Researchers are exploring some age-old questions about human intelligence. You know it: You are smarter than some people and not as smart as others. So what in that heart of smarts—the brain—creates this difference? Is it our brain’s relative size? (Was Einstein big-brained?) Our amounts of certain brain tissue? Our brain networks’ speed? These are among the possibilities that researchers have identified.

Here we will focus on two other questions: How stable is intelligence over the life span? (Will the precocious 5-year-old likely mature into a talented collegian and a brilliant senior citizen?) And what are the traits and talents of those at the low and high extremes of intelligence?

10-7 How stable are intelligence scores over the life span, and how does aging affect crystallized and fluid intelligence?

If we retested people periodically throughout their lives, would their intelligence scores be stable? Let’s first explore the stability of mental abilities in later life.

What happens to our intellectual muscles as we age? Do they gradually decline, as does our body strength? Or do they remain constant? The quest for answers to these questions illustrates psychology’s self-correcting process. This research developed in phases.

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Phase I: Cross-Sectional Evidence for Intellectual Decline In cross-sectional studies, researchers at one point in time test and compare people of different ages. In such studies, older adults give fewer correct answers on intelligence tests than do younger adults. WAIS-creator David Wechsler (1972) therefore concluded that “the decline of mental ability with age is part of the general [aging] process of the organism as a whole.”

For a long time, this rather dismal view went unchallenged. Many corporations established mandatory retirement policies, assuming the companies would benefit by replacing aging workers with younger, more capable, employees. As “everyone knows,” you can’t teach an old dog new tricks.

Phase II: Longitudinal Evidence for Intellectual Stability After colleges in the 1920s began giving intelligence tests to entering students, several psychologists saw their chance to study intelligence longitudinally. They retested the same cohort—the same group of people—over a period of years (Schaie & Geiwitz, 1982). What they found was a surprise: Until late in life, intelligence remained stable (FIGURE 10.7). On some tests, it even increased.

How then are we to account for the cross-sectional findings? In retrospect, researchers saw the problem. When older cross-sectional studies compared 70-year-olds and 30-year-olds, they compared people not only of two different ages but of two different eras. It compared generally less-educated people (born, say, in the early 1900s) with better-educated people (born after 1950), people raised in large families with people raised in smaller families, people growing up in less affluent families with people raised in more affluent families.

With the more optimistic results from longitudinal studies, the myth that intelligence sharply declines with age was laid to rest. Famed painter Anna Mary Robertson Moses (“Grandma Moses”) took up painting in her seventies, and at age 88 a popular magazine named her “Young Woman of the Year.” At age 89, architect Frank Lloyd Wright designed New York City’s Guggenheim Museum. As “everyone knows,” given good health, you’re never too old to learn.

Phase III: It All Depends With “everyone knowing” two different and opposing facts about age and intelligence, something was wrong. As it turns out, longitudinal studies have their own pitfalls. Those who survive to the end of such studies may be bright, healthy people whose intelligence is least likely to decline. (Perhaps people who died younger and were removed from the study had declining intelligence.) Adjusting for the loss of participants, as did a study following more than 2000 people over age 75 in Cambridge, England, revealed a steeper intelligence decline, especially after 85 (Brayne et al., 1999).

Research is further complicated by the finding that intelligence is not a single trait, but rather several distinct abilities. Intelligence tests that assess speed of thinking may place older adults at a disadvantage because of their slower neural processing. Meeting old friends on the street, names rise to the mind’s surface more slowly—“like air bubbles in molasses,” said David Lykken (1999). But slower processing need not mean less intelligence. In four studies in which players were given 15 minutes to complete New York Times crossword puzzles, the highest average performance was achieved by adults in their fifties, sixties, and seventies (Salthouse, 2004). “Wisdom” tests—which assess “expert knowledge about life in general and good judgment and advice about how to conduct oneself in the face of complex, uncertain circumstances”—also suggested that older adults more than hold their own (Baltes et al., 1993, 1994, 1999).

So the answers to our age-and-intelligence questions depend on what we assess and how we assess it. Crystallized intelligence—our accumulated knowledge as reflected in vocabulary and analogies tests—increases up to old age. Fluid intelligence—our ability to reason speedily and abstractly, as when solving novel logic problems—decreases beginning in the twenties and thirties, slowly up to age 75 or so, then more rapidly, especially after age 85 (Cattell, 1963; Horn, 1982; Salthouse, 2009, 2013). With age we lose and we win. We lose recall memory and processing speed, but we gain vocabulary knowledge (FIGURE 10.8). Our decisions also become less distorted by negative emotions such as anxiety, depression, and anger (Blanchard-Fields, 2007; Carstensen & Mikels, 2005). And despite their lesser fluid intelligence, older people also display greater wisdom in seeing multiple perspectives, allowing for compromise, and recognizing the limits of what they know (Grossman et al., 2010, 2012).

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Age-related cognitive differences help explain why older adults are less likely to embrace new technologies (Charness & Boot, 2009). These cognitive differences also help explain why mathematicians and scientists produce much of their most creative work during their thirties, when fluid intelligence is at its peak (Jones et al., 2014). In contrast, authors, historians, and philosophers tend to produce their best work in their forties, fifties, and beyond—after accumulating more knowledge (Simonton, 1988, 1990). Poets, for example, who depend on fluid intelligence, reach their peak output earlier than prose authors, who need the deeper knowledge reservoir that accumulates with age. This finding holds in every major literary tradition, for both living and dead languages.

Now what about the stability of intelligence scores early in life? Infants’ and toddlers’ attention, processing speed, and learning give some clue to their intelligence score in later childhood and early adulthood (Fagan, 2011; Rose et al., 2012). For extremely impaired or very precocious children, those early indications can be very predictive. Yet for most children, casual observation and intelligence tests before age 3 only modestly predict their future aptitudes (Humphreys & Davey, 1988; Tasbihsazan et al., 2003). Even Albert Einstein was once thought “slow”—as he was in learning to talk (Quasha, 1980).

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By age 4, however, children’s performance on intelligence tests begins to predict their adolescent and adult scores. The consistency of scores over time increases with the age of the child. The remarkable stability of aptitude scores by late adolescence is seen in a U.S. Educational Testing Service study of 23,000 students who took the SAT and then later took the GRE (Angoff, 1988). On either test, verbal scores correlated only modestly with math scores—revealing that these two aptitudes are distinct. Yet scores on the SAT verbal test correlated +.86 with the scores on the GRE verbal tests taken four to five years later. An equally astonishing +.86 correlation occurred between the two math tests. Given the time lapse and differing educational experiences of these 23,000 students, the stability of their aptitude scores is remarkable.

Ian Deary and his colleagues (2004, 2009, 2013) set the record for long-term follow-up. Their amazing longitudinal studies have been enabled by their country, Scotland, doing something that no nation has done before or since. On June 1, 1932, essentially every child in the country born in 1921—87,498 children around age 11—took an intelligence test. The aim was to identify working-class children who would benefit from further education. Sixty-five years later to the day, Patricia Whalley, the wife of Deary’s co-worker, Lawrence Whalley, discovered the test results on dusty storeroom shelves at the Scottish Council for Research in Education, not far from Deary’s Edinburgh University office. “This will change our lives,” Deary replied when Whalley told him the news.

And so it has, with dozens of studies of the stability and the predictive capacity of these early test results. For example, when the intelligence test administered to 11-year-old Scots in 1932 was readministered to 542 survivors as turn-of-the-millennium 80-year-olds, the correlation between the two sets of scores—after nearly 70 years of varied life experiences—was striking (FIGURE 10.9). Ditto when 106 survivors were retested at age 90 (Deary et al., 2013). Another study that followed Scots born in 1936 from ages 11 to 70 confirmed the remarkable stability of intelligence, independent of life circumstance (Johnson et al., 2010).

High-scoring 11-year-olds also were more likely to be living independently as 77-year-olds and were less likely to have suffered Alzheimer’s disease (Starr et al., 2000; Whalley et al., 2000). Among girls scoring in the highest 25 percent, 70 percent were still alive at age 76—as were only 45 percent of those scoring in the lowest 25 percent (FIGURE 10.10). (World War II prematurely ended the lives of many of the male test-takers.) Follow-up studies with other large samples confirm the phenomenon: More intelligent children and adults live healthier and longer (Calvin et al., 2011; Deary et al., 2008, 2010; Johnson et al., 2011). One study that followed 93 nuns found that those exhibiting less verbal ability in essays written when entering convents in their teens were more at risk for Alzheimer’s disease after age 75 (Snowdon et al., 1996).

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Pause a moment: Have you any idea why more intelligent people might live longer? Deary (2008) reports four possible explanations:

10-8 What are the traits of those at the low and high intelligence extremes?

One way to glimpse the validity and significance of any test is to compare people who score at the two extremes of the normal curve. The two groups should differ noticeably, and they do.

At one extreme of the intelligence test normal curve are those with unusually low scores. To be diagnosed with an intellectual disability (formerly referred to as mental retardation), a person must meet two criteria. The first is a low test score. American Association on Intellectual and Developmental Disabilities guidelines specify performance that is approximately two standard deviations below average (Schalock et al., 2010). For an intelligence test with 100 as average and a standard deviation of 15, that means (allowing for some variation in one’s test score) an intelligence score of approximately 70 or below. The second criterion is that the person must have difficulty adapting to the normal demands of independent living, as expressed in three areas:

Intellectual disability is a developmental condition that is apparent before age 18, sometimes with a known physical cause. Down syndrome, for example, is a disorder of varying severity caused by an extra copy of chromosome 21 in the person’s genetic makeup.

Consider one reason why people diagnosed with a mild intellectual disability—those just below the 70 score—might be better able to live independently today than many decades ago, when they were institutionalized. Recall that, thanks to the Flynn effect, the tests have been periodically restandardized. As that happened, individuals who scored near 70 on earlier tests have suddenly lost about 6 test-score points. Two people with the same ability level could thus be classified differently, depending on when they were tested (Kanaya et al., 2003; Reynolds et al., 2010). As the intellectual disability boundary has shifted, more people have become eligible for special education and for Social Security payments.

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And in the United States (one of only a few industrialized countries with the death penalty), fewer people are now eligible for execution: The U.S. Supreme Court ruled in 2002 that the execution of people with an intellectual disability is “cruel and unusual punishment.” For people near the cutoff score of 70, intelligence testing can be a high-stakes competition. And so it was for Teresa Lewis, a “dependent personality” with limited intellect, who was executed by the state of Virginia in 2010. Lewis, whose reported test score was 72, allegedly agreed to a plot in which two men killed her husband and stepson in exchange for a split of a life insurance payout (Eckholm, 2010). If only she had scored 69.

In 2014, the U.S. Supreme Court recognized the imprecision and arbitrariness of a fixed cutoff score of 70, and required states with death row inmates who have scored just above 70 to consider other evidence. Thus, Ted Herring, who had scored 72 and 74 on intelligence tests—but without knowing that summer follows spring or how to transfer between buses—could be taken off Florida’s death row (Alvarez & Schwartz, 2014).

In one famous project begun in 1921, Lewis Terman studied more than 1500 California schoolchildren with IQ scores over 135. Contrary to the popular notion that intellectually gifted children are frequently maladjusted, Terman’s high-scoring children (the “Termites”), like those in later studies, were healthy, well-adjusted, and unusually successful academically (Friedman & Martin, 2012; Koenen et al., 2009; Lubinski, 2009a). When restudied over the next seven decades, most people in Terman’s group had attained high levels of education (Austin et al., 2002; Holahan & Sears, 1995). Many were doctors, lawyers, professors, scientists, and writers, though no Nobel Prize winners.

A more recent study of precocious youths who had aced the math SAT at age 13—by scoring in the top quarter of 1 percent of their age group—found them at age 33 twice as likely to have patents as were those in the bottom quarter of the top 1 percent (Wai et al., 2005). Compared with the math aces, 13-year-olds scoring high on verbal aptitude were, by age 38, more likely to have become humanities professors or written a novel (Kell et al., 2013). About 1 percent of Americans earn doctorates. But among those scoring in the top 1 in 10,000—on the SAT at age 12 or 13—63 percent had done so.

One of psychology’s whiz kids was Jean Piaget, who by age 15 was publishing scientific articles on mollusks and who went on to become the twentieth century’s most famous developmental psychologist (Hunt, 1993). Children with extraordinary academic gifts are sometimes more isolated, shy, and in their own worlds (Winner, 2000). But most thrive.

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There are critics who question many of the assumptions of “gifted child” programs, such as the belief that only 3 to 5 percent of children are gifted and that it pays to identify and “track” these special few—segregating them in special classes and giving them academic enrichment not available to their peers. Critics note that tracking by aptitude sometimes creates a self-fulfilling prophecy: Those implicitly labeled “ungifted” may be influenced to become so (Lipsey & Wilson, 1993; Slavin & Braddock, 1993). Denying lower-ability students opportunities for enriched education can widen the achievement gap between ability groups and increase their social isolation from one another (Carnegie, 1989; Stevenson & Lee, 1990). Because minority and low-income youth are more often placed in lower academic groups, tracking can also promote segregation and prejudice—hardly, note critics, a healthy preparation for working and living in a multicultural society.

Critics and proponents of gifted education do, however, agree on this: Children have differing gifts, whether at math, verbal reasoning, art, or social leadership. Some children exhibit exceptional potential or talent in a given domain. Educating children as if all were alike is as naive as assuming that giftedness is something, like blue eyes, that you either have or do not have. One need not hang labels on children to affirm their special talents and to challenge them all at the frontiers of their own ability and understanding. By providing appropriate placement suited to each child’s talents (as when allowing a math whiz to study math at a higher level), we can promote both equity and excellence for all (Subotnik et al., 2011).

LEARNING OBJECTIVES

RETRIEVAL PRACTICE Take a moment to answer each of these Learning Objective Questions (repeated here from within this section). Then click the 'show answer' button to check your answers. Research suggests that trying to answer these questions on your own will improve your long-term retention (McDaniel et al., 2009).

10-7 How stable are intelligence scores over the life span, and how does aging affect crystallized and fluid intelligence?

10-8 What are the traits of those at the low and high intelligence extremes?

TERMS AND CONCEPTS TO REMEMBER

RETRIEVAL PRACTICE Match each of the terms on the left with its definition on the right. Click on the term first and then click on the matching definition. As you match them correctly they will move to the bottom of the activity.