Remember Christopher, the boy who could learn languages but not tic-tac-toe? Would you call him intelligent? It seems odd to say that someone is intelligent when he can’t master a child’s game, but it seems equally odd to say that someone is unintelligent when he can master 16 languages. In a world of Albert Einsteins and Homer Simpsons, we’d have no trouble distinguishing the geniuses from the dullards. But ours is a world of people like Christopher and people like us—people who are sometimes brilliant, often bright, usually competent, and occasionally dimmer than broccoli. Psychologists generally define intelligence as the ability to direct one’s thinking, adapt to one’s circumstances, and learn from one’s experiences (Gottfredson, 1997), and as you will see, that definition captures much of what scientists and lay-people mean by that term.

Few things are more dangerous than a man with a mission. In the 1920s, psychologist Henry Goddard administered intelligence tests to arriving immigrants at Ellis Island and concluded that the overwhelming majority of Jews, Hungarians, Italians, and Russians were “feebleminded.” Goddard also used his tests to identify feebleminded American families (whom, he claimed, were largely responsible for the nation’s social problems) and suggested that the government should segregate them in isolated colonies and “take away from these people the power of procreation” (Goddard, 1913, p. 107). The United States subsequently passed laws restricting the immigration of people from southern and eastern Europe, and 27 states passed laws requiring the sterilization of “mental defectives.”

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From Goddard’s day to our own, intelligence tests have been used to rationalize prejudice and discrimination against people of different races, religions, and nationalities. This is especially ironic because such tests were originally developed for the most noble of purposes: to help underprivileged children succeed in school. At the end of the 19th century, France instituted a sweeping set of education reforms that made a primary school education available to children of every social class, and suddenly French classrooms were filled with a diverse mix of children who differed dramatically in their readiness to learn. The French government called on Alfred Binet and Theodore Simon to create a test that would allow educators to develop remedial programs for those children who lagged behind their peers (Siegler, 1992). “Before these children could be educated,” Binet (1909) wrote, “they had to be selected. How could this be done?”

Binet and Simon set out to develop an objective test that would provide an unbiased measure of a child’s ability. They began, sensibly enough, by looking for tasks that the best students in a class could perform and that the worst students could not—in other words, tasks that could distinguish the best and worst students and thus predict a future child’s success in school. The tasks they tried included solving logic problems, remembering words, copying pictures, distinguishing edible and inedible foods, making rhymes, and answering questions such as, “When anyone has offended you and asks you to excuse him, what ought you to do?” Binet and Simon settled on 30 of these tasks and assembled them into a test that they claimed could measure a child’s “natural intelligence,” meaning the aptitude for learning independent of the child’s prior educational achievement. They suggested that teachers could use their test to estimate a particular child’s “mental level” simply by computing the average test score of many children in different age groups and then finding the age group whose average test score was most like that of the particular child’s. For example, a child who was 10 years old but whose score was about the same as the score of the average 8-year-old was considered to have the mental level of an 8-year-old and thus to need remedial education.

This simple idea became the basis for what is now known as the intelligence quotient or IQ score. There are two ways to compute an IQ score. One is the ratio IQ, which is a statistic obtained by dividing a person’s mental age by the person’s physical age and then multiplying the quotient by 100. According to this formula, a 10-year-old child whose test score was about the same as the average 10-year-old child’s test score would have a ratio IQ of 100 because (10/10) × 100 = 100. But a 10-year-old child whose test score was about the same as the average 8-year-old child’s test score would have a ratio IQ of 80 because (8/10) × 100 = 80. Ratio IQ is a fine measure for children, but it doesn’t work so well for adults. After all, there’s nothing wrong with a 60-year-old who has the mental level of a 30-year-old, is there? That’s why adult intelligence is usually measured using the deviation IQ, which is a statistic obtained by dividing a person’s test score by the average test score of people in the same age group and then multiplying the quotient by 100. According to this formula, a person who scored the same as the average person his or her age would have a deviation IQ of 100.

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The most widely used modern intelligence test is the Wechsler Adult Intelligence Scale (WAIS), named after its originator, psychologist David Wechsler. Like Binet and Simon’s original test, it measures intelligence by asking respondents to solve problems, to articulate the meaning of words, to recall general knowledge, to explain practical actions in everyday life, and so forth. Some sample problems from the WAIS are shown in TABLE 9.2. Decades of research show that a person’s performance on tests like the WAIS predict a wide variety of important life outcomes, including health, educational level, and income (Deary, Batty, & Gale, 2008; Deary et al., 2008; Der, Batty, & Deary, 2009; Gottfredson & Deary, 2004; Leon et al., 2009; Richards et al., 2009; Rushton & Templer, 2009; Whalley & Deary, 2001). One study compared siblings who had significantly different IQs and found that the less intelligent sibling earned roughly half of what the more intelligent sibling earned over the course of their lifetimes (Murray, 2002; see FIGURE 9.11). Perhaps that’s because intelligent people perform better at their jobs (Hunter & Hunter, 1984; see the Real World box).

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During the 1990s, Michael Jordan won the National Basketball Association’s Most Valuable Player award five times, led the Chicago Bulls to six league championships, and had the highest regular season scoring average in the history of the game. ESPN named him the greatest athlete of the century. So when Jordan quit professional basketball in 1993 to join professional baseball, he was as surprised as anyone else to find that he … well, sucked. One of his teammates lamented that Jordan “couldn’t hit a curveball with an ironing board,” and a major-league manager called him “a disgrace to the game” (Wulf, 1994).

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Michael Jordan’s brilliance on the basketball court and his mediocrity on the baseball field proved beyond all doubt that these two sports require different abilities that are not necessarily possessed by the same individual. But if basketball and baseball require different abilities, then what does it mean to say that someone is the greatest athlete of the century? Is athleticism a meaningless word? The science of intelligence has grappled with a similar question for more than a century. As we have seen, intelligence test scores predict important outcomes—from academic success to health. But is that because they measure a real ability that allows people to do well at everything?

To investigate this question, Charles Spearman (a student of Wilhelm Wundt, whom you met in the Psychology: Evolution of a Science chapter), measured how well school-age children could discriminate small differences in color, auditory pitch, and weight, and he then correlated these scores with the children’s grades in different academic subjects (Spearman, 1904). Spearman’s research revealed that these measures were positively correlated: that is, children who scored well on one measure (e.g., distinguishing the musical note C# from D) tended to score well on the other measures (e.g., solving algebraic equations). But, although these different measures were positively correlated, they were not perfectly correlated: that is, the child who had the best score on one measure didn’t necessarily have the best score on every measure. Spearman combined these two facts into a two-factor theory of intelligence, which suggested that every task requires a combination of a general ability (which he called g) and skills that are specific to the task (which he called s).

As sensible as Spearman’s conclusions were, not everyone agreed with them. Louis Thurstone (1938) noticed that although childrens’ scores on different tests were indeed positively correlated with one another, scores on one kind of verbal test were more highly correlated with scores on another verbal test than they were with scores on other kinds of tests. Thurstone took this “clustering of correlations” to mean that there was actually no such thing as g and that there were, instead, a few stable and independent mental abilities such as perceptual ability, verbal ability, and numerical ability, which he called the primary mental abilities. In essence, Thurstone argued that just as we have games called baseball and basketball but no game called athletics, so we have abilities such as verbal ability and perceptual ability but no general ability called intelligence. TABLE 9.3 shows the primary mental abilities that Thurstone identified.

The debate among Spearman, Thurstone, and other intelligence researchers raged for half a century as psychologists argued about the existence of g. But in the 1980s, new mathematical techniques brought the debate to a quiet close by revealing that Spearman and Thurstone had each been right in his own way. We now know that most intelligence test data are best described by a three-level hierarchy (see FIGURE 9.12). At the top is a general factor (like Spearman’s g) called intelligence, which is made up of a small set of middle-level factors (like Thurstone’s primary mental abilities), which in turn are made up of a large set of specific abilities that are unique to particular tasks (like Spearman’s s). Although this resolution to 100 years of disagreement is not particularly thrilling, it appears to have the compensatory benefit of being true.

So what are these middle level abilities, and how many are there? Psychologist John Carroll (1993) analyzed intelligence test scores from nearly 500 studies conducted over a half century, and he concluded that there are eight independent middle-level abilities: memory and learning, visual perception, auditory perception, retrieval ability, cognitive speediness, processing speed, crystallized intelligence, and fluid intelligence. Although most of the abilities on this list are self-explanatory, the last two are not. Fluid intelligence is the ability to see abstract relationships and draw logical inferences; crystallized intelligence is the ability to retain and use knowledge that was acquired through experience (Horn & Cattell, 1966). Whereas crystallized intelligence is generally assessed by tests of vocabulary, factual information, and so on, fluid intelligence is generally assessed by tests that pose novel, abstract problems that must be solved under time pressure, such as Raven’s Progressive Matrices Test (shown in FIGURE 9.13).

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So was that the end of the debate? Not exactly, because some psychologists have argued that there are kinds of intelligence that traditional tests simply do not measure. For example, Robert Sternberg (1999) distinguishes between analytic intelligence (which is the ability to identify and define problems and to find strategies for solving them), practical intelligence (which is the ability to apply and implement these solutions in everyday settings), and creative intelligence (which is the ability to generate solutions that other people do not). According to Sternberg, standard intelligence tests measure analytic intelligence by giving people clearly defined problems that have one right answer. But everyday life confronts people with situations in which they must formulate the problem, find the information needed to solve it, and then choose among multiple acceptable solutions. These situations require practical and creative intelligence.

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Another kind of intelligence that standard tests don’t measure is emotional intelligence, which is the ability to reason about emotions and to use emotions to enhance reasoning (Mayer, Roberts, & Barsade, 2008; Salovey & Grewal, 2005). Emotionally intelligent people know what kinds of emotions a particular event will trigger; they can identify, describe, and manage their emotions; and they can identify other people’s emotions from facial expressions and tones of voice. Emotionally intelligent people have better social skills and more friends (Eisenberg et al., 2000; Mestre et al., 2006; Schultz, Izard, & Bear, 2004), they are judged to be more competent in their interactions (Brackett et al., 2006), and they have better romantic relationships (Brackett, Warner, & Bosco, 2005). Given all this, it isn’t surprising that emotionally intelligent people tend to be happier (Brackett & Mayer, 2003; Brackett et al., 2006) and more satisfied with their lives (Ciarrochi, Chan, & Caputi, 2000; Mayer, Caruso, & Salovey, 1999).

Not only are there different kinds of intelligence, but the concept itself seems to differ across cultures. For instance, Westerners regard people as intelligent when they speak quickly and often, but Africans regard people as intelligent when they are deliberate and quiet (Irvine, 1978). The Confucian tradition emphasizes the ability to behave properly, the Taoist tradition emphasizes humility and self-knowledge, and the Buddhist tradition emphasizes determination and mental effort (Yang & Sternberg, 1997). Unlike Western societies, many African and Asian societies conceive of intelligence as including social responsibility and cooperativeness (Azuma & Kashiwagi, 1987; Serpell, 1974; White & Kirkpatrick, 1985), which is why the Mashona word for intelligence, ngware, means “to be wise in one’s relationships.” Definitions of intelligence may even differ within a culture: Californians of Latino ancestry are more likely to equate intelligence with social competence, whereas Californians of Asian ancestry are more likely to equate it with cognitive skill (Okagaki & Sternberg, 1993). Some researchers take all this to mean that different cultures have radically different conceptualizations of intelligence, but others are convinced that apparent differences in the conceptualization of intelligence are really just differences in ways of talking about it. They argue that every culture values the ability to solve important problems and that what really distinguishes cultures is the kinds of problems that are considered to be important.

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