"There are inheritable differences in structure, but we no longer
believe in inherited capacities.... Give me a dozen healthy infants,
and my own world to bring them up in, and I'll guarantee to train any
one of them to become any type of specialist I might select-doctor,
lawyer, artist, merchant chief, and even beggar-man or thief."
J.B.WATSON, 1931, Behaviourism.
London : Kegan Paul, Trench, Trübner & Co.
|
"Much of what we ascribe to human nature is no more than a reaction to
the restraints put upon us by our civilization."
Frank BOAS (social anthropologist), 1928, introducing Margaret Mead's
Coming of Age in Samoa. New York : William Morrow.
|
"Studies of twins provide strong evidence for the heritability of
manic-depressive illness. If an identical twin has manic-depressive
illness, the other twin has a 70 to 100 percent chance of also having
the disease; if the other twin is fraternal, the chances are
considerably lower (approximately 20 percent)."
Kay R. JAMISON, 1995, 'Manic-depressive illness and creativity.'
Scientific American 272, ii, 47-51.
|
|
POPULIST, n. A fossil patriot of the early agricultural period, found
in the old red soapstone underlying Kansas; characterized by an
uncommon spread of ear, which some naturalists contend gave him the
power of flight, though Professors Morse and Whitney, pursuing
independent lines of thought, have ingeniously pointed out that had he
possessed it he would have gone elsewhere. In the picturesque speech
of his period, some fragments of which have come down to us, he was
known as "The Matter with Kansas."
[The Devil's Dictionary A.B.]
|
Intelligence: Knowns and Unknowns
Report of a Task Force established
by the Board of Scientific Affairs of the American Psychological
Association
August 7, 1995
Members of the Task Force:
Ulric Neisser, PhD, Chair; Emery
University
Gwyneth Boodoo, PhD, Educational
Testing Service
Thomas J. Bouchard, Jr., PhD,
University of Minnesota
A. Wade Boykin, PhD, Howard University
Nathan Brody, PhD, Wesleyan University
Stephen J. Ceci, PhD, Cornell
University
Diane F. Halpem, PhD, California
State University, San Bernadino
John C. Loehlin, PhD, University
of Texas, Austin
Robert Perloff, PhD, University
of Pittsburgh
Robert J. Sternberg, PhD, Yale
University
Susana Urbina, PhD, University
of North Florida
Science Directorate
750 First Street, NE
Washington, DC 20002-4242
(202) 336-6000
PREFACE
In the fall of 1994, the publication
of Hermstein and Murray's book The Bell Curve sparked a new round
of debate about the meaning of intelligence test scores andthe
nature of intelligence. The debate was characterized by strong
assertions as well as by strong feelings. Unfortunately, those
assertions often revealed serious misunder- standings of what
has land has not) been demonstrated by scientific research in
this field. Although a great deal is now known, the issues remain
complex and in many cases still unresolved. Another unfortunate
aspect of the debate was that many par- ticipants made little
effort to distinguish scientific issues from. political ones,
Research findings were often assessed not so much on their merits
or their scientific standing as on their supposed political implications.
In such a climate. individuals who wish to make their own judgments
find it hard to know what to believe. Reviewing the intelligence
debate at its meeting of November 1994, the Board of Scientific
Affairs (BSA) of the American Psychological Association (APA)
concluded that there was urgent need for an authoritative report
on these issues - one that all sides could use as a basis for
discussion. Acting by unanimous vote, BSA established a Task Force
charged with preparing such a report. Ulric Neisser, Professor
of Psychol- ogy at Emery University and a member of BSA, was appointed
Chair. The APA Board on the Advancement of Psychology in the Public
Interest (BAPPI), which was consulted extensively during this
process, nominated one member of the Task Force; the Committee
on Psychological Tests and Assessment nominated another; a third
was nominated by the Council of Representatives. Other members
were chosen by an extended consultative process, with the aim
of representing a broad range of expertise and opinion. The Task
Force met twice, in January and March of 1995. Between and after
these meetings, drafts of the various sections were circulated,
revised, and revised yet again. Disputes were resolved by discussion.
As a result, the report presented here has the unanimous support
of the entire Task Force. In July 1495, members of BSA and BAPPI
were asked to comment on a preliminary draft of the report. Many
of their helpful responses have been incorporated in this final
version, and we are grateful for their assistance. We also wish
to acknowledge the energetic and indispensable logistical support
of the APA Science Directorate, especially Suzanne Wandersman
and Dianne Brown. It is our hope that the result of all these
efforts will prove to be a constructive contribution to the intelligence
debate.
TABLE OF CONTENTS
I Concepts of Intelligence
The Psychometric Approach
Intelligence Tests
Interrelations among Tests
Multiple Forms of intelligence
Gardner's Theory
Sternberg's Theory
Related Findings
Cultural Variation
Developmental Progressions
Piaget's Theory
Vygotsky's Theory
Biological Approaches
II. Intelligence Tests and
their Correlates
Basic Characteristics of Test
Scores
Stability
Factors and g
Tests as Predictors
School Performance
Years of Education
Social Status and Income
Job Performance
Social Outcomes
Test Scores and Measures of Processing
Speed
Cognitive Correlates
Choice Reaction Time
Inspection Time
Neurological Measures
Problems of Interpretation
III. The Genes and Intelligence
Sources of Individual Differences
Partitioning the Variation
How Genetic Estimates are Made
Results for IQ Scores
Parameter Estimates
Implications
IV. Environmental Effects
on Intelligence
Social Variables
Occupation
Schooling
Interventions
Family Environment
Biological Variables
Nutrition
Lead
Alcohol
Perinatal Factors
Continuously Rising Test Scores
Individual Life Experiences
V. Group Differences
Sex Differences
Spatial and Quantitative Abilities
Verbal Abilities
Causal Factors
Hormonal Influences
Mean Scores of Different Ethnic
Groups
Asian Americans
Hispanic Americans
Native Americans
African Americans
Test Bias
Characteristics of Tests
Interpreting Group Differences
Socio-economic Factors
Caste-like Minorities
African-American Culture
The Genetic Hypothesis
Summary and Conclusions
References
I. CONCEPTS OF INTELLIGENCE
Individuals differ from one another
in their ability to understand complex ideas, to adapt effectively
to the environment, to learn from experience, to engage in various
forms of reasoning, to overcome obstacles by taking thought. Although
these individual differences can be substantial, they are never
entirely consistent: a given person's intellectual performance
will vary on different occasions, in different domains, as judged
by different criteria. Concepts of "intelligence" are
attempts to clarify and organize this complex set of phenomena.
Although considerable clarity has been achieved in some areas,
no such conceptualization has yet answered all the important questions
and none commands universal assent. Indeed, when two dozen prominent
theorists were recently asked to define intelligence, they gave
two dozen somewhat different definitions (Sternberg & Detterman,
1986). Such disagreements are not cause for dismay. Scientific
research rarely begins with fully agreed definitions, though it
may eventually lead to them.
This first section of our report
reviews the approaches to intelligence that are currently influential,
or that seem to be becoming so. Here (as in later sections) much
of our discussion is devoted to the dominant psychometric approach,
which has not only inspired the most research and attracted the
most attention (up to this time) but is by far the most widely
used in practical settings. Nevertheless, other points of view
deserve serious consideration. Several current theorists argue
that there are many different 'intelligences" (systems of
abilities), only a few of which can be captured by standard psychometric
tests. Others emphasize the role of culture, both in establishing
different conceptions of intelligence and in influencing the acquisition
of intellectual skills. Developmental psychologists, taking yet
another direction, often focus more on the processes by which
all children come to think intelligently than on measuring individual
differences among them. There is also a new interest in the neural
and biological bases of intelligence, a field of research that
seems certain to expand in the next few years.
In this brief report, we cannot
do full justice to even one such approach. Rather than trying
to do so, we focus here on a limited and rather specific set of
questions:
* What are the significant conceptualizations
of intelligence
at this time? (Section I)
* What do intelligence test scores
mean, what do they predict, and how well do they predict it? (Section
II)
* Why do individuals differ in
intelligence, and especially in their scores on intelligence tests?
Our discussion of these questions implicates both genetic factors
(Section III) and environmental factors (Section IV).
* Do various ethnic groups display
different patterns of performance on intelligence tests, and if
so what might explain those differences? (Section V)
* What significant scientific
issues are presently unresolved? (Section VI)
Public discussion of these issues
has been especially vigorous since the 1994 publication of Hermstein
and Murray's The Bell Curve, a controversial volume which stimulated
many equally controversial reviews and replies. Nevertheless,
we do not directly enter that debate. Hermstein and Murray (and
many of their critics) have gone well beyond the scientific findings,
making explicit recommendations on various aspects of public policy.
Our concern here, however, is with science rather than policy.
The charge to our Task Force was to prepare a dispassionate survey
of the state of the art: to make clear what has been scientifically
established, what is presently in dispute, and what is still unknown.
In fulfilling that charge, the only recommendations we shall make
are for further research and calmer debate.
The Psychometric Approach
Ever since Alfred Binet's great
success in devising tests to distinguish mentally retarded children
from those with behavior problems, psychometric instruments have
played an important part in European and American life. Tests
are used for many purposes, such as selection, diagnosis, and
evaluation. Many of the most widely used tests are not intended
to measure intelligence itself but some closely related construct:
scholastic aptitude, school achievement, specific abilities, etc.
Such tests are especially important for selection purposes. For
preparatory school, it's the SSAT; for college, the SAT or ACT;
for graduate school, the GRE; for medical school, the MOAT; for
law school, the LSAT; for business school, the GMAT. Scores on
intelligence-related tests matter, and the stakes can be high.
Intelligence tests. Tests of intelligence
itself (in the psychometric sense) come in many forms. Some use
only a single type of item or question; examples include the Peabody
Picture Vocabulary Test (a measure of children's verbal intelligence)
and Raven's Progressive Matrices (a nonverbal, untimed test that
requires inductive reasoning about perceptual patterns). Although
such instruments are useful for specific purposes, the more familiar
measures of general intelligence, such as the Wechsler tests and
the Stanford-Binet, include many different types of items, both
verbal and nonverbal. Test-takers may be asked to give the meanings
of words, to complete a series of pictures, to indicate which
of several words does not belong with the others, and the like.
Their performance can then be scored to yield several subscores
as well as an overall score.
By convention, overall intelligence
test scores are usuallv converted to a scale in which the mean
is 100 and the standard deviation is 15. (The standard deviation
is a measure of the variability of the distribution of scores.)
Approximately 95% of the population has scores within two standard
deviations of the mean, i.e. between 70 and 130. For historical
reasons, the term "IQ" is often used to describe scores
on tests of intelligence. It originally referred to an "intelligence
Quotient" that was formed by dividing a so-called mental
age by a chronological age, but this procedure is no longer used.
Intercorrelations among Tests.
Individuals rarely perform equally well on all the different kinds
of items included in a test of intelligence. One person may do
relatively better on verbal than on spatial items, for example,
while another may show the opposite pattern. Nevertheless, subtests
measuring different abilities tend to be positively correlated:
people who score high on one such subtest are likely to be above
average on others as well. These complex patterns of correlation
can be clarified by factor analysis, but the results of such analyses
are often controversial themselves. Some theorists (e.g., Spearman,
1927) have emphasized the importance of a general factor, g, which
represents what all the tests have in common; others (e.g., Thurstone,
1938) focus on more specific group factors such as memory, verbal
comprehension, or number facility. As we shall see in Section
2, one common view today envisages something like a hierarchy
of factors with g at the apex. But there is no full agreement
on what g actually means: it has been described as a mere statistical
regularity (Thompson, 1939), a kind of mental energy(Spearman,
1927), a generalized abstract reasoning ability (Gustafsson 1984),
or an index measure of neural processing speed (Reed & Jensen,
1992).
There have been many disputes
over the utility of IQ and g. Some theorists are critical of the
entire psychometric approach (e.g., Ceci, 1990; Gardner, 1983;
Could, 1978), while others regard it as firmly established (e.g.,
Carroll, 1993; Eysenck, 1973; Hermstein & Murray, 1994; Jensen,
1972). The critics do not dispute the stability of test scores,
nor the fact that they predict certain forms of achievement-especially
school achievement--rather effectively (see Section 2). They do
argue, however, that to base a concept of intelligence on test
scores alone is to ignore many important aspects of mental ability.
Some of those aspects are emphasized in other approaches reviewed
below.
Multiple Forms of Intelligence
Gardner's Theory. A relatively
new approach is the theory of multiple intelligences" proposed
by Howard Gardner (1983). On this view conceptions of intelligence
should be informed not only by work with normal children and adults
but also by studies of gifted individuals (including so-called
'savants"), of persons who have suffered brain damage, of
experts and virtuosos, and of individuals from diverse cultures.
These considerations have led Gardner to include musical, bodlily-kinesthetic,
and various forms of personal intelligence as well as more familiar
spatial, linguistic, and logical mathematical abilities in the
scope of his theory. He argues that psychometric tests address
only linguistic and logical plus some aspects of spatial intelligence;
other forms have been entirely ignored. Moreover, the paper and-pencil
format of most tests rules out many kinds of intelligent performance
that matter in everyday life, such as giving an extemporaneous
talk (linguistic) or being able to find one's way in a new town
(spatial). While Gardner's arguments have attracted considerable
interest, the stability and validity of performance tests in these
new domains has yet to be conclusively demonstrated. It is also
possible to doubt whether some of these abilities-bodily-kinesthetic,"
for example--are appropriately described as forms of intelligence
rather than as special talents.
Sternberg's Theory. Robert Sternberg's
(1985) triarchic theory proposes three fundamental aspects of
intelligence-analytic, creative, and practical--of which only
the first is measured to any significant extent by mainstream
tests. His investigations suggest the need for a balance between
analytic intelligence, on the one hand, and creative and especially
practical intelligence on the other. The distinction between analytic
(or "academic") and practical intelligence has also
been made by others (e.g., Neisser, 1976). Analytic problems,
of the type suitable for test construction, tend to (a) have been
formulated by other people, (b) be clearly defined, (c) come with
all the information needed to solve them, (d) have only a single
right answer, which can be reached by only a single method, (e)
be disembodied from ordinary experience, and (f) have little or
no intrinsic interest. Practical problems, in contrast, tend to
(a) require problem recognition and formulation, (b) be poorly
defined, (c) require information seeking, (d) have various acceptable
solutions, (e) be embedded in and require prior everyday experience,
and (f) require motivation and personal involvement.
As part of their study of practical
intelligence, Sternberg and his collaborators have developed measures
of "tacit knowledge" in various domains, especially
business management. In these measures, individuals are given
written scenarios of various work related situations and then
asked to rank a number of options for dealing with the situation
presented. The results show that tacit knowledge predicts such
criteria such as job performance fairly well, even though it is
relatively independent of intelligence test scores and other common
selection measures (Sternberg & Wagner, 1993; Sternberg, Wagner,
Williams & Horvath, in press). This work, too, has its critics
(Jensen, 1993; Schmidt & Hunter, 1993).
Related Findings. Other investigators
have also demonstrated the relative independence of academic and
practical intelligence. Brazilian street children, for example,
are quite capable of doing the math required for survival in their
street business even though they have failed mathematics in school
(Carraher, Carraher, and Schliemann, 1985). Similarly, women shoppers
in California who had no difficulty in comparing product values
at the supermarket were unable to carry out the same mathematical
operations in paper-and pencil tests (Lave, 1988). In a study
of expertise in wagering on harness races, Ceci and Liker (1986)
found that the skilled handicappers implicitly used a highly complex
interactive model with as many as seven variables; the ability
to do this successfully was unrelated to scores on intelligence
tests.
Cultural Variation
It is very difficult to compare
concepts of intelligence across cultures. English is not alone
in having many words for different aspects of intellectual power
and cognitive skill (wise, sensible, smart, bright, clever; cunning,
etc.); if another language has just as many, which of them shall
we say corresponds to its speakers' "concept of intelligence"?
The few attempts to examine this issue directly have typically
found that, even within a given society, different cognitive characteristics
are emphasized from one situation to another and from one subculture
to another(Serpell, 1974; Super, 1983; Wober, 1974). These differences
extend not just to conceptions of intelligence but to what is
considered adaptive or appropriate in a broader sense.
These issues have occasionally
been addressed across sub-cultures and ethnic groups in America.
In a study conducted in San Jose California, Okagaki and Sternberg
(1993) asked immigrant parents from Cambodia, Mexico, the Philippines
and Vietnam, as well as native-born Angle-Americans and Mexican-Americans,
about their conceptions of child-rearing, appropriate teaching,
and children's intelligence. Parents from all groups except Angle-Americans
indicated that such characteristics as motivation, social skills,
and practical school skills were as or more important than cognitive
characteristics for their conceptions of an intelligent first-grade
child.
Heath (1983) found that different
ethnic groups in North Carolina have different conceptions of
intelligence. To be considered as intelligent or adaptive, one
must excel in the skills valued by one's own group. One particularly
interesting contrast was in the importance ascribed to verbal
vs. nonverbal communication skills--to saying things explicitly
as opposed to using and understanding gestures and facial expressions.
Note that while both these forms of communicative skill have their
uses, they are not equally well represented in psychometric tests.
How testing is done can have different
effects in different cultural groups. This can happen for many
reasons, including differential familiarity with the test materials
themselves. Serpell (1979), for example, asked Zambian and English
children to reproduce patterns in three media: wire models, clay
models, or pencil and paper. The Zambian children excelled in
the wire medium with which they were familiar, while the English
children were best with pencil and paper. Both groups performed
equally well with clay.
Developmental Progressions
Piaget's Theory. The best-known
developmentally-based conception of intelligence is certainly
that of the Swiss psychologist Jean Piaget (1972). Unlike most
of the theorists considered here, Piaget had relatively little
interest in individual differences. Intelligence develops in all
children through the continually shifting balance between the
assimilation of new information into existing cognitive structures
and the accommodation of those structures themselves to the new
information. To index the development of intelligence in this
sense, Piaget devised methods that are rather different from conventional
tests. To assess the understanding of "conservation."
for example, (roughly, the principle that material quantity is
not affected by mere changes of shape), children who have watched
water being poured from a shallow to a tall beaker may be asked
if there is now more water than before. (A positive answer would
suggest that the child has not yet mastered the principle of conservation.)
Piaget's tasks can be modified to serve as measures of individual
differences; when this is done, they correlate fairly well with
standard psychometric tests (for a review see Jensen, 1980).
Vygotsky's Theory. The Russian
psychologist Lev Vygotsky (1978) argued that all intellectual
abilities are social in origin. Language and thought first appear
in early interactions with parents, and continue to develop through
contact with teachers and others. Traditional intelligence tests
ignore what Vygotsky called the "zone of proximal development."
i.e., the level of performance that a child might reach with appropriate
help from a supportive adult. Such tests are 'static." measuring
only the intelligence that is already fully developed. 'Dynamic"
testing, in which the examiner provides guided and graded feedback,
can go further to give some indication of the child's latent potential.
These ideas are being developed and extended by a number of contemporary
psychologists (Brown & French, 1979; Feuerstein, 1980; Pascual-Leone
& Ijaz, 1989).
Biological Approaches
Some investigators have recently
turned to the study of the brain as a basis for new ideas about
what intelligence is and how to measure it. Many aspects of brain
anatomy and physiology have been suggested as potentially relevant
to intelligence: the arborization of cortical neurons (Ceci, 1990),
cerebral glucose metabolism (Haler 1993), evoked potentials (Caryl,
1994), nerve conduction velocity (Reed & Jensen, 1992), sex
hormones (see Section 4), and still others (cf. Vernon, 1993).
Advances in research methods, including new forms of brain imaging
such as PET and MRI scans, will surely add to this list. In the
not-too-distant future it may be possible to relate some aspects
of test performance to specific characteristics of brain function.
This brief survey has revealed
a wide range of contemporary conceptions of intelligence and of
how it should be measured. The psychometric approach is the oldest
and best established, but others also have much to contribute.
We should be open to the possibility that our understanding of
intelligence in the future will be rather different from what
it is today.
II INTELLIGENCE TESTS AND
THEIR CORRELATES
The correlation coefficient, r,
can be computed whenever the scores in a sample are paired in
some way. Typically this is because each individual is measured
twice: he or she takes the same test on two occasions, or takes
two different tests, or has both a test score and some criterion
measure such as grade point average or job performance. (In Section
3 we consider cases where the paired scores are those of two different
individuals, such as twins or parent and child.) The value of
r measures the degree of relationship between the two sets of
scores in a convenient way, by assessing how well one of them
(computationally it doesn't matter which one) could be used to
predict the value of the other. Its sign indicates the direction
of relationship: when r is negative, high scores on one measure
predict low scores on the other. Its magnitude indicates the strength
of the relationship. If r=0, there is no relation at all; if r
is 1 (or -1), one score can be used to predict the other score
perfectly. Moreover, the square of r has a particular meaning
in cases where we are concerned with predicting one variable from
another. When r=.50, for example, r^2 is .25: this means (given
certain linear assumptions) that 25% of the variance in one set
of scores is predictable from the correlated values of the other
set, while the remaining 75% is not.
Basic Characteristics of Test
Scores
Stability. Intelligence test scores
are fairly stable during development. When Jones and Bayley (1941)
tested a sample of children annually throughout childhood and
adolescence, for example, scores obtained at age 18 were correlated
r=.77 with scores that had been obtained at age 6, r=.89 with
scores from age 12. When scores were averaged across several successive
tests to remove short-term fluctuations, the correlations were
even higher. The mean for ages 17 and 18 was correlated r=.86
with the mean for ages 5, 6 and 7, r=.96 with the mean for ages
11, 12 and 13. (For comparable findings in a more recent study,
see Moffitt, Caspi, Harkness, & Silva, 1993.) Nevertheless,
IQ scores do change over time. In the same study (Jones &
Bayley, 1941), the average change between age 12 and age 17 was
7. 1 IQ points; some individuals changed as much as 18 points.
Is it possible to measure the
intelligence of young infants in a similar way? Conventional tests
of "infant intelligence" do not predict later test scores
very well, but certain experimental measures of infant attention
and memory that were originally developed for other purposes have
turned out to be more successful. In the most common procedure,
a particular visual pattern is shown to a baby over and over again.
The experimenter records how long the infant subject looks at
the pattern on each trial; these looks get shorter and shorter
as the baby becomes "habituated" to it. The time required
to reach a certain level of habituation, or the extent to which
the baby now "prefers" (looks longer at) a new pattern,
are regarded as measures of some aspect of his or her information-processing
capability.
These habituation-based measures,
obtained from babies at ages ranging from three months to a year,
are significantly correlated with the intelligence test scores
of the same children when they get to be 2 or 4 or 6 years old
(for reviews see Bornstein, 1989; Columbo, 1993; McCall &
Garriger, 1993). A few studies have found such correlations even
at ages 8 or 11 (Rose & Feldman, 1995). A recent meta analysis,
based on 31 different samples, estimates the average magnitude
of the correlations at about r=.36 (McCall & Ganriger. 1993).
(The largest rs often appear in samples that include 'at risk'
infants.) It is possible that these habituation scores (and other
similar measures of infant cognition) do indeed reflect real cognitive
differences, perhaps in 'speed of information processing"
[Colombo, 1993). It is also possible, however, that - to a presently
unknown extent - they reflect early differences in temperament
or inhibition.
It is important to understand
what remains stable and what changes in the development of intelligence.
A child whose IQ score remains the same from age 6 to age 18 does
not exhibit the same performance throughout that period. On the
contrary, steady gains in general knowledge vocabulary, reasoning
ability, etc. will be apparent. What does not change is his or
her score in comparison to that of other individuals of the same
age. A six-year old with an IQ of 100 is at the mean of six-year-olds;
an 11 year-old with that score is at the mean of 18-year-olds.
Factors and g. As noted in Section
1, the patterns of intercorrelation among tests (i.e. among different
kinds of items) are complex. Some pairs of tests are much more
closely related than others, but all such correlations are typically
positive and form what is called a "positive manifold."
Spearman (1927) showed that in any such manifold, some portion
of the variance of scores on each test can be mathematically attributed
to a "general factor." or g. Given this analysis, the
overall pattern of correlations can be roughly described as produced
by individual differences in g plus differences in the specific
abilities sampled by particular tests. In addition, however, there
are usually patterns of intercorrelation among groups of tests.
These commonalities, which played only a small role in Spearman's
analysis, were emphasized by other theorists. Thurstone (1938),
for example, proposed an analysis based primarily on the concept
of group factors.
While some psychologists today
still regard g as the most fundamental measure of intelligence
(e.g., Jensen, 1980), others prefer to emphasize the distinctive
profile of strengths and weaknesses present in each person's performance.
A recently published review identifies over 70 different abilities
that can be distinguished by currently available tests (Carroll,
1993). One way to represent this structure is in terms of a hierarchical
arrangement with a general intelligence factor at the apex and
various more specialized abilities arrayed below it. Such a summary
merely acknowledges that performance levels on different tests
are correlated; it is consistent with, but does not prove, the
hypothesis that a common factor such as g underlies those correlations.
Different specialized abilities might also be correlated for other
reasons, such as the effects of education. Thus while the g-based
factor hierarchy is the most widely accepted current view of the
structure of abilities, some theorists regard it as misleading
(Ceci, 1990). Moreover, as noted in Section 1, a wide range of
human abilities, including many that seem to have intellectual
components, are outside the domain of standard psychometric tests.
Tests as Predictors
School Performance. Intelligence
tests were originally devised by Alfred Binet to measure children's
ability to succeed in school. They do in fact predict school performance
fairly well: the correlation between IS scores and grades is about
.50. They also predict scores on school achievement tests, designed
to measure knowledge of the curriculum. Note, however, that correlations
of this magnitude account for only about 25% of the overall variance.
Successful school learning depends on many personal characteristics
other than intelligence, such as persistence, interest in school,
and willingness to study. The encouragement for academic achievement
that is received from peers, family and teachers may also be important,
together with more general cultural factors (see Section 5).
The relationship between test
scores and school performance seems to be ubiquitous. Wherever
it has been studied, children with high scores on tests of intelligence
tend to learn more of what is taught in school than their lower-scoring
peers. There may be styles of teaching and methods of instruction
that will decrease or increase this correlation, but none that
consistently eliminates it has yet been found (Cronbach and Snow,
1977).
What children learn in school
depends not only on their individual abilities but also on teaching
practices and on what is actually taught. Recent comparisons among
pupils attending school in different countries have made this
especially obvious. Children in Japan and China, for example,
know a great deal more math than American children even though
their intelligence test scores are quite similar (see Section
5). This difference may result from many factors, including cultural
attitudes toward schooling as well as the sheer amount of time
devoted to the study of mathematics and how that study is organized
(Stevenson & Stigler, 1992). In principle it is quite possible
to improve the school learning of American children--even very
substantially-without changing their intelligence test scores
at all.
Years of Education. Some children
stay in school longer than others; many go on to college and perhaps
beyond. Two variables that can be measured as early as elementary
school correlate with the total amount of education individuals
will obtain: test scores and social class background. Correlations
between IQ scores and total years of education are about .55,
implying that differences in psychometric intelligence account
for about 30% of the outcome variance. The correlations of years
of education with social class background (as indexed by the occupation/
education of a child's parents) are also positive, but somewhat
lower.
There are a number of reasons
why children with higher test scores tend to get more education.
They are likely to get good grades, and to be encouraged by teachers
and counselors; often they are placed in "college preparatory"
classes, where they make friends who may also encourage them.
In general, they are likely to find the process of education rewarding
in a way that many low-scoring children do not (Rehberg and Rosenthal,
1978). These influences are not omnipotent: some high scoring
children do drop out of school. Many personal and social characteristics
other than psychometric intelligence determine academic success
and interest, and social privilege may also play a role. Nevertheless,
test scores are the best single predictor of an individual's years
of education.
In contemporary American society,
the amount of schooling that adults complete is also somewhat
predictive of their social status. Occupations considered high
in prestige (e.g., law, medicine, even corporate business) usually
require at least a college degree-16 or more years of education-as
a condition of entry. It is partly because intelligence test scores
predict years of education so well that they also predict occupational
status, and even income to a smaller extent, (Jencks, 1979). Moreover,
many occupations can only be entered through professional schools
which base their admissions at least partly on test scores: the
MCAT, the GMAT, the LSAT, etc. Individual scores on admission-related
tests such as these are certainly correlated with scores on tests
of intelligence.
Social Status and Income. How
well do IQ scores (which can be obtained before individuals enter
the labor force) predict such outcome measures as the social status
or income of adults? This question is complex, in part because
another variable also predicts such outcomes: namely, the socioeconomic
status (SES) of one's parents. Unsurprisingly, children of privileged
families are more likely to attain high social status than those
whose parents are poor and less educated. These two predictors
(IQ and parental SES) are by no means independent of one another;
the correlation between them is around .33 (White, 1982).
One way to look at these relationships
is to begin with SES. According to Jencks (1979), measures of
parental SES predict about one-third of the variance in young
adults' social status and about one-fifth of the variance in their
income. About half of this predictive effectiveness depends on
the fact that the SES of parents also predicts children's intelligence
test scores, which have their own predictive value for social
outcomes; the other half comes about in other ways.
We can also begin with IQ scores,
which by themselves account for about one-fourth of the social
status variance and one-sixth of the income variance. Statistical
controls for parental SES eliminate only about a quarter of this
predictive power. One way to conceptualize this effect is by comparing
the occupational status (or income) of adult brothers who grew
up in the same family and hence have the same parental SES. In
such cases, the brother with the higher adolescent Ig score is
likely to have the higher adult social status and income (Jencks,
1979). This effect, in turn, is substantially mediated by education:
the brother with the higher test scores is likely to get more
schooling, and hence to be better credentialled as he enters the
workplace.
Do these data imply that psychometric
intelligence is a major determinant of social status or income?
That depends on what one means by major. In fact, individuals
who have the same test scores may differ widely in occupational
status and even more widely in income. Consider for a moment the
distribution of occupational status scores for all individuals
in a population, and then consider the conditional distribution
of such scores for just those individuals who test at some given
I8. Jencks (1979) notes that the standard deviation of the latter
distribution may still be quite large; in some cases it amounts
to about 88% of the standard deviation for the entire population.
Viewed from this perspective, psychometric intelligence appears
as only one of a great many factors that influence social outcomes.
Job Performance. Scores on intelligence
tests predict various measures of job performance: supervisor
ratings, work samples, etc. Such correlations, which typically
lie between r=.30 and r=.50, are partly restricted by the limited
reliability of those measures themselves. They become higher when
ris statistically corrected for this unreliability: in one survey
of relevant studies (Hunter, 1983), the mean of the corrected
correlations was .54. This implies that, across a wide range of
occupations, intelligence test performance accounts for some 29%
of the variance in job performance.
Although these correlations can
sometimes be modified by changing methods of training or aspects
of the job itself, intelligence test scores are at least weakly
related to job performance in most settings. Sometimes 19 scores
are described as the 'best available predictor" of that performance.
It is worth noting, however, that such tests predict considerably
less than half the variance of job-related measures. Other individual
characteristics such as interpersonal skills, aspects of personality,
etc., are probably of equal or greater importance, but at this
point we do not have equally reliable instruments to measure them.
Social Outcomes. Psychometric
intelligence is negatively correlated with certain socially undesirable
outcomes. For example, children with high test scores are less
likely than lower-scoring children to engage in juvenile crime.
in one study, Moffitt, Gabrielli, Mednick & Schulsinger (1981)
found a correlation of -.19 between IQ scores and number of juvenile
offenses in a large Danish sample; with social class controlled,
the correlation dropped to -. 17. The correlations for most "negative
outcome" variables are typically smaller than .20, which
means that test scores are associated with less than 4% of their
total variance. It is important to realize that the causal links
between psychemetric ability and social outcomes may be indirect.
Children who are unsuccessful in-and hence alienated from-school
may be more likely to engage in delinquent behaviors for that
very reason, compared to other children who enjoy school and are
doing well.
in summary, intelligence test
scores predict a wide range of social outcomes with varying degrees
of success. Correlations are highest for school achievement, where
they account for about a quarter of the variance. They are somewhat
lower for job performance, and very low for negatively valued
outcomes such as criminality. In general, intelligence tests measure
only some of the many personal characteristics that are relevant
to life in contemporary America. Those characteristics are never
the only influence on outcomes, though in the case of school performance
they may well be the strongest.
Test Scores and Measures of Processing
Speed
Many recent studies show that
the speeds with which people perform very simple perceptual and
cognitive tasks are correlated with psychometric intelligence
/for reviews see Ceci, 1990; Deary, 1995; Vernon, 1987). In general,
people with higher intelligence test scores apprehend, scan, retrieve,
and respond to stimuli more quickly than those who score lower.
Cognitive Correlates, The modern
study of these relations began in the 1970s, as part of the general
growth of interest in chronometric measures of cognition. Many
of the new cognitive paradigms required subjects to make same/different
judgments or other types of speeded responses to visual displays.
Although those paradigms had not been devised with individual
differences in mind, they could be interpreted as providing measures
of the speed of certain information processes. Those speeds turned
out to correlate with psychometrically-measured verbal ability
(Hunt, 1978; Jackson & McClelland, 1979). In some problem
solving tasks, it was possible to analyze the subjects' overall
response times into theoretically motivated 'cognitive components"
(Sternberg, 1977); component times could then be correlated with
test scores in their own right.
Although the size of these correlations
was modest (seldom accounting for more than 10% of the variance),
they did increase as the basic tasks were made more complex by
requiring increased memory or attentional capacity. For instance,
the correlation between paired associate learning and intelligence
increased as the pairs were presented at faster rates (Christal
et al., 1984).
Choice Reaction Time. In another
popular cognitive paradigm, the subject simply moves his or her
finger from a 'home" button to one of eight others arranged
in a semicircle around it; these are marked by small lights that
indicate which one is the target on a given trial (Jensen, 1987).
Various aspects of the choice reaction times obtained in this
paradigm are correlated with scores on intelligence tests, sometimes
with values of r as high as .30 or -.40 (r is negative because
higher test scores go with shorter times). Nevertheless it has
proved difficult to make theoretical sense of the overall pattern
of correlations, and the results obtained in this paradigm are
still hard to interpret (cf. Longstreth, 1984; Brody, 1992). A
later modification, the 'odd-man-out" procedure of Frearson
and Eysenck (1986), seems to be more promising.
Inspection Time. A more recently
developed measure of processing speed, which seems relatively
independent of response factors, is the method of "inspection
time" (IT). In the standard version of this paradigm (Vickers,
Nettelbeck & Wilson, 1972; Nettelbeck, 1987), two vertical
lines are shown very briefly on each trial, followed by a pattern
mask; the subject must judge which line was shorter. For a given
subject, IT is defined as the minimum exposure duration (up to
the onset of the mask) for which the lines must be displayed if
he or she is to meet a preestablished criterion of accuracy -
e.g., nine correct trials out of ten.
Inspection times defined in this
way are consistently correlated with measures of psychometric
intelligence. In a recent meta-analysis, Kranzler and Jensen (1989)
reported an overall correlation of-.30 between IQ scores and IT;
this rose to -.55 when corrected for measurement error and attenuation.
More recent findings confirm this general result (e.g., Bates
& Eysenck, 1993, Deary, 1993]. IT usually correlates best
with performance subtests of intelligence; its correlation with
verbal intelligence is usually weaker and sometimes zero.
One apparent advantage of IT over
other chronometric methods is that the task itself seems particularly
simple. At first glance, it is hard to imagine that any differences
in response strategies or stimulus familiarity could affect the
outcome. Nevertheless, it seems that they do. Brian Mackenzie
and his colleagues (e.g. Mackenzie et al, 1991) discovered that
some subjects use apparent-movement cues in the basic IT task
while others do not; only in the latter group is IT correlated
with intelligence test scores. Moreover, standard IT paradigms
require an essentially spatial judgment; it is not surprising,
then, that they correlate with intelligence tests which emphasize
spatial ability. With this in mind, Mackenzie et al (1991) devised
a verbal inspection time task based on Posner's classical same-letter/different-letter
paradigm (Posner et al, 1969). As predicted, the resulting ITs
correlated with verbal but not with spatial intelligence. It is
clear that the apparently simple IT task actually involves complex
modes of information processing (cf. Chaiken, 1993) that are as
yet poorly understood.
Neurological Measures. Recent
research has begun to explore what seem to be still more direct
measures of neural processing. Reed and Jensen (1992) have used
visual evoked potential (VEP) techniques to assess what they call
'nerve conduction velocity. To estimate this velocity, each subject's
head length (a rough index of the distance between the eye and
the occipital cortex) is divided by the mean latency of an early
component (N70 or P100) in his or her VEP pattern. In a study
with 147 college-student subjects, this measure correlated r-.26
with scores on an unspeeded test of intelligence. (A statistical
correction for the restricted subject range raised the correlation
to r=.37.) Interestingly, however, the same 'conduction velocities"
were not significantly correlated with the subjects' choice reaction
times (Reed & Jensen, 1993). Other researchers have also reported
correlations between VEP parameters and intelligence test scores
(Caryl, 1994).
Problems of lnterpretation. Some
researchers believe that psychometric intelligence, especially
g, depends directly on the 'neural efficiency" of the brain
(Vernon, 1987; Eysenck, 1986). They regard the observed correlations
between test scores and measures of processing speed as evidence
for their view. If choice reaction times, inspection times, and
VEP latencies actually reflect the speed of basic neural processes,
such correlations are only to be expected. In fact, however, the
observed patterns of correlation are rarely as simple as this
hypothesis would predict. Moreover, it is quite possible that
high- and low-IQ individuals differ in other ways that affect
speeded performance (cf. Ceci, 1990). Those variables include
motivation, response criteria (emphasis on speed vs. accuracy),
perceptual strategies (cf. Mackenzie et al, 1991), attentional
strategies, and in some cases differential familiarity with the
material itself. Finally, we do not know the direction of causation
that underlies many of these correlations. Do high levels of neural
efficiency" promote the development of intelligence, or do
more intelligent people just find faster ways to carry out perceptual
tasks? Or both? These questions are still open.
III. THE GENES AND INTELLIGENCE
In this section of the report
we first discuss individual differences generally, without reference
to any particular trait. We then focus on intelligence, as measured
by conventional IQ tests or other tests intended to measure general
cognitive ability. The different and more controversial topic
of group differences will be considered in Section V.
We focus here on the relative
contributions of genes and environments to individual differences
in particular traits. To avoid misunderstanding, it must be emphasized
from the outset that gene action always involves an environment--at
least a biochemical environment, and often an ecological one.
(For humans, that ecology is usually interpersonal or cultural.)
Thus all genetic effects on the development of observable traits
are potentially modifiable by environmental input, though the
practicability of making such modifications may be another matter.
Conversely, all environmental effects on trait development involve
the genes or structures to which the genes have contributed. Thus
there is always a genetic aspect to the effects of the environment
(cf. Plomin & Bergeman, 1991).
Sources of Individual Differences
Partitioning the Variation. Individuals
differ from one another on a wide variety of traits: familiar
examples include height. intelligence, and aspects of personality.
Those differences are often of considerable social importance.
Many interesting questions can be asked about their nature and
origins. One such question is the extent to which they reflect
differences among the genes of the individuals involved, as distinguished
from differences among the environments to which those individuals
have been exposed. The issue here is not whether genes and environments
are both essential for the development of a given trait (this
is always the case), and it is not about the genes or environment
of any particular person. We are concerned only with the observed
variation of the trait across individuals in a given population.
A figure called the "heritability" (h2) of the trait
represents the proportion of that variation that is associated
with genetic differences among the individuals. The remaining
variation (1 - h2] is associated with environmental differences
and with errors of measurement. These proportions can be estimated
by various methods described below.
Sometimes special interest attaches
to those aspects of environments that family members have in common
(for example, characteristics of the home). The part of the variation
that derives from this source, called "shared" variation
or C2, can also be estimated. Still more refined estimates can
be made: c2 is sometimes subdivided into several kinds of shared
variation; h2 is sometimes subdivided into so-called "additive"
and "non-additive" portions /the part that is transmissible
from parent to child vs. the part expressed anew in each generation
by a unique patterning of genes.) Variation associated with correlations
and statistical interactions between genes and environments may
also be identifiable. In theory, any of the above estimates may
vary with the age of the individuals involved.
A high heritability does not mean
that the environment has no impact on the development of a trait,
or that learning is not involved. Vocabulary size, for example,
is very substantially heritable (and highly correlated with general
intelligence) although every word in an individual's vocabulary
is learned. In a society in which plenty of words are available
in everyone's environment, especially for individuals who are
motivated to seek them out, the number of words that individuals
actually learn depends to a considerable extent on their genetic
predispositions.
Behavior geneticists have often
emphasized the fact that individuals can be active in creating
or selecting their own environments. Some describe this process
as active or reactive genotype-environment correlation (Plomin,
DeFries, & Loehlin, 1977). (The distinction is between the
action of the organism in selecting its own environment and the
reaction of others to its gene-based traits.) Others suggest that
these forms of gene-environment relationship are typical of the
way that genes are normally expressed, and simply include them
as part of the genetic effect (Roberts, 1967). This is a matter
of terminological preference, not a dispute about facts.
How Genetic Estimates are Made.
Estimates of the magnitudes of these sources of individual differences
are made by exploiting natural and social 'experiments" that
combine genotypes and environments in informative ways. Monozygotic
(MZ) and dyzygotic (DZ) twins, for example, can be regarded as
experiments of nature. MZ twins are paired individuals of the
same age growing up in the same family who have all their genes
in common; DZ twins are otherwise similar pairs who have only
half their genes in common. Adoptions, in contrast, are experiments
of society. They allow one to compare genetically unrelated persons
who are growing up in the same family as well as genetically related
persons who are growing up in different families. They can also
provide information about genotype-environment correlations: in
ordinary families genes and environments are correlated because
the same parents provide both, whereas in adoptive families one
set of parents provides the genes and another the environment.
An experiment involving both nature and society is the study of
monozygotic twins who have been reared apart (Bouchard, Lykken,
McGue, Segal & Tellegen, 1990; Pedersen, Plomin, Nesselroade
& McClearn, 1992). Relationships in the families of monozygotic
twins also offer unique possibilities for analysis (e.g., Rose,
Harris, Christian, & Nance, 1979). Because these comparisons
are subject to different sources of potential error, the results
of studies involving several kinds of kinship are often analyzed
together to arrive at robust overall conclusions. (For general
discussions of behavior genetic methods, see Plomin, DeFries,
& McClearn, 1990, or Hay, 1985.)
Results for IQ scores
Parameter Estimates Across the
ordinary range of environments in modern Western societies, a
sizable part of the variation in intelligence test scores is associated
with genetic differences among individuals. Quantitative estimates
vary from one study to another, because many are based on small
or selective samples. If one simply combines all available correlations
in a single analysis, the heritability (h^2) works out to about
.50 and the between-family variance (C2) to about .25 (e.g., Chipuer,
Rovine, & Plomin, 1990; Loehlin, 1989). These overall figures
are misleading, however, because most of the relevant studies
have been done with children. We now know that the heritability
of IQ changes with age: h2 goes up and C2 goes down from infancy
to adulthood (McCartney, Harris, & Bernieri, 1990; McGue,
Bouchard, Iacono, & Lykken, 1993). In childhood h2 and C2
for IQ are of the order of .45 and .35; by late adolescence h2
is around .75 and c^2 is quite low (zero in some studies). Substantial
environmental variance remains, but it primarily reflects within-family
rather than between-family differences.
These adult parameter estimates
are based on a number of independent studies. The correlation
between MZ twins reared apart, which directly estimates h^2, ranged
from .68 to .78 in five studies involving adult samples from Europe
and the U.S. (McGue et al., 1993). The correlation between unrelated
children reared together in adoptive families, which directly
estimates C2, was approximately zero for adolescents in two adoption
studies (Scarr & Weinberg, 1978; Loehlin, Horn, & Willerman,
1989) and .19 in a third (the Minnesota transracial adoption study:
Scarr, Weinberg & Waldman, 1993).
These particular estimates derive
from samples in which the lowest socioeconomic levels were underrepresented
(i.e., there were few very poor families), so the range of between
family differences was smaller than in the population as a whole.
This means that we should be cautious in generalizing the findings
for between-family effects across the entire social spectrum.
The samples were also mostly white, but available data suggest
that twin and sibling correlations in African-American and similarly
selected White samples are more often comparable than not (Loehlin,
Lindzey, & Spuhler, 1975).
Why should individual differences
in intelligence [as measured by test scores) reflect genetic differences
more strongly in adults than they do in children's One possibility
is that as individuals grow older their transactions with their
environments are increasingly influenced by the characteristics
that they bring to those environments themselves, decreasingly
by the conditions imposed by family life and social origins. Older
persons are in a better position to select their own effective
environments, a form of genotype-environment correlation. In any
case the popular view that genetic influences on the development
of a trait are essentially frozen at conception while the effects
of the early environment cumulate inexorably is quite misleading,
at least for the trait of psychometric intelligence.
Implications. Estimates ofh2 and
c2 for IQ (or any other trait) are descriptive statistics for
the populations studied. (In this respect they are like means
and standard deviations.) They are outcome measures, summarizing
the results of a great many diverse, intricate, individually variable
events and processes, but they can nevertheless be quite useful.
They can tell us how much of the variation in a given trait the
genes and family environments explain, and changes in them place
some constraints on theories of how this occurs. On the other
hand they have little to say about specific mechanisms, i.e. about
how genetic and environmental differences get translated into
individual physiological and psychological differences. Many psychologists
and neuroscientists are actively studying such processes; data
on heritabilities may give them ideas about what to look for and
where or when to look for it.
A common error is to assume that
because something is heritable it is necessarily unchangeable
This is wrong. Heritability does not imply immutability. As previously
noted, heritable traits can depend on learning, and they may be
subject to other environmental effects as well. The value of h2
can change if the distribution of environments (or genes) in the
population is substantially altered. On the other hand, there
can be effective environmental changes that do not change heritability
at all. If the environment relevant to a given trait improves
in a way that affects all members of the population equally, the
mean value of the trait will rise without any change in its heritability
(because the differences among individuals in the population will
stay the same). This has evidently happened for height: the heritability
of stature is high, but average heights continue to increase (Olivier,
1980). Something of the sort may also be taking place for IQ scores
the so-called 'Flynn effect" discussed in Section IV.
In theory, different subgroups
of a population might have different distributions of environments
or genes and hence different values of h2. This seems not to be
the case for high and low IQ levels, for which adult heritabilities
appear to be much the same (Saudino, Plomin, Pedersen, & McClearn,
1994). It is also possible that an impoverished or suppressive
environment could fail to support the development of a trait,
and hence restrict individual variation. This could affect estimates
of h2, c2, Or both, depending on the details of the process. Again
(as in the case of whole populations), an environmental factor
that affected every member of a subgroup equally might alter the
group's mean without affecting heritabilities at all.
Where the heritability of IQ is
concerned, it has sometimes seemed as if the findings based on
differences between group means were in contradiction with those
based on correlations. For example, children adopted in infancy
into advantaged families tend to have higher IQs in childhood
than would have been expected if they had been reared by their
birth mothers; this is a mean difference implicating the environment.
Yet at the same time their individual resemblance to their birth
mothers persists, and this correlation is most plausibly interpreted
in genetic terms. There is no real contradiction: the two findings
simply call attention to different aspects of the same phenomenon.
A sensible account must include both aspects: there is only a
single developmental process, and it occurs in individuals. By
looking at means or correlations one learns somewhat different
but compatible things about the genetic and environmental contributions
to that process (Turkheimer, 1991).
As far as behavior genetic methods
are concerned, there is nothing unique about psychometric intelligence
relative to other traits or abilities. Any reliably measured trait
can be analyzed by these methods, and many traits including personality
and attitudes have been. The methods are neutral with regard to
genetic and environmental sources of variance: if individual differences
on a trait are entirely due to environmental factors, the analysis
will reveal this. These methods have shown that genes contribute
substantially to individual differences in intelligence test performance,
and that their role seems to increase from infancy to adulthood.
They have also shown that variations in the unique environments
of individuals are important, and that between-family variation
contributes significantly to observed differences in IQ scores
in childhood although this effect diminishes later on. All these
conclusions are wholly consistent with the notion that both genes
and environment, in complex interplay, are essential to the development
of intellectual competence.
IV. ENVIRONMENTAL EFFECTS
ON INTELLIGENCE
The 'environment" includes
a wide range of influences on intelligence. Some of those variables
affect whole populations, while others contribute to individual
differences within a given group. Some of them are social, some
are biological; at this point some are still mysterious. It may
also happen that the proper interpretation of an environmental
variable requires the simultaneous consideration of genetic effects.
Nevertheless, a good deal of solid information is available.
Social Variables
It is obvious that the cultural
environment - how people live, what they value, what they do -
has a significant effect on the intellectual skills developed
by individuals. Rice farmers in Liberia are good at estimating
quantities of rice (Gay & Cole, 1967); children in Botswana,
accustomed to storytelling, have excellent memories for stories
(Dube, 1982). Both these groups were far ahead of American controls
on the tasks in question. On the other hand Americans and other
Westernized groups typically outperform members of traditional
societies on psychometric tests, even those designed to be 'culture-fair.
Cultures typically differ from
one another in so many ways that particular differences can rarely
be ascribed to single causes. Even comparisons between subpopulations
are often difficult to interpret. If we find that groups living
in different environments (e.g., middle-class and poor Americans)
differ in their test scores, it is easy to suppose that the environmental
difference causes the IQ difference. But there is also an opposite
direction of causation: individuals may come to be in one environment
or another because of differences in their own abilities, including
the abilities measured by intelligence tests. Waller(1971) has
shown, for example, that sons whose IQ scores are above those
of their fathers also tend to achieve a higher social class status;
conversely, those with scores below their fathers' tend to achieve
lower status. Such an effect is not surprising, given the relation
between IQ scores and years of education reviewed in Section II.
Occupation. In section II we noted
that intelligence test scores predict occupational level, not
only because some occupations require more intelligence than others
but also because admission to many professions depends on test
scores in the first place. There can also be an effect in the
opposite direction, i.e. workplaces may affect the intelligence
of those who work in them. Kohn and Schooler (1973), who interviewed
some 3000 men in various occupations (farmers, managers, machinists,
porters...), argued that more "complex" jobs produce
more "intellectual flexibility" in the individuals who
hold them. Although the issue of direction of effects complicates
the interpretation of their study, this remains a plausible suggestion.
Among other things, Kohn &
Schooler's hypothesis may help us understand urban/rural differences.
A generation ago these were substantial in the United States,
averaging about six IQ points or 0.4 standard deviations (Terman
& Merrill, 1937; Seashore, Wesman & Doppelt, 1950). In
recent years the difference has declined to about two points (Kaufman
& Doppelt, 1976; Reynolds, Chastain, Kaufman & McLean,
1987). In all likelihood this urban/ rural convergence primarily
reflects environmental changes: a decrease in rural isolation
(due to increased travel and mass communications), an improvement
in rural schools, the greater use of technology on farms. All
these changes can be regarded as increasing the "complexity"
of the rural environment in general or of farm work in particular.
(However, processes with a genetic component, e.g., changes in
the selectivity of migration from farm to city, cannot be completely
excluded as contributing factors.)
Schooling. Attendance at school
is both a dependent and an independent variable in relation to
intelligence. On the one hand, children with higher test scores
are less likely to drop out, more likely to be promoted from grade
to grade and then to attend college. Thus the number of years
of education that adults complete is roughly predictable from
their childhood scores on intelligence tests. On the other hand
schooling itself changes mental abilities, including those abilities
measured on psychometric tests. This is obvious for tests like
the SAT that are explicitly designed to assess school learning,
but it is almost equally true of intelligence tests themselves.
The evidence for the effect of
schooling on intelligence test scores takes many forms (Ceci,
1991). When children of nearly the same age go through school
a year apart (because of birthday-related admission criteria),
those who have been in school longer have higher mean scores.
Children who attend school intermittently score below those who
go regularly, and test performance tends to drop over the summer
vacation. A striking demonstration of this effect appeared when
the schools in one Virginia county closed for several years in
the 1960s to avoid integration, leaving most Black children with
no formal education at all. Compared to controls, the intelligence-test
scores of these children dropped by about 0.4 standard deviations
(6 points) per missed year of school (Green et al, 1964).
Schools affect intelligence in
several ways, most obviously by transmitting information. The
answers to questions like "Who wrote Hamlet?" and "What
is the boiling point of water?" are typically learned in
school, where some pupils learn them more easily and thoroughly
than others. Perhaps at least as important are certain general
skills and attitudes: systematic problem-solving, abstract thinking,
categorization, sustained attention to material of little intrinsic
interest, repeated manipulation of basic symbols and operations.
There is no doubt that schools promote and permit the development
of significant intellectual skills, which develop to different
extents in different children. It is because tests of intelligence
draw on many of those same skills that they predict school achievement
as well as they do.
To achieve these results, the
school experience must meet at least some minimum standard of
quality. In very poor schools, children may learn so little that
they fall farther behind the national IQ norms for every year
of attendance. When this happens, older siblings have systematically
lower scores than their younger counterparts. This pattern of
scores appeared in at least one rural Georgia school system in
the 1970s (Jensen, 1977). Before desegregation, it must have been
characteristic of many of the schools attended by Black pupils
in the South. In a study based on Black children who had moved
to Philadelphia at various ages during this period, Lee (1951)
found that their IQ scores went up more than half a point for
each year that they were enrolled in the Philadelphia system.
Interventions. Intelligence test
scores reflect a child's standing relative to others in his or
her age cohort. Very poor or interrupted schooling can lower that
standing substantially; are there also ways to raise it? In fact
many interventions have been shown to raise test scores and mental
ability 'in the short run" (i.e. while the program itself
was in progress), but long-run gains have proved more elusive.
One noteworthy example of (at least short-run) success was the
Venezuelan Intelligence Project (Hermstein et al, 1986), in which
hundreds of seventh-grade children from underprivileged backgrounds
in that country were exposed to an extensive, theoretically based
curriculum focused on thinking skills. The intervention produced
substantial gains on a wide range of tests, but there has been
no follow-up.
Children who participate in 'Head
Start" and similar programs are exposed to various school-related
materials and experiences for one or two years. Their test scores
often go up during the course of the program, but these gains
fade with time. By the end of elementary school, there are usually
no significant I9 or achievement-test differences between children
who have been in such programs and controls who have not. There
may, however, be other differences. Follow-up studies suggest
that children who participated in such programs as preschoolers
are less likely to be assigned to special education, less likely
to be held back in grade, and more likely to finish high school
than matched controls (Consortium for Longitudinal Studies, 1983;
Darlington, 1986; but see Locurto, 1991).
More extensive interventions might
be expected to produce larger and more lasting effects, but few
such programs have been evaluated systematically. One of the more
successful is the Carolina Abecedarian Project (Campbell &
Ramey, 1994), which provided a group of children with enriched
environments from early infancy through preschool and also maintained
appropriate controls. The test scores of the enrichment-group
children were already higher than those of controls at age two;
they were still some five points higher at age twelve, seven years
after the end of the intervention. Importantly, the enrichment
group also outperformed the controls in academic achievement.
Family environment. No one doubts
that normal child development requires a certain minimum level
of responsible care. Severely deprived, neglectful, or abusive
environments must have negative effects on a great many aspects
of development, including intellectual aspects. Beyond that minimum,
however, the role of family experience is now in serious dispute
(Baumrind, 1993; Jackson, 1993; Scarr, 1992, 1993). Psychometric
intelligence is a case in point. Do differences between children's
family environments (within the normal range) produce differences
in their intelligence test performance? The problem here is to
disentangle causation from correlation. There is no doubt that
such variables as resources of the home (Gottfried, 1984) and
parents' use of language (Hart & Risley, 1992, in press) are
correlated with children's IQ scores, but such correlations may
be mediated by genetic as well as (or instead of) environmental
factors.
Behavior geneticists frame such
issues in quantitative terms. As noted in Section 3, environmental
factors certainly contribute to the overall variance of psychometric
intelligence. But how much of that variance results from differences
between families, as contrasted with the varying experiences of
different children in the same family? Between-family differences
create what is called "shared variance" or c2 (all children
in a family share the same home and the same parents). Recent
twin and adoption studies suggest that while the value of c2 (for
IQ scores) is substantial in early childhood, it becomes quite
small by late adolescence.
These findings suggest that differences
in the life styles of families whatever their importance may be
for many aspects of children's lives make little long-term difference
for the skills measured by intelligence tests. We should note,
however, that low-income and non-white families are poorly represented
in existing adoption studies as well as in most twin samples.
Thus it is not yet clear whether these surprisingly small values
of (adolescent) c2 apply to the population as a whole. It re-mains
possible that, across the full range of income and ethnicity,
between-family differences have more lasting consequences for
psychometric intelligence.
Biological Variables
Every individual has a biological
as well as a social environment, one that begins in the womb and
extends throughout life. Many aspects of that environment can
affect intellectual development. We now know that a number of
biological factors, including malnutrition, exposure to toxic
substances, and various prenatal and perinatal stressors, result
in lowered psychometric intelligence under at least some conditions.
Nutrition. There has been only
one major study of the effects of prenatal malnutrition (i.e.
malnutrition of the mother during pregnancy) on long-term intellectual
development. Stein et al (1975) analyzed the test scores of Dutch
19-year-old males in relation to a wartime famine that had occurred
in the winter of 1944-45, just before their birth. In this very
large sample (made possible by a universal military induction
requirement), exposure to the famine had no effect on adult intelligence.
Note, however, that the famine itself lasted only a few months;
the subjects were exposed to it prenatally but not after birth.
In contrast, prolonged malnutrition
during childhood does have long-term intellectual effects. These
have not been easy to establish, in part because many other unfavorable
socioeconomic conditions are often associated with chronic malnutrition
(Ricciuti, 1993; but cf. Sigman, 1995). In one intervention study,
however, pre-schoolers in two Guatemalan villages (where undernourishment
is common) were given ad lib access to a protein dietary supplement
for several years. A decade later, many of these children (namely,
those from the poorest socio-economic levels) scored significantly
higher on school related achievement tests than comparable controls
(Pollitt et al, 1993). It is worth noting that the effects of
poor nutrition on intelligence may well be indirect. Malnourished
children are typically less responsive to adults, less motivated
to learn, and less active in exploration than their more adequately
nourished counterparts.
Although the degree of malnutrition
prevalent in these villages rarely occurs in the United States,
there may still be nutritional influences on intelligence. In
studies of so-called "micro-nutrients," experimental
groups of children have been given vitamin/mineral supplements
while controls got placebos. in many of these studies (e.g., Schoenthaler
et al, 1991), the experimental children showed test-score gains
that significantly exceeded the controls. In a somewhat different
design, Rush, Stein, Susser, & Brody (1980) gave dietary supplements
of liquid protein to pregnant women who were thought to be at
risk for delivering low birth-weight babies. At one year of age,
the babies born to these mothers showed faster habituation to
visual patterns than did control infants. (Other research has
shown that infant habituation rates are positively correlated
with later psychometric test scores: Colombo, 1993.) Although
these results are encouraging, there has been no long-term follow-up
of such gains.
Lead. Certain toxins have well
established negative effects on intelligence. Exposure to lead
is one such factor. In one long-term study (McMichael et al, 1988;
Baghurst et al, 1992), the blood lead levels of children growing
up near a lead smelting plant were substantially and negatively
correlated with intelligence test scores throughout childhood.
No "threshold dose" for the effect of lead appears in
such studies. Although ambient lead levels in the United States
have been reduced in recent years, there is reason to believe
that some American children - especially those in inner cities
- may still be at risk from this source (cf. Needleman, Geiger
& Frank, 1985).
Alcohol Extensive prenatal exposure
to alcohol (which occurs if the mother drinks heavily during pregnancy)
can give rise to fetal alcohol syndrome, which includes mental
retardation as well as a range of physical symptoms. Smaller doses"
of prenatal alcohol may have negative effects on intelligence
even when the full syndrome does not appear. Streissguth et al
(1989) found that mothers who reported consuming more than 1.5
oz, of alcohol daily during pregnancy had children who scored
some five points below controls at age four. Prenatal exposure
to aspirin and antibiotics had similar negative effects in this
study.
Perinatal Factors. Complications
at delivery and other negative perinatal factors may have serious
consequences for development. Nevertheless, because they occur
only rarely, they contribute relatively little to the population
variance of intelligence [Broman et al, 1975). Down's syndrome,
a chromosomal abnormality that produces serious mental retardation,
is also rare enough to have little impact on the overall distribution
of test scores.
The correlation between birth
weight and later intelligence deserves particular discussion.
In some cases low birth weight simply reflects premature delivery;
in others, the infant's size is below normal for its gestational
age. Both factors apparently contribute to the tendency of low-birth-weight
infants to have lower test scores in later childhood (Lubchenko,
1976). These correlations are small, ranging from .05 to .13 in
different groups (Broman et al, 1975). The effects of low birth
weight are substantial only when it is very low indeed (less than
1500 gm). Premature babies born at these very low birth weights
are behind controls on most developmental measures; they often
have severe or permanent intellectual deficits (Rosetti, 1986).
Continuously Rising Test Scores
Perhaps the most striking of all
environmental effects is the steady worldwide rise in intelligence
test performance. Although many psychometricians had noted these
gains, it was James Mynn (1984, 1987) who first described them
systematically. His analysis shows that performance has been going
up ever since testing began. The "Flynn Effect" is now
very well documented, not only in the United States but in many
other technologically advanced countries. The average gain is
about three IQ points per decade; more than a full standard deviation
since, say, 1940.
Although it is simplest to describe
the gains as increases in population IQ, this is not exactly what
happens. Most intelligence tests are "re-standardized"
from time to time, in part to keep up with these very gains. As
part of this process the mean score of the new standardization
sample is typically set to 100 again, so the increase more or
less disappears from view. In this context, the Flynn effect means
that if twenty years have passed since the last time the test
was standardized, people who now score 100 on the new version
would probably average about 106 on the old one.
The sheer extent of these increases
is remarkable, and the rate of gain may even be increasing. The
scores of nineteen-year-olds in the Netherlands, for example,
went up more than 8 points--over half a standard deviation-between
1972 and 1982. What's more, the largest gains appear on the types
of tests that were specifically designed to be free of cultural
influence (Flynn, 1987). One of these is Raven's Progressive Matrices,
an untimed non-verbal test that many psychometricians regard as
a good measure of g.
These steady gains in intelligence
test performance have not always been accompanied by corresponding
gains in school achievement. Indeed, the relation between intelligence
and achievement test scores can be complex. This is especially
true for the Scholastic Aptitude Test (SAT), in part because the
ability range of the students who take the SAT has broadened over
time. That change explains some portion, but not all, of the prolonged
decline in SAT scores that took place from the mid nineteen-sixties
to the early eighties, even as IQ scores were continuing to rise(Flynn,
1984). Meanwhile, however, other more representative measures
show that school achievement levels have held steady or in some
cases actually increased [Hermstein & Murray, 1994). The National
Assessment of Educational Progress (NAEP), for example, shows
that the average reading and math achievement of American 13-
and l7-year-olds improved somewhat from the early nineteen-seventies
to 1990 (Grissmer, Kirby, Berends & Williamson, 1994). An
analysis of these data by ethnic group, reported in Section 5,
shows that this small overall increase actually reflects very
substantial gains by Blacks and Latinos combined with little or
no gain by Whites.
The consistent IQ gains documented
by Flynn seem much too large to result from simple increases in
test sophistication. Their cause is presently unknown, but three
interpretations deserve our consideration. Perhaps the most plausible
of these is based on the striking cultural differences between
successive generations. Daily life and occupational experience
both seem more "complex" (Kohn & Schooler, 1973)
today than in the time of our parents and grandparents. The population
is increasingly urbanized; television exposes us to more information
and more perspectives on more topics than ever before; children
stay in school longer; almost everyone seems to be encountering
new forms of experience. These changes in the complexity of life
may have produced corresponding changes in complexity of mind,
and hence in certain psychometric abilities.
A different hypothesis attributes
the gains to modern improvements in nutrition. Lynn (1990) points
out that large nutritionally-based increases in height have occurred
during the same period as the IQ gains: perhaps there have been
increases in brain size as well. As we have seen, however, the
effects of nutrition on intelligence are themselves not firmly
established.
The third interpretation addresses
the very definition of intelligence. Flynn himself believes that
real intelligence-whatever it may be--cannot have increased as
much as these data would suggest. Consider, for example, the number
of individuals who have IQ scores of 140 or more. (This is slightly
above the cutoff used by L.M. Terman (1925) in his famous longitudinal
study of "genius.") In 1952 only 0.38% of Dutch test
takers had IQs over 140; in 1982, scored by the same norms,
9. 12% exceeded this figure! Judging
by these criteria, the Netherlands should now be experiencing
"...a cultural renaissance too great to be overlooked"
(Flynn, 1987, p.187). So too should France, Norway, the United
States, and many other countries. Because Flynn (1987) finds this
conclusion implausibie or absurd, he argues that what has risen
cannot be intelligence itself but only a minor sort of "abstract
problem solving ability." The issue remains unresolved.
Individual Life Experiences
Although the environmental variables
that produce large differences in intelligence are not yet well
understood, genetic studies assure us that they exist. With a
heritability well below 1.00, IQ must be subject to substantial
environmental influences. Moreover, available heritability estimates
apply only within the range of environments that are well-represented
in the present population. We already know that some relatively
rare conditions, like those reviewed earlier, have large negative
effects on intelligence. Whether there are (now equally rare)
conditions that have large positive effects is not known.
As we have seen, there is both
a biological and a social environment. For any given child, the
social factors include not only an overall cultural/ social/school
setting and a particular family but also a unique "micro-environment"
of experiences that are shared with no one else. The adoption
studies reviewed in Section 3 show that family variables, such
as differences in parenting style, in the resources of the home,
etc., have smaller long-term effects than we once supposed. At
least among people who share a given SES level and a given culture,
it seems to be unique individual experience that makes the largest
environmental contribution to adult IQ differences.
We do not yet know what the key
features of those micro-environments may be. Are they biological?
Social? Chronic? Acute? Is there something especially important
in the earliest relations between the infant and its caretakers?
Whatever the critical variables may be, do they interact with
other aspects of family life? Of culture? At this point we cannot
say, but these questions offer a fertile area for further research.
V. GROUP DIFFERENCES
Group means have no direct implications
for individuals. What matters for the next person you meet (to
the extent that test scores matter at all) is that person's own
particular score, not the mean of some reference group to which
he or she happens to belong. The commitment to evaluate people
on their own individual merit is central to a democratic society.
It also makes quantitative sense. The distributions of different
groups inevitably overlap, with the range of scores within any
one group always wider than the mean differences between any two
groups. In the case of intelligence test scores, the variance
attributable to individual differences far exceeds the variance
related to group membership (Jensen, 1980).
Because claims about ethnic differences
have often been used to rationalize racial discrimination in the
past, all such claims must be subjected to very careful scrutiny.
Nevertheless, group differences continue to be the subject of
intense interest and debate. There are many reasons for this interest:
some are legal and political, some social and psychological. Among
other things, facts about group differences may be relevant to
the need for (and the effectiveness of) affirmative action programs.
But while some recent discussions of intelligence and ethnic differences
(e.g., Hermstein & Murray, 1994) have made specific policy
recommendations in this area, we will not do so here. Such recommendations
are necessarily based on political as well as scientific considerations,
and so fall outside the scope of this report.
Besides European-Americans ("Whites"),
the ethnic groups to be considered are Chinese- and Japanese Americans,
Hispanic Americans ("Latinos"), Native Americans ("Indians")
and African-Americans ("Blacks"). These groups (we avoid
the term "race") are defined and self-defined by social
conventions based on ethnic origin as well as on observable physical
characteristics such as skin color. None of them are internally
homogeneous. Asian Americans, for example, may have roots in many
different cultures: not only China and Japan but Korea, Laos,
Vietnam, the Philippines, India, Pakistan. Hispanic Americans,
who share a common linguistic tradition, actually differ along
many cultural dimensions. In their own minds they may be less
"Latinos" than Puerto Ricans, Mexican-Americans Cuban
Americans, or representatives of other Latin cultures. "Native
American" is an even more diverse category, including a great
many culturally distinct tribes living in a wide range of environments.
Although males and females are
not ethnic or cultural groups, possible sex differences in cognitive
ability have also been the subject of widespread interest and
discussion. For this reason, the evidence relevant to such differences
is briefly reviewed in the next section.
Sex Differences
Most standard tests of intelligence
have been constructed so that there are no overall score differences
between females and males. Some recent studies do report sex differences
in IQ, but the direction is variable and the effects are small
(Held, Alderton, Foley, & Segall, 1993; Lynn, 1994). This
overall equivalence does not imply equal performance on every
individual ability. While some tasks show no sex differences,
there are others where small differences appear and a few where
they are large and consistent.
Spatial and quantitative Abilities.
Large differences favoring males appear on visual-spatial tasks
like mental rotation and spatio-temporal tasks like tracking a
moving object through space (Law, Pellegrino, & Hunt, 1993;
Linn & Petersen, 1985). The sex difference on mental rotation
tasks is substantial: a recent meta-analysis (Masters & Sanders,
1993) puts the effect size at d = 0.9.
(Effect sizes are measured in
standard deviation units. Here, the mean of the male distribution
is nearly one standard deviation above that for females.) Males'
achievement levels on movement-related and visual-spatial tests
are relevant to their generally better performance in tasks that
involve aiming and throwing (Jardine & Martin, 1983).
Some quantitative abilities also
show consistent differences. Females have a clear advantage on
quantitative tasks in the early years of school (Hyde, Fennema,
& Lamon, 1990), but this reverses sometime before puberty;
males then maintain their superior performance into old age. The
math portion of the Scholastic Aptitude Test shows a substantial
advantage for males (d = 0.33 to 0.50), with many more males scoring
in the highest ranges (Benbow, 1988; Halpern, 1992). Males also
score consistently higher on tests of proportional and mechanical
reasoning [Meehan, 1984; Stanley, Benbow, Brody, Dauber, &
Lupkowski, 1992).
Verbal Abilities. Some verbal
tasks show substantial mean differences favoring females. These
include synonym generation and verbal fluency (e.g., naming words
that start with a given letter), with effect sizes ranging from
d = 0.5 to 1.2 (Gordon & Lee, 1986; Hines, 1990). On average
females score higher on college achievement tests in literature,
English composition, and Spanish(Stanley, 1993) they also excel
at reading and spelling Many more males than females are diagnosed
with dyslexia and other reading disabilities (Sutaria, 1985),
and there are many more male stutterers [Yairi & Ambrose,
1992). Some memory tasks also show better performance by females,
but the size (and perhaps even the direction) of the effect varies
with the type of memory being assessed.
Causal Factors. There are both
social and biological reasons for these differences. At the social
level there are both subtle and overt differences between the
experiences, expectations, and gender roles of females and males.
Relevant environmental differences appear soon after birth. They
range from the gender-differentiated toys that children regularly
receive to the expectations of adult life with which they are
presented, from gender-differentiated household and leisure activities
to assumptions about differences in basic ability. Models that
include many of these psychosocial variables have been successful
in predicting academic achievement (Eccles, 1987).
Many biological variables are
also relevant. One focus of current research is on differences
in the sizes or shapes of particular neural structures. Numerous
sexually dimorphic brain structures have now been identified,
and they may well have implications for cognition. There are,
for example, sex related differences in the sizes of some portions
of the corpus callosum; these differences are correlated with
verbal fluency (Hines, Chiu, McAdams, Bentler, & Lipcamon,
1992). Recent brain imaging studies have found what may be differences
in the lateralization of language (Shaywitz et al., 1995). Note
that such differences in neural structure could result from differences
in patterns of life experience as well as from genetically-driven
mechanisms of brain development; moreover, brain development and
experience may have bi-directional effects on each other. This
research area is still in a largely exploratory phase.
Hormonal Influences. The importance
of prenatal exposure to sex hormones is well established. Hormones
influence not only the developing genitalia but also the brain
and certain immune system structures (Geschwind & Gaiaburda,
1987; Halpern & Cass, 1994). Several studies have tested individuals
who were exposed to abnormally high androgen levels in utero,
due to a condition known as congenital adrenal hyperplasia(CAH).
Adult CAH females score significantly higher than
controls on tests of spatial ability
(Resnick, Berenbaum, Gottesman & Bouchard, 1986); CAH girls
play more with "boys' toys" and less with 'girls' toys"
than controls (Berenbaum & Hines, 1992).
Other experimental paradigms confirm
the relevance of sex hormones for performance levels in certain
skills. Christiansen and Knussman (1987) found testosterone levels
in normal males to be correlated positively (about .20) with some
measures of spatial ability and negatively (about -.20) with some
measures of verbal ability. Older males given testosterone show
improved performance on visual-spatial tests (Janowsky, Oviatt,
& Orwoll, 1994). Many similar findings have been reported,
though the effects are often non-linear and complex (Gouchie &
Kimura, 1991; Nyborg, 1984). It is clear that any adequate model
of sex differences in cognition will have to take both biological
and psychological variables (and their interactions) into account.
Mean Scores of Different Ethnic
Groups
Asian Americans. In the years
since the Second World War, Asian Americans, especially those
of Chinese and Japanese extraction, have compiled an outstanding
record of academic and professional achievement. This record is
reflected in school grades, in scores on content-oriented achievement
tests like the SAT and GRE, and especially in the disproportionate
representation of Asian Americans in many sciences and professions.
Although it is often supposed that these achievements reflect
correspondingly high intelligence test scores, this is not the
case. In more than a dozen studies from the 1960s and 1970s analyzed
by Flynn (1991), the mean IQs of Japanese- and Chinese American
children were always around 97 or 98; none was over 100. Even
Lynn (1993), who argues for a slightly higher figure concedes
that the achievements of these Asian Americans far outstrip what
might have been expected on the basis of their test scores.
It may be worth noting that the
interpretation of test scores obtained by Asians in Asia has been
controversial in its own right. Lynn (1982) reported a mean Japanese
IQ of ill, Flynn (1991) estimated it to be between 101 and 105;
Stevenson et al (1985), comparing the intelligence-test performance
of children in Japan, Taiwan and the United States, found no substantive
differences at all. Given the general problems of cross-cultural
comparison, there is no reason to expect precision or stability
in such estimates. Nevertheless some interest attaches to these
particular comparisons: they show that the well-established differences
in school achievement among the same three groups (Chinese and
Japanese children are much better at math than American children)
do not simply reflect differences in psychometric intelligence.
Stevenson et a1(1986) suggest that they result from structural
differences in the schools of the three nations as well as from
varying cultural attitudes toward learning itself. It is also
possible that spatial ability, in which Japanese and Chinese obtain
somewhat higher scores than Americans, plays a particular role
in the learning of mathematics.
One interesting way to assess
the achievements of Chinese- and Japanese-Americans is to reverse
the usual direction of prediction. Data from the 1980 census shows
that the proportion of Chinese Americans employed in managerial,
professional, or technical occupations was 55% and that of Japanese
was 46%. (For whites, the corresponding figure was 34%.) Using
the well-established correlation between intelligence test scores
and occupational level, Flynn (1991, p.99) calculated the mean
IQ that a hypothetical White group "would have to have"
to predict the same proportions of upper-level employment. He
found that the occupational success of these Chinese Americans,
whose mean IQ was in fact slightly below 100, was what would be
expected of a White group with an IQ of almost 120! A similar
calculation for Japanese-Americans shows that their level of achievement
matched that of Whites averaging 110. These "over-achievements"
serve as sharp reminders of the limitations of IQ-based prediction.
Various aspects of Chinese-American and Japanese American culture
surely contribute to them (Schneider, Hieshima, Lee & Plank,
1994); gene-based temperamental factors could conceivably be playing
a role as well (Freedman & Freedman, 1969).
Hispanic Americans. Hispanic immigrants
have come to America from many countries. In 1993, the largest
Latino groups in the continental United States were Mexican Americans
(64%), Puerto Ricans (110/o), Central and South Americans (13%),
and Cubans (5%) (U.S. Bureau of the Census, 1994). There are very
substantial cultural differences among these nationality groups,
as well as differences in academic achievement (Duran, 1983; USNCEP,
1982). Taken together, Latinos make up the second largest and
the fastest-growing minority group in America (Davis, Haub &
Willette, 1983; Eyde, 1992).
The mean intelligence test scores
of Hispanics typically lie between those of Blacks and Whites.
There are also differences in the patterning of scores across
different abilities and subtests (Hennessy & Merrifield, 1978;
Lesser, Fifer & Clark, 1965). Linguistic factors play a particularly
important role for Hispanic Americans, who may know relatively
little English. (By one estimate, 25% of Puerto Ricans and Mexican
Americans and at least 40% of Cubans speak English 'not well"
or 'not at all" - Rodriguez, 1992). Even those who describe
themselves as bilingual may be at a disadvantage if Spanish was
their first and best-learned language. It is not surprising that
Latino children typically score higher on the performance than
on the verbal subtests of the English-based WISC-R (Kaufman, 1994).
Nevertheless, the predictive validity of Latino test scores is
not negligible. In young children, the WISC-R has reasonably high
correlations with school achievement measures (McShane & Cook,
1985). For high school students of moderate to high English proficiency,
standard aptitude tests predict first-year college grades about
as well as they do for non Hispanic Whites (Pennock-Roman, 1992).
Native Americans. There are a
great many culturally distinct North American Indian tribes (Driver,
1969), speaking some 200 different languages (Leap, 1981). Many
Native Americans live on reservations, which themselves represent
a great variety of ecological and cultural settings. Many others
presently live in metropolitan areas (Brandt, 1984). Although
few generalizations can be appropriate across so wide a range,
two or three points seem fairly well established. The first is
a specific relation between ecology and cognition: the Inuit (Eskimo)
and other groups that live in the arctic tend to have particularly
high visual-spatial skills. (For a review see McShane & Berry,
1988.) Moreover, there seem to be no substantial sex differences
in those skills (Berry, 1974). It seems likely that this represents
an adaptation-genetic or learned or both--to the difficult hunting,
traveling and living conditions that characterize the arctic environment.
On the average Indian children
obtain relatively low scores on tests of verbal intelligence,
which are often administered in school settings. The result is
a performance-test/verbal-test discrepancy similar to that exhibited
by Hispanic Americans and other groups whose first language is
generally not English. Moreover, many Indian children suffer from
chronic middle-ear infection (otitis media), which is "the
leading identifiable disease among Indians since record-keeping
began in 1962" (McShane & Plas, 1984b, p.84). Hearing
loss can have marked negative effects on verbal test performance
(McShane & Plas, 1984a).
African Americans, The relatively
low mean of the distribution of African-American intelligence
test scores has been discussed for many years. Although studies
using different tests and samples yield a range of results, the
Black mean is typically about one standard deviation (about 15
points) below that of Whites (Loehlin et al, 1975; Jensen, 1980;
Reynolds et al, 1987). The difference is largest on those tests
(verbal or non-verbal) that best represent the general intelligence
factor g (Jensen, 1985). It is possible, however, that this differential
is diminishing. In the most recent re-standardization of the Stanford-Binet
test, the Black/White differential was 13 points for younger children
and 10 points for older children (Thorndike et al, 1986). In several
other studies of children since 1980, the Black mean has consistently
been over 90 and the differential has been in single digits (Vincent,
1991). Larger and more definitive studies are needed before this
trend can be regarded as established.
Another reason to think the IQ
mean might be changing is that the Black/ White differential in
achievement scores has diminished substantially in the last few
years. Consider, for example, the mathematics achievement of five
year olds as measured by the National Assessment of Educational
Progress (NAEP). The differential between Black and White scores,
about 1.1 standard deviations as recently as 1978, had shrunk
to .65 SD by 1990 (Grissmer et al, 1994) because of Black gains.
Hispanics showed similar but smaller gains; there was little change
in the scores of Whites. Other assessments of school achievement
also show substantial recent gains in the performance of minority
children.
In their own analysis of these
gains, Grissmer et al (1994) cite both demographic factors and
the effects of public policy. They found the level of parents'
education to be a particularly good predictor of children's' school
achievement; that level increased for all groups between 1970
and 1990, but most sharply for Blacks. Family size was another
good predictor (children from smaller families tend to achieve
higher scores); here too, the largest change over time was among
Blacks. Above and beyond these demographic effects, Grissmer et
al believe that some of the gains can be attributed to the many
specific programs, geared to the education of minority children,
that were implemented during that period.
Test Bias. It is often argued
that the lower mean scores of African Americans reflect a bias
in the intelligence tests themselves. This argument is right in
one sense of "bias" but wrong in another. To see the
first of these, consider how the term is used in probability theory.
When a coin comes up heads consistently for any reason it is said
to be 'biased," regardless of any consequences that the outcome
may or may not have. In this sense the Black/White score differential
is ipso facto evidence of what may be called "outcome bias."
African Americans are subject to outcome bias not only with respect
to tests but along many dimensions of American life. They have
the short end of nearly every stick: average income, representation
in high-level occupations, health and health care, death rate,
confrontations with the legal system, and so on. With this situation
in mind, some critics regard the test score differential as just
another example of a pervasive outcome bias that characterizes
our society as a whole (Jackson, 1975; Mercer, 1984). Although
there is a sense in which they are right, this critique ignores
the particular social purpose that tests are designed to serve.
From an educational point of view,
the chief function of mental tests is as predictors (Section 2).
Intelligence tests predict school performance fairly well, at
least in American schools as they are now constituted. Similarly,
achievement tests are fairly good predictors of performance in
college and postgraduate settings. Considered in this light, the
relevant question is whether the tests have a "predictive
bias" against Blacks, Such a bias would exist if African-American
performance on the criterion variables (school achievement, college
GPA, etc.) were systematically higher than the same subjects'
test scores would predict. This is not the case. The actual regression
lines (which show the mean criterion performance for individuals
who got various scores on the predictor) for Blacks do not lie
above those for Whites; there is even a slight tendency in the
other direction (Jensen, 1980; Reynolds &:Brown, 1984). Considered
as predictors of future performance, the tests do not seem to
be biased against African Americans.
Characteristics of Tests. It has
been suggested that various aspects of the way tests are formulated
and administered may put African Americans at an disadvantage.
The language of testing is a standard form of English with which
some Blacks may not be familiar; specific vocabulary items are
often unfamiliar to Black children; the tests are often given
by White examiners rather than by more familiar Black teachers;
African Americans may not be motivated to work hard on tests that
so clearly reflect White values; the time demands of some tests
may be alien to Black culture. (Similar suggestions have been
made in connection with the test performance of Hispanic Americans,
e.g., Rodriguez, 1992.) Many of these suggestions are plausible,
and such mechanisms may play a role in particular cases. Controlled
studies have shown, however, that none of them contributes substantially
to the Black/White differential under discussion here (Jensen,
1980; Reynolds 82 Brown, 1984; for a different view see Helms,
1992). Moreover, efforts to devise reliable and valid tests that
would minimize disadvantages of this kind have been unsuccessful.
Interpreting Group Differences
If group differences in test performance
do not result from the simple forms of bias reviewed above, what
is responsible for them? The fact is that we do not know. Various
explanations have been proposed, but none is generally accepted.
It is clear, however, that these differences, whatever their origin,
are well within the range of effect sizes that can be produced
by environmental factors. The Black/White differential amounts
to one standard deviation or less, and we know that environmental
factors have recently raised mean test scores in many populations
by at least that much (Flynn, 1987: see Section 4). To be sure,
the "Flynn effect" is itself poorly understood: it may
reflect generational changes in culture, improved nutrition, or
other factors as yet unknown. Whatever may be responsible for
it, we cannot exclude the possibility that the same factors play
a role in contemporary group differences.
Socio-economic Factors. Several
specific environmental/cultural explanations of those differences
have been proposed. All of them refer to the general life situation
in which contemporary African Americans find themselves, but that
situation can be described in several different ways. The simplest
such hypothesis can be framed in economic terms. On the average,
Blacks have lower incomes than Whites; a much higher proportion
of them are poor. It is plausible to suppose that many inevitable
aspects of poverty, such as poor nutrition, frequently inadequate
prenatal care, and lack of intellectual resources, have negative
effects on children's developing intelligence. Indeed, the correlation
between "socio-economic status" (SES) and scores on
intelligence tests is well known (White, 1982).
Several considerations suggest
that this cannot be the whole explanation. For one thing, the
Black/White differential in test scores is not eliminated when
groups or individuals are matched for SES (Loehlin et al, 1975).
Moreover, the data reviewed in Section 4 suggest that excluding
extreme conditions, nutrition and other biological factors that
may vary with SES account for relatively little of the variance
in such scores. Finally the (relatively weak) relationship between
test scores and income is much more complex than a simple SES
hypothesis would suggest. The living conditions of children result
in part from the accomplishments of their parents: if the skills
measured by psychometric tests actually matter for those accomplishments.
intelligence is affecting SES rather than the other way around.
We do not know the magnitude of these various effects in various
populations, but it is clear that no model in which 'SES"
directly determines "IQ" will do.
A more fundamental difficulty
with explanations based on economics alone appears from a different
perspective. To imagine that any simple income- and education-based
index can adequately describe the situation of African Americans
is to ignore important categories of experience. The sense of
belonging to a group with a distinctive culture, one that has
long been the target of oppression, and the awareness or anticipation
of racial discrimination are profound personal experiences, not
just aspects of socio-economic status. Some of these more deeply
rooted differences are addressed by other hypotheses, based on
caste and culture.
Caste-like Minorities. Most discussions
of this issue treat Black/ White differences as aspects of a uniquely
"American Dilemma" (Myriad, 1944). The fact is, however,
that comparably disadvantaged groups exist in many countries:
the Maori in New Zealand, scheduled castes ("untouchables")
in India, non-European Jews in Israel, the Burakumin in Japan.
All these are "caste-like" (Ogbu, 1978) or "involuntary"
(Ogbu, 1994) minorities. John Ogbu distinguishes this status from
that of "autonomous" minorities who are not politically
or economically subordinated (like Amish or Mormons in the U.S.),
and from that of "immigrant" or 'voluntary" minorities
who initially came to their new homes with positive expectations.
Immigrant minorities expect their situations to improve; they
tend to compare themselves favorably with peers in the old country,
not unfavorably with members of the dominant majority. In contrast,
to be born into a caste-like minority is to grow up firmly convinced
that one's life will eventually be restricted to a small and poorly-rewarded
set of social roles.
Distinctions of caste are not
always linked to perceptions of race. In some countries lower
and upper caste groups differ by appearance and are assumed to
be racially distinct; in others they are not. The social and educational
consequences are the same in both cases. All over the world, the
children of castelike minorities do less well in school than upper-caste
children and drop out sooner. Where there are data, they have
usuallv been found to have lower test scores as well.
In explaining these findings,
Ogbu (1978) argues that the children of caste-like minorities
do not have 'effort optimism," i.e., the conviction that
hard work (especially hard schoolwork) and serious commitment
on their part will actually be rewarded. As a result they ignore
or reject the forms of learning that are offered in school. Indeed
they may practice a sort of cultural inversion, deliberately rejecting
certain behaviors (such as academic achievement or other forms
of "acting white") that are seen as characteristic of
the dominant group. While the extent to which the attitudes described
by Ogbu (1978, 1994) are responsible for African-American test
scores and school achievement has not been empirically established,
it does seem that familiar problems can take on quite a different
look when they are viewed from an international perspective.
African-American Culture. According
to Boykin (1986, 1994), there is a fundamental conflict between
certain aspects of African-American culture on the one hand and
the implicit cultural commitments of most American schools on
the other. "When children are ordered to do their own work,
arrive at their own individual answers, work only with their own
materials, they are being sent cultural messages. When children
come to believe that getting up and moving about the classroom
is inappropriate, they are being sent powerful cultural messages.
When children come to confine their 'learning' to consistently
bracketed time periods, when they are consistently prompted to
tell what they know and not how they feel, when they are led to
believe that they are completely responsible for their own success
and failure, when they are required to consistently put forth
considerable effort for effort's sake on tedious and personally
irrelevant tasks ... then they are pervasively having cultural
lessons imposed on them" (1994, pp. 180-181).
In Boykin's view, the combination
of constriction and competition that most American schools demand
of their pupils conflicts with certain themes in the 'deep structure"
of African-American culture. That culture includes an emphasis
on such aspects of experience as spirituality, harmony, movement,
verve, affect, expressive individualism, communalism, orality,
and a socially defined time perspective(Boykin, 1986, 1994). While
it is not shared by all African Americans to the same degree,
its accessibility and familiarity give it a profound influence.
The result of this cultural conflict,
in Boykin's view, is that many Black children become alienated
from both the process and the products of the education to which
they are exposed. One aspect of that process, now an intrinsic
aspect of the culture of most American schools, is the psychometric
enterprise itself. He argues (Boykin, 1994) that the successful
education of African-American children will require an approach
that is less concerned with talent sorting and assessment, more
concerned with talent development.
One further factor should not
be overlooked. Only a single generation has passed since the Civil
Rights movement opened new doors for African Americans, and many
forms of discrimination are still all too familiar in their experience
today. Hard enough to bear in its own right, discrimination is
also a sharp reminder of a still more intolerable past. It would
be rash indeed to assume that those experiences, and that historical
legacy, have no impact on intellectual development.
The Genetic Hypothesis. It is
sometimes suggested that the Black/ White differential in psychometric
intelligence is partly due to genetic differences (Jensen, 1972).
There is not much direct evidence on this point, but what little
there is fails to support the genetic hypothesis. Once piece of
evidence comes from a study of the children of American soldiers
stationed in Germany after the Second World War (Eyferth, 1961):
there was no mean difference between the test scores of those
children whose fathers were White and those whose fathers were
Black. (For a discussion of possible confounds in this study,
see Mynn, 1980.) Moreover, several studies have used blood-group
methods to estimate the degree of African ancestry of American
Blacks; there were no significant correlations between those estimates
and IQ scores (Loehlin et al, 1973; Scarr et al, 1977).
It is clear (Section III) that
genes make a substantial contribution to individual differences
in intelligence test scores, at least in the white population.
The fact is, however, that the high heritability of a trait within
a given group has no necessary implications for the source of
a difference between groups (Loehlin et al, 1975). This is now
generally understood (e.g., Hermstein & Murray, 1994). But
even though no such implication is necessary, some have argued
that a high value of h2 makes a genetic hypothesis more plausible.
Does it?
That depends on one's assessment
of the actual difference between the two environments. Consider
Lewontin's (1970) well-known example of seeds from the same genetically
variable stock that are planted in two different fields. If the
plants in field X are fertilized appropriately while key nutrients
are withheld from those in field Y, we have produced an entirely
environmental group difference. This example works (i.e., h2 is
genuinely irrelevant to the differential between the fields) because
the differences between the effective environments of X and Y
are both large and consistent. Are the environmental and cultural
situations of American Blacks and Whites also substantially and
consistently different - different enough to make this a good
analogy? If so, the within-group heritability of IQ scores is
irrelevant to the issue. Or are those situations similar enough
to suggest that the analogy is inappropriate, and that one can
plausibly generalize from within-group heritabilities? Thus the
issue ultimately comes down to a personal judgment: how different
are the relevant life experiences of Whites and Blacks in the
United States today? At present, this question has no scientific
answer.
VI. SUMMARY AND CONCLUSIONS
Because there are many ways to
be intelligent, there are also many conceptualizations of intelligence.
The most influential approach, and the one that has generated
the most systematic research, is based on psychometric testing.
This tradition has produced a substantial body of knowledge, though
many questions remain unanswered. We know much less about the
forms of intelligence that tests do not easily assess: wisdom,
creativity, practical knowledge, social skill, and the like.
Psychometricians have successfully
measured a wide range of abilities, distinct from one another
and yet intercorrelated. The complex relations among those abilities
can be described in many ways. Some theorists focus on the variance
that all such abilities have in common, which Spearman termed
g ("general intelligence"); others prefer to describe
the same manifold with a set of partially independent factors;
still others opt for a muitifactorial description with factors
hierarchically arranged and something like g at the top. Standardized
intelligence test scores ("IQs"), which reflect a person's
standing in relation to his or her age cohort, are based on tests
that tap a number of different abilities. Recent studies have
found that these scores are also correlated with information processing
speed in certain experimental paradigms (choice reaction time,
inspection time, evoked brain potentials, etc.), but the meaning
of those correlations is far from clear.
Intelligence test scores predict
individual differences in school achievement moderately well,
correlating about .50 with grade point average and .55 with the
number of years of education that individuals complete. In this
context the skills measured by tests are clearly important. Nevertheless,
population levels of school achievement are not determined solely
or even primarily by intelligence or any other individual difference
variable. The fact that children in Japan and Taiwan learn much
more math than their peers in America, for example, can be attributed
primarily to differences in culture and schooling rather than
in abilities measured by intelligence tests.
Test scores also correlate with
measures of accomplishment outside of school, e.g. with adult
occupational status. To some extent those correlations result
directly from the tests' link with school achievement and from
their roles as "gatekeepers." In the United States today,
high test scores and grades are prerequisites for entry into many
careers and professions. This is not quite the whole story, however:
a significant correlation between psychometric intelligence and
occupational status remains even when measures of education and
family background have been statistically controlled. There are
also modest (negative) correlations between intelligence test
scores and certain undesirable behaviors such as juvenile crime.
Those correlations are necessarily low: all social outcomes result
from complex causal webs in which psychometric skills are only
one factor.
Like every trait, intelligence
is the joint product of genetic and environmental variables. Gene
action always involves a (biochemical or social) environment;
environments always act via structures to which genes have contributed.
Given a trait on which individuals vary, however, one can ask
what fraction of that variation is associated with differences
in their genotypes (this is the heritability of the trait) as
well as what fraction is associated with differences in environmental
experience. So defined, heritability (h2) can and does vary from
one population to another. In the case of IQ, h2 is markedly lower
for children (about .45) than for adults (about .75). This means
that as children grow up, differences in test scores tend increasingly
to reflect differences in genotype and in individual life experience
rather than differences among the families in which they were
raised.
The factors underlying that shift-and
more generally the pathways by which genes make their undoubted
contributions to individual differences in intelligence--are largely
unknown. Moreover, the environmental contributions to those differences
are almost equally mysterious. We know that both biological and
social aspects of the environment are important for intelligence,
but we are a long way from understanding how they exert their
effects.
One environmental variable with
clear-cut importance is the presence of formal schooling. Schools
affect intelligence in many ways, not only by transmitting specific
information but by developing certain intellectual skills and
attitudes. Failure to attend school (Or attendance at very poor
schools) has a clear negative effect on intelligence test scores.
Pre-school programs and similar interventions often have positive
effects, but in most cases the gains fade when the program is
over.
A number of conditions in the
biological environment have clear negative consequences for intellectual
development. Some of these conditions, which are very important
when they occur, nevertheless do not contribute much to the population
variance of IQ scores because they are relatively rare. (Perinatal
complications are one such factor.) Exposure to environmental
lead has well-documented negative effects; so too does prenatal
exposure to high blood levels of alcohol. Malnutrition in childhood
is another negative factor for intelligence, but the level at
which its effects become significant has not been clearly established.
Some studies suggest that dietary supplements of certain micro-nutrients
can produce gains even in otherwise well-nourished individuals,
but the effects are still controversial and there has been no
long-term follow-up.
One of the most striking phenomena
in this field is the steady world-wide rise in test scores, now
often called the "Flynn effect." Mean IQs have increased
more than 15 points--a full standard deviation--in the last fifty
years, and the rate of gain may be increasing. These gains may
result from improved nutrition, cultural changes, experience with
testing, shifts in schooling or child-rearing practices, or some
other factor as yet unknown,
Although there are no important
sex differences in overall intelligence test scores, substantial
differences do appear for specific abilities. Males typically
score higher on visual-spatial and (beginning in middle childhood)
mathematical skills; females excel on a number of verbal measures.
Sex hormone levels are clearly related to some of these differences,
but social factors presumably play a role as well. As for all
the group differences reviewed here, the range of performance
within each group is much larger than the mean difference between
groups.
Because ethnic differences in
intelligence reflect complex patterns, no overall generalization
about them is appropriate. The mean IQ scores of Chinese- and
Japanese-Americans, for example, differ little from those of Whites
though their spatial ability scores tend to be somewhat higher.
The outstanding record of these groups in terms of school achievement
and occupational status evidently reflects cultural factors. The
mean intelligence test scores of Hispanic Americans are somewhat
lower than those of Whites, in part because Hispanics are often
less familiar with English. Nevertheless their test scores, like
those of African Americans, are reasonably good predictors of
school and college achievement.
African-American 19 scores have
long averaged about 15 points below those of Whites, with correspondingly
lower scores on academic achievement tests. In recent years the
achievement-test gap has narrowed appreciably. It is possible
that the IQ-score differential is narrowing as well, but this
has not been clearly established. The cause of that differential
is not known; it is apparently not due to any simple form of bias
in the content or administration of the tests themselves. The
Flynn effect shows that environmental factors can produce differences
of at least this magnitude, but that effect is mysterious in its
own right. Several culturally based explanations of the Black/
White IQ differential have been proposed; some are plausible,
but so far none has been conclusively supported. There is even
less empirical support for a genetic interpretation. In short,
no adequate explanation of the differential between the IQ means
of Blacks and Whites is presently available.
It is customary to conclude surveys
like this one with a summary of what has been established. Indeed,
much is now known about intelligence. A near century of research,
most of it based on psychometric methods, has produced an impressive
body of findings. Although we have tried to do justice to those
findings in this report, it seems appropriate to conclude on a
different note. In this contentious arena, our most useful role
may be to remind our readers that many of the critical questions
about intelligence are still unanswered. Here are a few of those
questions:
1) Differences in genetic endowment
contribute substantially to individual differences in (psychometric)
intelligence, but the pathway by which genes produce their effects
is still unknown. The impact of genetic differences appears to
increase with age, but we do not know why.
2) Environmental factors also
contribute substantially to the development of intelligence, but
we do not clearly understand what those factors are or how they
work. Attendance at school is certainly important, for example,
but we do not know what aspects of schooling are critical.
3) The role of nutrition in intelligence
remains obscure. Severe childhood malnutrition has clear negative
effects, but the hypothesis that particular "micro-nutrients"
may affect intelligence in otherwise adequately-fed populations
has not yet been convincingly demonstrated.
4) There are significant correlations
between measures of information processing speed and psychometric
intelligence, but the overall pattern of these findings yields
no easy theoretical interpretation.
5) Mean scores on intelligence
tests are rising steadily. They have gone up a full standard deviation
in the last fifty years or so, and the rate of gain may be increasing.
No one is sure why these gains are happening or what they mean.
6) The differential between the
mean intelligence test scores of Blacks and Whites (about one
standard deviation, although it may be diminishing) does not result
from any obvious biases in test construction and administration,
nor does it simply reflect differences in socio-economic status.
Explanations based on factors of caste and culture may be appropriate,
but so far have little direct empirical support. There is certainly
no such support for a genetic interpretation. At present, no one
knows what causes this differential.
7) It is widely agreed that standardized
tests do not sample all forms of intelligence. Obvious examples
include creativity, wisdom, practical sense and social sensitivity;
there are surely others. Despite the importance of these abilities
we know very little about them: how they develop, what factors
influence that development, how they are related to more traditional
measures.
In a field where so many issues
are unresolved and so many questions unanswered, the confident
tone that has characterized most of the debate on these topics
is clearly out of place. The study of intelligence does not need
politicized assertions and recriminations; it needs self-restraint,
reflection, and a great deal more research. The questions that
remain are socially as well as scientifically important. There
is no reason to think them unanswerable, but finding the answers
will require a shared and sustained effort as well as the commitment
of substantial scientific resources. Just such a commitment is
what we strongly recommend.
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