42 9.1 Defining and Measuring Intelligence

Psychologists have long debated how to best conceptualize and measure intelligence (Sternberg, 2003a). These questions include how many types of intelligence there are, the role of nature versus nurture in intelligence, how intelligence is represented in the brain, and the meaning of group differences in intelligence.

General versus specific intelligences

In the early 1900s, the French psychologist Alfred Binet (1857–1914) and colleague Henri Simon (1872–1961) began working in Paris to develop a measure that would differentiate students who were expected to be better learners from students who were expected to be slower learners. The goal was to help teachers better educate these two groups of students. Binet and Simon developed what most psychologists today regard as the first intelligence test (see Figure 9.1), which consisted of a wide variety of questions that included the ability to name objects, define words, draw pictures, complete sentences, compare items, and construct sentences.

Binet and Simon (1915; Siegler, 1992) believed that the questions they asked their students, even though they were on the surface dissimilar, all assessed the basic abilities to understand, reason, and make judgments. It turned out that the correlations among these different types of measures were, in fact, all positive; students who got one item correct were more likely to also get other items correct, even though the questions themselves were very different.

On the basis of these results, psychologist Charles Spearman (1863–1945) hypothesized that there must be a single underlying construct that all of these items measure. Spearman called the construct that the different abilities and skills measured on intelligence tests have in common the general intelligence factor (g). Virtually all psychologists now believe that there is a generalized intelligence factor, g, that relates to abstract thinking and that includes the abilities to acquire knowledge, to reason abstractly, to adapt to novel situations, and to benefit from instruction and experience (Gottfredson, 1997; Sternberg, 2003a). People with higher general intelligence learn faster.

Soon after Binet and Simon introduced their test, American psychologist Lewis Terman (1877–1956) developed an American version of Binet’s test that became known as the Stanford-Binet Intelligence Test. The Stanford-Binet is a measure of general intelligence made up of a wide variety of tasks including vocabulary, memory for pictures, naming of familiar objects, repeating sentences, and following commands.

Although there is general agreement among psychologists that g exists, there is also evidence for specific intelligence (s), which is a measure of specific skills in narrow domains. One empirical result in support of the idea of s comes from intelligence tests themselves. Although the different types of questions do correlate with each other, some items correlate more highly with each other than do other items; they form clusters or clumps of intelligences.

One distinction is between fluid intelligence, which refers to the capacity to learn new ways of solving problems and performing activities, and crystallized intelligence, which refers to the accumulated knowledge of the world we have acquired throughout our lives (Salthouse, 2004). These intelligences must be different because crystallized intelligence increases with age — older adults are as good as or better than young people in solving crossword puzzles — whereas fluid intelligence tends to decrease with age (Horn, Donaldson, & Engstrom, 1981; Salthouse, 2004).

Other researchers have proposed even more types of intelligences. Louis Thurstone (1938) proposed that there were seven clusters of primary mental abilities, made up of word fluency, verbal comprehension, spatial ability, perceptual speed, numerical ability, inductive reasoning, and memory. However, even these dimensions tend to be at least somewhat correlated, showing again the importance of g.

One advocate of the idea of multiple intelligences is the psychologist Robert Sternberg, who has proposed a triarchic theory of intelligence, focusing on three types of intelligence, that proposes people may display more or less analytical intelligence, creative intelligence, and practical intelligence. Sternberg (1985, 2003b) argued that traditional intelligence tests assess analytical intelligence, the ability to answer problems with a single right answer, but that they do not assess creativity well, like the ability to adapt to new situations and create new ideas, or practicality, like the ability to write good memos or to effectively delegate responsibility.

As Sternberg proposed, research has found that creativity is not highly correlated with analytical intelligence (Furnham & Bachtiar, 2008), and exceptionally creative scientists, artists, mathematicians, and engineers do not score higher on intelligence than do their less creative peers (Simonton, 2000). Furthermore, the brain areas that are associated with convergent thinking, which is thinking that is directed toward finding the correct answer to a given problem, are different from those associated with divergent thinking (Tarasova, Volf, & Razoumnikova, 2010), which is the ability to generate many different ideas for or solutions to a single problem (see Figure 9.2). On the other hand, being creative often takes some of the basic abilities measured by g, including the abilities to learn from experience, to remember information, and to think abstractly (Bink & Marsh, 2000).

Studies of creative people suggest at least five components that are likely to be important for creativity:

1. Expertise — Creative people have carefully studied and know a lot about the topic that they are working in. Creativity comes with a lot of hard work (Ericsson, 1998; Weisberg, 2006).
2. Imaginative thinking — Creative people often view a problem in a visual way, allowing them to see it from a new and different point of view.
3. Risk taking — Creative people are willing to take on new but potentially risky approaches.
4. Intrinsic interest — Creative people tend to work on projects because they love doing them, not because they are paid for them. In fact, research has found that people who are paid to be creative are often less creative than those who are not (Hennessey & Amabile, 2010).
5. Working in a creative environment — Creativity is in part a social phenomenon. Dean Simonton (1992) found that the most creative people were supported, aided, and challenged by other people working on similar projects.

The last aspect of the triarchic model, practical intelligence, refers primarily to intelligence that cannot be gained from books or formal learning. Practical intelligence represents a type of street smarts or common sense that is learned from life experiences. Although a number of tests have been devised to measure practical intelligence (Sternberg, Wagner, & Okagaki, 1993; Wagner & Sternberg, 1985), research has not found much evidence that practical intelligence is distinct from g or that it is predictive of success at any particular tasks (Gottfredson, 2003). Practical intelligence may include, at least in part, certain abilities that help people perform well at specific jobs, and these abilities may not always be highly correlated with general intelligence (Sternberg, Wagner, & Okagaki, 1993). On the other hand, these abilities or skills are very specific to particular occupations and thus do not seem to represent the broader idea of intelligence.

Another champion of the idea of multiple intelligences is the psychologist Howard Gardner. Gardner (1983, 1999) argued that it would be evolutionarily functional for different people to have different talents and skills. Additionally, Gardner proposed that there are eight intelligences that can be differentiated from each other, as seen in the table below. Gardner noted that some evidence for multiple intelligences comes from the abilities of “autistic savants,” people who score low on intelligence tests overall but who nevertheless may have exceptional skills in a given domain, such as math, music, art, or in being able to recite statistics in a given sport (Treffert & Wallace, 2004).

The idea of multiple intelligences has been influential in the field of education, and teachers have used these ideas to try to teach differently to different students (see Figure 9.3). For instance, to teach math problems to students who have particularly good kinesthetic intelligence, a teacher might encourage the students to move their bodies or hands according to the numbers. On the other hand, some have argued that these intelligences sometimes seem more like abilities or talents rather than real intelligence, and there is no clear conclusion about how many intelligences there are. Are sense of humour, artistic skills, dramatic skills, and so forth also separate intelligences? Furthermore, and again demonstrating the underlying power of a single intelligence, the many different intelligences are in fact correlated and thus represent, in part, the general intelligence factor (Brody, 2003).

Measuring intelligence: Standardization and the intelligence quotient

The goal of most intelligence tests is to measure g, the general intelligence factor. Good intelligence tests are reliable, meaning that they are consistent over time, and also demonstrate construct validity, meaning that they actually measure intelligence rather than something else. Because intelligence is such an important individual difference dimension, psychologists have invested substantial effort in creating and improving measures of intelligence, and these tests are now the most accurate of all psychological tests. In fact, the ability to accurately assess intelligence is one of the most important contributions of psychology to everyday public life.

Intelligence changes with age. A three-year-old who could accurately multiply 183 by 39 would certainly be intelligent, but a 25-year-old who could not do so would be seen as unintelligent. Thus, understanding intelligence requires that we know the norms or standards in a given population of people at a given age. The standardization of a test involves giving it to a large number of people at different ages and computing the average score on the test at each age level. Once a large enough representative sample of people have been tested, any individual’s score can be compared to norms that are based on the population.

The results of studies assessing the measurement of intelligence show that IQ is distributed in the population in the form of a normal distribution (or bell curve), which is the pattern of scores usually observed in a variable that clusters around its average. In a normal distribution, the bulk of the scores fall toward the middle, with many fewer scores falling at the extremes. The normal distribution of intelligence shows that on IQ tests, as well as on most other measures, the majority of people cluster around the average, where IQ = 100 (see Figure 9.4). Because the standard deviation of an IQ test is about 15, this means that about 2% of people score above an IQ of 130, often considered the threshold for giftedness, and about the same percentage score below an IQ of 70, often being considered the threshold for an intellectual disability.

It is important that intelligence tests be standardized on a regular basis because the overall level of intelligence in a population may change over time. The Flynn effect refers to the observation that scores on intelligence tests worldwide have increased substantially over the past decades (Flynn, 1999). Although the increase varies somewhat from country to country, the average increase is about three intelligence points in IQ test scores every 10 years. There are many explanations for the Flynn effect, including better nutrition, increased access to information, and more familiarity with multiple-choice tests (Neisser, 1998), but whether people are actually getting smarter is debatable (Neisser, 1997).

Once the standardization has been accomplished, we have a picture of the average abilities of people at different ages and can calculate a person’s mental age, which is the age at which a person is performing intellectually. If we compare the mental age of a person to the person’s chronological age, the result is the IQ, a measure of intelligence that is adjusted for age. A simple way to calculate IQ is by using the following formula:

IQ = mental age ÷ chronological age × 100.

Thus, a 10-year-old child who does as well as the average 10-year-old child has an IQ of 100 (10 ÷ 10 × 100), whereas an eight-year-old child who does as well as the average 10-year-old child would have an IQ of 125 (10 ÷ 8 × 100). Most modern intelligence tests are based the relative position of a person’s score among people of the same age, rather than on the basis of this formula, but the idea of an intelligence ratio, or intelligence quotient, provides a good description of the score’s meaning.

A number of scales are based on IQ. The Wechsler Adult lntelligence Scale (WAIS) is the most widely used intelligence test for adults (Watkins, Campbell, Nieberding, & Hallmark, 1995). The current version of the WAIS, the WAIS-IV, was standardized on 2,200 people ranging from 16 to 90 years of age. It consists of 15 different tasks, each designed to assess intelligence, including working memory, arithmetic ability, spatial ability, and general knowledge about the world (see Figure 9.5). The WAIS-IV yields scores on four domains: verbal, perceptual, working memory, and processing speed. The reliability of the test is high, more than 0.95, and it shows substantial construct validity. The WAIS-IV is correlated highly with other IQ tests such as the Stanford-Binet, as well as with criteria of academic and life success, including grades, measures of work performance, and occupational level.

The Wechsler scale has also been adapted for preschool children, in the form of the Wechsler Primary and Preschool Scale of Intelligence (WPPSI-III), and for older children and adolescents, in the form of the Wechsler Intelligence Scale for Children (WISC-IV).

The intelligence tests that you may be most familiar with are aptitude tests, which are designed to measure one’s ability to perform a given task, such as doing well in undergraduate, graduate, or post-graduate training. Canadian post-secondary institutions request official high school transcripts demonstrating minimum grade admission requirements, while most American colleges and universities require students to take the Scholastic Assessment Test (SAT) or the American College Test (ACT). Post-graduate schools in both countries require the Graduate Record Examination (GRE), Graduate Management Admission Test (GMAT), Medical College Admissions Test (MCAT), or the Law School Admission Test (LSAT). These tests are useful for selecting students because they predict success in the programs that they are designed for, particularly in the first year of the program (Kuncel, Hezlett, & Ones, 2010). These aptitude tests also measure, in part, intelligence. Meredith Frey and Douglas Detterman (2004) found that the SAT correlated highly (i.e., between about r = .7 and r = .8) with standard measures of intelligence.

Intelligence tests are also used by industrial and organizational psychologists in the process of personnel selection. Personnel selection is the use of structured tests to select people who are likely to perform well at given jobs (Schmidt & Hunter, 1998). The psychologists begin by conducting a job analysis in which they determine what knowledge, skills, abilities, and personal characteristics (KSAPs) are required for a given job. This is normally accomplished by surveying or interviewing current workers and their supervisors. Based on the results of the job analysis, the psychologists choose selection methods that are most likely to be predictive of job performance. Measures include tests of cognitive and physical ability and job knowledge tests, as well as measures of IQ and personality.

The biology of intelligence

The brain processes underlying intelligence are not completely understood, but current research has focused on four potential factors: brain size, sensory ability, speed and efficiency of neural transmission, and working memory capacity.

There is at least some truth to the idea that smarter people have bigger brains. Studies that have measured brain volume using neuroimaging techniques find that larger brain size is correlated with intelligence (McDaniel, 2005), and intelligence has also been found to be correlated with the number of neurons in the brain and with the thickness of the cortex (Haier, 2004; Shaw et al., 2006). It is important to remember that these correlational findings do not mean that having more brain volume causes higher intelligence. It is possible that growing up in a stimulating environment that rewards thinking and learning may lead to greater brain growth (Garlick, 2003), and it is also possible that a third variable, such as better nutrition, causes both brain volume and intelligence.

Another possibility is that the brains of more intelligent people operate faster or more efficiently than the brains of the less intelligent. Some evidence supporting this idea comes from data showing that people who are more intelligent frequently show less brain activity, suggesting that they need to use less capacity, than those with lower intelligence when they work on a task (Haier, Siegel, Tang, & Abel, 1992), and the brains of more intelligent people also seem to run faster than the brains of the less intelligent. Research has found that the speed with which people can perform simple tasks — such as determining which of two lines is longer or pressing, as quickly as possible, one of eight buttons that is lighted — is predictive of intelligence (Deary, Der, & Ford, 2001). Intelligence scores also correlate at about r = .5 with measures of working memory (Ackerman, Beier, & Boyle, 2005), and working memory is now used as a measure of intelligence on many tests.

Although intelligence is not located in a specific part of the brain, it is more prevalent in some brain areas than others. John Duncan and colleagues (Duncan et al., 2000) administered a variety of intelligence tasks and observed the places in the cortex that were most active. Although different tests created different patterns of activation (see Figure 9.6), these activated areas were primarily in the outer parts of the cortex, the area of the brain most involved in planning, executive control, and short-term memory.

Is intelligence nature or nurture?

Intelligence has both genetic and environmental causes, and these have been systematically studied through a large number of twin and adoption studies (Neisser et al., 1996; Plomin, 2003). These studies have found that between 40% and 80% of the variability in IQ is due to differences in genes, meaning that overall, genetics plays a bigger role than environment does in creating IQ differences among individuals (Plomin & Spinath, 2004). The IQs of identical twins correlate very highly, where r = .86, which is much higher than do the scores of fraternal twins who are less genetically similar, where r = .60, and the correlations between the IQs of parents and their biological children, where r = .42, is significantly greater than the correlation between parents and adopted children, where r = .19. The role of genetics gets stronger as children get older. The intelligence of very young children that are less than 3 years old does not predict adult intelligence, but by age 7 it does, and IQ scores remain very stable in adulthood (Deary, Whiteman, Starr, Whalley, & Fox, 2004).

There is also evidence for the role of nurture, indicating that individuals are not born with fixed, unchangeable levels of intelligence. Twins raised together in the same home have more similar IQs than do twins who are raised in different homes, and fraternal twins have more similar IQs than do non-twin siblings, which is likely due to the fact that they are treated more similarly than non-twin siblings are.

The fact that intelligence becomes more stable as we get older provides evidence that early environmental experiences matter more than later ones. Environmental factors also explain a greater proportion of the variance in intelligence for children from lower-class households than they do for children from upper-class households (Turkheimer, Haley, Waldron, D’Onofrio, & Gottesman, 2003). This is because most upper-class households tend to provide a safe, nutritious, and supporting environment for children, whereas these factors are more variable in lower-class households.

Social and economic deprivation can adversely affect IQ. Children from households in poverty have lower IQs than do children from households with more resources even when other factors such as education, race, and parenting are controlled (Brooks-Gunn & Duncan, 1997). Poverty may lead to diets that are undernourishing or lacking in appropriate vitamins, and poor children may also be more likely to be exposed to toxins such as lead in drinking water, dust, or paint chips (Bellinger & Needleman, 2003). Both of these factors can slow brain development and reduce intelligence.

If impoverished environments can harm intelligence, we might wonder whether enriched environments can improve it. Government-funded after-school programs such as Head Start are designed to help children learn. Research has found that attending such programs may increase intelligence for a short time, but these increases rarely last after the programs end (McLoyd, 1998; Perkins & Grotzer, 1997). Other studies suggest that Head Start and similar programs may improve emotional intelligence and reduce the likelihood that children will drop out of school or be held back a grade (Reynolds, Temple, Robertson, & Mann 2001).

Intelligence is improved by education; the number of years a person has spent in school correlates at about r = .6 with IQ (Ceci, 1991). In part, this correlation may be due to the fact that people with higher IQ scores enjoy taking classes more than people with low IQ scores, and thus they are more likely to stay in school. However, education also has a causal effect on IQ. Comparisons between children who are almost exactly the same age but who just do or just do not make a deadline for entering school in a given school year show that those who enter school a year earlier have higher IQ than those who have to wait until the next year to begin school (Baltes & Reinert, 1969; Ceci & Williams, 1997). Children’s IQs tend to drop significantly during summer vacations (Huttenlocher, Levine, & Vevea, 1998), a finding that suggests that a longer school year, as is used in Europe and East Asia, is beneficial.

It is important to remember that the relative roles of nature and nurture can never be completely separated. A child who has higher than average intelligence will be treated differently than a child who has lower than average intelligence, and these differences in behaviours will likely amplify initial differences. This means that modest genetic differences can be multiplied into big differences over time.

Image Attributions

Figure 9.1. Az-Writing Boy E-Citizen by Moonsun1981 is used under a CC BY-SA 3.0 license.

Figure 9.2. Wanzikia by Hawyih is in the public domain.

Figure 9.3. Klutz the Clown by Herald Post is used under a CC BY-NC 2.0 license; Women Heptathlon LJ French Athletics Championships 2013 t144221 by Marie-Lan Nguyen is used under a CC BY 3.0 license; Street Painter by Pedro Ribeiro Simões is used under a CC BY 2.0 license; Vardan Mamikonyan Armenian Pianist by Chaojoker is used under a CC BY-SA 3.0 license; Teacher at Chalkboard by cybrarian77 is used under a CC BY-NC 2.0 license.

Figure 9.4. Used under a CC BY-NC-SA 4.0 license.

Figure 9.5. Used under a CC BY-NC-SA 4.0 license.

Figure 9.6. Used under a CC BY-NC-SA 4.0 license.

Long Description

Figure 9.5. Sample items from the Wechsler Adult Intelligence Scale:

1. What day of the year is Independence Day?
2. If eggs cost 60 cents a dozen, what does 1 egg cost?
3. Tell me the meaning of “corrupt.”
4. Why do people buy fire insurance?
5. Say the following numbers after me: 7 3 4 1 8 6
6. Say the following numbers backwards: 3 8 4 1 6

The last two questions involve making pictures out of blocks.

[Return to Figure 9.5]

References

Ackerman, P. L., Beier, M. E., & Boyle, M. O. (2005). Working memory and intelligence: The same or different constructs? Psychological Bulletin, 131(1), 30–60.

Ackley, D. (2016). Emotional intelligence: A practical review of models, measures, and applications. Consulting Psychology Journal: Practice and Research, 68(4), 269–286.

Ayduk, O., Mendoza-Denton, R., Mischel, W., Downey, G., Peake, P. K., & Rodriguez, M. (2000). Regulating the interpersonal self: Strategic self-regulation for coping with rejection sensitivity. Journal of Personality and Social Psychology, 79(5), 776–792.

Baltes, P. B., & Reinert, G. (1969). Cohort effects in cognitive development of children as revealed by cross-sectional sequences. Developmental Psychology, 1(2), 169–177.

Bellinger, D. C., & Needleman, H. L. (2003). Intellectual impairment and blood lead levels [Letter to the editor]. The New England Journal of Medicine, 349(5), 500.

Binet, A., & Simon, T. (1915). A method of measuring the development of intelligence of young children (3rd ed., C. H. Town, Trans.) Chicago, IL: Chicago Medical Book.

Bink, M. L., & Marsh, R. L. (2000). Cognitive regularities in creative activity. Review of General Psychology, 4(1), 59–78.

Brody, N. (2003). Construct validation of the Sternberg Triarchic Abilities Test: Comment and reanalysis. Intelligence, 31(4), 319–329.

Brody, N. (2004). What cognitive intelligence is and what emotional intelligence is not. Psychological Inquiry, 15(3), 234–238.

Brooks-Gunn, J., & Duncan, G. J. (1997). The effects of poverty on children. The Future of Children, 7(2), 55–71.

Ceci, S. J. (1991). How much does schooling influence general intelligence and its cognitive components? A reassessment of the evidence. Developmental Psychology, 27(5), 703–722.

Ceci, S. J., & Williams, W. M. (1997). Schooling, intelligence, and income. American Psychologist, 52(10), 1051–1058.

Deary, I. J., Der, G., & Ford, G. (2001). Reaction times and intelligence differences: A population-based cohort study. Intelligence, 29(5), 389–399.

Deary, I. J., Whiteman, M. C., Starr, J. M., Whalley, L. J., & Fox, H. C. (2004). The impact of childhood intelligence on later life: Following up the Scottish mental surveys of 1932 and 1947. Journal of Personality and Social Psychology, 86(1), 130–147.

Duncan, J., Seitz, R. J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A., . . . Emslie, H. (2000). A neural basis for general intelligence. Science, 289(5478), 457–460.

Eigsti, I.-M., Zayas, V., Mischel, W., Shoda, Y., Ayduk, O., Dadlani, M. B., . . . Casey, B. J. (2006). Predicting cognitive control from preschool to late adolescence and young adulthood. Psychological Science, 17(6), 478–484.

Ericsson, K. (1998). The scientific study of expert levels of performance: General implications for optimal learning and creativity. High Ability Studies, 9(1), 75–100.

Flynn, J. R. (1999). Searching for justice: The discovery of IQ gains over time. American Psychologist, 54(1), 5–20.

Føllesdal, H., & Hagtvet, K. A. (2009). Emotional intelligence: The MSCEIT from the perspective of generalizability theory. Intelligence, 37(1), 94–105.

Frey, M. C., & Detterman, D. K. (2004). Scholastic assessment or g? The relationship between the scholastic assessment test and general cognitive ability. Psychological Science, 15(6), 373–378.

Furnham, A., & Bachtiar, V. (2008). Personality and intelligence as predictors of creativity. Personality and Individual Differences, 45(7), 613–617.

Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York, NY: Basic Books.

Gardner, H. (1999). Intelligence reframed: Multiple intelligences for the 21st century. New York, NY: Basic Books.

Garlick, D. (2003). Integrating brain science research with intelligence research. Current Directions in Psychological Science, 12(5), 185–189.

Goleman, D. (1998). Working with emotional intelligence. New York, NY: Bantam Books.

Gottfredson, L. S. (1997). Mainstream science on intelligence: An editorial with 52 signatories, history and bibliography. Intelligence, 24(1), 13–23.

Gottfredson, L. S. (2003). Dissecting practical intelligence theory: Its claims and evidence. Intelligence, 31(4), 343–397.

Haier, R. J. (2004). Brain imaging studies of personality: The slow revolution. In R. M. Stelmack (Ed.), On the psychobiology of personality: Essays in honor of Marvin Zuckerman (pp. 329–340). New York, NY: Elsevier Science.

Haier, R. J., Siegel, B. V., Tang, C., & Abel, L. (1992). Intelligence and changes in regional cerebral glucose metabolic rate following learning. Intelligence, 16(3–4), 415–426.

Harms, P. D., & Credé, M. (2010). Emotional intelligence and transformational and transactional leadership: A meta-analysis. Journal of Leadership & Organizational Studies, 17(1), 5–17.

Hennessey, B. A., & Amabile, T. M. (2010). Creativity. Annual Review of Psychology, 61, 569–598.

Horn, J. L., Donaldson, G., & Engstrom, R. (1981). Apprehension, memory, and fluid intelligence decline in adulthood. Research on Aging, 3(1), 33–84.

Huttenlocher, J., Levine, S., & Vevea, J. (1998). Environmental input and cognitive growth: A study using time-period comparisons. Child Development, 69(4), 1012–1029.

Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2010). A comprehensive meta-analysis of the predictive validity of the graduate record examinations: Implications for graduate student selection and performance. Psychological Bulletin, 127(1), 162–181.

Landy, F. J. (2005). Some historical and scientific issues related to research on emotional intelligence. Journal of Organizational Behavior, 26, 411–424.

Martins, A., Ramalho, N., & Morin, E. (2010). A comprehensive meta-analysis of the relationship between emotional intelligence and health. Personality and Individual Differences, 49(6), 554–564.

Mayer, J. D., & Cobb, C. D. (2000). Educational policy on emotional intelligence: Does it make sense? Educational Psychology Review, 12(2), 163–183.

McDaniel, M. A. (2005). Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence, 33(4), 337–346.

McLoyd, V. C. (1998). Children in poverty: Development, public policy and practice. In W. Damon, I. E. Sigel, & K. A. Renninger (Eds.), Handbook of child psychology: Child psychology in practice (5th ed., Vol. 4, pp. 135–208). Hoboken, NJ: Wiley.

Mischel, W., & Ayduk, O. (Eds.). (2004). Willpower in a cognitive-affective processing system: The dynamics of delay of gratification. New York, NY: Guilford Press.

Neisser, U. (1997). Rising scores on intelligence tests. American Scientist, 85, 440–447.

Neisser, U. (Ed.). (1998). The rising curve. Washington, DC: American Psychological Association.

Neisser, U., Boodoo, G., Bouchard, T. J., Jr., Boykin, A. W., Brody, N., Ceci, S. J., . . . Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51(2), 77–101.

Perkins, D. N., & Grotzer, T. A. (1997). Teaching intelligence. American Psychologist, 52(10), 1125–1133.

Plomin, R. (2003). General cognitive ability. In R. Plomin, J. C. DeFries, I. W. Craig, & P. McGuffin (Eds.), Behavioral genetics in the postgenomic era (pp. 183–201). Washington, DC: American Psychological Association.

Plomin, R., & Spinath, F. M. (2004). Intelligence: Genetics, genes, and genomics. Journal of Personality and Social Psychology, 86(1), 112–129.

Reynolds, A. J., Temple, J. A., Robertson, D. L., & Mann, E. A. (2001). Long-term effects of an early childhood intervention on educational achievement and juvenile arrest: A 15-year follow-up of low-income children in public schools. Journal of the American Medical Association, 285(18), 2339–2346.

Salthouse, T. A. (2004). What and when of cognitive aging. Current Directions in Psychological Science, 13(4), 140–144.

Schmidt, F. L., & Hunter, J. E. (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.

Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., . . . Giedd, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440(7084), 676–679.

Siegler, R. S. (1992). The other Alfred Binet. Developmental Psychology, 28(2), 179–190.

Simonton, D. K. (1992). The social context of career success and course for 2,026 scientists and inventors. Personality and Social Psychology Bulletin, 18(4), 452–463.

Simonton, D. K. (2000). Creativity: Cognitive, personal, developmental, and social aspects. American Psychologist, 55(1), 151–158.

Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. New York, NY: Cambridge University Press.

Sternberg, R. J. (2003a). Our research program validating the triarchic theory of successful intelligence: Reply to Gottfredson. Intelligence, 31(4), 399–413.

Sternberg, R. J. (2003b). Contemporary theories of intelligence. In W. M. Reynolds & G. E. Miller (Eds.), Handbook of psychology: Educational psychology (Vol. 7, pp. 23–45). Hoboken, NJ: Wiley.

Sternberg, R. J., Wagner, R. K., & Okagaki, L. (1993). Practical intelligence: The nature and role of tacit knowledge in work and at school. In J. M. Puckett & H. W. Reese (Eds.), Mechanisms of everyday cognition (pp. 205–227). Mahwah, NJ: Lawrence Erlbaum.

Tarasova, I. V., Volf, N. V., & Razoumnikova, O. M. (2010). Parameters of cortical interactions in subjects with high and low levels of verbal creativity. Human Physiology, 36(1), 80–85.

Thorndike, R. L., & Hagen, E. P. (1997). Cognitive Abilities Test (Form 5) research handbook. Chicago, IL: Riverside.

Thurstone, L. L. (1938). Primary mental abilities. Chicago, IL: University of Chicago Press.

Treffert, D. A., & Wallace, G. L. (2004). Islands of genius. Scientific American, 14(1), 14–23.

Turkheimer, E., Haley, A., Waldron, M., D’Onofrio, B., & Gottesman, I. I. (2003). Socioeconomic status modifies heritability of IQ in young children. Psychological Science, 14(6), 623–628.

Wagner, R., & Sternberg, R. (1985). Practical intelligence in real-world pursuits: The role of tacit knowledge. Journal of Personality and Social Psychology, 49(2), 436–458.

Watkins, C. E., Campbell, V. L., Nieberding, R., & Hallmark, R. (1995). Contemporary practice of psychological assessment by clinical psychologists. Professional Psychology: Research and Practice, 26(1), 54–60.

Weisberg, R. (2006). Creativity: Understanding innovation in problem solving, science, invention, and the arts. Hoboken, NJ: Wiley.