View sample theories of intelligence research paper. Browse research paper examples for more inspiration. If you need a psychology research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our writing service for professional assistance. We offer high-quality assignments for reasonable rates.
Hundreds of tests of intelligence are currently available to those who wish to test intelligence. Some are household names; others are known only to small groups of aficionados. Can such tests be justified in terms of psychological theory? If so, what are the theories, and what is the evidence in favor of them? Do all the theories lead to the same kinds of tests, or might alternative theories lead to different kinds of tests? And if alternative theories lead to different kinds of tests, might people’s fates be changed if other types of tests are used? These are the kinds of questions that are addressed in this research paper.
Need a Custom-Written Essay or a Research Paper?
Academic Writing, Editing, Proofreading, And Problem Solving Services
This research paper is divided into four parts following this introduction. First, I argue that theories of intelligence matter not only in theory, but also in practical everyday life. The ways in which these theories matter has a profound effect on societies, including that of the United States. Second, classical theories of intelligence are presented and critically evaluated. They are presented not only for historical purposes. Rather, they are presented because these theories continue to be highly influential in the contemporary world, much more so than many contemporary theories. Their influence is contemporary, even though their origins are in the past. Third, contemporary theories of intelligence are presented and critically evaluated. There are many such theories, but consistent with the topic of the volume in which this research paper is embedded, the emphasis is on those theories that have some kind of educational impact. Fourth and finally, the paper presents some challenges to all current conceptions of intelligence and draws some conclusions.
The second and third parts of the paper are each divided into two sections. One section considers implicit theories of intelligence, or people’s informal conceptions of what intelligence is. Asecond section considers explicit theories of intelligence, or experts’ formal conceptions of what intelligence is. Each part considers the extent to which implicit and explicit theories correspond, and why the correspondence is, at best, partial.
Why Theories of Intelligence Matter to Society
Underlying every measurement of intelligence is a theory. The theory may be transparently obvious, or it may be hidden. It may be a formal explicit theory or an informal implicit one. But there is always a theory of some kind lurking beneath the test. And in the United States and some other countries, tests seem to be everywhere.
The Pervasiveness of Intelligence-Related Measurements
Students who apply to competitive independent schools in many locations and notably in NewYork City must present an impressive array of credentials. Among these credentials, for many of these schools, is a set of scores on either theWechsler Preschool and Primary Scale of Intelligence–Revised (WPPSI-R; Wechsler, 1980) or the Stanford-Binet Intelligence Scale–Fourth Edition (Thorndike, Hagen, & Sattler, 1985). If the children are a bit older, they may take instead the Wechsler Intelligence Scale for Children–Third Edition (WISC-3; Wechsler, 1991). The lower level version of the Wechsler test is used only for children ages 3 to 7 1/2 years. The higher level version of theWechsler test is used for somewhat older children ages 6 to 16 years, 11 months of age. The Stanford-Binet test is used across a wider range of ages, from 2 years through adult.
Children applying to independent schools in other locations are likely to take either these or similar tests. The names may be different, and the construct they are identified as measuring may differ as well: intelligence, intellectual abilities, mental abilities, scholastic aptitude, and so forth. But the tests will be highly correlated with each other, and ultimately, one will serve the schools’ purposes about as well as another. These tests will henceforth be referred to as measuring intelligence-related abilities in order to group them together but to distinguish them from tests explicitly purported to measure intelligence.
The need to take tests such as these will not end with primary school. For admission to independent schools, in general, regardless of level, the children may take one of the Wechsler tests, the Stanford-Binet test, or some other intelligence test. More likely, they will take either the Educational Records Bureau (ERB) or the Secondary School Admissions Test (SSAT).
Of course, independent schools are supported by fees, not tax dollars. But children attending public schools will be exposed to a similar regimen.At one time, these children would have been likely to take group intelligence (IQ) tests, which likely would have been used to track them or, at the very least, predict their futures. Today, the students are less likely to take intelligence tests, unless they are being considered for special services, such as services for educable mentally retarded (EMR) children, learning-disabled (LD) children, or gifted children. If the children wish to go to a competitive college or university, they will likely take the SAT (an acronym originally standing for Scholastic Aptitude Test, then for Scholastic Assessment Test, and now for nothing in particular) or the American CollegeTest (ACT), the two most widely used tests used for college admissions. If individuals’ scores are within the normal range of a particular college or university to which they apply for admission, the scores may not much affect their admission prospects. But if their scores are outside this range, they may be a crucial factor in determining acceptance, in the case of high scores, or rejection, in the case of low scores. These tests may be required whether the school is publicly or privately funded. The story still is not over.
If the individuals (now adults) wish to puruse further study, they will have to take tests of various kinds. These include the Graduate Record Examination (GRE) for graduate school, the Law SchoolAdmission Test (LSAT) for law, the Graduate Management Admission Test (GMAT) for business school, the Medical College Admission Test (MCAT) for medical school, and so forth.And the story of intelligence testing may not end with graduate-level study: Many kinds of occupational placements, especially in business, may require applicants to take intelligence tests as well.
This rather lengthy introduction to the everyday world of tests of intelligence-related abilities shows the extent to which such tests permeate U.S. society, and some other contemporary societies as well. It is hard not to take such tests very seriously because they can be influential in or even determinative of a person’s educational and even occupational fate.
The Societal System Created by Tests
Tests of intelligence-related skills are related to success in many cultures. People with higher test scores seem to be more successful in a variety of ways, and those with lower test scores seem to be less successful (Herrnstein & Murray, 1994; Hunt, 1995). Why are scores on intelligence-related tests closely related to societal success? Consider two points of view.
According to Herrnstein and Murray (1994), Wigdor and Garner (1982), and others, conventional tests of intelligence account for about 10% of the variation, on average, in various kinds of real-world outcomes. This figure increases if one makes various corrections to it (e.g., for attenuation in measures or for restriction of range in particular samples). Although this percentage is not particularly large, it is not trivial either. Indeed, it is difficult to find any other kind of predictor that fares as well. Clearly, the tests have some value (Gottfredson, 1986, 1997; Hunt, 1995; Schmidt & Hunter, 1981, 1998). They predict success in many jobs and predict success even better in schooling for jobs. Rankings of jobs by prestige usually show higher prestige jobs associated with higher levels of intelligence-related skills. Theorists of intelligence differ as to why the tests have some success in prediction of job level and competency.
The Discovery of an Invisible Hand of Nature?
Some theorists believe that the role of intelligence is society is along the lines of some kind of natural law. In their book, Herrnstein and Murray (1994) refer to an “invisible hand of nature” guiding events such that people with high IQs tend to rise toward the top socioeconomic strata of a society and people with low IQs tend to fall toward the bottom strata. Jensen (1969, 1998) has made related arguments, as have many others (see, e.g., the largely unfavorable reviews by Gould, 1981; Lemann, 1999; Sacks, 1999; Zenderland, 1998). Herrnstein and Murray presented data to support their argument, although many aspects of their data and their interpretations of these data are arguable (Fraser, 1995; Gould, 1995; Jacoby & Glauberman, 1995; Sternberg, 1995).
This point of view has a certain level of plausibility to it. First, more complex jobs almost certainly do require higher levels of intelligence-related skills. Presumably, lawyers need to do more complex mental tasks than do street cleaners. Second, reaching the complex jobs via the educational system almost certainly requires a higher level of mental performance than does reaching less complex jobs. Finally, there is at least some heritable component of intelligence (Plomin, DeFries, McClearn, & Rutter, 1997), so nature must play some role in who gets what mental skills. Despite this plausibility, there is an alternative point of view.
A Societal Invention?
An alternative point of view is that the sorting influence of intelligence in society is more a societal invention than a discovery of an invisible hand of nature (Sternberg, 1997). The United States and some other countries have created societies in which test scores matter profoundly. High test scores may be needed for placement in higher tracks in elementary and secondary school. They may be needed for admission to selective undergraduate programs. They may be needed again for admission to selective graduate and professional programs. Test scores help individuals gain the access routes to many of the highest paying and most prestigious jobs. Low GRE scores, for example, may exclude an individual not only from one selective graduate school, but from many others as well. To the extent that there is error of measurement, there will be comparable effects in many schools.
According to this point of view, there are many able people who may be disenfranchised because the kinds of abilities that they have are not important for test performance, even though they may be important for job performance. For example, the kinds of creative and practical skills that matter to success on the job typically are not measured on the tests used for admissions to educational programs. At the same time, society may be overvaluing those who have a fairly narrow range of skills, and a range of skills that may not serve these individuals particularly well on the job, even if they do lead to success in school and on the tests.
On this view, it is scarcely surprising that ability tests predict school grades, because the tests originally were designed explicitly for this purpose (Binet & Simon, 1905/1916). In effect, U.S. society and other societies have created closed systems: Certain abilities are valued in instruction (e.g., memory and analytical abilities). Ability tests are then created that measure these abilities and thus predict school performance. Then assessments of achievement are designed that also assess for these abilities. Little wonder that ability tests are more predictive in school than in the work place: Within the closed system of the school, a narrow range of abilities leads to success on ability tests, in instruction, and on achievement tests. But these same abilities are less important later on in life.
According to the societal-invention view, closed systems can be and have been constructed to value almost any set of attributes at all. In some societies, caste is used. Members of certain castes are allowed to rise to the top; members of other castes have no chance. Of course, the members of the successful castes believe they are getting their due, much as did members of the nobility in the Middle Ages when they rose to the top and subjugated their serfs. Even in the United States, if one were born a slave in the early 1800s, one’s IQ would make little difference: One would die a slave. Slave owners and others rationalized the system, as social Darwinists always have, by believing that the fittest were in the roles in which they rightfully belonged.
The general conclusion is that societies can and do choose a variety of criteria to sort people. Some societies have used or continue to use caste systems, whether explicit, as in India, or implicit, as in the United States. Others use or have used race, religion, or wealth of parents as bases for sorting people. Many societies use a combination of criteria. Once a system is in place, those who gain access to the power structure, whether via their passage through elite education or elsewhere, are likely to look for others like themselves to enter into positions of power. The reason, quite simply, is that there probably is no more powerful basis of interpersonal attraction than similarity, so that people in a power structure look for others similar to themselves. The result is a potentially endlessly looping closed system.
It seems fair to say that some closed systems may be better, in some sense, than are others. For example, scores on intelligence-related measures would seem more relevant to school or job performance than would social class. But it is hard to draw definitive conclusions because the various attributes that are favored by a society often tend to correlate with each other. Socialization advantages may lead people of societally preferred racial, ethnic, religious, or other groups to have higher test scores. Thus, the extent to which correlations between test scores and status attributes are natural versus manufactured is unknown because it has not been possibly to conduct a study that would look systematically and comparatively at predictors of success across societies. The closest to doing so probably comes from the work of Ogbu (1978, 1991, 1994; Ogbu & Stern, 2001), who has compared the performance of groups that in one society are of low caste but in another society are of high caste. Ogbu found that performance varies not with group but with caste: When a group is of high social caste, it performs well; when it is of low social caste, it does not.
In sum, there may be some work by an invisible hand of nature, although this hand of nature almost certainly sorts on many attributes in addition to intelligence (such as height, beauty, health, and so forth). There also may be some work through societal inventions, although societies, like nature, sort on many attributes. The role of intelligence in society needs further (and unbiased) research.
Studies of sorting use psychological tests of intelligence and intelligence-related skills. What are the psychological theories on which these tests are based? Consider first some of the classical theories and then some contemporary ones.
Classical Theories of Intelligence and Their Contemporary Counterparts
Implicit theories are people’s conceptions of intelligence. Why even bother to study or report on implicit theories of intelligence? There are several reasons.
First, people’s day-to-day interactions are far more likely to be affected by their implicit theories than by any explicit theories. In job interviews, admission interviews, and even daily conversations, people are continually judging each other’s intelligence, based not on any formal and explicit theories but on their own implicit theories of intelligence. Second, implicit theories are of interest in their own right. Part of the study of psychology is seeking an understanding how people think, and given the importance of intelligence to society, learning how people think about intelligence is a worthy endeavor. Third, implicit theories often serve as the basis for generating explicit theories. The formal explicit theories of many psychologists (and other scientists) had their origins in these individual’s implicit theories.
How have psychologists conceived of intelligence? Almost none of these views are adequately expressed by Boring’s (1923) operationistic view of intelligence as what intelligence tests test. For example, a symposium on experts’ definitions of intelligence (“Intelligence and its measurement: A symposium,” 1921) asked leading researchers how they conceptualized intelligence. Among those asked were leaders in the field such as Edward L. Thorndike, Lewis M. Terman, Lewis L. Thurstone, and Herbert Woodrow. The researchers emphasized the importance of the ability to learn and the ability to adapt to the environment. These skills seem important. Are they the skills that play a major role in explicit theories of intelligence?
We consider here the three classical theories that today have the most influence: g theory, the theory of primary mental abilities, and the theory of fluid and crystallized abilities.
Probably the most influential theory in the history of intelligence research is the two-factor theory, which was first proposed by Spearman (1904, 1927) but has been carried forth by many modern theorists as g theory. Jensen (1998), himself a g theorist, summarizes much of this work.
Spearman (1904) noticed that tests purported to measure intelligence exhibit a positive manifold: They tend to correlate positively with each other. He invented a technique called factor analysis that was designed to analyze these intercorrelations in order to identify the purported sources of individual differences underlying the observed patterns of test scores. His factor analyses revealed two types of factors (hence the original name of his theory): the general factor (g), whose influence pervades all tests of mental abilities, and specific factors (s), whose influence is limited to a single test.
Spearman proposed two separate theories to explain the pervasive presence of g. One theory (Spearman, 1927) attributed the general factor to mental energy, a concept that he believed originated with Aristotle. The other theory was a more cognitive theory. Spearman (1923) suggested that three information-processing components (termed qualitative principles of cognition) were common to all of the tests. The three components were apprehension of experience, or encoding of stimuli; eduction of relations, or inferring the relation between two terms; and eduction of correlates, or applying the inferred relation in a new domain. In the analogy BLACK : WHITE :: HIGH : ?, for example, apprehension of experience would be used to encode the terms; eduction of relations is used to infer the relation between BLACK and WHITE; and eduction of correlates is used to apply the inferred relation from HIGH to produce LOW.
Spearman’s g theory continues today in more modern form. Indeed, two books published in the late 1990s both were called The g Factor (Brand, 1996; Jensen, 1998). Jensen (1998, 2002) has defined g as a distillate of the common source of individual differences in all mental tests. He has proposed that underlying g are individual differences in the speed or efficiency of the neural processes that affect the kinds of behavior measured by tests of mental ability.
Jensen (1998) has built his argument in terms of converging operations that, to him, seem to indicate unequivocally the presence of some biologically based common source of variation in performance on mental tests. For example, he cited eight studies prior to 1998 using magnetic resonance imaging (MRI) that showed a correlation between IQ and brain volume (p. 147). Anumber of other studies have shown correlations between aspects of spontaneously measured electroencephalogram (EEG) waves and IQ and between averaged evoked potentials (AEPs) and IQ (pp. 152–157). Other studies using positron-emission tomography (PET) scanning also have shown correlations with IQ (pp. 157– 159), as have studies of peripheral nerve conduction velocity (pp. 159–160) and brain-nerve conduction velocity (pp. 160– 162). Some of these kinds of works are described in more detail later.
Other studies have also suggested the viability of the general factor. One example is the heritability study (see Bouchard, 1997; Jensen, 1998; Petrill, in press; Plomin, 1997; Plomin et al., 1997; Scarr, 1997). Such studies typically are designed to study identical twins separated at or near birth, to study identical versus fraternal twins, or to study adopted children (of known biological parentage) and biological children living in the same household. These kinds of studies enable investigators to separate, to some extent, genetic from environmental contributions to intelligence.Today it is recognized, however, that pure influences of genetics and environment are extremely difficult to disentangle (Sternberg & Grigorenko, 1997).
As mentioned earlier, the theory of general intelligence has been the longest lasting and perhaps the most widely accepted in all of the psychological literature. The evidence is impressive—certainly more so than that garnered for any competing theory. Nevertheless, the available evidence requires at least some skepticism.
First, some theorists (e.g., Gardner, 1983, 1999; Sternberg, 1997, 1999a, 1999c, 1999d; whose work is described later) suggest that a general factor is obtained in tests of intelligence because the tests are limited to a class of fairly academic and somewhat artificial tasks. They argue that the general factor disappears or at least is greatly weakened when a broader range of tasks is used.
Second, contrary to the claim of Jensen (1998), a general factor does tend to appear as a mathematical regularity when factorial solutions are left unrotated. Such a factor tends to be produced because the methods of both common-factor and principal-components analysis in widespread use today maximize the amount of variance that they place in each successive factor, with the most possible variance going into the first factor. Thus, the first factor maximizes the loadings of variables on it.
Third, the sheer number of studies supporting a general factor does not necessarily engender support of the theory in proportion to the number of studies (Sternberg, 1999a). The large majority of these studies tends to use a somewhat restricted range of tasks, situations in which intelligence is tested, and even participants.
The Theory of Primary Mental Abilities
Thurstone (1938) proposed a theory of primary mental abilities. Although this theory is not widely used today, the theory forms the basis of many contemporary theories, including two contemporary theories discussed later, those of Gardner (1983) and Carroll (1993). It is also the basis for many contemporary group tests of intelligence, which comprise items roughly of the types described next.
Thurstone (1938) analyzed the data from 56 different tests of mental abilities and concluded that to the extent that there is a general factor of intelligence, it is unimportant and possibly epiphenomenal. From this point of view there are seven primary mental abilities:
- Verbal comprehension. This factor involves a person’s ability to understand verbal material. It is measured by tests such as vocabulary and reading comprehension.
- Verbal fluency. This ability is involved in rapidly producing words, sentences, and other verbal material. It is measured by tests such as one that requires the examinee to produce as many words as possible beginning with a particular letter in a short amount of time.
- This ability is involved in rapid arithmetic computation and in solving simple arithmetic word problems.
- Perceptual speed. This ability is involved in proofreading and in rapid recognition of letters and numbers. It is measured by tests such as those requiring the crossing out of As in a long string of letters or in tests requiring recognition of which of several pictures at the right is identical to the picture at the left.
- Inductive reasoning. This ability requires generalization—reasoning from the specific to the general. It is measured by tests, such as letter series, number series, and word classifications, in which the examinee must indicate which of several words does not belong with the others.
- Spatial visualization. This ability is involved in visualizing shapes, rotations of objects, and how pieces of a puzzle fit together. An example of a test would be the presentation of a geometric form followed by several other geometric forms. Each of the forms that follows the first is either the same rotated by some rigid transformation or the mirror image of the first form in rotation. The examinee has to indicate which of the forms at the right is a rotated version of the form at the left, rather than a mirror image.
Today, Thurstone’s theory is not used as often in its original form, but it has served as a basis for many subsequent theories of intelligence, including hierarchical theories and modern theories such as Gardner’s (1983). Thus, to the extent that a theory is judged by its heuristic value, Thurstone’s has been one of the most important in the field.
Fluid-Crystallized Ability Theory
The theory of fluid and crystallized abilities is one of a class of hierarchical theories of intelligence (Burt, 1949; Gustafsson, 1988; Jensen, 1970; Vernon, 1971), not all of which can be described here. The theory is still current. It was proposed by Cattell (1971) but now has been proposed in a contemporary and elaborated form by Horn (1994). Only the simple form is described here.
According to this theory, fluid ability (Gf) is flexibility of thought and the ability to reason abstractly. It is measured by tests such as number series, abstract analogies, matrix problems, and the like. Crystallized ability (Gc), which is alleged to derive from fluid ability, is essentially the accumulation of knowledge and skills through the life course. It is measured by tests of vocabulary, reading comprehension, and general information. Sometimes a further distinction is made between fluid and crystallized abilities and a third ability, visual ability (Gv), which is the ability to manipulate representations mentally, such as those found in tests of spatial ability (as described earlier for Thurstone’s theory).
A number of contemporary tests of intelligence are based on this theory. One is the Test of g: Culture Fair (Cattell & Cattell, 1963), which seeks to capture general ability through tests of fluid abilities. Two other such tests are the Kaufman Adolescent and Adult Intelligence Test (KAIT; Kaufman & Kaufman, 1993) and the Woodcock-Johnson Tests of Cognitive Ability–Revised (Woodcock & Johnson, 1989; see Daniel, 2000, for a review of these and other tests).
The theory of fluid and crystallized intelligence has been extremely influential in the psychological literature on intelligence. If one includes visual ability (Gv), the theory seems to capture three of the most pervasive abilities constituting intelligence. Some questions remain unresolved.
First, it is unclear whether fluid ability is statistically separable from general intelligence (Gustafsson, 1984, 1988). Such a separation appears to be difficult, and even Cattell’s own allegedly culture-fair test of g is actually a test of fluid ability, as is the Raven’s Progressive Matrices test.
Second, it is unclear whether crystallized ability really derives from or somehow springs out of fluid ability. Such a view seemed plausible when Cattell and many others could argue persuasively that tests of fluid ability were culture-fair and that fluid ability is largely unaffected by environmental factors. It now appears that both these views are erroneous. Fluid-ability tests often show greater differences between cultural groups than do crystallized ability tests; more important, they are more susceptible to the Flynn effect (considered later) than are tests of crystallized abilities. This effect refers to secular increases in scores over time. If fluid-ability scores are increasing over time more rapidly than crystallizedability scores, one can hardly argue that they are unaffected by enculturation or, most likely, by schooling. Indeed, Ceci (1991, 1996; Ceci & Williams, 1997) has suggested that schooling has a large effect on measured intelligence of all kinds.
Third, it appears likely that there are other kinds of abilities beyond those specified by the theory of fluid and crystallized abilities. Some of the contemporary theories considered next attempt to specify what these abilities might be.
Contemporary Theories of Intelligence
Sixty-five years after the symposium in the Journal of Educational Psychology on intelligence, Sternberg and Detterman (1986) conducted a similar symposium, again asking experts about their views on intelligence. Experts such as Earl Butterfield, Douglas Detterman, Earl Hunt, Arther Jensen, and Robert Sternberg gave their views. Learning and adaptive abilities retained their importance, and a new emphasis crept in—metacognition, or the ability to understand and control one’s self. Of course, the name is new, but the idea is not, because long ago Aristotle emphasized the importance for intelligence of knowing oneself.
The 1921 and 1986 symposia could be criticized for being overly Western in the composition of their contributors. In some cases, Western notions about intelligence are not shared by other cultures. For example, the Western emphasis on speed of mental processing (Sternberg, Conway, Ketron, & Bernstein, 1981) is absent in many cultures. Other cultures may even be suspicious of the quality of work that is done very quickly. Indeed, other cultures emphasize depth rather than speed of processing. They are not alone: Some prominent Western theorists have pointed out the importance of depth of processing for full command of material (e.g., Craik & Lockhart, 1972). Even L. L. Thurstone (1924) emphasized the importance to human intelligence of withholding a quick, instinctive response, a view that Stenhouse (1973) argued is supported by evolutionary theory. Today, unlike in the past, psychologists have a better idea of the implicit theories of people in diverse cultures.
Laypersons’ Views (Across Cultures)
Yang and Sternberg (1997a) reviewed Chinese philosophical conceptions of intelligence. The Confucian perspective emphasizes the characteristic of benevolence and of doing what is right. As in the Western notion, the intelligent person spends much effort in learning, enjoys learning, and persists in lifelong learning with a great deal of enthusiasm. The Taoist tradition, in contrast, emphasizes the importance of humility, freedom from conventional standards of judgment, and full knowledge of oneself as well as of external conditions.
The difference between Eastern and Western conceptions of intelligence may persist even in the present day. Yang and Sternberg (1997b) studied contemporary Taiwanese Chinese conceptions of intelligence and found five factors underlying these conceptions: (a) a general cognitive factor, much like the g factor in conventional Western tests; (b) interpersonal intelligence; (c) intrapersonal intelligence; (d) intellectual self-assertion; and (d) intellectual self-effacement. In a related study but with different results, Chen (1994) found three factors underlying Chinese conceptualizations of intelligence: nonverbal reasoning ability, verbal reasoning ability, and rote memory. The difference may be due to different subpopulations of Chinese, to differences in methodology, or to differences in when the studies were done.
The factors uncovered in both studies differ substantially from those identified in U.S. people’s conceptions of intelligence by Sternberg et al. (1981). The factors uncovered by this study were (a) practical problem solving, (b) verbal ability, and (c) social competence, although in both cases people’s implicit theories of intelligence seem to go far beyond what conventional psychometric intelligence tests measure. Of course, comparingtheChen(1994)totheSternbergetal.(1981)study simultaneously varies both language and culture.
Chen and Chen (1988) varied only language. They explicitly compared the concepts of intelligence of Chinese graduates from Chinese-language versus English-language schools in Hong Kong. They found that both groups considered nonverbal reasoning skills as the most relevant skill for measuring intelligence. Verbal reasoning and social skills came next, and then numerical skill. Memory was seen as least important. The Chinese-language group, however, tended to rate verbal skills as less important than did the English-language group. Moreover, in an earlier study, Chen, Braithwaite, and Huang (1982) found that Chinese students viewed memory for facts as important for intelligence, whereas Australian students viewed these skills as being of only trivial importance.
Das (1994), also reviewing Eastern notions of intelligence, has suggested that in Buddhist and Hindu philosophies, intelligence involves waking up, noticing, recognizing, understanding, and comprehending, but also includes such things as determination, mental effort, and even feelings and opinions in addition to more intellectual elements.
Differences between cultures in conceptions of intelligence have been recognized for some time. Gill and Keats (1980) noted that Australian university students value academic skills and the ability to adapt to new events as critical to intelligence, whereas Malay students value practical skills, as well as speed and creativity. Dasen (1984) found Malay students to emphasize both social and cognitive attributes in their conceptions of intelligence.
The differences between East and West may be due to differences in the kinds of skills valued by the two kinds of cultures (Srivastava & Misra, 1996). Western cultures and their schools emphasize what might be called technological intelligence (Mundy-Castle, 1974), so things like artificial intelligence and so-called smart bombs are viewed, in some sense, as intelligent, or smart.
Western schooling emphasizes other things as well (Srivastava & Misra, 1996), such as generalization, or going beyond the information given (Connolly & Bruner, 1974; Goodnow, 1976), speed (Sternberg, 1985), minimal moves to a solution (Newell & Simon, 1972), and creative thinking (Goodnow, 1976). Moreover, silence is interpreted as a lack of knowledge (Irvine, 1978). In contrast, the Wolof tribe in Africa views people of higher social class and distinction as speaking less (Irvine, 1978). This difference between the Wolof and Western notions suggests the usefulness of looking at African notions of intelligence as a possible contrast to U.S. notions.
In fact, studies in Africa provide yet another window on the substantial differences. Ruzgis and Grigorenko (1994) have argued that, in Africa, conceptions of intelligence revolve largely around skills that help to facilitate and maintain harmonious and stable intergroup relations; intragroup relations are probably equally important and at times more important. For example, Serpell (1974, 1982, 1993) found that Chewa adults in Zambia emphasize social responsibilities, cooperativeness, and obedience as important to intelligence; intelligent children are expected to be respectful of adults. Kenyan parents also emphasize responsible participation in family and social life as important aspects of intelligence (Super, 1983; Super & Harkness, 1982). In Zimbabwe, the word for intelligence, ngware, actually means to be prudent and cautious, particularly in social relationships. Among the Baoule, service to the family and community and politeness toward and respect for elders are seen as key to intelligence (Dasen, 1984).
Similar emphasis on social aspects of intelligence has been found as well among two other African groups, the Songhay of Mali and the Samia of Kenya (Putnam & Kilbride, 1980). The Yoruba, another African tribe, emphasize the importance of depth—of listening rather than just talking—to intelligence, and of being able to see all aspects of an issue and of being able to place the issue in its proper overall context (Durojaiye, 1993).
The emphasis on the social aspects of intelligence is not limited to African cultures. Notions of intelligence in many Asian cultures also emphasize the social aspect of intelligence more than does the conventional Western or IQ-based notion (Azuma & Kashiwagi, 1987; Lutz, 1985; Poole, 1985; White, 1985).
It should be noted that neither African nor Asian cultures emphasize exclusively social notions of intelligence. In one village in Kenya (near Kisumu), many and probably most of the children are at least moderately infected with a variety of parasitic infections. As a result, they experience stomachaches quite frequently. Traditional medicine suggests the usefulness of a large variety (actually, hundreds) of natural herbal medicines that can be used to treat such infections. It appears that at least some of these—although perhaps a small percentage—actually work. More important for our purposes, however, children who learn how to self-medicate via these natural herbal medicines are viewed as being at an adaptive advantage over those who do not have this kind of informal knowledge. Clearly, the kind of adaptive advantage that is relevant in this culture would be viewed as totally irrelevant in the West, and vice versa.
Grigorenko and her colleagues (2001) have studied conceptions of intelligence in this village in some detail. There appear to be four parts to the conception.
First, the concept of rieko can be translated as intelligence, smartness, knowledge, ability, skill, competence, and power. Along with the general concept of rieko, the Luo people distinguish among various specialized representations of this concept. Some representations are characterized by the source of rieko: rieko mar sikul (knowledge acquired in school), or rieko mzungu (the White man’s technical powers); others by different domains of action: rieko mar ot (competence in household tasks, including planning skills and resource management), or rieko mar kite (being versed in traditional customs and rules). Other representations are characterized by specific outcomes, such as rieko mar lupo (fishing skills, including knowledge of magic to provide rich catches), rieko mar yath (knowledge of healing with herbal medicines), and so forth.
Luoro is the second main quality of children and people in general. It encompasses a whole field of concepts roughly corresponding to social qualities such as respect and care for others, obedience, diligence, consideration, and readiness to share. Luoro has an unequivocal positive meaning and was always mentioned as a necessity in response to questions such as “What is most important for a good child to have?” and “What should people have to lead a happy life?” When people were asked to compare the relative importance for an individual’s life of rieko and luoro, respondents generally gave preference to luoro. It is interesting that the only two respondents ranking rieko higher than luoro were outsiders to the local community who had a tertiary education and considerable wealth by village standards. Rieko and luoro are complementary. Rieko is a positive attribute only if luoro is also present. Ideally, the power of pure individual abilities should be kept under control by social rules.
Third, paro overlaps with both luoro and rieko and, roughly translated, means thinking. Specifically, paro refers to the thought processes required to identify a problem and its solution and to the thought processes involved in caring for other people. A child with good thinking (paro maber) could thus, for example, be a child who is able to react rationally in case of another person’s accident or one who is able to collect wood, burn charcoal, and sell it favorably in order to help his old grandmother. The concept of paro stresses the procedural nature of intelligence. In essence, paro occupies an intermediate position between the potentiality of rieko (its ability aspects) and the partially moral connotation of an outcome (the deed) done with or without luoro. Paro also reflects the idea of initiative and innovation, for example, in designing a new technical device. Paro encompasses the process of thinking, the ability to think, and the specific kind of thinking that an individual demonstrates.
Fourth, winjo, like paro, is linked to both rieko and luoro. Winjo means comprehending and understanding. It points to the child’s abilities to comprehend, that is, to process what is said or what is going on. But it also involves the ability to grasp what is appropriate and inappropriate in a situation, that is, to understand and do what you are told by adults or to derive from the situation what is appropriate to do. It shares with the other key terms the feature that its meaning is a function of context. For a teacher in school it means that a child runs an errand as told. In contrast, a grandmother teaching a child about healing might emphasize the aspect of procedural learning combined with attention to another person.
A “good child” as well as a “good community member” needs a balanced mixture of all positive qualities, in which the contradictory aspects counterbalance each other. Specifically, the ambiguous powers of individual rieko (which could be either positive or negative) need to be controlled by social values and rules (luoro).
These conceptions of intelligence emphasize social skills much more than do conventional U.S. conceptions of intelligence, but at the same time they recognize the importance of cognitive aspects of intelligence. It is important to realize, again, that there is no one overall U.S. conception of intelligence. Indeed, Okagaki and Sternberg (1993) found that different ethnic groups in San Jose, California, had rather different conceptions of what it means to be intelligent. For example, Latino parents of schoolchildren tended to emphasize the importance of social-competence skills in their conceptions of intelligence, whereas Asian parents tended rather heavily to emphasize the importance of cognitive skills. Anglo parents also emphasized cognitive skills more. Teachers, representing the dominant culture, emphasized cognitive skills more than social-competence skills. The rank order of children of various groups’ performances (including subgroups within the Latino and Asian groups) could be perfectly predicted by the extent to which parents shared the teachers’ conceptions of intelligence. In other words, teachers tended to reward those children who were socialized into a view of intelligence that happened to correspond to the teachers’ own.
A Psychometric Theory
The psychometric approach to intelligence is among the oldest of approaches, dating back to Galton’s (1883) psychophysical theory of intelligence in terms of psychophysical abilities (such as strength of hand grip or visual acuity) and later to Binet and Simon’s (1905/1916) theory of intelligence as judgment, involving adaptation to the environment, direction of one’s efforts, and self-criticism.
Carroll (1993) has proposed a hierarchical model of intelligence, based on a factor analysis of more than 460 data sets obtained between 1927 and 1987. His analysis encompasses more than 130,000 people from diverse walks of life and even countries of origin (although non-English-speaking countries are poorly represented among his data sets). The model Carroll proposed, based on his monumental undertaking, is a hierarchy comprising three strata: Stratum I, which includes many narrow, specific abilities (e.g., spelling ability, speed of reasoning); Stratum II, which includes various group-factor abilities (e.g., fluid intelligence, involved in flexible thinking and seeing things in novel ways; and crystallized intelligence, the accumulated knowledge base); and Stratum III, which is just a single general intelligence, much like Spearman’s (1904) general intelligence factor.
Of these strata, the most interesting is perhaps the middle stratum, which includes (in addition to fluid and crystallized abilities) learning and memory processes, visual perception, auditory perception, facile production of ideas (similar to verbal fluency), and speed (which includes both sheer speed of response and speed of accurate responding). Although Carroll does not break much new ground, in that many of the abilities in his model have been mentioned in other theories, he does masterfully integrate a large and diverse factoranalytic literature, thereby giving great authority to his model. At the same time, his meta-analysis assumes that conventional psychometric tests cover the entire domain of intelligence that needs to be covered by a theory of intelligence. Some theorists, discussed next, question this assumption.
Cronbach (1957) called for a merging of the two disciplines of scientific psychology: the differential and experimental approaches. The idea is that the study of individual differences (differential psychology) and of cross-individual commonalities (experimental psychology) need not be separate disciplines. They can be merged.
Serious responses to Cronbach came in the 1970s, with cognitive approaches to intelligence attempting this merger. Two of the responses were the cognitive-correlates approach to intelligence and the cognitive-correlates approach.
Hunt, Frost, and Lunneborg (1973; see also Hunt, Lunneborg, & Lewis, 1975) introduced the cognitivecorrelates approach, whereby scores on laboratory cognitive tests were correlated with scores on psychometric intelligence tests. The theory underlying this work was that fairly simple components of information processing studied in the laboratory—such as the time to retrieve lexical information from long-term memory—could serve as a basis for understanding human intelligence. Intelligence tests, on this view, present complex problems whose solution nevertheless relies on fairly simple information processing. Thus, a participant in a cognitive study might be asked whether two letters, A and a, are identical in identity (answer: yes) or identical in case (answer: no). The tasks were directly out of the literature of experimental psychology, including the letter-comparison task, which is based on work by Posner and Mitchell (1967).
Sternberg (1977; see also Sternberg, 1983) introduced the cognitive-components approach, whereby performance on complex psychometric tasks was decomposed into elementary information-processing components.The underlying theory was that intelligence comprises a series of component information processes. In contrast to the cognitive-correlates approach, however, the underlying components were seen as complex rather than as simple. For example, solving an analogy of the form A : B :: C : ? involves components such as encoding the terms, inferring the relation between A and B, applying this relation from C to ?, and so forth (see review by Lohman, 2000).
The cognitive approaches of Hunt and Sternberg are now primarily of historical interest. Both authors have expanded their conceptualizations of intelligence since this work. They were forced to do so. Neither approach yielded consistently high correlations between the tasks and task components and psychometric tests of intelligence used as criteria. Moreover, sometimes the components showing the highest correlations were the ones least expected to show them. Sternberg and Gardner (1983), for example, consistently found the regression-constant component to have the highest correlations with psychometric test scores, leading them to wonder whether they had rediscovered through information-processing analysis the general factor that had been discovered through psychometric analysis.
In the 1990s cognitive and biological approaches (discussed next) began to merge (Vernon, Wickett, Bazana, & Stelmack, 2000). A prototypical example is the inspectiontime task (Nettlebeck, 1982; see reviews by Deary, 2000; Deary & Stough, 1996). In this task, two adjacent vertical lines are presented tachistoscopically or by computer, followed by a visual mask (to destroy the image in visual iconic memory). The two lines differ in length, as do the lengths of time for which the two lines are presented. The participant’s task is to say which line is longer. But instead of using raw response time as the dependent variable, investigators typically use measures derived from a psychophysical function estimated after many trials. For example, the measure might be the duration of a single inspection trial at which 50% accuracy is achieved. Correlations between this task and measures of IQ appear to be about .4, a bit higher than is typical in psychometric tasks. Much of this correlation may be mediated by the visual ability component of intelligence (Gv). There are differing theories as to why such correlations are obtained. All such theories generally attempt to relate the cognitive function of visual inspection time to some kind of biological function, such as speed of neuronal conduction. Let us consider, then, some of the biological functions that may underlie intelligence.
An important approach to studying intelligence is to understand it in terms of the functioning of the brain, in particular, and of the nervous system, in general. Earlier theories relating the brain to intelligence tended to be global in nature, although they were not necessarily backed by strong empirical evidence. Because these earlier theories are still used in contemporary writings and, in the case of Halstead and Luria, form the bases for test batteries still in contemporary use, they are described here briefly.
Early Biological Theories. Halstead (1951) suggested that there are four biologically based abilities, which he called (a) the integrative field factor, (b) the abstraction factor, (c) the power factor, and (d) the directional factor. Halstead attributed all four of these abilities primarily to the functioning of the cortex of the frontal lobes.
More influential than Halstead has been Hebb (1949), who distinguished between two basic types of intelligence: Intelligence Aand Intelligence B. Hebb’s distinction is still used by some theorists. According to Hebb, Intelligence A is innate potential, and Intelligence B is the functioning of the brain as a result of the actual development that has occurred. These two basic types of intelligence should be distinguished from Intelligence C, or intelligence as measured by conventional psychometric tests of intelligence. Hebb also suggested that learning, an important basis of intelligence, is built up through cell assemblies, by which successively more and more complex connections among neurons are constructed as learning takes place.
A third biologically based theory is that of Luria (1973, 1980), which has had a major impact on tests of intelligence (Kaufman & Kaufman, 1983; Naglieri & Das, 1997). According to Luria, the brain comprises three main units with respect to intelligence: (a) a unit of arousal in the brain stem and midbrain structures; (b) a sensory-input unit in the temporal, parietal, and occipital lobes; and (c) an organization and planning unit in the frontal cortex. The more modern form of this theory is PASS theory (Das, Kirby, & Jarman, 1979; Naglieri & Das, 1990, 2002), which distinguishes among planning, attentional, successive processing, and simultaneous processing abilities. These latter two abilities are subsets of the sensory-input abilities referred to by Luria.
The early biological theories continue to have an influence on theories of intelligence. Oddly, their influence on contemporary psychometric work is substantially greater than their influence on contemporary biological work, which largely (although not wholly) has left these theories behind.
Contemporary Biological Theories. More recent theories have dealt with more specific aspects of brain or neural functioning. One contemporary biological theory is based on speed of neuronal conduction. For example, one theory has suggested that individual differences in nerve-conduction velocity are a basis for individual differences in intelligence (e.g., Reed & Jensen, 1992; Vernon & Mori, 1992). Two procedures have been used to measure conduction velocity, either centrally (in the brain) or peripherally (e.g., in the arm).
Reed and Jensen (1992) tested brain-nerve conduction velocities via two medium-latency potentials, N70 and P100, which were evoked by pattern-reversal stimulation. Subjects saw a black-and-white checkerboard pattern in which the black squares would change to white and the white squares to black. Over many trials, responses to these changes were analyzed via electrodes attached to the scalp in four places. Correlations of derived latency measures with IQ were small (generally in the .1 to .2 range of absolute value), but were significant in some cases, suggesting at least a modest relation between the two kinds of measures.
Vernon and Mori (1992) reported on two studies investigating the relation between nerve-conduction velocity in the arm and IQ. In both studies nerve-conduction velocity was measured in the median nerve of the arm by attaching electrodes to the arm. In the second study, conduction velocity from the wrist to the tip of the finger was also measured. Vernon and Mori found significant correlations with IQ in the .4 range, as well as somewhat smaller correlations (around .2) with response-time measures. They interpreted their results as supporting the hypothesis of a relation between speed of information transmission in the peripheral nerves and intelligence. However, these results must be interpreted cautiously, as Wickett and Vernon (1994) later tried unsuccessfully to replicate these earlier results.
Other work has emphasized P300 as a measure of intelligence. Higher amplitudes of P300 are suggestive of higher levels of extraction of information from stimuli (Johnson, 1986, 1988) and also more rapid adjustment to novelty in stimuli (Donchin, Ritter, & McCallum, 1979). However, attempts to relate P300 and other measures of amplitudes of evoked potentials to scores on tests of intelligence have led to inconclusive results (Vernon et al., 2000). Indeed, the field has gotten a mixed reputation because so many successful attempts have later been met with failures to replicate.
There could be a number of reasons for these failures. One is almost certainly that there are just so many possible sites, potentials to measure, and ways of quantifying the data that the huge number of possible correlations creates a greater likelihood of Type I errors than would be the case for more typical cases of test-related measurements. Investigators using such methods therefore have to take special care to guard against Type II errors.
Another approach has been to study glucose metabolism. The underlying theory is that when a person processes information, there is more activity in a certain part of the brain. The better the person is at the behavioral activity, the less is the effort required by the brain. Some of the most interesting recent studies of glucose metabolism have been done by Richard Haier and his colleagues. For example, Haier et al. (1988) showed that cortical glucose metabolic rates as revealed by PET scan analysis of subjects solving Raven Progressive Matrices problems were lower for more intelligent than for less intelligent subjects. These results suggest that the more intelligent participants needed to expend less effort than the less intelligent ones in order to solve the reasoning problems. A later study (Haier, Siegel, Tang, Abel, & Buchsbaum, 1992) showed a similar result for more versus less practiced performers playing the computer game of Tetris. In other words, smart people or intellectually expert people do not have to work as hard as less smart or intellectually expert people at a given problem.
What remains to be shown, however, is the causal direction of this finding. One could sensibly argue that the smart people expend less glucose (as a proxy for effort) because they are smart, rather than that people are smart because they expend less glucose. Or both high IQ and low glucose metabolism may be related to a third causal variable. In other words, we cannot always assume that the biological event is a cause (in the reductionist sense). It may be, instead, an effect.
Another approach considers brain size. The theory is simply that larger brains are able to hold more neurons and, more important, more complex intersynaptic connections between neurons. Willerman, Schultz, Rutledge, and Bigler (1991) correlated brain size with Wechsler Adult Intelligence Scale–Revised(WAIS-R)IQs,controllingforbodysize.They found that IQ correlated .65 in men and .35 in women, with a correlationof.51forbothsexescombined.Afollow-upanalysis of the same 40 subjects suggested that, in men, a relatively larger left hemisphere better predicted WAIS-R verbal than it predicted nonverbal ability, whereas in women a larger left hemisphere predicted nonverbal ability better than it predicted verbal ability (Willerman, Schultz, Rutledge, & Bigler, 1992). These brain-size correlations are suggestive, but it is difficult to say what they mean at this point.
Yet another approach that is at least partially biologically based is that of behavior genetics.Afairly complete review of this extensive literature is found in Sternberg and Grigorenko (1997).The basic idea is that it should be possible to disentangle genetic from environmental sources of variation in intelligence. Ultimately, one would hope to locate the genes responsible for intelligence (Plomin, McClearn, & Smith, 1994, 1995; Plomin & Neiderhiser, 1992; Plomin & Petrill, 1997). The literature is complex, but it appears that about half the total variance in IQ scores is accounted for by genetic factors (Loehlin, 1989; Plomin, 1997). This figure may be an underestimate because the variance includes error variance and because most studies of heritability have been with children, but we know that heritability of IQ is higher for adults than for children (Plomin, 1997). Also, some studies, such as the Texas Adoption Project (Loehlin, Horn, & Willerman, 1997), suggest higher estimates: .78 in the Texas Adoption Project, .75 in the Minnesota Study of Twins Reared Apart (Bouchard, 1997; Bouchard, Lykken, McGue, Segal, & Tellegen, 1990), and .78 in the Swedish Adoption Study of Aging (Pedersen, Plomin, Nesselroade, & McClearn, 1992).
At the same time, some researchers argue that effects of heredity and environment cannot be clearly and validly separated (Bronfenbrenner & Ceci, 1994; Wahlsten & Gottlieb, 1997). Perhaps, the direction of future research should be to figure out how heredity and environment work together to produce phenotypic intelligence (Scarr, 1997), concentrating especially on within-family environmental variation, which appears to be more important than between-family variation (Jensen, 1997). Such research requires, at the very least, very carefully prepared tests of intelligence, perhaps some of the newer tests described in the next section.
Many contemporary theories of intelligence can be viewed as systems theories because they are more complex, in many respects, than past theories, and attempt to deal with intelligence as a complex system.
The Theory of Multiple Intelligences. Gardner (1983, 1993, 1999) proposed that there is no single, unified intelligence, but rather a set of relatively distinct, independent, and modular multiple intelligences. His theory of multiple intelligences (MI theory) originally proposed seven multiple intelligences: (a) linguistic, as used in reading a book or writing a poem; (b) logical-mathematical, as used in deriving a logical proof or solving a mathematical problem; (c) spatial, as used in fitting suitcases into the trunk of a car; (d) musical, as used in singing a song or composing a symphony; (e) bodily-kinesthetic, as used in dancing or playing football; (f) interpersonal, as used in understanding and interacting with other people; and (g) intrapersonal, as used in understanding oneself.
Recently, Gardner (1999) has proposed an additional intelligence as a confirmed part of his theory: naturalist intelligence, the kind shown by people who are able to discern patterns in nature. Charles Darwin would be a notable example. Gardner has also suggested that there may be two other intelligences: spiritual intelligence and existential intelligence. Spiritual intelligence involves a concern with cosmic or existential issues and the recognition of the spiritual as the achievement of a state of being. Existential intelligence involves a concern with ultimate issues. Gardner believes that the evidence for these latter two intelligences is less powerful than the evidence for the other eight intelligences. Whatever the evidence may be for the other eight, we agree that the evidence for these two new intelligences is speculative at this point.
Most activities will involve some combination of these different intelligences. For example, dancing might involve both musical and bodily-kinesthetic intelligences. Reading a mathematical textbook might require both linguistic and logical-mathematical intelligences. Often it will be hard to separate these intelligences in task performance.
In the past, factor analysis served as the major criterion for identifying abilities. Gardner (1983, 1999) proposed a new set of criteria, including but not limited to factor analysis, for identifying the existence of a discrete kind of intelligence: (a) potential isolation by brain damage, in that the destruction or sparing of a discrete area of the brain may destroy or spare a particular kind of intelligent behavior; (b) the existence of exceptional individuals who demonstrate extraordinary ability (or deficit) in a particular kind of intelligent behavior; (c) an identifiable core operation or set of operations that are essential to performance of a particular kind of intelligent behavior; (d) a distinctive developmental history leading from novice to master, along with disparate levels of expert performance; (e) a distinctive evolutionary history, in which increases in intelligence may be plausibly associated with enhanced adaptation to the environment; (f) supportive evidence from cognitive-experimental research; (g) supportive evidence from psychometric tests; and (h) susceptibility to encoding in a symbol system.
Gardner (1993, 1995, 1997) has suggested that the multiple intelligences can be understood as bases not only for understanding intelligence, but for understanding other kinds of constructs as well, such as creativity and leadership. For example, Gardner has analyzed some of the great creative thinkers of the twentieth century in terms of their multiple intelligences, arguing that many of them were extraordinarily creative by virtue of extremely high levels of one of the intelligences. For example, Martha Graham was very high in bodily-kinesthetic intelligence, T. S. Eliot in linguistic intelligence, and so forth.
The theory of multiple intelligences has proved to be enormously successful in capturing the attention both of the psychological public and of the public in general. Nevertheless, some caution must be observed before accepting the theory.
First, since the theory was proposed in 1983, there have been no published empirical tests of the theory as a whole. Given that a major goal of science is empirically to test theories, this fact is something of a disappointment, but it certainly suggests the need for such testing.
Second, the theory has been justified by Gardner on the basis of post hoc reviews of various literatures. Although these reviews are persuasive, they are also highly selective. For example, there is virtually no overlap between the literatures reviewed by Gardner in his various books and the literatures reviewed by Carroll (1993) or Jensen (1998). This is not to say that his literature is wrong or that theirs is right. Rather, all literature reviews are selective and probably tend more to dwell on studies that support the proposed point of view.Adifference between the literature reviewed by Gardner and that reviewed by Carroll and Jensen is that the literature Gardner reviews was not intended to test his theory of intelligence or anything like it. In contrast, the literatures reviewed by Carroll and Jensen largely comprise studies designed specifically to test psychometric theories of intelligence.
Third, even if one accepts Gardner’s criteria for defining an intelligence, it is not clear whether the eight or ten intelligences proposed by Gardner are the only ones that would fit. For example, might there be a sexual intelligence? And are these intelligences really intelligences, per se, or are some of them better labeled talents? Obviously, the answer to this question is definitional, and hence there may be no ultimate answer at all.
Finally, there is a real need for psychometrically strong assessments of the various intelligences, because without such assessments it will be difficult ever to validate the theory. Assessments exist (Gardner, Feldman, & Krechevsky, 1998), but they seem not to be psychometrically strong. Without strong assessments, the theory is likely to survive without or because of the lack of serious attempts at disconfirmation.
Since the theory was first proposed, a large number of educational interventions have arisen that are based on the theory, sometimes closely and other times less so (Gardner, 1993). Many of the programs are unevaluated, and evaluations of other programs seem still to be ongoing, so it is difficult to say at this point what the results will be. In one particularly careful evaluation of a well-conceived program in a large southern city, there were no significant gains in student achievement or changes in student self-concept as a result of an intervention program based on Gardner’s (1983, 1999) theory (Callahan, Tomlinson, & Plucker, 1997). There is no way of knowing whether these results are representative of such intervention programs, however.
Successful Intelligence. Sternberg (1997, 1999c, 1999d) has suggested that we may wish to pay less attention to conventional notions of intelligence and more to what he terms successful intelligence, or the ability to adapt to, shape, and select environments to accomplish one’s goals and those of one’s society and culture. A successfully intelligent person balances adaptation, shaping, and selection, doing each as necessary. The theory is motivated in part by repeated findings that conventional tests of intelligence and related tests do not predict meaningful criteria of success as well as they predict scores on other similar tests and school grades (e.g., Sternberg & Williams, 1997).
Successful intelligence involves an individual’s discerning his or her pattern of strengths and weaknesses and then figuring out ways to capitalize on the strengths and at the same time compensate for or correct the weaknesses. People attain success, in part, in idiosyncratic ways that involve their finding how best to exploit their own patterns of strengths and weaknesses.
According to the proposed theory of human intelligence and its development (Sternberg, 1980, 1984, 1985, 1990, 1997, 1999a, 1999b), a common set of processes underlies all aspects of intelligence. These processes are hypothesized to be universal. For example, although the solutions to problems that are considered intelligent in one culture may be different from the solutions considered to be intelligent in another culture, the need to define problems and translate strategies to solve these problems exists in any culture.
Metacomponents, or executive processes, plan what to do, monitor things as they are being done, and evaluate things after they are done. Examples of metacomponents are recognizing the existence of a problem, defining the nature of the problem, deciding on a strategy for solving the problem, monitoring the solution of the problem, and evaluating the solution after the problem is solved.
Performance components execute the instructions of the metacomponents. For example, inference is used to decide how two stimuli are related, and application is used to apply what one has inferred (Sternberg, 1977). Other examples of performance components are comparison of stimuli, justification of a given response as adequate although not ideal, and actually making the response.
Knowledge-acquisition components are used to learn how to solve problems or simply to acquire declarative knowledge in the first place (Sternberg, 1985). Selective encoding is used to decide what information is relevant in the context of one’s learning. Selective comparison is used to bring old information to bear on new problems. Selective combination is used to put together the selectively encoded and compared information into a single and sometimes insightful solution to a problem.
Although the same processes are used for all three aspects of intelligence universally, these processes are applied to different kinds of tasks and situations depending on whether a given problem requires analytical thinking, creative thinking, practical thinking, or a combination of these kinds of thinking. Data supporting the theory cannot be presented fully here but are summarized elsewhere (Sternberg, 1977, 1985; Sternberg et al., 2000).
Three broad abilities are important to successful intelligence: analytical, creative, and practical abilities.
Analytical abilities are required to analyze and evaluate the options available to oneself in life. They include things such as identifying the existence of a problem, defining the nature of the problem, setting up a strategy for solving the problem, and monitoring one’s solution processes.
Creative abilities are required to generate problem-solving options in the first place. Creative individuals typically “buy low and sell high” in the world of ideas (Sternberg & Lubart, 1995, 1996): They are willing to generate ideas that, like stocks with low price-earnings ratios, are unpopular and perhaps even deprecated. Having convinced at least some people of the value of these ideas, they then sell high, meaning that they move on to the next unpopular idea. Research shows that these abilities are at least partially distinct from conventional IQ and that they are moderately domain specific, meaning that creativity in one domain (such as art) does not necessarily imply creativity in another (such as writing; Sternberg & Lubart, 1995). Not all creative work is crowd defying, of course. Some work is creative by virtue of extending existing paradigms (see Sternberg, 1999b).
Practical abilities are required to implement options and to make them work. Practical abilities are involved when intelligence is applied to real-world contexts. A key aspect of practical intelligence is the acquisition and use of tacit knowledge, which is knowledge of what one needs to know to succeed in a given environment that is not explicitly taught and that usually is not verbalized. Research shows several generalizations about tacit knowledge. First, it is acquired through mindful utilization of experience. What matters, however, is not the experience, per se, but how much one profits from it. Second, tacit knowledge is relatively domain specific, although people who are likely to acquire it in one domain are likely to acquire it in another domain. Third, acquisition and utilization are relatively independent of conventional abilities. Fourth, tacit knowledge predicts criteria of job success about as well as and sometimes better than does IQ. Fifth, tacit knowledge predicts these criteria incrementally over IQ and other kinds of measures, such as of personality and of styles of learning and thinking (McClelland, 1973; Sternberg et al., 2000; Sternberg & Wagner, 1993; Sternberg, Wagner, Williams, & Horvath, 1995).
The separation of practical intelligence from IQ has been shown in a number of different ways in a number of different studies (see Sternberg et al., 2000, for a review). Scribner (1984, 1986) showed that experienced assemblers in a milkprocessing plant used complex strategies for combining partially filled cases in a manner that minimized the number of moves required to complete an order. Although the assemblers were the least educated workers in the plant, they were able to calculate in their heads quantities expressed in different base number systems, and they routinely outperformed the more highly educated white-collar workers who substituted when the assemblers were absent. Scribner found that the order-filling performance of the assemblers was unrelated to measures of academic skills, including intelligence test scores, arithmetic test scores, and grades.
Ceci and Liker (1986) carried out a study of expert racetrack handicappers and found that expert handicappers used a highly complex algorithm for predicting post time odds that involved interactions among seven kinds of information. Use of a complex interaction term in their implicit equation was unrelated to the handicappers’ IQs.
Aseries of studies showed that shoppers in California grocery stores were able to choose which of several products represented the best buy for them (Lave, Murtaugh, & de la Roche, 1984; Murtaugh, 1985). They were able to do so even though they did very poorly on the same kinds of problems when the problems were presented in the form of a paper-and-pencil arithmetic computation test. The same principle that applies to adults appears to apply to children as well: Carraher, Carraher, and Schliemann (1985) found that Brazilian street children who could apply sophisticated mathematical strategies in their street vending were unable to do the same in a classroom setting (see also Ceci & Roazzi, 1994; Nuñes, 1994).
One more example of a study of practical intelligence was provided by individuals asked to play the role of city managers for the computer-simulated city of Lohhausen (Dörner & Kreuzig, 1983; Dörner, Kreuzig, Reither, & Staudel, 1983).A variety of problems were presented to these individuals, such as how best to raise revenue to build roads. The simulation involved more than one thousand variables. No relation was found between IQ and complexity of strategies used.
There is also evidence that practical intelligence can be taught (Gardner, Krechevsky, Sternberg, & Okagaki, 1994; Sternberg, Okagaki, & Jackson, 1990), at least in some degree. For example, middle-school children given a program for developing their practical intelligence for school (strategies for effective reading, writing, execution of homework, and taking of tests) improved more from pretest to posttest than did control students who received an alternative but irrelevant treatment.
None of these studies suggest that IQ is unimportant for school or job performance or other kinds of performance; indeed, the evidence suggests the contrary (Barrett & Depinet, 1991; Gottfredson, 1986, 1997; Hunt, 1995; Hunter & Hunter, 1984; Schmidt & Hunter, 1981, 1993, 1998; Wigdor & Garner, 1982). What the studies do suggest, however, is that there are other aspects of intelligence that are relatively independent of IQ, and that are important as well. A multiple abilities prediction model of school or job performance would probably be most satisfactory.
According to the theory of successful intelligence, children’s multiple abilities are underutilized in educational institutions because teaching tends to value analytical (as well as memory) abilities at the expense of creative and practical abilities. Sternberg, Ferrari, Clinkenbeard, and Grigorenko (1996; Sternberg, Grigorenko, Ferrari, & Clinkenbeard, 1999) designed an experiment in order to illustrate this point. They identified 199 high school students from around the United States who were strong in either analytical, creative, or practical abilities, or all three kinds of abilities, or none of the kinds of abilities. Students were then brought to Yale University to take a college-level psychology course that was taught in a way that emphasized either memory, analytical, creative, or practical abilities. Some students were matched, and others mismatched, to their own strengths. All students were evaluated for memory-based, analytical, creative, and practical achievements.
Sternberg and his colleagues found that students whose instruction matched their pattern of abilities performed significantly better than did students who were mismatched. They also found that prediction of course performance was improved by taking into account creative and practical as well as analytical abilities.
In subsequent studies (Grigorenko, Jarvin, & Sternberg, 2002; Sternberg, Torff, & Grigorenko, 1998), students were taught a subject matter in a variety of ways in order to compare instruction based on the theory of successful intelligence with other forms of instruction. For example, one set of studies compared such instruction with instruction based on critical thinking and instruction based on traditional, memory-based learning in social studies and science (Sternberg et al., 1998). Another study compared instruction based on successful intelligence to traditional instruction in reading (Grigorenko et al., 2002). Participants in these experiments ranged from middle-school to high-school levels and covered the range of socioeconomic levels from very low to very high. In general, instruction based on the theory of successful intelligence was superior to the other forms of instruction, even if tests of achievement measured only memory-based learning.
At a theoretical level, why should instruction based on the theory of successful intelligence be more effective than conventional or other forms of instruction? Five reasons have been proffered. First, instruction based on the theory of successful intelligence encourages students to capitalize on strengths.Second,itencouragesthemtocorrectortocompensate for weaknesses. Third, it enables them to encode material in three different ways, which, by increasing the number of retrieval routes to the information, facilitates memory retrieval later on. Fourth, it encourages elaborative rather than maintenance rehearsal, which results in more elaborated memory traces for the material. Fifth, it is more motivating to students because it typically renders the material more interesting than do conventional forms of presentation.
The theory of successful intelligence has been tested more extensively than many other contemporary theories of intelligence. Nevertheless, questions remain. For example, even some who might accept the existence of distinctive creative and practical abilities might argue that they represent psychological attributes distinct from intelligence. Second, the pervasiveness of the general factor in psychological investigations must make one wary of Type I errors in accepting the notion that the general factor is not truly general, but rather applies primarily to academic kinds of tasks. Third, there is as yet no published test that measures the triarchic abilities, and the research-based tests clearly need further development. Without published tests, it will be difficult for laboratories other than those of the principal proponents of the theory to test the theory adequately.
True Intelligence. Perkins (1995) proposed a theory of what he refers to as true intelligence, which he believes synthesizes classic views as well as new ones. According to Perkins, there are three basic aspects to intelligence: neural, experiential, and reflective.
Neural intelligence concerns what Perkins believes to be the fact that some people’s neurological systems function better than do the neurological systems of others, running faster and with more precision. He mentions “more finely tuned voltages” and “more exquisitely adapted chemical catalysts” as well as a “better pattern of connectivity in the labyrinth of neurons” (Perkins, 1995, p. 97), although it is not entirely clear what any of these phrases means. Perkins believes this aspect of intelligence to be largely genetically determined and unlearnable. This kind of intelligence seems to be somewhat similar to Cattell’s (1971) idea of fluid intelligence. The experiential aspect of intelligence is what has been learned from experience. It is the extent and organization of the knowledge base, and thus is similar to Cattell’s (1971) notion of crystallized intelligence. The reflective aspect of intelligence refers to the role of strategies in memory and problem solving and appears to be similar to the construct of metacognition or cognitive monitoring (Brown & DeLoache, 1978; Flavell, 1981).
There have been no published empirical tests of the theory of true intelligence, so it is difficult to evaluate the theory at this time. Like Gardner’s (1983) theory, Perkins’s theory is based on literature review, and as noted earlier, such literature reviews often tend to be selective and then interpreted in a way to maximize the theory’s fit to the available data.
The Bioecological Model of Intelligence. Ceci (1996) proposed a bioecological model of intelligence, according to which multiple cognitive potentials, context, and knowledge all are essential bases of individual differences in performance. Each of the multiple cognitive potentials enables relationships to be discovered, thoughts to be monitored, and knowledge to be acquired within a given domain. Although these potentials are biologically based, their development is closely linked to environmental context, and hence it is difficult if not impossible cleanly to separate biological from environmental contributions to intelligence. Moreover, abilities may express themselves very differently in different contexts. For example, children given essentially the same task in the context of a video game and in the context of a laboratory cognitive task performed much better when the task was presented in the context of the video game.
The bioecological model appears in many ways to be more a framework than a theory. At some level, the theory must be right. Certainly, both biological and ecological factors contribute to the development and manifestation of intelligence. Perhaps what the theory needs most at this time are specific and clearly falsifiable predictions that would set it apart from other theories.
Emotional Intelligence. Emotional intelligence is the ability to perceive accurately, appraise, and express emotion; the ability to access or generate feelings when they facilitate thought; the ability to understand emotion and emotional knowledge; and the ability to regulate emotions to promote emotional and intellectual growth (Mayer et al., 2000). The concept was introduced by Salovey and Mayer (Mayer & Salovey, 1993; Salovey & Mayer, 1990) and popularized and expanded by Goleman (1995).
There is some evidence—though still tentative—for the existence of emotional intelligence. For example, Mayer and Gehr (1996) found that emotional perception of characters in a variety of situations correlated with SAT scores, with empathy, and with emotional openness. Full convergentdiscriminant validation of the construct, however, appears to be needed. The results to date are mixed, with some studies supportive (Mayer, Salovey, & Caruso, 2000) and others not (Davies, Stankov, & Roberts, 1998).
The study of intelligence has come far in the century since Spearman (1904) published his seminal paper on general intelligence. Although there is no consensus as to what intelligence is or how to measure it, there are many viable alternatives. More research needs to distinguish among these alternatives rather than simply adducing evidence for any one of the alternatives.
Among the psychometric theories, Carroll’s (1993) has achieved fairly widespread acclaim, perhaps because it is based on a meta-analysis of so much empirical work. Because of its complexity, however, it is likely to have less influence on measurement than simpler theories, such as the theory of fluid and crystallized abilities (Cattell, 1971; Horn, 1994). History suggests that very complicated theories (e.g., Guilford, 1967, 1982; Guilford & Hoepfner, 1971; Guttman, 1954) tend not to have a long shelf life. In Guilford’s case, however, it is more a compliment to than a criticism of his theory, because the demise of Guilford’s theory is related to its falsifiability (Horn & Knapp, 1973), a property that not all modern theories have shown themselves to possess.
There are some questions that no existing theories of intelligence answer. Consider a few of these.
Challenges to Traditional Theories and Beliefs About Intelligence
Within recent years, several challenges from unexpected quarters have been proposed to theories and conceptions of intelligence. Two such challenges are the Flynn effect and dynamic testing.
The Flynn Effect. An empirical phenomenon challenges many theories of intelligence that view intelligence as some kind of fixed, largely genetically based trait. We know that the environment has powerful effects on cognitive abilities. Perhaps the simplest and most potent demonstration of this effect is what is called the Flynn effect (Flynn, 1984, 1987, 1994, 1998). The basic phenomenon is that IQ has increased over successive generations around the world through most of the century—at least since 1930. The effect must be environmental because a successive stream of genetic mutations obviously could not have taken hold and exerted such an effect over such a short period of time. The effect is powerful—about 15 points of IQ per generation for tests of fluid intelligence. And it occurs all over the world. The effect has been greater for tests of fluid intelligence than for tests of crystallized intelligence. The difference, if linearly extrapolated (a hazardous procedure, obviously), would suggest that a person who in 1892 fell at the 90th percentile on the Raven Progressive Matrices Test, a test of fluid intelligence, would, in 1992, score at the 5th percentile.
There have been many potential explanations of the Flynn effect, and in 1996 Ulric Neisser organized a conference at Emory University to try to explain the effect (Neisser, 1998). Some of the possible explanations include increased schooling, greater educational attainment of parents, better nutrition, and less childhood disease. A particularly interesting explanation is that of more and better parental attention to children (see Bronfenbrenner & Ceci, 1994). Whatever the answer, the Flynn effect suggests that we need to think carefully about the view that IQ is fixed. It probably is not fixed within individuals (Campbell & Ramey, 1994; Ramey, 1994), and it is certainly not fixed across generations.
Dynamic Assessment. In dynamic assessment, individuals learn at the time of test. If they answer an item correctly, they are given guided feedback to help them solve the item, either until they get it correct or until the examiner has run out of clues to give them.
The notion of dynamic testing appears to have originated with Vygotsky (1934/1962, 1978) and was developed independently by Feuerstein, Rand, Haywood, Hoffman, and Jensen (1985). Dynamic assessment is generally based on the notion that cognitive abilities are modifiable and that there is some zone of proximal development (Vygotsky, 1978), which represents the difference between actually developed ability and latent capacity. Dynamic assessments attempt to measure this zone of proximal development, or an analogue to it.
Dynamic assessment is cause for both celebration and caution (Grigorenko & Sternberg, 1998). On the one hand, it represents a break from conventional psychometric notions of a more or less fixed level of intelligence. On the other hand, it is more a promissory note than a realized success. The Feuerstein test, the Learning Potential Assessment Device (Feuerstein et al., 1985), is of clinical use but is not psychometrically normed or validated. There is only one formally normed test available in the United States (Swanson, 1995). This test yields scores for working memory before and at various points during and after training, as well as scores for amount of improvement with intervention, number of hints that have been given, and a subjective evaluation by the examiner of the examinee’s use of strategies. Other tests are perhaps on the horizon (Guthke & Stein, 1996), but their potential for standardization and validity, too, remains to be shown.
Intelligence as Typical Performance. Traditionally, intelligence has been thought of as something to be conceptualized and measured in terms of maximum performance. The tests of intelligence have been maximum-performance tests, requiring examinees to work as hard as they can to maximize their scores.Ackerman (1994;Ackerman & Heggestad, 1997; Goff & Ackerman, 1992) has recently argued that typicalperformance tests—which, like personality tests, do not require extensive intellectual effort—ought to supplement maximal-performance ones. On such tests individuals might be asked to what extent statements like “I prefer my life to be filled with puzzles I must solve” or “I enjoy work that requires conscientious, exacting skills” match their attitudes. A factor analysis of such tests yielded five factors: intellectual engagement, openness, conscientiousness, directed activity, and science-technology interest.
Ackerman’s data suggest a weak relationship between his measures of typical performance and more conventional measures of maximum performance. What is needed most at this time are incremental validity studies that show that this theory provides significant incremental validity with respect to real-world task performance over the validity provided by available measures of intelligence. Because our intelligence so often is used in typical performance settings (Sternberg et al., 1981), future theorists will need to cope with the challenge of typical performance, followingAckerman’s lead.
- Ackerman, P. (1994). Intelligence, attention, and learning: Maximal and typical performance. In D. K. Detterman (Ed.), Current topics in human intelligence: Theories of intelligence (Vol. 4, pp. 1–27). Norwood, NJ: Ablex.
- Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin, 121, 219–245.
- Azuma, H., & Kashiwagi, K. (1987). Descriptions for an intelligent person: A Japanese study. Japanese Psychological Research, 29, 17–26.
- Barrett, G. V., & Depinet, R. L. (1991). A reconsideration of testing for competence rather than for intelligence. American Psychologist, 46, 1012–1024.
- Binet, A., & Simon, T. (1916). The development of intelligence in children. Baltimore: Williams & Wilkins. (Originally published 1905)
- Boring, E. G. (1923, June 6). Intelligence as the tests test it. New Republic, 35–37.
- Bouchard, T. J., Jr. (1997). IQ similarity in twins reared apart: Findings and responses to critics. In R. J. Sternberg & E. L. Grigorenko (Eds.), Intelligence, heredity, and environment (pp. 126–160). New York: Cambridge University Press.
- Bouchard, T. J., Jr., Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990). Sources of human psychological differences: The Minnesota study of twins reared apart. Science, 250, 223–228.
- Brand, C. (1996). The g factor: General intelligence and its implications. Chichester, England: Wiley.
- Bronfenbrenner, U., & Ceci, S. J. (1994). Nature-nurture reconceptualized in developmental perspective: A bioecological model. Psychological Review, 101, 568–586.
- Brown, A. L., & DeLoache, J. S. (1978). Skills, plans, and selfregulation. In R. Siegler (Ed.), Children’s thinking: What develops? Hillsdale, NJ: Erlbaum.
- Burt, C. (1949). Alternative methods of factor analysis and their relations to Pearson’s method of “principal axis.” British Journal of Psychology, Statistical Section, 2, 98–121.
- Callahan, C. M., Tomlinson, C. A., & Plucker, J. (1997). Project START using a multiple intelligences model in identifying and promoting talent in high-risk students (Research Monograph 95136). Storrs: University of Connecticut, National Research Center on the Gifted and Talented.
- Campbell, F. A., & Ramey, C. T. (1994). Effects of early intervention on intellectual and academic achievement: A follow-up study of children from low-income families. Child Development, 65, 684–698.
- Carraher, T. N., Carraher, D., & Schliemann, A. D. (1985). Mathematics in the streets and in schools. British Journal of Developmental Psychology, 3, 21–29.
- Carroll, J. B. (1993). Human cognitive abilities: A survey of factoranalytic studies. New York: Cambridge University Press.
- Cattell, R. B. (1971). Abilities: Their structure, growth and action. Boston: Houghton Mifflin.
- Cattell, R. B., & Cattell,A. K. (1963). Test of g: Culture fair, scale 3. Champaign, IL: Institute for Personality and Ability Testing.
- Ceci, S. J. (1991). How much does schooling influence general intelligence and its cognitive components? Areassessment of the evidence. Developmental Psychology, 27, 703–722.
- Ceci, S. J. (1996). On intelligence . . . More or less. Cambridge, MA: Harvard University Press.
- Ceci, S. J., & Liker, J. (1986). Academic and nonacademic intelligence: An experimental separation. In R. J. Sternberg & R. K. Wagner (Eds.), Practical intelligence: Nature and origins of competence in the everyday world (pp. 119–142). New York: Cambridge University Press.
- Ceci, S. J., & Roazzi,A. (1994). The effects of context on cognition: Postcards from Brazil. In R. J. Sternberg & R. K. Wagner (Eds.), Mind in context: Interactionist perspectives on human intelligence (pp. 74–101). New York: Cambridge University Press.
- Ceci, S. J., & Williams, W. M. (1997). Schooling, intelligence, and income. American Psychologist, 52(10), 1051–1058.
- Chen, M. J. (1994). Chinese and Australian concepts of intelligence. Psychology and Developing Societies, 6, 101–117.
- Chen,M.J.,Braithwaite,V.,&Huang,J.T.(1982).Attributesofintelligent behaviour: Perceived relevance and difficulty byAustralian and Chinese students. Journal of Cross-Cultural Psychology, 13, 139–156.
- Chen, M. J., & Chen, H. C. (1988). Concepts of intelligence: Acomparison of Chinese graduates from Chinese and English schools in Hong Kong. International Journal of Psychology, 223, 471–487.
- Connolly, H., & Bruner, J. (1974). Competence: Its nature and nurture. In K. Connolly & J. Bruner (Eds.), The growth of competence. New York: Academic Press.
- Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11, 671–684.
- Cronbach, L. J. (1957). The two disciplines of scientific psychology. American Psychologist, 12, 671–684.
- Daniel, M. H. (2000). Interpretation of intelligence test scores. In R. J. Sternberg (Ed.), Handbook of intelligence. New York: Cambridge University Press.
- Das, J. P. (1994). Eastern views of intelligence. In R. J. Sternberg (Ed.), Encyclopedia of human intelligence (Vol. 1, p. 391). New York: Macmillan.
- Das, J. P., Kirby, J. R., & Jarman, R. F. (1979). Simultaneous and successive cognitive processes. New York: Academic Press.
- Dasen, P. (1984). The cross-cultural study of intelligence: Piaget and the Baoule. International Journal of Psychology, 19, 407–434.
- Davies, M., Stankov, L., & Roberts, R. D. (1998). Emotional intelligence: In search of an elusive construct. Journal of Personality & Social Psychology, 75, 989–1015.
- Deary, I. J. (2000). Simple information processing. In R. J. Sternberg (Ed.), Handbook of intelligence (pp. 267–284). New York: Cambridge University Press.
- Deary, I. J., & Stough, C. (1996). Intelligence and inspection time: Achievements, prospects, and problems. American Psychologist, 51, 599–608.
- Donchin, E., Ritter, W., & McCallum, W. C. (1979). Cognitive psychophysiology: The endogenous components of the ERP. In E. Callaway, P. Teuting, & S. H. Koslow (Eds.), Event-related potentials in man (pp. 349–441). San Diego:Academic Press.
- Dörner, D., & Kreuzig, H. (1983). Problemlosefahigkeit und intelligenz [Problem-solving ability and intelligence]. Psychologische Rundschaus, 34, 185–192.
- Dörner, D., Kreuzig, H., Reither, F., & Staudel, T. (1983). Lohhausen: Vom Umgang mit Unbestimmtheir und Komplexitat [Lonhauser: On handling uncertainty and complexity]. Bern: Huber.
- Durojaiye, M. O. A. (1993). Indigenous psychology in Africa. In U. Kim & J. W. Berry (Eds.), Indigenous psychologies: Research and experience in cultural context. Newbury Park, CA: Sage.
- Feuerstein, R., Rand, Y., Haywood, H. C., Hoffman, M., & Jensen, M. (1985). The learning potential assessment device (LPAD): Examiners’Manual.Jerusalem:Hadassah–Wizo–CanadaResearch
- Flavell, J. H. (1981). Cognitive monitoring. In W. P. Dickson (Ed.), Children’s oral communication skills (pp. 35–60). New York: Academic Press.
- Flynn, J. R. (1984). The mean IQ of Americans: Massive gains 1932 to 1978. Psychological Bulletin, 95, 29–51.
- Flynn, J. R. (1987). Massive IQ gains in 14 nations. Psychological Bulletin, 101, 171–191.
- Flynn, J. R. (1994). Giving g a fair chance: How to define intelligence, survive falsification, and resist behaviorism. Psychological Inquiry, 5, 204–208.
- Flynn, J. R. (1998). WAIS-III and WISC-III gains in the United States from 1972 to 1995: How to compensate for obsolete norms. Perceptual & Motor Skills, 86, 1231–1239.
- Fraser, S. (Ed.). (1995). The bell curve wars: Race, intelligence and the future of America. New York: Basic Books.
- Galton, F. (1883). Inquiry into human faculty and its development. London: Macmillan.
- Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York: Basic Books.
- Gardner, H. (1993). Multiple intelligences: The theory in practice. New York: Basic Books.
- Gardner, H. (1995). Leading minds. New York: Basic Books.
- Gardner, H. (1997). Extraordinary minds: Portraits of exceptional individuals and an examination of our extraordinariness. New York: Basic Books.
- Gardner, H. (1999). Intelligence reframed: Multiple intelligences for the 21st century. New York: Basic Books.
- Gardner, H., Feldman, D., & Krechevsky, M. (Eds.) (1998). Project Zero frameworks for early childhood education (3 vols.). New York: Teachers College Press.
- Gardner, H., Krechevsky, M., Sternberg, R. J., & Okagaki, L. (1994). Intelligence in context: Enhancing students’ practical intelligence for school. In K. McGilly (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 105–127). Cambridge, MA: MIT Press.
- Gill, R., & Keats, D. M. (1980). Elements of intellectual competence: Judgments by Australian and Malay university students. Journal of Cross-Cultural Psychology, 11, 233–243.
- Goff, M., & Ackerman, P. L. (1992). Personality-intelligence relations: Assessment of typical intellectual engagement. Journal of Educational Psychology, 84, 537–552.
- Goleman, D. (1995). Emotional intelligence. New York: Bantam Books.
- Goodnow, J. J. (1976). The nature of intelligent behavior: Questions raised by cross-cultural studies. In L. Resnick (Ed.), The nature of intelligence (pp. 169–188). Hillsdale, NJ: Erlbaum.
- Gottfredson, L. S. (Ed.). (1986). The g factor in employment [Special issue]. Journal of Vocational Behavior, 29, 293–450.
- Gottfredson, L. S. (1997). Why g matters: The complexity of everyday life. Intelligence, 24, 79–132.
- Gould, S. J. (1981). The mismeasure of man. New York: Norton.
- Gould, S. J. (1995). Curveball. In S. Fraser (Ed.), The bell curve wars (pp. 11–22). New York: Basic Books.
- Grigorenko, E. L., Geissler, P. W., Prince, R., Okatcha, F., Nokes, C., Kenny, D. A., Bundy, D. A., & Sternberg, R. J. (2001). The organization of Luo conceptions of intelligence: A study of implicit theories in a Kenyan village. International Journal of Behavioral Development, 25(4), 367–378.
- Grigorenko, E. L., Jarvin, L., & Sternberg, R. J. (2002). Schoolbased tests of the triarchic theory of intelligence: Three settings, three samples, three syllabi. Contemporary Educational Psychology, 27, 167–208.
- Grigorenko, E. L., & Sternberg, R. J. (1998). Dynamic testing. Psychological Bulletin, 124, 75–111.
- Guilford, J. P. (1967). The nature of human intelligence. New York: McGraw-Hill.
- Guilford, J. P. (1982). Is some creative thinking irrational? Journal of Creative Behavior, 16, 151–154.
- Guilford, J. P., & Hoepfner, R. (1971). The analysis of intelligence. New York: McGraw-Hill.
- Gustafsson, J. E. (1984). A unifying model for the structure of intellectual abilities. Intelligence, 8, 179–203.
- Gustafsson, J. E. (1988). Hierarchical models of the structure of cognitive abilities. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 4, pp. 35–71). Hillsdale, NJ: Erlbaum.
- Guthke, J., & Stein, H. (1996). Are learning tests the better version of intelligence tests? European Journal of Psychological Assessment, 12, 1–13.
- Guttman, L. (1954). A new approach to factor analysis: The radex. In P. F. Lazarsfeld (Ed.), Matermatical thinking in the social sciences (pp. 258–348). New York: Free Press.
- Haier, R. J., Nuechterlein, K. H., Hazlett, E., Wu, J. C., Pack, J., Browning, H. L., & Buchsbaum, M. S. (1988). Cortical glucose metabolic rate correlates of abstract reasoning and attention studiedwithpositronemissiontomography.Intelligence,12,199– 217.
- Haier, R. J., Siegel, B., Tang, C., Abel, L., & Buchsbaum, M. S. (1992). Intelligence and changes in regional cerebral glucose metabolic rate following learning. Intelligence, 16, 415–426.
- Halstead, W. C. (1951). Biological intelligence. Journal of Personality, 20, 118–130.
- Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley.
- Herrnstein, R., & Murray, C. (1994). The bell curve. New York: Free Press.
- Horn, J. L. (1994). Theory of fluid and crystallized intelligence. In R. J. Sternberg (Ed.), The encyclopedia of human intelligence (Vol. 1, pp. 443–451). New York: Macmillan.
- Horn, J. L., & Knapp, J. R. (1973). On the subjective character of the empirical base of Guilford’s structure-of-intellect model. Psychological Bulletin, 80, 33–43.
- Hunt, E. (1995). Will we be smart enough? A cognitive analysis of the coming workforce. New York: Russell Sage Foundation.
- Hunt, E., Frost, N., & Lunneborg, C. (1973). Individual differences in cognition: A new approach to intelligence. In G. Bower (Ed.), The psychology of learning and motivation (Vol. 7, pp. 87–122). New York: Academic Press.
- Hunt, E. B., Lunneborg, C., & Lewis, J. (1975). What does it mean to be high verbal? Cognitive Psychology, 7, 194–227.
- Hunter, J. E., & Hunter, R. F. (1984). Validity and utility of alternative predictors of job performance. Psychological Bulletin, 96, 72–98.
- “Intelligence and its measurement”: A symposium. (1921). Journal of Educational Psychology, 12, 123–147, 195–216, 271–275.
- Irvine,J.T. (1978). “Wolofmagicalthinking”:Culture andconservationrevisited.JournalofCross-CulturalPsychology,9,300–310.
- Jacoby, R., & Glauberman, N. (Eds.). (1995). The bell curve debate. New York: Times Books.
- Jensen, A. R. (1969). How much can we boost IQ and scholastic achievement? Harvard Educational Review, 39, 1–123.
- Jensen, A. R. (1970). Hierarchical theories of mental ability. In W. B. Dockrell (Ed.), On intelligence (pp. 119–190). Toronto, Ontario, Canada: Ontario Institute for Studies in Education.
- Jensen, A. R. (1997). The puzzle of nongenetic variance. In R. J. Sternberg & E. L. Grigorenko (Eds.), Intelligence, heredity, and environment (pp. 42–88). New York: Cambridge University Press.
- Jensen, A. R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger/Greenwoood.
- Jensen,A. R. (2002). Psychometric g: Definition and substantiation. In R. J. Sternberg & E. L. Grigorenko (Eds.), General factor of intelligence: Fact or fiction. Mahwah, NJ: Erlbaum.
- Johnson, R., Jr. (1986). A triarchic model of P300 amplitude. Psychophysiology, 23, 367–384.
- Johnson, R., Jr. (1988). The amplitude of the P300 component of the vent-relatedpotential:Reviewandsynthesis.InP.K.Ackles,J.R. Jennings, & M. G. H. Coles (Eds.), Advances in psychophysiology: A research manual (Vol. 3, pp. 69–138). Greenwich, CT: CAI Press.
- Kaufman, A. S., & Kaufman, N. L. (1983). Kaufman assessment battery for children: Interpretive manual. Circle Pines, MN: American Guidance Service.
- Kaufman, A. S., & Kaufman, N. L. (1993). Kaufman Adolescent and Adult Intelligence Test. Circle Pines, MN: American Guidance Service.
- Lave, J., Murtaugh, M., & de la Roche, O. (1984). The dialectic of arithmetic in grocery shopping. In B. Rogoff & J. Lace (Eds.), Everyday cognition: Its development in social context (pp. 67–94). Cambridge, MA: Harvard University Press.
- Lemann, N. (1999). The big test: The secret history of the American meritocracy. New York: Farrar, Straus, & Giroux.
- Loehlin, J. C. (1989). Partitioning environmental and genetic contributions to behavioral development. American Psychologist, 44, 1285–1292.
- Loehlin, J. C., Horn, J. M., & Willerman, L. (1997). Heredity, environment, and IQ in the Texas adoption project. In R. J. Sternberg & E. L. Grigorenko (Eds.), Intelligence, heredity, and environment (pp. 105–125). New York: Cambridge University Press.
- Lohman, D. F. (2000). Complex information processing and intelligence. In R. J. Sternberg (Ed.), Handbook of intelligence (pp. 285–340). New York: Cambridge University Press.
- Luria, A. R. (1973). The working brain. New York: Basic Books.
- Luria, A. R. (1980). Higher cortical functions in man (2nd ed., rev. & expanded). New York: Basic.
- Lutz, C. (1985). Ethnopsychology compared to what? Explaining behaviour and consciousness among the Ifaluk. In G. M. White & J. Kirkpatrick (Eds.), Person, self, and experience: Exploring Pacific ethnopsycholgoies (pp. 35–79). Berkeley: University of California Press.
- Mayer, J. D., & Gehr, G. (1996). Emotional intelligence and the identification of emotion. Intelligence, 22, 89–114.
- Mayer, J. D., & Salovey, P. (1993). The intelligence of emotional intelligence. Intelligence, 17, 433–442.
- Mayer, J. D., Salovey, P., & Caruso, D. (2000). Emotional intelligence. In R. J. Sternberg (Ed.), Handbook of intelligence (pp. 396–421). New York: Cambridge University Press.
- McClelland, D. C. (1973). Testing for competence rather than for “intelligence.” American Psychologist, 28, 1–14.
- Mundy-Castle, A. C. (1974). Social and technological intelligence in Western or Nonwestern cultures. Universitas, 4, 46–52.
- Murtaugh, M. (1985). The practice of arithmetic by American grocery shoppers. Anthropology and Education Quarterly, 16, 186–192.
- Naglieri, J. A., & Das, J. P. (1990). Planning, attention, simultaneous, and successive cognitive processes as a model for intelligence. Journal of Psychoeducational Assessment, 8, 303–337.
- Naglieri, J. A., & Das, J. P. (1997). Cognitive Assessment System. Itasca, IL: Riverside Publishing.
- Naglieri, J. A., & Das, J. P. (2002). Practical implications of general intelligence and PASS cognitive processes. In R. J. Sternberg & E. L. Grigorenko (Eds.), The general factor of intelligence: Fact or fiction. Mahwah, NJ: Erlbaum.
- Neisser, U. (Ed.). (1998). The rising curve. Washington, DC: American Psychological Association.
- Nettlebeck, T. (1982). Inspection time: An index for intelligence. Quarterly Journal of Experimental Psychology, 34, 299–312.
- Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
- Nuñes, T. (1994). Street intelligence. In R. J. Sternberg (Ed.), Encyclopedia of human intelligence (Vol. 2, pp. 1045–1049). New York: Macmillan.
- Ogbu, J. U. (1978). Minority education and caste: The American system in cross-cultural perspective. New York: Academic Press.
- Ogbu, J. U. (1991). Low school performance as an adaptation: The case of blacks in Stockton, CA. In M. A. Gibson & J. U. Ogbu (Eds.), Minority status and schooling: A comparative study of immigrant and involuntary minorities (pp. 249–286). New York: Garland.
- Ogbu,J.U.(1994).Fromculturaldifferencestodifferencesincultural frame of reference. In P. M. Greenfield & R. R. Cocking (Ed.), Cross-cultural roots of minority child development (pp. 365– 391). Hillsdale, NJ: Erlbaum.
- Ogbu, J. U., & Stern, P. (2001). Caste status and intellectual development. In R. S. Sternberg & E. L. Grigorenko (Eds.), Environmental effects on intellectual functioning (pp. 3– 37). Hillsdale, NJ: Erlbaum.
- Okagaki, L., & Sternberg, R. J. (1993). Parental beliefs and children’s school performance. Child Development, 64, 36–56.
- Pedersen, N. L., Plomin, R., Nesselroade, J. R., & McClearn, G. E. (1992). A quantitative genetic analysis of cognitive abilities during the second half of the life span. Psychological Science, 3, 346–353.
- Perkins, D. N. (1995). Outsmarting IQ: The emerging science of learnable intelligence. New York: Free Press.
- Petrill, S. A. (in press). The case for general intelligence: A behavioral genetic perspective. In R. J. Sternberg & E. L. Grigorenko (Eds.), General factor of intelligence: Fact or fiction. Mahwah, NJ: Erlbaum.
- Plomin, R. (1997). Identifying genes for cognitive abilities and disabilities. In R. J. Sternberg & E. L. Grigorenko (Eds.), Intelligence, heredity, and environment (pp. 89–104). New York: Cambridge University Press.
- Plomin, R., DeFries, J. C., McClearn, G. E., & Rutter, M. (1997). Behavioral genetics (3rd ed.). New York: W. H. Freeman.
- Plomin, R., McClearn, D. L., & Smith, D. L. (1994). DNA markers associated with high versus low IQ: The IQ QTL Project. Behavior Genetics, 24, 107–118.
- Plomin, R., McClearn, D. L., & Smith, D. L. (1995). Allelic associations between 100 DNA markers and high versus low IQ. Intelligence, 21, 31–48.
- Plomin, R., & Neiderhiser, J. M. (1992). Quantitative genetics, molecular genetics, and intelligence. Intelligence, 15, 369– 387.
- Plomin, R., & Petrill, S. A. (1997). Genetics and intelligence: What is new? Intelligence, 24, 53–78.
- Poole, F. J. P. (1985). Coming into social being: Cultural images of infants in Bimin-Kuskusmin folk psychology. In G. M. White & J. Kirkpatrick (Eds.), Person, self, and experience: Exploring Pacific ethnopsychologies (pp. 183–244). Berkeley: University of California Press.
- Posner, M. I., & Mitchell, R. F. (1967). Chronometric analysis of classification. Psychological Review, 74, 392–409.
- Putnam, D. B., & Kilbride, P. L. (1980). A relativistic understanding of social intelligence among the Songhay of Mali and Smaia of Kenya. Paper presented at the meeting of the Society for CrossCultural Research, Philadelphia, PA.
- Ramey, C. T. (1994). Abecedarian Project. In R. J. Sternberg (Ed.), Encyclopedia of human intelligence (Vol. 1, pp. 1–2). New York: Macmillan.
- Reed, T. E., & Jensen, A. R. (1992). Conduction velocity in a brain nerve pathway of normal adults correlates with intelligence level. Intelligence, 16, 259–272.
- Ruzgis, P. M., & Grigorenko, E. L. (1994). Cultural meaning systems, intelligence and personality. In R. J. Sternberg & P. Ruzgis (Eds.), Personality and intelligence (pp. 248–270). New York: Cambridge.
- Sacks, P. (1999). Standardized minds: The high price of America’s testing culture and what we can do to change it. Cambridge, MA: Perseus Books.
- Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition, and Personality, 9, 185–211.
- Scarr, S. (1997). Behavior-genetic and socialization theories of intelligence: Truce and reconciliation. In R. J. Sternberg & E. L. Grigorenko (Eds.), Intelligence, heredity and environment (pp. 3–41). New York: Cambridge University Press.
- Schmidt, F. L., & Hunter, J. E. (1981). Employment testing: Old theories and new research findings. American Psychologist, 36, 1128–1137.
- Schmidt, F. L., & Hunter, J. E. (1993). Tacit knowledge, practical intelligence, general mental ability, and job knowledge. Current Directions in Psychological Science, 1, 8–9.
- Schmidt, F., & Hunter, J. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124, 262–274.
- Scribner, S. (1984). Studying working intelligence. In B. Rogoff & J. Lave (Eds.), Everyday cognition: Its development in social context (pp. 9–40). Cambridge, MA: Harvard University Press.
- Scribner, S. (1986).Thinking in action: Some characteristics of practical thought. In R. J. Sternberg & R. K. Wagner (Eds.), Practical intelligence: Nature and origins of competence in the everyday world (pp. 13–30). New York: Cambridge University Press.
- Serpell, R. (1974). Aspects of intelligence in a developing country. African Social Research, 17, 576–596.
- Serpell, R. (1982). Measures of perception, skills, and intelligence. In W. W. Hartup (Ed.), Review of child development research (Vol. 6, pp. 392–440). Chicago: University of Chicago Press.
- Serpell, R. (1993). The significance of schooling: Life journeys in an African society. New York: Cambridge University Press.
- Spearman, C. (1904). “General intelligence,” objectively determined and measured. American Journal of Psychology, 15(2), 201–293.
- Spearman, C. (1923). The nature of “intelligence” and the principles of cognition (2nd ed.). London: Macmillan.
- Spearman, C. (1927). The abilities of man. London: Macmillan.
- Srivastava, A. K., & Misra, G. (1996). Changing perspectives on understanding intelligence: An appraisal. Indian Psychological Abstracts and Review, 3, 1–34.
- Stenhouse, D. (1973). The evolution of intelligence: A general theory and some of its implications. New York: Harper & Row.
- Sternberg, R. J. (1977). Intelligence, information processing, and analogical reasoning: The componential analysis of human abilities. Hillsdale, NJ: Erlbaum.
- Sternberg, R. J. (1980). Representation and process in linear syllogistic reasoning. Journal of Experimental Psychology: General, 109, 119–159.
- Sternberg, R. J. (1983). Components of human intelligence. Cognition, 15, 1–48.
- Sternberg, R. J. (1984). Toward a triarchic theory of human intelligence. Behavioral and Brain Sciences, 7, 269–287.
- Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. New York: Cambridge University Press.
- Sternberg, R. J. (1990). Metaphors of mind: Conceptions of the nature of intelligence. New York: Cambridge University Press.
- Sternberg, R. J. (1995). For whom the Bell Curve tolls: A review of The Bell Curve. Psychological Science, 6, 257–261.
- Sternberg, R. J. (1997). Successful intelligence. New York: Plume.
- Sternberg, R. J. (1999a). Human intelligence: A case study of how more and more research can lead us to know less and less about a psychological phenomenon, until finally we know much less than we did before we started doing research. In E. Tulving (Ed.), Memory, consciousness, and the brain: The Tallinn Conference (pp. 363–373). Philadelphia: Psychology Press.
- Sternberg, R. J. (1999b). A propulsion model of types of creative contributions. Review of General Psychology, 3, 83–100.
- Sternberg, R. J. (1999c). Successful intelligence: Finding a balance. Trends in Cognitive Sciences, 3, 436–442.
- Sternberg, R. J. (1999d). The theory of successful intelligence. Review of General Psychology, 3, 292–316.
- Sternberg, R. J., Conway, B. E., Ketron, J. L., & Bernstein, M. (1981). People’s conceptions of intelligence. Journal of Personality and Social Psychology, 41, 37–55.
- Sternberg, R. J., & Detterman, D. K. (1986). What is intelligence? Norwood, NJ: Ablex Publishing.
- Sternberg, R. J., Ferrari, M., Clinkenbeard, P. R., & Grigorenko, E. L. (1996). Identification, instruction, and assessment of gifted children: A construct validation of a triarchic model. Gifted Child Quarterly, 40, 129–137.
- Sternberg, R. J., Forsythe, G. B., Hedlund, J., Horvath, J., Snook, S., Williams, W. M., Wagner, R. K., & Grigorenko, E. L. (2000). Practical intelligence. New York: Cambridge University Press.
- Sternberg, R. J., & Gardner, M. K. (1983). Unities in inductive reasoning. Journal of Experimental Psychology: General, 112, 80–116.
- Sternberg, R. J., & Grigorenko, E. L. (Eds.). (1997). Intelligence, heredity,andenvironment.NewYork:CambridgeUniversityPress.
- Sternberg, R. J., Grigorenko, E. L., Ferrari, M., & Clinkenbeard, P. (1999). A triarchic analysis of an aptitude-treatment interaction. European Journal of Psychological Assessment, 15, 1–11.
- Sternberg, R. J., & Lubart, T. I. (1995). Defying the crowd: Cultivating creativity in a culture of conformity. New York: Free Press.
- Sternberg, R. J., & Lubart, T. I. (1996). Investing in creativity. American Psychologist, 51, 677–688.
- Sternberg, R. J., Okagaki, L., & Jackson, A. (1990). Practical intelligence for success in school. Educational Leadership, 48, 35–39.
- Sternberg, R. J., Torff, B., & Grigorenko, E. L. (1998). Teaching triarchically improves school achievement. Journal of Educational Psychology, 90, 1–11.
- Sternberg, R. J., & Wagner, R. K. (1993). The g-ocentric view of intelligence and job performance is wrong. Current Directions in Psychological Science, 2, 1–4.
- Sternberg, R. J., Wagner, R. K., Williams, W. M., & Horvath, J. A. (1995). Testing common sense. American Psychologist, 50, 912– 927.
- Sternberg, R. J., & Williams, W. M. (1997). Does the Graduate Record Examination predict meaningful success in the graduate training of psychologists? A case study. American Psychologist, 52, 630–641.
- Super, C. M. (1983). Cultural variation in the meaning and uses of children’s intelligence. In J. B. Deregowski, S. Dziurawiec, & R. C. Annis (Eds.), Expiscations in cross-cultural psychology (pp. 199–212). Lisse, the Netherlands: Swets and Zeitlinger.
- Super, C. M., & Harkness, S. (1982). The development of affect in infancy and early childhood. In D. Wagnet & H. Stevenson (Eds.), Cultural perspectives on child development (pp. 1–19). San Francisco: W. H. Freeman.
- Swanson, H. L. (1995). Effects of dynamic testing on the classification of learning disabilities: The predictive and discriminant validity of the Swanson Cognitive Processing Test. Journal of Psychoeducational Assessment, 1, 204–229.
- Thorndike, R. L., Hagen, E., & Sattler, J. (1985). Stanford-Binet Intelligence Scale. Boston: Houghton-Mifflin.
- Thurstone, L. L. (1924). The nature of intelligence. New York: Harcourt Brace.
- Thurstone, L. L. (1938). Primary mental abilities. Chicago: University of Chicago Press.
- Vernon, P. A., & Mori, M. (1992). Intelligence, reaction times, and peripheral nerve conduction velocity. Intelligence, 8, 273–288.
- Vernon, P. E. (1971). The structure of human abilities. London: Methuen.
- Vernon, P. A., Wickett, J. C., Bazana, P. G., & Stelmack, R. M. (2000). The neuropsychology and psycholophysiology of human intelligence In R. J. Sternberg (Ed.), Handbook of intelligence (pp. 245–264). New York: Cambridge University Press.
- Vygotsky, L. S. (1962). Thought and language. Cambridge, MA: MIT Press. (Original work published 1934)
- Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.
- Wahlsten, D., & Gottlieb, G. (1997). The invalid separation of effects of nature and nurture: Lessons from animal experimentation. In R. J. Sternberg & E. L. Grigorenko (Eds.), Intelligence, heredity, and environment (pp. 163–192). New York: Cambridge University Press.
- Wechsler, D. (1980). Wechsler Preschool and Primary Scale of Intelligence– San Antonio, TX: The Psychological Corporation.
- Wechsler, D. (1991). Manual for the Wechsler Intelligence Scales for Children (3rd ed.). San Antonio, TX: Psychological Corporation.
- White, G. M. (1985). Premises and purposes in a Solomon Islands ethnopsychology. In G. M. White & J. Kirkpatrick (Eds.), Person, self, and experience: Exploring Pacific ethnopsychologies (pp. 328–366). Berkeley: University of California Press.
- Wickett, J. C., & Vernon, P. A. (1994). Peripheral nerve conduction velocity, reaction time, and intelligence: An attempt to replicate Vernon and Mori. Intelligence, 18, 127–132.
- Wigdor, A. K., & Garner, W. R. (Eds.). (1982). Ability testing: Uses, consequences, and controversies. Washington, DC: National Academy Press.
- Willerman, L., Schultz, R., Rutledge, J. N., & Bigler, E. D. (1991). In vivo brain size and intelligence. Intelligence, 15, 223– 228.
- Willerman, L., Schultz, R., Rutledge, J. N., & Bigler, E. D. (1992). Hemisphere size asymmetry predicts relative verbal and nonverbal intelligence differently in the sexes: An MRI study of structure function relations. Intelligence, 16, 315–328.
- Woodcock, R. W., & Johnson, M. B. (1989). Woodcock-Johnson Tests of Cognitive Ability–Revised. Itasca, IL: Riverside.
- Yang, S., & Sternberg, R. J. (1997a). Conceptions of intelligence in ancient Chinese philosophy. Journal of Theoretical and Philosophical Psychology, 17(2), 101–119.
- Yang, S., & Sternberg, R. J. (1997b). Taiwanese Chinese people’s conceptions of intelligence. Intelligence, 25(1), 21–36.
- Zenderland, L. (1998). Measuring minds: Henry Goddard and the origins of American intelligence testing. New York: Cambridge University Press.