sample consisted of intact families of
relatively high SES.

The findings on the interaction between SES and
genetic influences described above suggest that the prob-
lem goes beyond the concerns about restrictions of range
addressed by Stoolmiller (1999). If there is heterogeneity in
the importance of environment in different social groups,
then exclusion of participants from poor socioeconomic
backgrounds can have a much more profound effect than
what might appear based on the reduction of variance in
SES alone. Since environment has a larger impact on
outcomes among lower SES individuals, then removing
them from the sample not only reduces the variance of
environment but also reduces the average impact of envi-
ronment on outcomes in the sample, thereby causing a
reduction in the measured role of environment for two
separate reasons. Thus, in addition to the bias introduced
into the estimates of environmental effects by the restric-
tion of range in SES, excluding participants from the low-
est SES levels would also bias the results by omitting the
portion of the distribution for which environmental effects
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are known to be strongest (Turkheimer, Harden,
D’Onofrio, & Gottesman, 2009).
The Search for Genes for IQ
A major development since the Neisser et al. (1996) report
has been the mapping of the human genome and the in-
creasing availability and practicality of genotyping tech-
nology. The high heritability of cognitive ability led many
to believe that finding specific genes that are responsible
for normal variation would be easy and fruitful.
So far, progress in finding the genetic locus for com-
plex human traits has been limited. Whereas 282 individual
genes responsible for specific forms of mental retardation
have been identified (Inlow & Restifo, 2004), very little
progress has been made in finding the genes that contribute
to normal variation (Butcher, Davis, Craig, & Plomin,
2008). For example, a recent large study—a genome-wide
scan using 7,000 subjects (Butcher et al., 2008)—found
only six genetic markers (SNPs) associated with cognitive
ability, and only one of those remained statistically signif-
icant once the critical values were adjusted for multiple
tests. When the six markers were considered together they
barely explained 1% of the variance in general cognitive
ability. Further, in the many studies of a similar nature that
have purported to find genes for cognitive ability, the (very
slight) influence of only one gene has been consistently
replicated in subsequent studies (Butcher et al., 2008).
This is an unfortunately common circumstance in the
search for genes for a wide range of human traits. When
there is a single gene responsible for individual variation it
is easy to find, but when a trait is more than slightly
polygenic (many genes contribute) finding genes that ex-
plain more than a tiny fraction of the variance in the trait
has so far proved daunting (Goldstein, 2009). Perhaps the

best example of this is height, for which a recent genome-
wide scan with 30,000 subjects found only 20 markers, and
these collectively explained less than 3% of the variance in
height. This was the case despite the fact that height is over
90% heritable in the population studied (Weedon et al.,
2008).
Figuring out why it has proved so difficult to identify
the specific genes responsible for genetic variation in
highly heritable behavioral traits is the most challenging
problem facing contemporary behavioral genetics.2 The
possible explanations can be divided into two broad
classes. The difficulty may originate either in technical
problems in specifying the genetic sequence or in broader
problems involving the prediction of complex human be-
havior more generally, or both.
Despite the astonishing gains that have been made in
the specification of the human genetic sequence over the
last two decades, the technologies that have been brought
to bear on the identification of specific genes related to
complex traits are still at some distance from the actual
genetic sequence. Linkage methods, the first to be applied
to traits such as intelligence, can query the entire genome,
but they have relatively low statistical power and only
identify chromosomal regions that may include relevant
genes. Candidate gene association studies estimate the as-
sociations between specific genes and a behavioral out-
come but are necessarily limited to the relatively small
number of candidate genes that can be tested. The most
recent method, genome-wide association studies (GWAS),
scan the genome for associations between outcomes and
variation in individual chromosomal markers known as
SNPs. GWAS is as close as researchers have come to
examining relations between the actual genetic sequence

and intelligence, but there are still limitations. Only rela-
tively common SNPs are included in GWAS, and relations
between densely packed SNPs and the genes with which
they are associated are quite complex.
Another class of technical difficulties includes the
possibility that a linear specification of the genetic se-
quence does not capture all of the information contained in
the genome. Statistical interactions or nonlinear associa-
tions among genes, or between genes and particular envi-
ronments, could foil an effort to understand intelligence by
simply adding up the small effects of many genes.
In the end, the most likely explanation of the missing
genes problem is the most straightforward, but unfortu-
nately it is also likely to be the most difficult to remedy. It
may simply be that that the number of genes involved in an
outcome as complex as intelligence is very large, and
therefore the contribution of any individual locus is just as
small as the number of genes is large and thus very difficult
to detect without huge samples. This problem is not likely
to be solved by advances in genetic technology that are
foreseeable at present.
New Knowledge About the Effects of
the Environment
Much new knowledge about relationships between envi-
ronmental factors and intelligence has accrued since the
Neisser et al. (1996) report appeared, especially regarding
the interplay of biological and social factors, which thus
has blurred the line between biological and environmental
effects on intelligence.
Biological Factors
A wide range of environmental factors of a biological

nature influence intelligence. Most of the known factors are
detrimental, having to do with a lack of micronutrients and
the presence of environmental toxins, and they were re-
viewed briefly by Neisser et al. (1996). Little of note
concerning these effects has been uncovered since then, but
there is not much research in this area.
There is, however, one biological factor that seems to
increase intelligence and that occurs early in life. Breast-
feeding may increase IQ by as much as 6 points (Anderson,
Johnstone, & Remley, 1999; Mortensen, Michaelsen,
Sanders, & Reinisch, 2002) for infants born with normal
weight and by as much as 8 points for those born prema-
turely (Anderson et al., 1999; Lucas, Morley, Cole, Lister,
2 For those who are interested in more detail on these questions,
the
April 23, 2009, issue of the New England Journal of Medicine
(Vol. 360,
No. 17) carried a symposium on this issue.
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& Leeson-Payne, 1992), and the advantage seems to persist
into adulthood (Mortensen et al., 2002). One meta-analysis
found only a 3-point effect of breastfeeding on IQ when
social class and IQ of the mother were controlled (Ander-
son et al., 1999), and another found essentially no effect on
academic achievement scores when the mother’s IQ was
controlled except for a modest effect for children breastfed
for more than seven months (Der, Batty, & Deary, 2006).
It is far from clear, however, that a confound between
social class and mother’s IQ is what accounts for the
relationship between breastfeeding and IQ. Human breast
milk contains fatty acids that are not found in formula and
that have been shown to prevent neurological deficits in
mice (Catalan et al., 2002). An important study indicates
that breastfeeding is effective in raising IQ by about six
points, but only for the large portion of the population
having one of two alleles at a particular site that regulates
fatty acids and is influenced by breast milk (Caspi et al.,
2007). Those having another allele at that site did not
benefit from breastfeeding, but people with that allele are in
a small minority. This finding of genetic contingency
speaks against the possibility of a social-class confound in
the breastfeeding–IQ relationship, because the benefit of
breastfeeding occurred for children with the two more
common alleles regardless of the mother’s social class and
did not occur for children with the less common allele,
again regardless of social class. Moreover, it was only the
child’s allele at the relevant site, not the mother’s, that
mediated the influence on IQ. Another study manipulated
breastfeeding in a large randomized control study and also
found about a 0.5 SD difference in IQ (Kramer, 2008).
The breastfeeding issue remains in doubt. There are
some contradictions in the literature up to this point. Nev-

ertheless, the prudent recommendation for new mothers is
that, absent any contraindications, they should breastfeed.
Social Factors
We can be confident that the environmental differences that
are associated with social class have a large effect on IQ.
We know this because adopted children typically score 12
points or more higher than comparison children (e.g., sib-
lings left with birth parents or children adopted by lower
SES parents), and adoption typically moves children from
lower to higher SES homes. A meta-analysis available at
the time of the Neisser et al. (1996) article found an effect
of adoption of lower SES children by upper-middle-class
parents of 12 points (Locurto, 1990). A subsequent adop-
tion study by Duyme, Dumaret, and Tomkiewicz (1999)
found that the IQ difference between children adopted by
upper-middle-class parents and those adopted by lower
SES parents was about 12 points. A recent meta-analysis
by van IJzendoorn, Juffer, and Klein Poelhuis (2005) found
an average effect of adoption of 18 points. However, these
authors considered some studies in which adoption was
compared with extremely deprived institutional settings.
Class and race differences in socialization
for intellectual abilities. The evidence from adop-
tion studies that social class greatly affects the IQ of
children raises questions about exactly what correlates of
SES affect IQ.
Some recent evidence indicates that there are marked
differences, beginning in infancy, between the environment
of higher SES families and lower SES families in factors
that plausibly influence intellectual growth. One of the
more important findings about cognitive socialization con-

cerns talking to children. Hart and Risley (1995) showed
that the child of professional parents has heard 30 million
words by the age of three, the child of working-class
parents has heard 20 million words, and the vocabulary is
much richer for the higher SES child. The child of unem-
ployed African American mothers has heard 10 million
words by the age of three. Hart and Risley also found a
large difference in the ratio of encouraging comments made
to children versus reprimands. The child of professional
parents received six encouragements for every reprimand,
the child of working-class parents received two encourage-
ments per reprimand, and the child of unemployed African
American mothers received two reprimands per encourage-
ment.
The Hart and Risley (1995) findings are amplified by
studies using the HOME (Home Observation for Measure-
ment of the Environment) technique mentioned earlier.
HOME researchers assess family environments for the
amount of intellectual stimulation that is present, as indi-
cated by how much the parent talks to the child; how much
access there is to books, magazines, newspapers, and com-
puters; how much the parents read to the child; how many
learning experiences outside the home (trips to museums,
visits to friends) there are; the degree of warmth versus
punitiveness of parents’ behavior toward the child; and so
on (Bradley et al., 1993; Phillips et al., 1998). These studies
find marked differences between the social classes, and
they find that the association between HOME scores and IQ
scores is very substantial. A 1 SD difference in summed
HOME scores is associated with a 9-point difference in IQ.
It should be acknowledged, however, that at present
there is no way of knowing how much of the IQ advantage
for children with excellent environments is due to the
environments per se and how much is due to the genes that

parents creating those environments pass along to their
children. In addition, some of the IQ advantage of children
living in superior environments may be due to the superior
genetic endowment of the child producing a phenotype that
rewards the parents for creating excellent environments for
intellectual development (Braungart, Plomin, DeFries, &
David, 1992; Coon, Fulker, DeFries, & Plomin, 1990;
Plomin, Loehlin, & DeFries, 1985). To the extent that such
processes play a role, the IQ advantage of children in
superior environments might be due to their own superior
genes rather than to the superior environments themselves.
It is almost surely the case, however, that a substantial
fraction of the IQ advantage is due to the environments
independent of the genes associated with them. This is
because we know that adoption adds 12–18 points to the IQ
of unrelated children, who are usually from lower SES
backgrounds. Home environments are not the only candi-
dates for explaining shared environment effects. Home
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environments are correlated with neighborhood, peer, and
school environments. These likely are also important com-
ponents of the shared environment effects that are reflected
in the adoption outcomes for children in families of differ-
ent social classes. But we stress that we have no direct
evidence of the impact of any particular environmental
factor on IQ.
Shared environment effects in childhood
and adulthood. Shared environment effects are
those shared by all members of a given family but that
differ between families. For example, social class is likely
to be shared by all family members. The children from one
randomly selected family will differ on average by about
1.13 SD in SES from the children of another randomly
selected family.
Shared environment effects are sometimes reported to
be very low or even zero by adulthood (Bouchard, 2004;
Johnson, 2010; McGue & Bouchard, 1998). If shared en-
vironment effects were really this low in adulthood, it
would prompt pessimistic conclusions about the degree to
which interventions in childhood would have enduring
effects. One basis for the claim that shared environment
effects are zero in adulthood is a review of three studies in
1993 by McGue, Bouchard, Iacono, and Lykken (1993),
which has been frequently cited since (e.g., Bouchard &
McGue, 2003; Rushton & Jensen, 2005a). But a large range
of shared environment effects has been reported. Bouchard
and McGue (2003) reproduced the 1993 review figure with
its assessment of zero adult shared environment effects, but
they also found a shared environment effect in excess of
.25 for 16 –20-year-olds. Johnson (2010) reported that
shared environment effects are zero in adulthood (but did
not provide sources) and in the same year reported a study
showing that the shared environment effect was .07 for

17-year-olds in Minnesota and .26 for 18-year-olds in
Sweden (Johnson, Deary, Silventoinen, Tynelius, & Ras-
mussen, 2010). Another recent study found shared envi-
ronment effects of .26 for 20-year-olds and .18 for 55-year-
olds (Lyons et al., 2009), and yet another found shared
environment effects of .20 for Swedish conscripts (Beau-
champ, Cesarini, Johannesson, Erik Lindqvist, & Apicella,
2011). A recent review of six well-conducted studies found
shared environment effects in adulthood to be .16 on av-
erage (Haworth et al., 2009).
Finally, it should be noted that most if not all twin
studies, especially studies of adults, likely result in higher
estimates of genetic effects and lower estimates of envi-
ronmental effects than would be found with genuine ran-
dom samples of all twins in a given population. This is
because lower SES individuals are difficult to recruit to
laboratories and testing sites (Dillman, 1978). Many studies
of heritability give no information about SES, and even
those that claim to have representative samples of the
various social-class groups may have self-selection prob-
lems: The lower SES individuals who volunteer may re-
semble higher SES individuals on variables relevant to
overestimation of heritability effects. On the other hand,
nearly all studies fail to take into account the effects of
assortative mating. Beauchamp et al. (2011) suggested that
assortative mating may completely offset their estimate of
.2 for Swedish conscripts, leaving shared environmental
effects at zero.
A comprehensive review of all studies providing ev-
idence about shared environment effects across the life
span, despite the obvious importance of such an enterprise,
has yet to be conducted. What we can say with some
confidence at present is that shared environment effects, as

typically measured, remain high at least through the early
20s. After that age, data are sparse and longitudinal studies
almost nonexistent except for much older adults.
Within-family effects (nonshared environ-
ment effects). Differences in IQ for siblings within a
family are partly due to differential environmental experi-
ences, including changes in the social-class status of a
given family, neighborhood differences over time, differ-
ences in the character of peer associations between children
in the same family, and birth order differences. There is a
large and contentious literature on the question of birth
order and IQ, with some researchers finding that firstborns
have higher IQs (Zajonc, 1976) and other researchers not
finding this to be the case (Wichman, Rodgers, & Mac-
Callum, 2006). Zajonc and Sulloway (2007) maintained
that the difference in findings could be explained by the
fact that the birth order effect is typically not found until
late adolescence. Some recent evidence from a particularly
rigorous study indicates that there is in fact a difference in
IQ of 3 points in early adulthood favoring firstborn children
over later-born children that may be understandable in
terms of differences in the home environment (Kristensen
& Bjerkedal, 2007).That it is social order of birth and not
biological order of birth that results in the difference in IQ
is indicated by the fact that second-born children in fami-
lies in which the firstborn child died early in life have IQs
as high as firstborns at age 18 (Kristensen & Bjerkedal,
2007); thus genetic or gestational factors do not account for
the difference. Zajonc and others have argued that the
intellectual environment of the firstborn is superior to that
of the later-born, partly because the firstborn has the full
attention of the parents for a period of time.
Interventions
A large number of interventions have been shown to have

substantial effects on IQ and academic achievement. In
particular, there is clear evidence that school affects intel-
ligence.
Education and Other Environmental
Interventions
It was known at the time of the Neisser et al. (1996) article
that school has a great impact on IQ (Ceci, 1991). Natural
experiments in which children are deprived of school for an
extended period of time show deficits in IQ of as much as
2 SD. A child who enters fifth grade approximately a year
earlier than a child of nearly the same age who enters fourth
grade will have a Verbal IQ more than 5 points higher at
the end of the school year (Cahan & Cohen, 1989) and as
much as 9 percentiles higher in eighth grade, as found in an
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international study of Organisation for Economic Cooper-
ation and Development countries studied by Bedard and
Dhuey (2006). Such children are more likely to enter

high-tracked high school programs and more likely to go to
college than are late starters (Bedard & Dhuey, 2006).
Children who miss a year of school show a drop of several
points in IQ. Children lose IQ and academic skills over the
summer (Ceci, 1991; Jencks et al., 1972). But the seasonal
change in intellectual skills, as we might expect given the
different home environments of children of different social
classes, is much greater for lower SES children. Indeed, the
knowledge and skills of children in the upper fifth in family
SES actually increase over the summer (Burkam, Ready,
Lee, & LoGerfo, 2004; Cooper, Charlton, Valentine, &
Muhlenbruck, 2000), an effect that is likely due to enriched
activities for the higher SES children. This effect is so
marked that by late elementary school, much of the differ-
ence in academic skills between lower and higher SES
children may be due to the loss of skills over the summer
for lower SES children versus the gains for higher SES
children (K. L. Alexander, Entwisle, & Olson, 2001). The
beneficial effects of schooling apparently continue at least
through junior high school. Brinch and Galloway (2011)
took advantage of the natural experiment created in Nor-
way when an extra two years of schooling beyond the
seventh grade began to be required. Effects on IQ were
substantial at age 19 — equal to one third the size of the
Flynn effect (the marked secular gain seen in developed
countries, which we discuss in detail later) in Norway at the
time.
The best prekindergarten programs for lower SES
children have a substantial effect on IQ, but this typically
fades by late elementary school, perhaps because the envi-
ronments of the children do not remain enriched. There are
two exceptions to the rule that prekindergarten programs
have little effect on later IQ. Both are characterized by
having placed children in average or above-average ele-
mentary schools following the prekindergarten interven-

tions. Children in the Milwaukee Project (Garber, 1988)
program had an average IQ 10 points higher than those of
controls when they were adolescents. Children in the in-
tensive Abecedarian prekindergarten program had IQs 4.5
points higher than those of controls when they were 21
years old (Campbell, Ramey, Pungello, Sparling, & Miller-
Johnson, 2002).3
Whether or not high-quality intervention programs
have sustained IQ effects, the effects on academic achieve-
ment and life outcomes can be very substantial. The gains
are particularly marked for intensive programs such as the
Perry School Project (Campbell, Pungello, Miller-Johnson,
Burchinal, & Ramey, 2001; Schweinhart et al., 2005;
Schweinhart & Weikart, 1980; Schweinhart & Weikart,
1993) and the Abecedarian program (Campbell et al., 2001;
Campbell & Ramey, 1995; C. T. Ramey et al., 2000; S. L.
Ramey & Ramey, 1999). By adulthood, individuals who
had participated in such interventions were about half as
likely to have repeated a grade in school or to have been
assigned to special education classes and were far more
likely to have completed high school, attended college, and
even to own their own home. The discrepancy between
school achievement effects and IQ effects (after early ele-
mentary school) is sufficiently great to suggest to some that
the achievement effects are produced more by attention,
self-control, and perseverance gains than by intellectual
gains per se (Heckman, 2011; Knudsen, Heckman, Cam-
eron, & Shonkoff, 2006).
Quality of teaching in kindergarten also has a mea-
surable impact on academic success and life outcomes.
Chetty et al. (2010) examined data from Project STAR in
Tennessee. Students who had been randomly assigned to
small kindergarten classrooms were more likely to subse-

quently attend college, attend a higher ranked college, and
have better life outcomes in a number of respects. Students
who had more experienced teachers had higher earnings as
adults, as did students for whom the quality of teaching as
measured by test scores was higher. Academic gains due to
having more experienced, superior teachers faded in later
grades, but noncognitive gains persisted, much as for the
pre-elementary intervention just discussed.
First-grade teaching quality also has a significant im-
pact on academic achievement in later grades. Hamre and
Pianta (2001) found that children who were at risk for poor
elementary school performance by virtue of relatively low
SES had achievement scores 0.4 SD higher if their first-
grade teacher was one whose teaching quality was consid-
ered to be in the top third as opposed to the bottom third.
Indeed, the performance of the children with the better
teachers was not significantly worse than that of children
with well-educated parents.
Many interventions in elementary school, including
lengthened schoolday, decreased class size, and interactive
computer programs, have been found to markedly affect
academic skills (Nisbett, 2009). The only enhanced school-
ing program that we know to have affected IQ was reported
on by Neisser et al. (1996). This was a year-long program
teaching a variety of reasoning skills to seventh-grade
children in Venezuela (Herrnstein, Nickerson, Sanchez, &
Swets, 1986). The program had a very substantial effect on
the individual reasoning skills taught and had a 0.4 SD
effect on intellectual ability as measured by typical tests.
There has been no exact replication of this study, although
Sanz de Acedo Lizarraga, Ugarte, Iriarte, & Sanz de Acedo
Baquedano (2003) devised an extensive educational inter-
vention that used the Herrnstein et al. (1986) materials.
Sanz de Acedo Lizarraga et al. found substantial increases

in several measures of intelligence, including the Culture
Fair Intelligence Test (Cattell, 1973), a matrix test similar
to Raven’s Progressive Matrices. Their intervention in-
cluded a variety of tasks intended to enhance self-regula-
tion, so it is unclear which aspects of their intervention
3 Another program similar to the Abecedarian program was
carried
out with low birth weight infants. Rather than starting at six
months and
continuing to age 5, the program started at age 1 and continued
to age 3.
Improvement in IQ was modest for children with birth weights
of more
than 2,000 grams and continued through adolescence. For
children with
birth weights less than 2,000 grams, and for the entire sample,
the control
group was not significantly different from the intervention
group.
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increased IQ. As we discuss below, attempts to improve
matrix reasoning of the Raven’s sort by providing working
memory training have proven quite successful.
Interventions to Raise Fluid Intelligence
Recent research has shown that g(F) can be enhanced by
training people in working memory skills. For example,
Jaeggi, Buschkuehl, Jonides, and Perrig (2008) used a dual
n-back working memory task that required participants to
remember both verbal and visuospatial information over
varying numbers of trials. In an n-back task, participants
are required to view sequential presentations of individual
letters or a stimulus in a specific location and to indicate
when one of the letters or locations is the same as that
presented on the previous one, two, or three presentations.
That is, in the 1-back condition, the participant indicates if
a letter is the same as that presented on the immediately
preceding presentation; on the 2-back, the same as that on
the presentation before last; on the 3-back, the same as that
presented two presentations before last; and so forth. Jaeggi
et al. (2008) found that participants who spent one month
training on a version of the n-back that required simulta-
neously remembering both a stimulus location presented
visually and a letter presented auditorily improved their
scores on the Bochumer Matrizen-Test, a matrix reasoning
test of fluid intelligence. This finding was taken as support
for the claim that training working memory leads to an
improvement in g(F). Jaeggi et al. (2010) replicated this
finding and extended it to include scores on Raven’s Pro-
gressive Matrices.
Klingberg, Forssberg, and Westerberg (2002) pro-
vided memory training to both children with attention-
deficit/hyperactivity disorder (ADHD) and to adults and

found marked improvement in performance on Raven’s
Progressive Matrices for both groups. Rueda, Rothbart,
McCandliss, Saccomanno, and Posner (2005) provided
children with attention control training and found an in-
crease of 0.5 SD for a matrices task. Work by Mackey and
her colleagues (Mackey, Hill, Stone, & Bunge, 2011)
showed that working memory training of low-SES children
using a variety of computer and noncomputer games re-
sulted in IQ gains of 10 points on a matrix reasoning task.
Research with aged participants showed that training with
a computer game that involved executive control skills
improved performance on a variety of tasks measuring
executive functioning, including Raven’s Progressive Ma-
trices (Basak, Boot, Voss, & Kramer, 2008). Similar effects
for elderly participants have been found by other investi-
gators using a working memory training paradigm, with
significant transfer of training and maintenance of gains on
g(F) tasks over an eight-month period (Borella, Carretti,
Riboldi, & De Beni, 2010). Gains in the range of 0.5 to 1
SD have been found for transfer of task switching training
in children, young adults, and the elderly (Karbach & Kray,
2009). See also Schmiedek, Lövdén, and Lindenberger
(2010), Tranter and Koutstaal (2007), and Stephenson and
Halpern (2012) for similar effects resulting from interven-
tions involving multiple tasks targeting executive control.
As might be expected, executive functioning and
working memory training have been shown to improve
only fluid intelligence performance and have little or no
effect on crystallized abilities or verbal reasoning.
Cognitive-Enhancing Pharmaceuticals
There is a rapidly increasing trend by healthy, normal
individuals to take drugs that can improve “memory, con-
centration, planning and reduce impulsive behavior and

risky decision making” (Sahakian & Morein-Zamir, 2007,
p. 1157). The majority of these drugs are being used
“off-label,” which means that they are being used in ways
that were not approved by drug administration agencies.
For example, methylphenidate (Ritalin) is routinely pre-
scribed for people with ADHD, but increasingly people
without ADHD are taking this drug for cognitive enhance-
ment. One survey found that 25% of college students on
some campuses use stimulants in this way in a given year
(McCabe, Knight, Teter, & Wechsler, 2005). Modest en-
hancements in attention, working memory, and executive
function in healthy, normal adults have been found with a
variety of stimulant drugs, with Modafinil, which is ap-
proved for the treatment of narcolepsy, and with Aricept,
which is approved for use for Alzheimer’s disease (Greely
et al., 2008). There are many indications that the use of
cognitive-enhancing pharmaceuticals among normal indi-
viduals is increasing.
There are multiple unknowns regarding the use of
cognitive-enhancing drugs, including the long-term bene-
fits and risks of these drugs for both the populations for
which they were developed and for their use in normal
populations. The use of cognitive-enhancing pharmaceuti-
cals is likely to be an emerging area of research and debate.
We already know about the beneficial effects of education,
exercise, and sound nutrition. A major question is whether
pharmaceutical interventions are similar to these noncon-
troversial interventions or whether they offer unknown
risks that make their use qualitatively different from other
types of interventions.
Exercise and Aging
Physical exercise. A meta-analysis of a large
number of studies has shown that aerobic exercise, at least

for the elderly, is very important for maintaining IQ, espe-
cially for executive functions such as planning, inhibition,
and scheduling of mental procedures (Colcombe &
Kramer, 2003). The effect of exercise on these functions is
more than 0.5 SD for the elderly (more for those past age
65 than for those younger). It is possible to begin cardio-
vascular exercise as late as the seventh decade of life and
substantially reduce the likelihood of Alzheimer’s disease
(Aamodt & Wang, 2007).
Cognitive exercise. Cognitive exercise does
not literally prevent cognitive abilities from declining with
age (G. D. Cohen, 2005; Maguire et al., 2000; Melton,
2005; Salthouse, 2006). But many studies show that the
cognitive skills of the elderly can be trained in the short
term (Salthouse, 2010). And there is some evidence that
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training older adults in memory, processing speed, and
particular narrow reasoning skills produces substantial im-
provements in these skills that remain over a period of

years (Ball et al., 2002). The evidence cited above in the
section on training of fluid intelligence includes two studies
of the elderly showing very significant transfer effects and
one study showing improvement on tasks requiring sub-
stantial transfer maintained over an eight-month period.
Additional evidence that cognitive exercise slows down the
process of intellectual decay comes from a study of retire-
ment. Episodic memory, which is memory for personally
relevant events, is not usually regarded as an aspect of
intelligence, but it is a cognitive ability of a sort and is
among the first to show a decline with age. A study of the
effects of retirement on episodic memory was conducted
with men aged 50 to 54 and aged 60 to 64 (Adam, Bonsang,
Germain, & Perelman, 2007). Twelve nations were ranked
in terms of the persistence of employment into old age. If
the percentage of men still working dropped by 90% from
the 50 –54 age range to the 60 – 64 age range (Austria,
France), there was a 15% decline in episodic memory. If
the percentage still working dropped by 25% (United
States, Sweden), the decline was only 7%. See also Ro-
hwedder and Willis (2010). These findings are consistent
with correlational evidence from a study in the United
Kingdom showing that an extra year of work was associ-
ated with a delay in the onset of Alzheimer’s on average by
six weeks (Adam et al., 2007; Lupton et al., 2010).
Massive IQ Gains Over Time
Flynn’s (1987) research showing that 14 nations had made
huge IQ gains from one generation to another was known
to Neisser et al. (1996). Data on IQ trends now exist for 30
nations. Gains differ as a function of the degree of moder-
nity that characterizes different nations. For nations that
were fully modern by the beginning of the 20th century, IQ
gains have been on the order of 3 points per decade (Flynn,
2007). Nations that have recently begun to modernize, such
as Kenya (Daley, Whaley, Sigman, Espinosa, & Neumann,

2003) and the Caribbean nations (Meisenberg, Lawless,
Lambert, & Newton, 2005), show extremely high rates of
gain. In Sudan, large fluid gains (on the Performance Scale
of the Wechsler Adult Intelligence Scale, or WAIS) have
accompanied a small loss for crystallized intelligence
(Khaleefa, Sulman, & Lynn, 2009).
Nations where modernization began during the early
to mid-20th century show large and persistent gains. Urban
Argentines (ages 13 to 24) made a 22-point gain on Ra-
ven’s Progressive Matrices between 1964 and 1998 (Flynn
& Rossi-Casé, 2011). Children in urban Brazil between
1930 and 2002 (Colom, Flores-Mendoza, & Abad, 2007),
in Estonia between 1935 and 1998 (Must, Must, & Raudik,
2003), and in Spain between 1970 and 1999 (Colom, Lluis-
Font, & Andrés-Pueyo, 2005) have gained at a rate of about
3 points per decade.
Results for nations that began modernization as far
back as the 19th century suggest that its effects on IQ can
reach an asymptote. Scandinavian nations show IQ peaking
or even in mild decline (Emanuelsson, Reuterberg, &
Svensson, 1993; Sundet, Barlaug, & Torjussen, 2004; Te-
asdale & Owen, 1989, 2000). Other highly developed na-
tions have not as yet experienced diminished gains. The
gains of British children on Raven’s Progressive Matrices
were at least as high from 1980 to 2008 as they were from
1943 to 1980 (Flynn, 2009a). Both American children (as
measured by the Wechsler Intelligence Scale for Children,
or WISC, 1989 –2002) and adults (WAIS, 1995–2006) are
gaining at their historic rate of 3 points per decade (Flynn,
2009b, 2009c).
If Sweden represents the asymptote that we are likely
to see for modern nations’ gains, the IQ gap between

developing and developed nations could close by the end of
the 21st century and falsify the hypothesis that some na-
tions lack the intelligence to fully industrialize. In 1917,
Americans matched the lowest IQs found in the developing
world today. It seems plausible that the first step toward
modernity raises IQ a bit, which paves the way for the next
step, which raises IQ a bit more, and so forth.
It is important to note that Flynn’s (2007) review
showed that gains on tests generally considered to measure
fluid intelligence showed substantially greater gains (18
points in IQ-equivalence terms) than tests considered to
measure crystallized intelligence (10 points).
The causes of IQ gains are still debated. Lynn (1989)
has proposed that they are due to improvements in nutri-
tion. In the developed world, better nutrition was probably
a factor before 1950, but probably not since. The nutrition
hypothesis posits greater IQ gains in the lower half of the
IQ distribution than the upper half because one would
assume that even in the past the upper classes were well
fed, whereas the nutritional deficiencies of the lower
classes have gradually diminished. IQ gains have indeed
been concentrated in the lower half of the curve in Den-
mark, Spain, and Norway, but not in France, the Nether-
lands, and the United States. Norway is actually a coun-
terexample: Height gains were larger in the upper half of
their distribution, whereas IQ gains were higher in the
lower half. It is unlikely that enhanced nutrition over time
had a positive effect on IQ but a negative effect on height.
British trends are quite incompatible with the nutrition
hypothesis. They do not show the IQ gap between the top
and bottom halves reducing over time. Differences were
large on the eve of the Great Depression, contracted from
1940 to 1942, expanded from 1964 to 1971, contracted
from 1972 to 1977, and have expanded ever since (Flynn,

2009a, 2009c).
People who marry within a small group suffer in-
breeding depression, which lowers IQ scores. It has been
proposed that less isolation of a breeding population might
contribute to IQ gains by way of “hybrid vigor” (Mingroni,
2004). Data from Norway comparing IQ scores for siblings
of different ages show IQ trends mirroring those of the
larger society (Sundet, Eriksen, Borren, & Tambs, 2010).
Since siblings cannot differ in the degree of inbreeding, this
indicates that hybrid vigor has not been a factor in modern
Scandinavia. Flynn (2009c) has shown that high mobility
in America dates back at least as far as 1872. Thus the
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hypothesis that a trend from substantial isolation to little
isolation lies behind IQ gains seems implausible.
It is easier to eliminate causes than to provide a
convincing causal scenario. It seems likely that the ultimate
cause of IQ gains is the Industrial Revolution, which pro-

duced a need for increased intellectual skills that modern
societies somehow rose to meet. The intermediate causes of
IQ gains may include such factors as a more favorable ratio
of adults to children, better schooling, more cognitively
demanding jobs, and more cognitively challenging leisure.
Flynn (2009c) has argued that one proximate cause is the
adoption of a scientific approach to reasoning with an
attendant emphasis on classification and logical analysis.
Blair and colleagues (Blair, Gamson, Thorne, & Baker,
2005) have shown that the teaching of mathematics, begin-
ning very early in school, has shifted from instruction in
mere counting and arithmetical operations to presentation
of highly visual forms of objects and geometrical figures
with patterns children have to figure out. Such instruction
seems a plausible explanation for at least part of the gain in
fluid intelligence of the kind measured by Raven’s Progres-
sive Matrices.
Analysis of trends on various Wechsler subtests and
Raven’s Progressive Matrices for the United States may be
helpful in understanding what the real-world implications
of IQ gains might be.
Raven’s Progressive Matrices. The very
large gains in performance on the Raven test—nearly 2 SD
in the period from 1947 to 2002—indicate that reasoning
about abstractions has improved in a very real way. The
very large gains for the Raven test render implausible the
claim that the test is “culture free.”
Similarities. The very large gains on the Simi-
larities subtest imply that skills in classification (dogs are
similar to owls because they are both animals) have in-
creased. This seems likely to be helpful for scientific rea-
soning and for preparation for tertiary education.

Performance subtests. These subtests, which
require operating on stimuli by means of manipulation or
detection of differences, such as creating a three-dimen-
sional representation of a two-dimensional picture, have
shown very large gains. These gains are more difficult to
interpret. Certainly, the gains on the Block Design subtest
indicate that there has been enhanced ability to solve prob-
lems on the spot that require more than the mere applica-
tion of learned rules.
Comprehension. Since 1947, adults have
gained the equivalent of almost 14 IQ points and children
have gained 11 points (SD � 15) on the Comprehension
subtest. This subtest measures the ability to comprehend
how the concrete world is organized (e.g., why streets are
numbered in sequence, why doctors go back to get more
education). These increases represent a real gain in ability
to understand the world one lives in.
Information. There have been gains for the In-
formation subtest of over 8 points for adults but only 2
points for children. It seems likely that the gain for adults
reflects the influence of the expansion of tertiary education.
Vocabulary. The vocabulary of adults has in-
creased by over 1 SD, but children’s has increased only
trivially (Flynn, 2010). The gain for adults likely reflects
the influence of increased tertiary education and more
cognitively demanding work roles.
Arithmetic. There have been very small gains for
arithmetic for both adults and children. Perhaps this is
because education in recent decades has not increased its
emphasis on these skills.
Obsolescence of IQ test norms has an important real-

world consequence. Someone who took an obsolete test
might get an IQ score of 74, whereas their IQ measured on
a more current test would be 69. Since 70 is the cutting
point for immunity from the death penalty in America,
obsolete tests have literally cost lives (Flynn, 2009b).
Biology of Intelligence
As presaged in the original Neisser et al. (1996) article,
advances in brain imaging research, including structural as
well as functional magnetic resonance imaging (fMRI) and
positron emission tomography, have resulted in a great deal
of research reporting brain correlates of intelligence.
Relationship Between IQ and Brain Structure
and Function
One of the clear findings of brain research over the past
decade, both in imaging research and in clinical neuropsy-
chology, has been a confirmation of an association between
activity in prefrontal cortex (PFC) and performance on
fluid reasoning and executive function and working mem-
ory tasks. Perhaps the most compelling demonstrations of
the association between PFC and fluid, visual-spatial rea-
soning are those indicating profound impairment on rea-
soning tasks such as the Raven’s Progressive Matrices and
the highly similar Cattell Culture Fair Intelligence Test in
individuals with damage to their PFCs (Duncan, Burgess,
& Emslie, 1995; Waltz et al., 1999). A remarkable result in
these studies is that other aspects of mental ability—
namely, well-learned and acculturated abilities such as
vocabulary knowledge that are grouped together as crys-
tallized intelligence, or g(C)—appear to be surprisingly
little affected when there is damage to the PFC (Waltz et
al., 1999). Indeed, individuals with average crystallized
intelligence functioning can have fluid functioning as much
2 SD below average (Blair, 2006).

These studies suggest that the PFC is necessary for the
solution of visual-spatial reasoning tasks in which the in-
formation is novel and nonsemantic but that the PFC is less
involved in tasks that require crystallized abilities. In con-
firmation of the distinction in clinical research between
brain areas important for g(F) and those important for g(C),
a large number of neuroimaging studies have demonstrated
that performance on fluid reasoning tasks of the type re-
quired by Raven’s Progressive Matrices, such as working
memory and executive function, is dependent on neural
circuitry associated with the PFC that also extends through-
out the neocortex—including the superior parietal temporal
and occipital cortex, as well as subcortical regions, partic-
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ularly the striatum (Duncan et al., 2000; Gray, Chabris, &
Braver, 2003; Jung & Haier, 2007; Olesen, Westerberg, &
Klingberg, 2004). Consistency across studies in brain areas
associated with reasoning, however, is limited. Patterns of
activation in response to various fluid reasoning tasks are

diverse, and brain regions activated in response to ostensi-
bly similar types of reasoning (inductive, deductive) appear
to be closely associated with task content and context. The
evidence is not consistent with the view that there is a
unitary reasoning neural substrate (Goel, 2007; Kroger,
Nystrom, Cohen, & Johnson-Laird, 2008).
Similarly, functional and structural imaging studies
indicate that brain regions associated with aspects of crys-
tallized intelligence, such as verbal ability, are located in
specific frontal and posterior temporal and parietal areas
and, as with fluid reasoning, are closely tied to task content
(Sowell et al., 2004). Common brain regions associated
with both fluid and crystallized abilities have been identi-
fied (Colom & Jung, 2004); however, there appears to be
limited similarity across studies searching for the neural
substrate for g. For example, direct comparison of the
structural correlates of g derived from two different intel-
ligence test batteries revealed only limited overlap in brain
regions identified (Haier, Colom, et al., 2009).
Although evidence for a single underlying neural basis
for intelligence appears mixed, it may be that the issue is
not one of identifying the relevant brain regions so much
as the possibility that individuals vary in how they use
areas of the brain that form the neural basis for intelligence
when engaging in a given mental task (Haier, Karama,
Leyba, & Jung, 2009). This possibility is suggested by a
review of 37 structural and functional neuroimaging studies
of “intelligence and reasoning tests” by Jung and Haier
(2007) in which they proposed a parieto-frontal integration
theory of intelligence (P-FIT) that was based on the iden-
tification of specific overall frontal and parietal brain re-
gions as well as specific temporal and occipital areas that
were reliably activated across most studies reviewed. The
authors, as well as several commentators on the theory,

however, noted the considerable heterogeneity in the find-
ings of the various studies included in the review. Many
brain regions were implicated by a small percentage of the
studies, whereas relatively few were identified by more
than 50% of the studies (Colom, 2007).
Overall, the conclusion seems to be that there is great
heterogeneity in the findings of imaging studies examining
the functional and structural basis of intelligence. An im-
portant area for future research on the neural basis for
intelligence will be the mapping of individual variation in
brain function and structure onto individual variation in
mental test performance. An important goal for this re-
search will be to determine the extent to which information
about brain structure and function can be used to accurately
estimate a given individual’s overall level of mental ability
(Colom, Karama, Jung, & Haier, 2010).
An additional indication of the potential limits of
identifying relations between brain structure and function
and general intelligence is provided by twin studies exam-
ining the heritability of both gray and white matter and
intelligence. Although the samples included in these stud-
ies are very small (N � 25), brain imaging with adults who
vary in degree of relatedness (monozygotic [MZ] twins,
dizygotic [DZ] twins, and unrelated individuals) has dem-
onstrated that regional structural variation in the brain is,
like cognitive abilities themselves, substantially heritable
(Chiang et al., 2009; Hulshoff Pol et al., 2006; Thompson
et al., 2001; Toga & Thompson, 2005; van Leeuwen et al.,
2009). Gray matter in frontal, parietal, and occipital cortex
in MZ twins is, as with most aspects of morphology in
these individuals, essentially identical (within-pair correla-
tion �.95.) In DZ twins, however, a very high but some-
what lesser degree of similarity is observed in gray matter

in parietal-occipital cortex and areas of frontal cortex as-
sociated with language. As with gray matter, similarity in
white matter between MZ twins is (relatively) high (r �
.45–.80), whereas many white matter regions, particularly
frontal and parietal, are actually uncorrelated in DZ twins
(r � .01–.19) (Chiang et al., 2009).
Although neuroimaging studies have established the
very high heritability of brain structure, the correlation of
brain volume to IQ in twin studies, as in all studies relating
brain structure to IQ, is moderate, at about .33 (McDaniel,
2005). It is not clear, however, that the relation between
brain size and IQ is causal. There is conflicting evidence as
to whether there is a correlation between siblings’ brain
size and IQ. A study by Jensen (1994) found a correlation
of .32 between head circumference and g for a small
sample of MZ twins and a correlation of .31 for DZ twins.
But several other studies found a weak or no relationship
between sibling brain size and sibling IQ. Jensen and
Johnson (1994) found a correlation of �.06 between head
circumference of siblings at age 4 and a .11 correlation for
siblings at age 7, but even the very weak positive relation
at age 7 is based on adjusting head size by height and
weight, and the truth is we have no idea of what is the
appropriate way to adjust for these variables. One small N
study found a correlation of .15 between brain volume and
IQ for siblings (Gignac, Vernon, & Wickett, 2003), but
another study with the same design and a larger N found a
correlation of �.05 between brain volume and the first
principal component of a battery of IQ subtests (Schoen-
emann, Budinger, Sarich, & Wang, 2000).
Cognitive Ability and Neural Efficiency
An important finding of brain imaging research over the
past decade is that individuals of higher ability exhibit
greater efficiency at the neural level. That is, high-ability

individuals are able to solve simple and moderately diffi-
cult problems more quickly and with less cortical activity,
particularly in PFC, than lower ability individuals (Neu-
bauer & Fink, 2009). Relations between neural activity and
intelligence, however, are related to task difficulty in a
manner characterized by an inverted U shape. For example,
one of the earliest imaging studies to examine the relation
of brain activity to fluid intelligence found that more intel-
ligent individuals exhibit less, rather than more, whole
brain activity (measured using glucose metabolic rate)
when completing items from Raven’s Progressive Matrices
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(Haier et al., 1988). However, when task difficulty was
matched to ability level, individuals with high g(F) exhib-
ited increased brain activity relative to individuals with low
g(F) (Larson, Haier, LaCasse, & Hazen, 1995). Subsequent
fMRI studies have confirmed that high-ability individuals
are more efficient problem solvers at the neural level and
that, as task difficulty increases, high-ability individuals
exhibit increasing neural activity in PFC, whereas lower

ability individuals exhibit decreasing activity (Callicott et
al., 1999; Rypma et al., 2006).
The inverted U relation between neural activity and IQ
helps to clarify the findings of neuroimaging studies de-
signed to identify the functional neural correlates of indi-
vidual differences in intelligence. For example, in a widely
cited fMRI study, neural activity in PFC and parietal cortex
in response to a working memory task was correlated with
fluid intelligence measured with Raven’s Progressive Ma-
trices (Gray et al., 2003). This correlation was present,
however, only on the most difficult items of the working
memory task, in which interference from distracting items
was very high. Similarly, a second study also found a
correlation between intelligence, using the Raven test, and
activation in frontal and parietal regions in response to
complex relative to simple visual-spatial reasoning items.
Regions most strongly associated with g(F), however, were
located in posterior parietal cortex (PPC) bilaterally rather
than in PFC (Lee et al., 2006). These findings suggest that
even on ostensibly more difficult problems, higher ability
individuals are able to solve matrices-type reasoning prob-
lems more efficiently through visual-spatial attention sys-
tems of the PPC rather than more effortful working mem-
ory processes associated with PFC. As task difficulty
increases, however, as with the most difficult working
memory trials in the study of Gray et al. (2003), high-
ability individuals exhibit increased prefrontal activity rel-
ative to individuals with lower ability.
Although much has been learned about the neural
basis for cognitive abilities through the proliferation of
neuroimaging studies, the results of these studies provide a
somewhat disjointed picture of the neural basis for intelli-
gence. There is much that remains to be learned about the
neural basis for the specific cognitive abilities important for

intelligence test performance. It appears, however, that like
most aspects of behavior, intelligence test performance is
multiply determined and that a single neural substrate for
intelligence that is activated in similar ways across diverse
individuals is not likely to be found.
Differentiation of g(F) and g(C)
In any case, the research to date does makes it clear that
g(F) and g(C) are different sets of abilities, subserved by
different parts of the brain: g(F) seems to be substantially
mediated by the PFC, whereas g(C) is not. This is not
surprising in view of the following facts, most of which
were reviewed by Blair (2006): (a) g(F) declines much
more rapidly with age than does g(C). (b) The PFC dete-
riorates with age more rapidly than does the rest of the
cortex. (c) Severe damage to PFC is associated with
marked impairment of g(F) but little or no impairment of
g(C). (d) Severe impairment of g(C), such as occurs in
autism, is often associated with near-normal or even supe-
rior g(F). (f) Gains in mental abilities in recent decades
have been much less for crystallized abilities (the equiva-
lent of about 10 points in IQ) than for fluid abilities (about
18 points), and they have been particularly dramatic for
performance on Raven’s Progressive Matrices (28 points),
which has traditionally been considered a measure of pure
fluid g (Flynn, 2007)4 (g) Training specifically in executive
functions such as attention control and working memory
can have a marked effect on fluid intelligence as measured
by Raven’s Progressive Matrices while having little or no
effect on crystallized intelligence. (h) Changes in g(F) and
g(C) across the teenage years can be substantial, and those
changes are independent of one another and are associated
with changes in gray matter in different parts of the brain
(Ramsden et al., 2011). It is important to note that of 35
investigators who replied to Blair’s (2006) article pointing

to evidence that g(F) and g(C) are quite separate constel-
lations of abilities, only two challenged this contention, and
none defended the strong view of g as being psychologi-
cally unitary and subserved by a single neural network. See
also Hunt (2011).
There is a complex relationship between aging and
relative declines of g(F) versus g(C) (J. R. Flynn, 2009c).
Intelligence declines in old age, but do brighter individuals
decline less or more? We do not have longitudinal data on
the question, but cross-sectional data from the WAIS tables
indicate that there may be a “bright tax” for fluid intelli-
gence. After age 65, the brighter the person the greater the
decline: Whereas those at the median lose an extra 6.35
points (SD � 15) compared to those 1 SD below the
median, those 1 SD above the median lose an extra 6.20
points compared to those at the median, and those 2 SDs
above the median lose an extra 3.40 points compared to
those 1 SD above. Those who are very bright, rather than
below average, pay a total penalty of 16 IQ points. The
reverse is true of verbal ability, where there is a “bright
bonus” of 6.30 points.
Reciprocal Causation Between Brain
Morphology and Intellectual Function
It is important to recognize that the causal arrow between
intellectual functioning and brain morphology points in
both directions. Exercise of particular skills increases the
size of particular areas of the brain. The hippocampus
mediates navigation in three-dimensional space. London
taxicab drivers have hippocampi that are enlarged—and
enlarged in proportion to the amount of time they have
been on the job (Maguire et al., 2000). Similarly, the
process of learning how to juggle over a three-month
period increased the size of gray matter in the mid-temporal

area and the left posterior intra-parietal sulcus (Draganski
et al., 2004). The extent of structural gray matter growth
4 German-speaking countries may constitute an exception to the
rule
for other societies that gains have been greater for fluid than for
crystal-
lized intelligence (Pietschnig, Voracek, & Formann, 2010).
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was correlated with increased juggling ability. Three
months after ceasing to juggle, the size of gray matter
expansions was reduced. Three months of playing the
visual-spatial game Tetris resulted in increases in cortical
thickness in two regions and also in functional changes
(though functional changes were not in the same areas as
structural changes; Haier, Karama, et al., 2009).
Group Differences in IQ
Two types of group differences in IQ have been exhaus-
tively explored. These are the differences between males

and females and the differences between Blacks and
Whites (mostly in the United States). Differences between
Asians and Westerners have been less well explored, and
differences between Jews and non-Jews still less well ex-
plored. We summarize the evidence for all four types of
differences below. Little is known of any value about
Hispanic and American Indian IQs other than that they are
lower than those of White Americans and slightly higher
than those of Blacks. Estimates of the Hispanic/White gap
range from two thirds the size of the Black/White gap for
IQ tests to only slightly less than the Black/White gap for
academic achievement as measured by the National As-
sessment of Educational Progress.
Sex Differences in Intelligence
Questions about sex differences in intelligence differ from
other group differences because there are some fundamen-
tal biological differences between males and females and,
despite sex-related differences in socialization practices,
both sexes share home environments and SES. However, as
for questions about racial differences, the topic is incendi-
ary because the answers we provide have political ramifi-
cations that include how and whom we educate, hire, and
select as leaders.
Some public school districts have begun to segregate
girls and boys on the basis of the belief that they are so
different intellectually that they need to be educated sepa-
rately, a belief that stems from faulty extrapolations from
research on sex differences in intelligence. An extensive
review conducted by the U.S. Department of Education
(Mael, Alonso, Gibson, Rogers, & Smith, 2005) found that
the majority of studies comparing single-sex with coedu-
cational schooling reported either no difference or mixed
results, and other reviews reported a host of negative con-

sequences associated with single-sex education, including
increased sex role stereotyping, which may harm both boys
and girls (Halpern et al., 2011; Karpiak, Buchanan, Hosey,
& Smith, 2007). As reviewed below, there are some cog-
nitive areas that show average sex differences, but the data
from the research literature on intelligence and cognitive
skills do not indicate that different learning environments
for females and males would be advisable.
Visuospatial, verbal, and quantitative
abilities. Jensen (1998) addressed the question of fe-
male–male differences in intelligence by analyzing tests
that “load heavily on g” but were not normed to eliminate
sex differences. He concluded, “No evidence was found for
sex differences in the mean level of g or in the variability
of g. . . . Males, on average, excel on some factors; females
on others” (Jensen, 1998, pp. 531–532). Jensen’s conclu-
sion that no overall sex differences exist for intelligence
has been bolstered by researchers who assessed intelligence
with a battery of 42 mental ability tests (Johnson &
Bouchard, 2007). They found that most of the tests showed
little or no sex differences. There were, however, several
tests that showed a difference between males and females
of 0.5 SD or more. These differences included an advantage
for females for verbal abilities such as fluency and memory
abilities and an advantage for males on visuospatial abili-
ties such as object rotation. These researchers recom-
mended that the usual partition of intelligence into fluid and
crystallized components be replaced with a model that
partitions intelligence into verbal, perceptual, and visu-
ospatial rotation. When this model is used, females excel at
verbal and perceptual tasks; males excel at visuospatial
tasks.
Sex differences favoring males in mental rotation,

which is the ability to imagine what an object would look
like if it were rotated, can be found in infants as young as
three months of age (Quinn & Liben, 2008). Although this
very early difference suggests a strong biological basis for
the large sex differences in mental rotation, there is also
strong evidence for a large sociocultural/learning contribu-
tion. For example, when female and male college students
were trained with computer games that required use of
spatial visualization, this intervention reduced the gap be-
tween male and female performance, though it was not
completely eliminated (Feng, Spence, & Pratt, 2007). Like-
wise, when male and female college students were primed
with positive stereotypes (“I’m a student from a selective
college”) before taking an object rotation task, the gender
gap in performance was nearly eliminated. When gender
was primed before the test, the gender gap widened (Mc-
Glone & Aronson, 2006).
Language difficulties associated with stuttering, dys-
lexia, and autism are much more prevalent in males than in
females (Wallentin, 2009). There is a large female advan-
tage in reading that is found internationally, with girls
significantly outscoring boys on the Progress in Interna-
tional Reading Literacy Study in fourth grade and at age 15
in all of the 25 countries that participated in 2003 and in 38
of the 40 countries that participated in 2006 (with no
significant between-sex differences in the other two coun-
tries (Mullis, Martin, Gonzalez, & Kennedy, 2003). There
are also large advantages favoring girls in writing achieve-
ment, with “the writing scores of female 8th graders . . .
comparable with those of 11th grade males’” (Bae, Choy,
Geddes, Sable, & Snyder, 2000, p. 18).
By contrast, mean differences on tests of quantitative
achievement tend to be small or nonexistent when mea-
sured with standardized tests that reflect the mathematics

content that is taught in school (Hyde, Lindberg, Lilnn,
Ellis, & Williams, 2008). On average, boys taking the SAT
have scored consistently about one third of a standard
deviation higher than girls over the last 25 years (College
Board, 2004; Halpern et al., 2007), but those values can be
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misleading because many more girls than boys take the
SAT (Hyde et al., 2008).5
There are many more mentally retarded (i.e., IQ below
70) males than females, which suggests an X-linked ge-
netic locus for many categories of mental retardation. A
review of the literature placed the ratio of males to females
at 3.6:1 across several categories of mental retardation
(Volkmar & Sparrow, 1993). Males are also more variable
in their performance on some tests of quantitative abilities,
which results in more males at both the high and low ends
of the distribution. The excess of males among the highest
scorers received considerable media attention three decades
ago when researchers found a 12:1 ratio of boys to girls

scoring above 700 on the mathematics portion of the SAT
for samples of highly gifted adolescents (Benbow & Stan-
ley, 1983). However, more recent assessments now place
the ratio of boys to girls somewhere between 4:1 and 3:1,
a very significant reduction that can best be explained by
increases in the number and level of mathematics courses
that girls and women take (Wai, Cacchio, Putallaz, &
Makel, 2010).
Raven’s Progressive Matrices is considered the best
measure of fluid g. Lynn and Irving (2004) reported that
males begin to show a significant advantage on Raven’s
Progressive Matrices at age 15. Flynn and Rossi-Casé
(2011) analyzed large and recent samples from five ad-
vanced nations. They showed that females matched males
on Raven’s Progressive Matrices at maturity as well as in
childhood. Samples that show a female deficit are biased by
the fact that more males drop out of secondary school than
females or by the fact that the university intake of females
has a lower IQ average than the male intake. Israel was an
instructive exception: The small female deficit there was
entirely a result of the low scores of Orthodox Jewish
women secluded from the modern world.
Causes of sex differences in intelligence.
For evolutionary psychologists, the answer to the “why”
questions about sex differences lies in the division of labor
in hunter-gatherer societies. For example, being able to
imagine what something looks like from different angles
(spatial rotation) might be more useful to a hunter than a
gatherer, and males traditionally do more hunting in prim-
itive societies. Although we recognize the importance of
evolutionary pressures in shaping modern humans, modern
mathematics and writing had no counterpart in early human
societies, and the changing nature of sex differences in
many intellectual tasks over the last century cannot neces-

sarily be explained with direct appeals to our evolutionary
past.
Because of the complexity of influences on intellec-
tual development, we endorse a biopsychosocial model that
recognizes the mutual influences of biological and psycho-
social effects. Consider, for example, sex differences in the
human brain. Overall, female and male brains are similar in
organization and structure, but closer inspection shows that
most areas are sexually dimorphic (Giedd, Castellanos,
Rajapakse, Vaituzis, & Rapoport, 1997). On average, the
male brain is between 8% and 14% larger than the female
brain, a difference that is comparable to the sex difference
in the mass of other organs such as the heart (Sarikouch et
al., 2010) and kidney (I. M. Schmidt, Molgaard, Main, &
Michaelsen, 2001). Overall brain size does not plausibly
account for differences in aspects of intelligence because
all areas of the brain are not equally important for cognitive
functioning. In general, females have more gray matter
(neuronal cell bodies and dendrites) and males have more
white matter in different regions of the brain (Eliot, 2011),
and different patterns of gray and white matter correlate
with intelligence for males and females (Haier, Jung, Yeo,
Head, & Alkire, 2005). Haier and colleagues (2005) con-
cluded, “Men and women apparently achieve similar IQ
results with different brain regions, suggesting that there is
no singular underlying neuroanatomical structure to gen-
eral intelligence and that different types of brain designs
may manifest equivalent intellectual performance” (p.
320).
Steroidal hormones also play a role in intellectual
ability. Prenatal hormones are critical to normal brain de-
velopment. As Neisser et al. (1996) noted, both prenatal
and postnatal hormones influence behavior, including cog-

nition, in characteristically male or female directions.
Sex hormones have effects on cognition throughout
life, with many studies showing that higher estrogen levels
slightly enhance some cognitive functions, particularly ver-
bal memory in older women, and lower testosterone levels
are weakly associated with better verbal fluency in older
men (Wolf & Kirschbaum, 2002). In general, the effects of
sex steroids on intelligence are small and inconsistent (Lu-
ine, 2008) because hormones interact in complex ways that
can enhance or diminish cognitive abilities. Contrary to
expectations, the Women’s Health Initiative, a large ran-
domized control study of hormone replacement therapy,
found that dementia rates were higher for women who took
hormone supplements; however, many researchers have
pointed out serious flaws in this study, including the fact
that the women in the hormone group were older, heavier,
and less compliant than those in the placebo control group.
A leading theory at this time is that there is a critical period
(close in time to menopause) when hormone therapy will
5 The state of Illinois requires all high school juniors to take
the ACT,
which is a standardized test used for college admissions
decisions. Unlike
the SAT, its content is more closely aligned with content that is
likely to
be taught in school. Data from 2009 show that 52% of test
takers from
Illinois were girls, so even with the requirement that all
students take this
test, more girls take the ACT. There are four separate scores,
with girls
scoring slightly higher on English and reading and boys scoring
higher on
mathematics and science. Although results are in the same

general direc-
tion as found with the SAT, the effect sizes are small. The ACT
also
reports the percentages of students who are ready for college-
level work
in different subjects. The data from Illinois were as follows:
64% for boys
and 68% for girls in English, 44% for boys and 37% for girls in
mathe-
matics, 48% for both boys and girls in reading, and 30% for
boys and 19%
for girls in science. Colorado also requires that all high school
juniors take
the ACT. As in Illinois, girls in Colorado scored higher than
boys in
English and reading and boys scored higher than girls in math,
but not by
very much (d � approximately .1); there was no difference in
science.
Thus, the direction of the sex differences is the same, and the
differences
are generally smaller than those found with the SAT, which
includes a
larger sample of girls than boys.
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be beneficial for verbal and working memory for women
(Luine, 2008) There have been fewer studies of the effects
of sex steroids on male cognition, and as with the literature
for female participants, hormonal effects are inconsistent
and generally small for men as well. In a review article,
Janowsky, Chavez, and Orwol (2000) concluded that men
with higher testosterone to estrogen ratios have better
working memories, but this conclusion is tentative and may
change as we gain a better understanding of the cumulative
lifetime effects of sex hormones on intelligence.
We are far from understanding the intricate interplay
of hormones, brain structures, and intelligence. This is an
active area of research, and we can expect important find-
ings in the near future.
Black–White Differences in IQ
About the Black–White difference in IQ, which at the time
was about 15 points, the Neisser et al. (1996) article stated,
“There is not much direct evidence on this point, but what
there is fails to support a genetic hypothesis.” That conclu-
sion stands today: There has been no new direct evidence
on the question. (See Rushton & Jensen, 2005a, 2005b,
Gottfredson, 2005, and Lynn & Vanhanen, 2002, for the
view that the Black–White IQ difference is owing substan-
tially to genetic differences between the races and that
indirect evidence having to do with such factors as reaction
time and brain size is probative. See Nisbett, 2005, 2009,
for the view that the direct evidence indicates that the
difference between the races is entirely due to environmen-
tal factors and that the indirect evidence has little value.)

Nisbett (2009) maintains that there is actually a substantial
amount of direct evidence stemming from the fact that the
“Black” gene pool in the United States contains a large
amount of European genes. He maintains that almost all the
research indicates no higher IQs for Blacks with a signif-
icant degree of European heritage than for those with much
less. One of the most telling of the studies was available at
the time of the Neisser et al. (1996) report but was appar-
ently not known to them. This is an adoption study by
Moore (1986). She examined the IQs of Black and mixed-
race children averaging 81⁄2 years of age who were adopted
by middle-class families who were either Black or White.
The children who were of half-European origin had virtu-
ally the same average IQ as the children who were of
exclusively Black origin. Hence European genes were of
no advantage to this group of “Blacks.” Children (both
Black and mixed-race) adopted by White families had IQs
13 points higher on average than those adopted by Black
families, indicating that there were marked differences in
the environments of Black and White families relevant to
socialization for IQ; indeed, the differences were large
enough to account for virtually the entire Black–White gap
in IQ at the time of the study.6
Brain size and IQ. The evidence cited by sup-
porters of the genetic view that has received the most
attention is the claim that because brain size is related to IQ
for both Whites and Blacks and because Blacks have
smaller brains than Whites, lower IQ for Blacks is genet-
ically determined and mediated by brain size. As indicated
above, it is not clear that the brain size correlation with IQ
is genetically mediated. Moreover, a within-group correla-
tion does not establish that between-group differences have
the same origin. Brain size differences between men and
women are much greater than the race differences in brain

size, yet men and women have the same average IQ. Brain
size of full-term Black and White infants is the same at
birth (Ho, Roessmann, Hause, & Monroe, 1981), and sev-
eral postnatal factors known to reduce brain size are more
common for Blacks than for Whites (Bakalar, 2007; Ho et
al., 1981; Ho, Roessmann, Straumfjord, & Monroe, 1980a,
1980b). Finally, sheer brain size is a rather blunt measure
of brain differences, which may be less predictive of IQ
than measures of the size of particular regions or measures
such as the ratio of gray matter to white matter. It is
noteworthy, for example, that at a given level of IQ, Chi-
nese have smaller frontal cortexes than Americans (Chee,
Zheng, Goh, & Park, 2011), although Chinese brains as a
whole may be larger than those of Americans (Rushton,
2010). Even with brain size equated between Chinese and
Americans, the frontal cortex is larger in Americans (Chee
et al., 2011).
Black gains in IQ. Dickens and Flynn (2006a)
analyzed data from nine standardization samples for four
major tests of cognitive ability. They found that Blacks
gained 5.5 IQ points on Whites between 1972 and 2002.
The gap between Blacks and Whites on a measure of g
had narrowed almost to the same degree, that is, by 5.13
points.
This reduction in the measured g gap occurred even
though the magnitude of Black gains on Whites by subtest
did not correlate highly with Wechsler subtest g loadings.
This is scarcely surprising, because the g loadings of
Wechsler subtests are very similar. If one multiplies the
Black on White gains by the g loadings of the subtests, and
then takes a weighted average, the downward shift from IQ
to “gQ” is very slight. Because all subtests measure g either
directly or indirectly, Blacks simply could not have gained
on Whites unless they had also gained in measured g. If

Blacks eliminate the racial IQ gap, the measured g gap
must at least be greatly reduced. Evidence for this comes
from Eyferth (1961), who compared the children fathered
by Black and White U.S. soldiers in Germany after World
War II. His data show that the half-Black children matched
the White children not only for IQ but also for measured g
(J. R. Flynn, 2008).
It is important to note that there is a dramatic decline
of Black IQ with age. Four-year-old Blacks are only about
5 points below Whites of the same age, whereas at age 24,
Blacks are 17 points below Whites. This could be, as it
seems, a loss with age. But it could be that younger cohorts
6 The sole study consistent with the possibility that European
genes
are advantageous for Blacks was an adoption study by Scarr and
col-
leagues (Scarr & Weinberg, 1976; Weinberg, Scarr, &
Waldman, 1992).
But their study was characterized by many flaws acknowledged
by the
authors. The most serious flaw was that Black children were
adopted
much later than other children, and age of adoption is strongly
nega-
tively associated with IQ.
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of Blacks (those born five years ago) have had more fa-
vorable life histories than older cohorts of Blacks (born 24
years ago). If it is an age effect, it could have either
environmental causes (the Black environment becomes
progressively worse and worse relative to the White envi-
ronment) or genetic causes (genes dictate that Black cog-
nitive growth with age is slower than that of Whites).
The evidence for Black gains on Whites has been
challenged in terms of the selection of studies, the magni-
tude of the gains, and their implications. Readers are di-
rected to the exchange between Rushton and Jensen (2006)
and Dickens and Flynn (2006b). A major objection of
Rushton and Jensen was that Dickens and Flynn failed to
analyze the standardization sample of the Woodcock-John-
son III IQ test. Murray (2006) did that analysis and found
Black gains of a magnitude similar to those found by
Dickens and Flynn on the four other tests, though the gains
on the Woodcock-Johnson test came at an earlier time
period than those on the other tests.
One other recent study supports an environmental
hypothesis. Fagan and Holland (2007) studied different
samples of Blacks and Whites and found that whereas the
Blacks were substantially lower than the Whites in word
knowledge, they were equally capable of learning new
concepts and of making inferences. It may be that knowl-
edge is more influenced by environmental factors than are

learning or inferential ability. This result seems to contra-
dict the finding that Blacks tend to underperform Whites on
more heavily g-loaded tasks such as Raven’s Progressive
Matrices, and it will be interesting to see if it can be
replicated.
Stereotype Threat
Our understanding of group differences in intellectual abil-
ity is furthered by the very large literature on psychological
reactions to negative stereotypes. Steele and Aronson
(1995) argued that when test takers are aware of wide-
spread stereotypes that impugn a group’s intelligence (e.g.,
“Black people are stupid,” “Girls can’t do math”), they
frequently experience the threat of devaluation— by the
self, by others, or by both. The resulting arousal and
anxiety can impair executive functioning on complex tasks
such as standardized aptitude tests. Steele and Aronson
called this response stereotype threat and demonstrated in
a series of experiments that Black test takers scored con-
siderably better—sometimes far better— on intellectual
tests when the test was presented in a manner that down-
played ability evaluation or downplayed the relevance of
race. Since the publication of Steele and Aronson’s 1995
article, some 200 replications of the effect have been pub-
lished, extending the findings to women and mathematics
abilities, Latinos and verbal abilities, elderly individuals
and short-term memory abilities, low-income students and
verbal abilities, and a number of nonacademic domains as
well. See Steele, Spencer, and Aronson (2002) and Aron-
son and McGlone (2009) for reviews of the literature.
Two recent meta-analyses reported by Walton and
Spencer (2009) that included the data from nearly 19,000
students indicate that stereotype threat can cause tests to
underestimate the true abilities of students likely to expe-

rience stereotype threat (Walton & Spencer, 2009). Walton
and Spencer’s analysis suggests a conservative estimate
that women’s math performance and Black students’ verbal
performance are suppressed by about 0.2 SD. In a number
of the individual studies, however, the suppression was
closer to a full standard deviation.
The stereotype threat formulation has led to a variety
of simple educational interventions conducted in schools
and colleges that have substantially raised the achievement
of Black students (e.g., Aronson, Fried, & Good, 2002;
G. L. Cohen, Garcia, Apfel, & Master, 2006) and the
achievement of girls in mathematics (Blackwell, Trzesni-
ewski, & Dweck, 2007; Good, Aronson, & Inzlicht, 2003).
The studies suggest that stereotype threat suppresses real-
world intellectual achievements. Some of the interventions
seem remarkably minor on the surface yet produce sub-
stantial gains in academic achievement. For example, sim-
ple efforts at persuading minority students that their intel-
ligence is under their control to a substantial extent have
nontrivial effects on academic performance (Aronson et al.,
2002; Blackwell et al., 2007).
A critique of the Steele–Aronson research by Sackett,
Hardison, and Cullen (2004) suggests caution in attributing
Black–White test score gaps to stereotype threat. In short,
the design and analysis of the studies leave the findings
open to an alternative interpretation, namely, that inducing
stereotype threat in the laboratory simply widens the exist-
ing Black–White gap but that reducing stereotype threat
leaves this preexisting gap intact. This is indeed a possi-
bility. However, if stereotype threat had no influence on
test scores, it would be hard to explain why the interven-
tions specifically targeted to reduce stereotype threat and its
effects would have such strong effects on reducing real-
world test and achievement gaps. Thus, the weight of the

evidence suggests that stereotype threat probably accounts
for some of the Black–White difference on intelligence-
related tests under some circumstances. At the least, these
effects should give one considerable pause when interpret-
ing group differences in intelligence test scores and aca-
demic achievement potential.
Asian–White Differences in IQ
The academic achievements and high occupational profile
of Chinese and Japanese Americans have inspired specu-
lation about genetic superiority (Lynn, 1987; Rushton,
1995; Weyl, 1969). Flynn (1991) analyzed data from the
Coleman Report for the high school graduating class of
1966. That large representative sample included a substan-
tial number of Asian Americans. The Asian Americans had
about the same mean IQ as White Americans (actually
slightly lower) but scored one third of a standard deviation
higher on the SAT than did White Americans. SAT scores
may reflect motivational differences—for example, taking
more and higher level math courses—to a greater degree
than do IQ tests. Remarkably, Chinese Americans in the
class of 1966 attained occupations of a professional, man-
agerial, or technical nature at a rate 62% higher than White
Americans. The picture that results is that Asian Americans
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capitalize on a given level of intellectual ability much more
than do European Americans.
The differences in achievement between Asian Amer-
icans and White Americans are not hard to explain on
cultural grounds. East Asians are members of cultures
having a Confucian background. An endemic belief in
those cultures is that intelligence is primarily a matter of
hard work (Chen & Stevenson, 1995; Choi & Markus,
1998; Choi, Nisbett, & Norenzayan, 1999; Heine et al.,
2001; Stevenson et al., 1990). For thousands of years in
China it was possible for the poorest villager to become the
highest magistrate in the land through study. Families with
a Confucian background exert far more influence on their
children than do most families of European culture (Fiske,
Kitayama, Markus, & Nisbett, 1998; Markus & Kitayama,
1991). They can demand of their children excellence in
education and preparation for high-status careers and ex-
pect their children to try to comply. Matters in the United
States have changed since the passage of immigration laws
in the late 1960s that encouraged the immigration of highly
skilled workers. That change resulted in a huge inflow of
talented East and South Asians. These people bring on
average very substantial educational and cultural capital
and undoubtedly some genetic advantage over the general
U.S. population.
Jewish–Non-Jewish Differences in IQ
There is little good evidence about just what IQ levels are
typical for Ashkenazi Jews. (There is even less evidence
available for Sephardic Jews, and in the rest of this section

“Jews” should be taken to mean “Ashkenazi Jews of Eu-
ropean descent.”) Jewish IQs have been variously esti-
mated to be 7–15 points higher than those of White non-
Jews in Britain and America (Flynn, 1991; Lynn, 2004,
2006). All available studies, however, are based on samples
of convenience.
It is not clear to what we should attribute the greater
overall intellectual ability popularly attributed to Jews and
supported by (weak) data. It is certainly possible to invoke
cultural explanations, and there are numerous armchair
genetic theories as well. One genetic theory may have more
substance to it than the others. This is the “sphingolipid”
theory of Cochran, Hardy, and Harpending (2005). Coch-
ran et al. noted that Jews are subject to several distinctive
genetic conditions that involve an excess of sphingolip-
ids—the substance that forms part of the insulating outer
sheaths that allow nerve cells to transmit electrical signals
and encourage growth of dendrites. These illnesses include
Tay-Sachs disease, Niemann-Pick disease, and Gaucher’s
disease. Having too many of these sphingolipids is fatal or
at least likely to result in serious illness that can prevent
reproduction. Why does natural selection not eliminate
these diseases? Cochran et al. enlisted the sickle-cell ane-
mia analogy. The sickle-cell gene produces illness for
individuals who have two copies of it (one from each
parent). But those with only one copy of the gene are
provided with protection against malaria. Cochran et al.
argued that because Jews were segregated into trade and
finance since their arrival in Europe in the 8th century AD,
intelligence conferred a larger reproductive advantage for
them than for others. The main evidence in favor of the
sphingolipid theory is that Jews with Gaucher’s disease are
more likely to be working in occupations demanding ex-
tremely high IQ than are other Jews. No direct test of the

association of the genes for these diseases with intelligence
has been made, so the theory remains merely an intriguing
suggestion.
It is important to note that even at the highest esti-
mates we have of Jewish IQ, Jewish accomplishment ex-
ceeds what would be predicted on the basis of IQ alone.
Nisbett (2009) has argued that the numbers of Jewish Ivy
Leaguers, professors at elite colleges, Supreme Court
clerks, and Nobel Prize winners are greater than one would
expect even if average Jewish IQ were 115. He has also
noted that remarkable as the superior achievement of Jews
is, the achievement difference between Jews and non-Jews
is far less extreme than differences between groups in many
other comparisons that cannot be explained on purely ge-
netic grounds, such as the achievements of the Italians
versus the English in the 15th century, of the English
versus the Italians after the 18th century, of Arabs versus
Europeans in the 8th century, of Europeans versus Arabs
after the 14th century, and of New Englanders versus
Southerners throughout American history.
Important Unresolved Issues
We can discuss some broad issues with greater clarity than
was possible at the time of the Neisser et al. (1996) review.
First, it has been proposed that working memory is essen-
tially identical to fluid intelligence. We discuss the evi-
dence on both sides of this issue. Second, the massive
Flynn effect gains in IQ seem to contradict the data indi-
cating that IQ is substantially heritable. We report an
attempted resolution of the paradox. Third, several recent
theories may throw new light on the concept of g. Fourth,
there is growing evidence that self-regulation or self-con-
trol abilities may have as much effect on academic and
other life outcomes as IQ. We explore the relationship
between self-regulation and IQ and examine possible

mechanisms through which self-control influences intellec-
tual achievement. Fifth, the issue of the effect of stress on
the central nervous system has become a focus of attention
recently, but many questions arise concerning its impor-
tance, the mechanisms by which it operates, and whether
class and race differences in stress contribute to group
differences in IQ.
The Relationship Between Working Memory
and Intelligence
Over the last two decades, many researchers have noted
that working memory and fluid intelligence, g(F), are
highly related concepts. Working memory is the active
processing system that simultaneously stores and manipu-
lates relevant information, often in the face of distracting or
competing information or the need to inhibit incorrect
responses (Engle, 2002). The predominant model of work-
ing memory posits verbal, visuospatial, and episodic mem-
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ory as three subsystems that are coordinated by an execu-
tive that is often conceptualized as attentional control
(Baddeley, 2002; Baddeley & Hitch, 1974). The capacity
of working memory is measured with tests that require
participants to keep verbal or visuospatial information in
memory while solving problems such as sentence compre-
hension, arithmetic, and abstract reasoning. Scores on tests
of working memory capacity have high correlations with
reading comprehension, reasoning abilities, and scores on
the SAT (Daneman & Carpenter, 1980; Kyllonen &
Christal, 1990; Turner & Engle, 1989).
The relation between working memory and the gen-
eral factor of intelligence has reached identity (r � 1.00) in
some studies using latent variable analysis (Gustafsson,
1984; Sü�, Oberauer, Wittmann, Wilhelm, & Schulze,
2002) and is, on average, .72 (Kane, Hambrick, & Conway,
2005). The extent of the relation between working memory
and general intelligence, however, remains in dispute. Pri-
mary points of contention concern the specific definition of
working memory (Ackerman, Beier, & Boyle, 2005) and
the extent to which speed of processing rather than working
memory best explains individual differences in intelligence
(Conway, Cowan, Bunting, Therriault, & Minkoff, 2002;
Fry & Hale, 2000; Salthouse, 1996; Salthouse & Pink,
2008; Sampson, 1988).
The argument about whether working memory ex-
plains individual differences in g(F) is important because of
the close association of g(F) with g. Following from this
association, as we noted earlier, scientists have recently
shown that g(F) can be markedly enhanced by training
people to increase the capacity of their working memory.
However, the research establishing this fact provides no
indication that training of these functions influences g(C).
Thus the case for identifying either working memory or

g(F) with g is rendered implausible. Nevertheless, the re-
lations among working memory and other executive func-
tions, g(F), and g are clearly complex, and much remains to
be learned.
Reconciling IQ Gains and Heritability
The discovery of IQ gains over time created a seeming
paradox. Identical twin studies (and other kinship studies)
indicated that genetic influence on IQ is strong and the
effects of environment on IQ are weak. But Dutch males
gained 20 IQ points on a test composed of 40 items from
Raven’s Progressive Matrices between 1952 and 1982,
which seems to imply environmental factors of enormous
potency. How can environment be so weak and so potent at
the same time? Dickens and Flynn (2001a, 2001b) offered
a model that distinguishes the dynamics of a situation in
which two separated twins develop highly similar IQs and
of a situation in which a whole society shows a huge IQ
gain over time.
When identical twins are separated at birth, they ini-
tially share only common genes. But soon those common
genes begin to access environments far more alike than
those of randomly selected individuals. If both twins are
taller and quicker than average, although raised in different
cities, they are more likely to play basketball a lot, get
selected for their school team, and get professional coach-
ing. There is a dynamic interplay each step of the way,
whereby better than average ability accesses an enriched
environment, which enhances the ability advantage, which
accesses an even more enriched environment, and so forth.
Similarly, if twins have slightly better brains than average,
although raised apart, they are both more likely to enjoy
school, get into an honors stream, go to a top university,
and so forth.

Dickens and Flynn (2001a, 2001b) call this process
the individual multiplier. Thanks to the individual multi-
plier, the fact that separated twins have powerful environ-
mental factors in common is “masked,” and their common
genes get all of the credit for their highly similar IQs.
Between generations, the quality of genes is relatively
constant, and the potency of environment is clearly re-
vealed. A persistent environmental factor like the advent of
TV can enhance the popularity of basketball, so that more
people play it more enthusiastically. This raises the average
performance, and the rising performance develops its own
momentum. There is a second kind of dynamic interaction
whereby the rising mean (some people learn to shoot with
either hand) encourages every individual to improve (more
people begin to shoot with either hand), which raises the
mean further, which influences the individual further, and
there is a great escalation of skills over time. If the Indus-
trial Revolution puts a premium on credentials, and lucra-
tive cognitive work expands, the more people in general
need increased cognitive skills. This seems likely to result
in increasing cognitive challenges in educational and en-
tertainment settings and an explosion of college enroll-
ments.
Dickens and Flynn (2001a, 2001b) call this process
the social multiplier. Thanks to the social multiplier, the
evolving environment can cause huge cognitive gains over
time with no assistance from better genes. The two multi-
pliers are operating simultaneously— genetic differences
between individuals that are rendered potent by individual
multipliers (registering in the twin studies) and environ-
mental differences between the generations that are ren-
dered potent by the social multiplier (registering huge IQ
gains over time).

To the extent that Blacks and Whites interact mainly
with members of their own groups, potent social multipli-
ers could be exacerbating the existing differences in intel-
ligence. If the primary environmental causes (e.g., discrim-
ination in jobs and housing) are removed, the multiplier
effects should unwind; that is, the removal of small persis-
tent environmental differences between groups could have
large effects on group differences in IQ.
Rowe and Rodgers (2002) argued that the Dickens–
Flynn model implies that IQ variance should have in-
creased at the same time that mean IQ increased. Loehlin
(2002) generalized the model and faulted the family of
models presented for failure to specify the time scale in-
volved and for failure to address developmental aspects.
Dickens and Flynn (2002) contended that the model does
not imply increased variance and is consistent with plau-
sible theories of development. Rushton and Jensen (2005a)
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criticized the model on the grounds that it (a) implied
mistakenly that Black–White differences in IQ should in-
crease over the course of development and (b) could not
explain the rise in heritability from 0.4 during childhood to
0.8 at maturity. Dickens (2009) replied as follows: (a) In
fact, the Black–White gap does increase with age, but even
if it did not, whether or not one could detect the increase
would depend on how quickly the multipliers work. If they
worked quickly enough, one might not see any change in
the Black–White difference once children were old enough
to test. (b) The model was actually proposed in part to
explain why heritability should increase with age. Min-
groni (2007) criticized the model on multiple grounds and
Dickens (2009) responded.
We emphasize that the Dickens–Flynn model, while
good at explaining a wide range of facts about intelligence,
has not been subject to very much empirical testing.
What Is g?
Some have suggested that whatever the cause of societal
gains in cognitive ability, they are irrelevant to debates
about the malleability of cognitive ability because they are
not g gains (Gottfredson, 2007; Rushton, 1999, 2000;
Rushton & Jensen, 2005a). It is argued that IQ tests derive
their predictive power from their ability to measure g and
that if secular gains are not g gains, then they have no
practical value.
In a simple model in which all cognitive abilities are
a linear function of one important underlying ability, an
increase in that unitary ability would lead the abilities that
derive from it to increase in proportion to their dependence
on that underlying cause. The gain on each ability would be
proportional to its loading on the first factor derived from
an analysis of all the abilities (i.e., the abilities’ g loadings).

Various studies using the method of correlated vectors
reach different conclusions about the extent to which sec-
ular gains are proportional to (correlated with) g loadings,7
but Wicherts, Jelte, Dolan, and Hessen (2004) rejected the
hypothesis that secular gains on different tests are purely g
gains. However, the same can be said for the trajectory of
abilities as children develop and as ability declines in old
age. A single-factor g model cannot explain either the
pattern of growth in ability or the pattern of decline in
ability (Finkel, Reynolds, McArdle, & Pedersen, 2007;
McArdle, Ferrer-Caja, Hamagami, & Woodcock, 2002;
McArdle, Hamagami, Meredith, & Bradway, 2000).
This lack of a relationship between g loading and
improving subtest scores across a range of different dimen-
sions is not terribly damaging to the notion of a dominant
underlying ability so long as one has a more nuanced view
as to how that general ability relates to the specific abilities
measured by the subtests. Cattell (1971) viewed fluid g as
the ability to solve new problems on the spot and the ability
to learn new things. Crystallized g was the knowledge that
one accumulated by applying one’s fluid ability in different
areas. Someone who always had more fluid g would also
have more crystallized g, but crystallized g could grow
while fluid g remained constant, and if fluid g began to fall,
crystallized g could still continue to rise as long as the rate
of building crystallized g surpassed the rate at which it was
lost. Thus, there would be no reason to expect tests of
crystallized and fluid abilities to rise and decline at the
same time or rate.
Studies of the rate of decline of different cognitive
abilities are consistent with such a view (McArdle et al.,
2002). Individuals’ scores on tests that tap mainly fluid

abilities (such as Raven’s Progressive Matrices) decline
with age much earlier and more rapidly on average than do
their scores on tests of crystallized abilities such as vocab-
ulary or general knowledge. However, studies that attempt
to determine whether fluid ability can predict changes in
crystallized abilities tell a more complicated story.
McArdle and colleagues (2000) found that fluid ability
does not predict crystallized ability (operationalized as
vocabulary) in individuals, but memory does, and memory
is somewhat influenced by fluid ability. Ferrer and
McArdle (2004) used a broader set of measures of crystal-
lized ability. Like McArdle et al. (2000), they found no
evidence that fluid abilities predict changes in vocabulary
in individuals, but they did find that fluid ability predicted
academic knowledge and quantitative abilities. Finally,
Hambrick, Pink, Meinz, Pettibone, and Oswald (2008)
estimated a structural equation model of the relationship
between a number of factors to see what could predict gains
in knowledge of current events. They found that g(F)
predicts the initial level of knowledge and that the initial
level of knowledge predicts gains, but they found no direct
role for g(F) in predicting gains.
These studies suggest a complex interplay of different
abilities in the dynamics both of early life development and
of later life decline. What then explains the relatively stable
factor structure of abilities described by Carroll (1993)?
Several recent papers have suggested how a g factor could
arise despite the independence of its components (Bar-
tholomew, Deary, & Lawn, 2009; Dickens, 2007; van der
Maas et al., 2006). Dickens (2007) showed that a general
intelligence factor would appear because people who are
better at any given cognitive skill are more likely to end up
in environments that cause them to develop a wide range of
skills. He attempted to account for the important facts
about g without postulating any underlying physiological

base for the correlation of abilities. People may indeed
have physiological advantages over one another for one or
more cognitive skills, but Dickens showed that even if
these skills are initially largely independent of one another,
after people interact with their environments, these skills
7 Colom, Juan-Espinosa, and Garcia (2001) and Juan-Espinosa
et al.
(2000) found strong correlations between g loadings and IQ
gains. Jensen
(1998, pp. 320 –321) reviewed a number of studies of the
relation between
subtest gains and g loadings, all of which showed weak positive
correla-
tions. Rushton (1999, 2000) found that a measure of g
developed on the
WISC has loadings that are negatively correlated with subtest
gains in
several countries. But Flynn (2006) argued that IQ gains are
greatest on
tests of fluid g rather than crystallized g, and he found a
positive (although
statistically insignificant) correlation between a measure of
fluid g he
developed and IQ gains in the same data used by Rushton. Must
et al.
(2003) found no correlation between g loadings and gains on
two tests in
Estonia, but these were achievement tests with a strong
crystallized bias.
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will no longer be independent. Someone who is good at any
intellectual skill is more likely to end up in environments
where all skills will be practiced, which will lead to the
development of all skills. It should be noted that if a
process such as this helps to explain the correlation of
different abilities, then there would be no reason to expect
that secular societal gains on IQ subtests would be propor-
tional to g loadings for those subtests. (See also Hunt,
2011.)
Van der Maas and colleagues (2006) also proposed a
model of the origin of g that introduces many interesting
properties. Initially uncorrelated abilities become more cor-
related over time because they are mutually reinforcing.
However, the model of van der Maas et al. does not
distinguish between genetic and environmental effects. In-
tegration of that model with Dickens’s (2007) model could
provide a more compelling description of the development
process. Again, if the van der Maas model explains the
correlation across different abilities, there would be no
reason to expect that secular gains on different tests would
come in proportion to their g loadings.
Bartholomew et al. (2009) provided a description of
how uncorrelated underlying abilities might be combined

by different test items, producing a g factor. As in the other
models of g just referred to, the underlying abilities are
assumed to be uncorrelated. A g factor emerges because
any test item taps multiple abilities. Thus there will be a
tendency for items that tap some of the same abilities to be
correlated, resulting in a correlation matrix between test
items having all positive elements. Such a matrix will
always yield a first principal factor that loads positively on
all test items. As in the van der Maas et al. (2006) model,
no explicit recognition is given to the role of genetics in
shaping g, and thus many of the issues that Dickens (2007)
addressed, such as the correlation of g loadings and the
heritability of different abilities, are unaddressed in the
Bartholomew et al. model. As with both the Dickens and
van der Maas models, there is no reason to expect that
secular gains in ability would be proportional to factor
loadings.
Self-Regulation and Schooling
What is it about school and preschool that enhances intel-
ligence and academic abilities? Content knowledge (e.g,,
learning about climate in different places in the world) and
procedural knowledge (e.g., sorting shapes) are of course
important, but increasingly scientists are recognizing the
importance of developing self-regulatory skills and other
noncognitive traits as requisite for high-level intellectual
functioning (Blair, 2002; Calero, Garcia-Martin, Jimenez,
Kazen, & Araque, 2007; Chetty et al., 2010; Diamond,
Barnett, Thomas, & Munro, 2007; Heckman, 2006, 2011).
Self-regulatory skills include behaviors such as being able
to wait in line, inhibiting the desire to call out in class, and
persevering at a task that may be boring or difficult. There
are many terms in the literature for the general idea that
people can recognize, alter, and maintain changes in their
behaviors and moods in ways that advance cognitive per-
formance. These terms include self-discipline (Duckworth

& Seligman, 2005), the ability to delay gratification (Mis-
chel, Shoda, & Peake, 1988), and self-regulated learning
(P. A. Alexander, 2008).
In a classic study of self-regulation, Mischel et al.
(1988) found that four-year-old children who delayed the
immediate gratification of eating one marshmallow so that
they would be allowed to eat two marshmallows later
scored higher on the SAT they took for college entrance
over a decade later. A study with similar implications was
conducted with eighth-grade students at a magnet public
school (Duckworth & Seligman, 2005). Students were
given envelopes that contained $1. They could either spend
the dollar or exchange the envelope for one containing $2
the following week. In addition, students were rated on
numerous other measures of self-discipline. The authors
reported that scores on a composite measure of self-disci-
pline predicted academic performance and learning gains
over the academic year in which the study was conducted
and did so better than IQ tests. Similar studies with college
students at Ivy League schools, students at a military acad-
emy, and spelling bee participants found that self-discipline
and ability to delay gratification predicted success across a
variety of academic measures (Duckworth, Peterson, Mat-
thews, & Kelly, 2007).
Several cognitive variables that could explain the ef-
fect of self-regulation on intellectual tasks have been in-
vestigated. For example, individuals with larger working
memory capacities are better able to regulate their emotions
(Schmeichel, Volokhov, & Demaree, 2008), and individu-
als who are better able to suppress their emotions have
higher scores than their more impulsive peers on the Ra-
ven’s Progressive Matrices, a standard measure of g(F)
(Shamosh & Gray, 2007). There are at least three possible

explanations for the relationship between self-regulation
and higher scores on tests of g(F): (a) The ability to
self-regulate could be a manifestation of intelligence; (b)
these constructs could share common variance such that
they are both affected by a third variable; or (c) self-
regulation could be one of the processes that facilitate the
development of intelligence.
There is evidence that self-control, or at any rate some
set of nonintellective motivational factors, contributes not
only to life outcomes but to IQ scores themselves. Duck-
worth, Quinn, Lynam, Loeber, and Stouthamer-Loeber
(2011) have shown in a meta-analysis of over 40 samples
that incentives for good test performance improve IQ
scores by about 10 points. For samples for which the
average baseline IQ was less than 100, the gain due to
incentives was about 14 points. The lower the baseline IQ,
the greater the gain due to incentives, and the larger the
incentives offered, the larger the IQ gain. The investigators
also examined the correlates of assessed test-taking moti-
vation (based on refusal to attempt parts of the test, re-
sponding rapidly with “I don’t know” answers, etc.) for a
group of middle-school boys. IQ predicted academic out-
comes in adolescence and total years of education by the
age of 24. So did the nonintellective traits, though to a
lesser degree. Nonintellective traits predicted nonacademic
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outcomes— criminal convictions and employment in adult-
hood—as well as did IQ.
The influence of nonintellective factors on life out-
comes is revealed by the sobering (to Americans, at least)
scores of 12th-graders on mathematics and science in the
Third International Mathematics and Science Study of 41
nations. The United States came in last for both fields of
study. Test motivation, as indicated by the number of
optional self-report questions answered in a background
questionnaire, accounted for 53% of between-nation vari-
ance and 22% of between-classroom variance within na-
tions (Boe, May, & Boruch, 2002).
Additional motivational issues for future examination
include the following: (a) If self-regulation is what lies
behind the academic success obtained by children who
were in the experimental conditions in effective preschool
interventions, as many have suggested, then it indicates that
self-regulation has aspects that are not directly related to
IQ, as these programs typically have little sustained effect
on IQ. (b) Would interventions designed specifically to
increase self-control improve performance on IQ tests
and—more importantly— on academic achievement tests?
Stress, Intelligence, and Social Class
One factor that Neisser and colleagues (1996) did not deal
with extensively is stress. Chronic, continuous stress—
what can be considered as “toxic” stress—is injurious over

time to organ systems, including the brain. Chronically
high levels of stress hormones damage specific areas of the
brain—namely, the neural circuitry of PFC and hippocam-
pus—that are important for regulating attention and for
short-term memory, long-term memory, and working
memory (McEwen, 2000). Although the extent to which
the effect of early stress on brain development and stress
physiology may affect the development of intelligence is
not currently known, we do know that (a) stress is greater
in low-income home environments (Evans, 2004) and (b) a
low level of stress is important for self-regulation and early
learning in school (Blair & Razza, 2007; Ferrer &
McArdle, 2004; Ferrer et al., 2007).
Research suggests that part of the Black–White IQ gap
may be attributable to the fact that Blacks, on average, tend
to live in more stressful environments than do Whites. This
is particularly the case in urban environments, where Black
children are exposed to multiple stressors. Sharkey (2010),
for example, has recently found that Black children living
in Chicago (ages 5–17) scored between 0.5 and 0.66 SD
worse on tests (both the WISC-Revised and the Wide
Range Achievement Test-3) in the aftermath of a homicide
in their neighborhood. Sharkey’s data show that debilitat-
ing effects were evident among children regardless of
whether they were witnesses to the homicide or had simply
heard about it.
An impressive study by Eccleston (2011) indicates
that even stress on the pregnant mother may have enduring
effects on her children. The children born to women in
New York City who were in the first six months of preg-
nancy when 9/11 occurred had lower birth weights than
children born before 9/11 or well after it, and the boys at
the age of six were more than 7% more likely to be in

special education and more than 15% more likely to be in
kindergarten rather than first grade. Oddly, girls’ academic
status was unaffected by mothers’ stress. Investigation of
relations between early stress and intelligence thus seems
an important direction for future research. A particularly
important issue concerns the degree to which the effects of
stress on the brain are reversible.
These five unresolved issues are merely examples of
some of the important contemporary paradoxes and un-
knowns in intelligence research. It is to be hoped that as
much progress on these and other issues will be made in the
next 15 years as has been made on some of the paradoxes
and unknowns since the time of the Neisser et al. (1996)
review.
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Correction to Nisbett et al. (2012)
In the article “Intelligence: New Findings and Theoretical
Developments,” by Richard E. Nisbett,
Joshua Aronson, Clancy Blair, William Dickens, James Flynn,
Diane F. Halpern, and Eric
Turkheimer (American Psychologist, Vol. 67, No. 2, pp. 130 –
159; this issue), two correlational
values are incorrect in the 10th line on p. 134. The relevant
sentences should read, “It appears, for

example, that socioeconomic differences in intelligence are not
as pronounced in modern Europe as
they are in the United States. In the Turkheimer et al. (2003)
study, the correlation of SES with IQ
was .46; in Asbury et al. (2005), it was about .2.”
DOI: 10.1037/a0027240
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