What is Special Intelligence andEducation? Individual Differences 1 10 iStockphoto/Thinkstock Pre-Test Voyagerix/iStock/Thinkstock 1. 1. You can use the terms disability and handicap interchangeably. T/F Learning Objectives 2. 2. The history of special education began in Europe. T/F 3. the 3. end Theoffirst legislation protected students with disabilities was passed in the 1950s. By thisAmerican chapter, you should that be able to: T/F • Analyze the qualities that determine IQ tests’ usefulness and adequacy for measuring intellectual differences 4. in 4. children. All students with disabilities should be educated in special education classrooms. T/F 5. 5. Special education law is constantly reinterpreted. T/F • Identify and relate the hierarchy of intellectual abilities posited by the psychometric approach to 6. intelligence. Answers can be found at the end of the chapter. • Explain how nature and nurture interact to influence intellectual differences in children. • Differentiate stability, change, and modifiability in intelligence. • Evaluate evidence that IQ scores can be improved in children through intervention. Pretest Questions  Pretest Questions 1. The modern intelligence test was first developed to identify gifted children so that they could receive advanced instruction. T/F 2. Children’s performance on measurements of nonverbal intelligence (for example, spatial reasoning) is positively related to verbal intelligence (for example, vocabulary). T/F 3. Because genetic differences influence intellectual differences between children, the impact of the environment is minimal. T/F 4. By age 6 or 7, a child’s IQ score is a fairly good predictor of subsequent IQ scores throughout the child’s development. T/F 5. Generally speaking, if an intervention boosts a young child’s IQ score, the improvement is permanent even after the intervention ends. T/F Michael is a 6-year-old who is struggling both academically and socially. His parents are recently divorced, and he is experiencing significant psychosocial stress. Michael had some issues prior to the divorce, but his symptoms have been exacerbated. Michael’s teacher is unsure if the child’s difficulties in school stem from problems coping with the divorce or from cognitive delays that make it difficult to keep pace with the other children. As part of the assessment procedure, the school psychologist, Dr. Williams, administers an intelligence test to measure Michael’s cognitive functioning. Intelligence test performance is a factor in determining whether a child is eligible for specialized services (Individuals with Disabilities Education Improvement Act, 2004). Questions on the intelligence test ask Michael to remember a string of randomly arranged digits (“Repeat after me: ‘3-1’”). Others require him to arrange puzzle pieces or solve addition and subtraction problems. During testing, the 6-year-old seems tired or perhaps unmotivated. However, Williams avoids providing minor hints and suggestions to prompt Michael and help him out a bit. When the test is over, Williams tallies the child’s correct answers and determines how his scores compare with a large sample of same-age children. He will look for patterns that indicate areas of relative weakness and strength. At the end of the assessment, Williams faces a dilemma. Michael’s outcome on the intelligence test finds him right on the borderline for placement in a special education program. Questions to Think About 1. Should Williams have provided some hints to help the child during testing? Explain your reasoning. 2. Do you believe an intelligence test would provide enough information to make a decision about Michael’s placement? Why or why not? If not, what other information should Williams gather? Explain your answer. 3. Michael is a very young child—how should his age factor into the decision to place him in a special education program? 4. Would the divorce impact Michael’s cognitive functioning? If so, how? Introduction Introduction Differences in children’s intelligence predict later income, occupational status, and educational attainment in adulthood (Firkowska-Mankiewicz, 2011). Children’s intelligence is even related to later health and length of life (Batty, Deary, & Gottfredson, 2007)! This broad influence raises a number of questions. What is intelligence? How is it measured? Why do children differ in intelligence? What steps, if any, can be taken to increase intelligence? These questions are heavily researched, and we will address them and other related questions in this chapter. Although there is no single, universally agreed-upon definition, many contemporary psychologists would agree with the description of intelligence as: “The ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience” (Gottfredson, 1997, p. 13; see also Kranzler & Floyd, 2013). Another definition is that intelligence is the “ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought” (Neisser et al., 1996, p. 77). Notice that these definitions include cognitive processes that are broadly applicable, like reasoning, abstract thinking, and learning from experiences. Processes like reasoning and learning from experiences are evident every day in multiple settings. As you reread the definitions, notice also how the cognitive processes relate to schooling. As we will see, from the beginnings of modern intelligence testing, the concept of intelligence has been closely tied to school performance. Intelligence can also be defined as an ability or set of abilities that extend beyond those found in the previous definitions, and we will discuss various theories of the nature of intelligence in Section 10.2. For now, however, we will work with a conception of intelligence that is tied to the definitions we have just presented. These definitions are widely accepted and underlie most tests of intelligence and contemporary research on the subject (Nisbett et al., 2012). Core Themes, Intelligence, and Individual Differences The nature and measurement of intelligence is a broad, expansive topic. The four overarching themes in our text help us organize our understanding of the wide-ranging material. The themes also place the study of intelligence into context by relating the material to topics and ideas we have encountered in previous chapters. Nature and nurture. The extent to which intellectual differences are related to genes or environment has generated considerable theorizing and large-scale investigations over the years. Consequently, the nature–nurture theme is evident throughout this chapter. Recall from Chapter 1 that the nature–nurture theme is often raised in the context of why people differ from one another. This concern with individual differences, idiographic development, is a focus as we explore why children differ from one another in intelligence. Intelligence Tests Section 10.1 Performance and competence. Second, intelligence testing is intended to accurately measure differences in children’s intellectual competencies. We will address whether intelligence tests are considered fair and accurate. Such concerns are related to the performance–competence theme of our book. Are the test scores really measuring intelligence (competence), or do the scores reflect, to some extent, performance limitations (for example, low motivation or cultural misunderstandings)? Continuity and discontinuity. Generally, intelligence testing is concerned with stable differences among individuals, rather than stage-like cognitive changes that occur within an individual (Miller, 2012a). Consequently, in this chapter we primarily focus on the question of whether there is continuity in intellectual differences. We address whether intellectual differences are relatively stable and continuous during childhood, whether children’s intelligence fluctuates during development, and whether childhood intelligence predicts later outcomes. Domain general and domain specific. Our fourth theme is concerned with whether cognition is better characterized as domain general or domain specific. In this chapter, we will see evidence indicating that intelligence is a general ability that cuts across a wide variety of cognitive domains. We will also encounter a theory that suggests there are many intelligences, each somewhat specific to a particular domain. 10.1 Intelligence Tests The results of intelligence tests can have a powerful impact on a child’s educational future, as we saw in the case study. Results can influence whether a child is placed in special education classes. Sometimes children are tested for placement into accelerated education programs. Intelligence tests are also used for clinical assessments; for instance, the results of a test can help determine if a child who recently suffered a head injury is experiencing cognitive impairments as a result of the injury. The influence of intelligence testing naturally raises two questions: How is intelligence measured, and how do we know if an intelligence test is dependable and accurate? Our focus on testing provides a foundation for subsequent sections in which we discuss theories of intelligence, its stability and change, and the factors that influence individual differences in intelligence. First, however, we will describe how intelligence tests developed and how they are used today. The Development of Intelligence Testing The French Third Republic passed laws in the early 1880s making school mandatory and free for all 6- to 13-year-old children (Nadeau & Barlow, 2010). The inclusiveness of the French education system meant that classrooms were filled with children of varying backgrounds Intelligence Tests Section 10.1 and skill levels. Soon government officials were faced with the dilemma of how to effectively handle children who had difficulty keeping pace with their peers. An instrument that could accurately identify at-risk children was sought, particularly when concerns arose over the accuracy and impartiality of a school official’s or parent’s opinion. Alfred Binet was a French psychologist who, along with his collaborator Theodore Simon, set out to resolve the problem by creating an effective measurement of children’s intellectual capabilities (Binet & Simon, 1916; Nicolas, Andrieu, Croizet, Santioso, & Burman, 2013; Wolf, 1973). Binet’s test ultimately contained three distinctive features still found in many intelligence tests (Siegler, 1992). First, it measured high-level cognitive abilities like memory, vocabulary, and reasoning that clearly related to the child’s schoolwork (Binet & Simon, 1916). These abilities are closely related to the definitions of intelligence presented in the previous section. Second, a single composite score summed across different cognitive subtests was used to estimate a child’s intelligence (Siegler, 1992). As we will see, this feature of intelligence testing remains influential. It also brings to the forefront the question of whether intelligence is really a singular entity. We discuss theories that address whether intelligence is made up of one or numerous entities in Section 10.2. Third, test items were arranged in sequence from less to more difficult. This arrangement allowed test administrators to determine how far children could progress before reaching their ceiling. A ceiling is typically established when a child misses a specified number of items. Once the child’s ceiling was established, the tester could then compare it with the average ceiling reached by same-age peers. In our case study at the beginning of the chapter, Michael’s score is low because he missed items that other children his age typically answer correctly. In other words, Michael did not progress through as many items as his peers. IQ Tests Today The conventional scoring method used to express intelligence is known as the intelligence quotient (IQ). Conceptually, an IQ score is derived by comparing an individual’s performance (called a “mental age”) against the typical performance of those who are the same chronological age. In this formulation, IQ = mental age ÷ chronological age (Stern, 1914). For instance, if an 8-year-old’s performance is at the level of a typical 10-year-old, she would have a score of 1.25 (that is, 10 ÷ 8). Conventionally, average IQ scores are set at 100 (Kranzler & Floyd, 2013). Thus, 10 ÷ 8, (or 1.25) multiplied by 100 results in an IQ score of 125. Figure 10.1 illustrates the types of items found on commonly used intelligence tests. These tests generally have (a) subtests that measure cognitive abilities (for example, math, vocabulary, math, and reasoning) and (b) a full-scale composite score that is derived from subtest performance. Section 10.1 Intelligence Tests Figure 10.1: Sample items found on IQ tests Examples of questions found on commonly administered intelligence tests. Note that items assess various abilities such as verbal comprehension (Information; Vocabulary) and reasoning (e.g., Block Design, Picture Completion). Typical IQ Subtests Matrix Reasoning Information On what continent is Argentina? Arithmetic If four toys cost six dollars, how much do seven cost? Find the missing piece from the six pictured below. Vocabulary What does “debilitating” mean? Comprehension Why are streets usually numbered in order? Picture Completion Indicate the missing part from an incomplete picture. Block Design Use blocks to replicate a two-color design. ? Object Assembly Assemble puzzles depicting common objects. Coding Using a key, match symbols with shapes or numbers. Picture Arrangement Reorder a set of scrambled picture cards to tell a story. Similarities In what way are dogs and rabbits alike? Source: Flynn, J. R. (2007). Solving the IQ puzzle. Scientific American Mind, 18(5), 24–31. Some commonly administered intelligence tests are summarized in Table 10.1. Table 10.1: Representative IQ tests administered to children Test Age range Stanford-Binet Intelligence Scales, fifth edition 2 to 85+ Features • Ten subtests assess cognitive processes such as working memory, general knowledge, quantitative and spatial reasoning, and vocabulary. • Subtests are administered in both verbal and nonverbal forms. • All 10 subtests are scored. The full-scale IQ score combines all 10 subtests. (continued) Section 10.1 Intelligence Tests Table 10.1: Representative IQ tests administered to children (continued) Test Age range Wechsler Preschool and Primary Scale of Intelligence, fourth edition (WPPSI-IV) 2 to 7 Wechsler Intelligence Scale for Children, fifth edition (WISC-V) 6 to 16 Kaufman Brief Intelligence Test, second edition (KBIT-2) 4 to 90 Features • Ages 2 to 3 assessed by three subtest areas of verbal comprehension, spatial knowledge, and working memory; testing for ages 4 to 7 also includes processing speed and reasoning subtest. • Subtest scores and full-scale IQ. • Ancillary scores enhance utility for special cases, such as the assessment of school readiness or suspected language delays. Ancillary scores include a cognitive proficiency score (working memory and processing speed) indicating informationprocessing efficiency. • Five subtests assess verbal comprehension, spatial knowledge, working memory, processing speed, and reasoning. • Subtest IQ scores and full-scale IQ • Ancillary index scores provide additional information relevant to clinical situations (such as a nonverbal index score for children with autism) and school achievement (such as a quantitative reasoning index). • Provides a quick screening measure for identifying children at academic risk or those eligible for enriched educational programs. • Three subtests. Two measure verbal knowledge such as vocabulary, and one measures nonverbal knowledge related to reasoning about novel problems (that is, choosing which of five pictures best matches the concept in a target picture). • Subjects receive verbal, nonverbal, and IQ composite scores. Sources: Bain & Jaspers, 2010; Kaufman & Kaufman, 2004; Raiford & Coalson, 2014; Roid, 2003; Wechsler, 2014. IQ Scores As noted earlier, a child’s IQ score is determined by comparing his or her performance with same-age peers. The comparison group is called the normative sample. A normative sample is a group of individuals who are representative of the larger population. In order to be representative, the sample must be inclusive and proportionately reflect the different backgrounds and ethnicities within a population. To illustrate, the normative sample for the Stanford-Binet (fifth edition) totaled nearly 5,000 individuals and was chosen to match demographic information derived from the 2000 U.S. Census (Roid, 2003). IQ scores in normative samples generally form a normal distribution (Gottfredson, 2008). This means scores within the sample most frequently occur in the middle—the overall average—and then occur less and less frequently as scores deviate further and further from the average (see Figure 10.2). Recall that most IQ tests are quantified so that the average score is 100 (Kranzler & Floyd, 2013). Thus, when a child is said to have “above average” intelligence, this means, in a literal sense, that the score is higher than 100, the average score of children the same age. Section 10.1 Intelligence Tests Figure 10.2: Normal distribution Scores within a normal distribution tend to cluster toward the middle (average) in their frequency. As scores deviate from the average, they occur with less and less frequency. IQ scores typically follow a normal distribution. 34% 0.1% Standard Deviation IQ Score 34% 13% 2% 13% 2% 0.1% –3 –2 –1 0 1 2 3 65 70 85 100 115 130 145 A standard deviation measures the extent that scores vary from the average. Conventionally, IQ scores change by 15 points for every standard deviation above or below the average (Kranzler & Floyd, 2013). In other words, IQ scores that are one standard deviation above the average are 115, while scores one standard deviation below the average are 85. When scores are normally distributed, 68% of scores fall within one standard deviation of the mean, and 95% of the scores fall within two standard deviations (see Figure 10.2). Thus, 95% of IQ scores typically fall between 70 and 130. Scores that are two standard deviations below the mean (scores of 70 or lower) indicate an intellectual disability (Prifitera, Saklofske, & Weiss, 2008). An intellectual disability is characterized by “significant limitations both in intellectual functioning and in adaptive behavior” that originate before age 18 (Schalock, Luckasson, & Shogren, 2007, p. 118). The transition to school is difficult for children diagnosed with intellectual disability—the cognitive and behavioral expectations of schooling can often exceed the child’s readiness. Social skills (for example, appropriate expression of feelings) and self-regulation skills can positively contribute to the ability of 5- to 6-year-old children with an intellectual disability to adapt to school (McIntyre, Blacher, & Baker, 2006). Consequently, early interventions that focus on social and behavioral competencies may be useful in smoothing the transition to schooling for children who have an intellectual disability. Children identified as gifted (also referred to as “talented”) exhibit extraordinary intellectual ability, promise, creativity, and motivation (McClain & Pfeiffer, 2012). Children are generally identified as “gifted” or “talented” in intelligence as their IQ scores approach 130 (McClain & Pfeiffer, 2012). Other measures, such as teacher ratings for motivation, are also typically incorporated into the process of determining giftedness. Intelligence Tests Section 10.1 The pace is often accelerated in educational settings for children identified as gifted (Subotnik, Olszewski-Kubilius, & Worrell, 2011). Also, educational opportunities generally provide an enriched curriculum that features topics (for example, robotics) not found in typical curricula (Subotnik et al., 2011). Longitudinal research has linked participation in enriched educational opportunities—such as taking college courses while in high school or participating in science fair/math competitions—to later accomplishments in STEM (science, technology, engineering, and math disciplines) for adolescents who scored exceptionally high on math assessments (Wai, Lubinski, Benbow, & Steiger, 2010). Such findings illustrate the role of nurture (that is, enriching experiences) in facilitating later accomplishment for students identified as gifted. Standards for Evaluating Intelligence Tests By what standards can we evaluate whether an intelligence test is actually useful? The question arises when a child is given a cognitive-related diagnosis. We want to know whether the test informing the diagnosis is trustworthy. Professional guidelines and legal requirements for fairness help ensure that intelligence tests are extensively vetted for their soundness (Gottfredson & Saklofske, 2009). Two fundamental criteria for evaluating an assessment are its reliability and validity. These constructs are key aspects of the vetting process. Each is discussed in turn in this section. As we discuss reliability and validity, keep in mind that these concepts apply to all psychological tests. In recent decades, testing has become increasingly common in schools. Education policy in the United States has emphasized guidelines that define what students should know and the assessment of whether those guidelines have been met (Hamilton, Stecher, & Yuan, 2012). The criteria we use to evaluate these educational assessments include reliability and validity. Recall our case study. If Michael was retested a few days later and his IQ score was vastly different from his first test, we would not know which result, if either, to believe. We would question the reliability of the test. Test reliability refers to the extent a measurement produces consistent results (Cook & Beckman, 2006). Consistency can be measured by comparing whether an individual’s test and retest scores are similar. The reliability of a test is different from the stability of the construct of intelligence (Miller, 2012a). Reliability is typically measured across a period of days or months. Stability is measured across a period of years. Commonly administered intelligence tests generally have very good reliability (Flanagan & Harrison, 2012). Second, an assessment is only useful to the extent it is accurate. Test validity is established when we are confident a test is measuring what it claims to measure (Cook & Beckman, 2006). Historically, the purpose of intelligence tests was to inform educators’ decisions about a child’s academic capabilities and potential. In the context of their historical purpose, school grades are a typical criterion for establishing whether an intelligence test is measuring what it purports to measure. A valid IQ test should bear some relationship to academic achievement. Generally speaking, the correlation between IQ scores and school grades is approximately .5, which means the relationship is moderate (Duckworth, Quinn, Lynam, Loeber, & StouthamerLoeber, 2011; Neisser et al., 1996). Additionally, those with higher IQs tend, on average, to have higher incomes, educational attainment, and occupations associated with attaining a Intelligence Tests Section 10.1 college education and holding a professional position (Gottfredson, 2004a; Gottfredson & Deary, 2004; McCall, 1977). Taken as a whole, these various findings are evidence for the validity of commonly administered intelligence tests. An ongoing question about intelligence test validity asks if the tests are fair to children from all cultural backgrounds. If individuals have a set of experiences that bias them to consistently misinterpret and miss test questions, then the test is not a valid measure of their ability. An invalid test would be a poor predictor of outcomes for members of that particular culture. There are cases in which intelligence test content does not meaningfully reflect a child’s background and experiences. For instance, questions asking about U.S. presidents and history would be unfair for a child who recently immigrated to the United States and has not studied U.S. history. Similarly, children learning English as a second language are at an obvious disadvantage on intelligence tests that assess verbal knowledge like vocabulary (Gottfredson & Saklofske, 2009). Consequently, the validity of IQ tests in the United States is in doubt when a child is not English-speaking and/or is new to the culture and unfamiliar with the knowledge and skills IQ tests assess (Kranzler & Floyd, 2013). In the News: Intelligence Tests and the Death Penalty The Supreme Court recently examined arguments about whether a death row inmate’s IQ score should make him ineligible for capital punishment. As you read the article, consider the arguments for and against the use of strict IQ cutoff points to determine whether a death sentence can be carried out. http://www.pbs.org/newshour/rundown/supreme-court-skeptical-iq-scores-decidingexecution Critical-Thinking Question What criteria should an intelligence test have to meet, in your opinion, to be a factor in legal questions such as death penalty cases? Aside from these exceptions, many researchers point out that the weight of evidence indicates intelligence tests possess acceptable validity across cultural and ethnic groups in the United States (Kranzler & Floyd, 2013). This is because intelligence tests predict outcomes in a comparable, reasonably effective manner for different groups. In short, validity across a wide spectrum of groups is evidence of fairness. Of course, each child brings a unique set of circumstances to the assessment process. Clinicians and educators must always strive for cultural fairness in testing on a case-by-case basis. Guidelines to this end include: • • • use assessments that are psychometrically sound, use multiple methods of assessment, and be aware of each individual’s background and circumstance when making evaluations (Reynolds & Ramsay, 2003). Intelligence Tests Section 10.1 Psychometrically sound tests (see the first point in the above list) are those that are reliable, valid, and have a normative sample that is culturally and ethnically diverse. Commonly administered intelligence tests like those summarized in Table 10.1 generally possess ample evidence of psychometric soundness (Bain & Jaspers, 2010; Flanagan & Harrison, 2012). Nonintellective Influences on IQ Scores Psychological assessment relies on the objective administration of tests. This means that each test should be administered in a consistent manner for all children through strict adherence to instructions and other testing procedures. If testers vary in how they present material—for example, if some offer hints and praise, while others simplify instructions—their well-intentioned modifications actually undercut the test’s usefulness. This is because the child’s score cannot be accurately compared to the normative sample if there are differences in the way the test was administered to the child and the comparison group. It would be impossible to know, for instance, if a child’s above average test score reflected above average intelligence or the advantages of receiving hints and suggestions that were unavailable to the normative sample. In our case study we asked if Dr. Williams should have provided prompts to help Michael during testing. Williams may have been concerned that the child’s performance was not reflecting his true competence. However, if Williams’s goal was to identify the child’s intellectual abilities by comparing his score to the normative sample (which is typically the goal of such assessments), then he should not have offered hints and prompts. Though psychologists and other professionals who administer intelligence tests are highly trained to minimize nonintellective influences on IQ scores, it is not possible in all cases. These influences refer to factors impacting a child’s IQ score that have nothing to do with the child’s intelligence per se. One factor we just noted is called assessor bias. This occurs when a test administrator strays from the testing procedures by, for instance, prompting children with suggestions, not enforcing time limits, or erroneously presenting items (McDermott, Watkins, & Rhoad, 2014). The term assessor bias can be misleading because it suggests deliberate fraud. In reality, it simply means that errors can occur in the administration of intelligence tests. Ensuring tests are appropriately and consistently administered is crucial for making the results useful. The concern over assessor bias is a longstanding one that dates to the creation of the modern intelligence test: An inexperienced examiner has no idea of the influence of words; he talks too much, he aids his subject, he puts him on the track, unconscious of the help he is thus giving. He plays the part of pedagogue, when he should remain psychologist. Thus his examination is vitiated. It is a difficult art to be able to encourage a subject, to hold his attention, to make him do his best without giving aid in any form by an unskillful suggestion. (Binet & Simon, 1916, pp. 44–45) Section 10.2 Theories of Intelligence Another potentially important source of nonintellective influence is the child’s level of motivation during testing. A child’s disinterest during testing can obscure his or her true cognitive abilities. A common experimental method for researching the effects of motivation is to offer children rewards that are tied to test performance. If rewards improve performance compared to typical test settings, then we can infer that outcomes are influenced, to some extent, by the child’s level of motivation. Fuse/Thinkstock Providing incentives can help improve children’s test performance. Questions to Consider A number of studies conclude that providing incentives (for example, tokens that can be cashed in for rewards) improves children’s test performance, particularly for those identified with below-average IQ scores (Duckworth et al., 2011). Such findings raise the possibility that correlations between IQ scores and academic outcomes are influenced to a degree by individuals’ motivation (Duckworth et al., 2011). That is, an individual with low motivation during IQ testing may also experience relatively low levels of motivation in academic assessments at school. Low motivation would impact performance in both settings. 1. What are some reasons why a child might be unmotivated to take an intelligence assessment? 2. If a child is generally unmotivated and disengaged when tested, does this mean his low IQ score is not valid? Why or why not? We caution that although motivation may influence test performance for some children, IQ tests are, as we have already noted, generally valid measurements that usefully predict school performance and other related outcomes (Duckworth et al., 2011). It would be misleading to immediately assume a relatively low IQ score simply reflects a lack of motivation. However, the influence of motivation is worth our consideration because it highlights the performance–competence theme. Considering the role of motivation usefully draws our attention to the possibility that a child’s intelligence score reflects factors that extend beyond the child’s actual intellectual competence. 10.2 Theories of Intelligence A score on an intelligence test is not identical to intelligence itself. Intelligence is a construct that is measured, or manifested in, intelligence test performance. Theories of intelligence influence how we define and measure the construct of intelligence and how we interpret the results of intelligence tests (Goldstein, 2013). Theories clarify our understanding of the nature of intelligence. Consequently, theories guide interventions that target specific cognitive processes and help answer why some interventions are more effective than others (Kaufman, Kaufman, & Plucker, 2013). By identifying the underlying cognitive processes of intelligence, theories also inform educational goals. For Theories of Intelligence Section 10.2 instance, if widely accepted theories target abstract reasoning as an important aspect of intelligence, then educational efforts to develop intelligence will focus curricular goals on fostering reasoning. Throughout the history of modern intelligence testing, theories and tests have had a mutual influence. If a theory defines quantitative reasoning as a critical component of intelligence, then a test will contain quantitative problems. It is also the case, as we will see, that the results of intelligence tests refine and clarify our understanding and our definitions of intelligence. For instance, the results of intelligence tests are analyzed to examine if intelligence is a singular ability that impacts performance across a range of problems. At present there is no single agreed-on and overarching theory of intelligence (Gardner, 2011). In this section, we will describe the dominant theoretical approach—the psychometric approach—and often-cited alternatives to this dominant approach. The Psychometric Approach Sometimes an individual will refer to herself as a “math person” who particularly enjoys, and excels at, working with numbers. A “history buff” may be someone whose cognitive strengths lie in quickly mastering and remembering lots of verbal information. Theories of intelligence ask how different cognitive tasks—like mastering math or history— may or may not be linked together by underlying cognitive processes (Gardner, 2011). How closely related are verbal and math abilities, and are they each influenced by a single, broad ability? The psychometric approach attempts to understand the nature and structure of intelligence by looking for patterns in the results of large data sets of intelligence test scores (Flanagan, Alfonso, Ortiz, & Dynda, 2013). In this approach we can understand the qualities of intelligence using statistical procedures like factor analysis. Factor analysis is a statistical technique for detecting how variables are structured and related to one another. For instance, factor analysis informs us whether performance on language tasks is related to performance on memory tasks. It also addresses whether abilities like language and memory are separate or related by an underlying cognitive ability. Intuitively, cognitive performance seems to vary from one task to another. One child may find math coursework easier than coursework that emphasizes reading comprehension. The reverse may be true for another child. However, decades of intelligence testing reveal this conventional wisdom is somewhat misleading. Scores on tests that assess seemingly diverse areas like spatial reasoning, vocabulary, memory, math, and others all tend to positively correlate with one another (Gottfredson, 2005). The phenomenon of positive intercorrelations among measurements of cognitive ability is known as the positive manifold. It is a consistent, frequently made observation that dates to the dawn of intelligence testing in the 20th century (Spearman, 1904; Van der Maas, Kan, & Borsboom, 2014). The positive manifold indicates that cognitive tasks have something in common with one another. That is, diverse tasks such as defining vocabulary items and solving math problems draw on the same general intellectual ability. This common factor underlying scores on a wide variety of intelligence tests is often denoted by psychologists as g (Spearman, 1904), which stands for a global, or general, intelligence that is responsible for widely impacting cognitive performance (Gottfredson, 2002). Theories of Intelligence Section 10.2 Findings suggest that g is not simply located in a single area of the brain. Instead, intelligence appears related to activity in a network of neural connections distributed throughout the brain (Barbey et al., 2012; Deary, 2012; Jung & Haier, 2007). The efficient flow of information across these regions appears to be associated with higher intelligence test scores (Deary, Penke, & Johnson, 2010). This indicates processing speed is an influential component of intelligence. Information-Processing Theory and Intelligence As just noted, the psychometric approach identifies processing speed as an underlying component of intelligence. In IP theory, processing speed refers to the efficiency of cognitive functioning (Sweet, 2011). It is measured by presenting timed cognitive tasks and calculating, often in milliseconds, the speed with which someone responds. To illustrate the measurement of processing speed, in one condition children would be instructed to push a button on a computer keyboard upon seeing a letter appear on a screen (Carlozzi, Tulsky, Kail, & Beaumont, 2013). In a second condition the particular button children push would depend on whether a lowercase or uppercase letter appeared. Notice that the second condition requires making a decision before responding as quickly as possible. From an IP theory perspective, the second condition requires the child to attend to the type of letter presented on the screen and decide how to respond based on the task instructions held in memory. The average difference in time it takes to complete the more difficult task is an index of processing speed. This is because the time difference reflects how long it takes the child to decide how to respond compared to how long it takes to respond when no decision is required (as in the first condition) (Carlozzi et al., 2013). When given such tasks, children and adults with faster processing speed times tend to have higher scores on measurements of g than those with lower times (Carlozzi et al., 2013; Deary, Der, & Ford, 2001; Sheppard & Vernon, 2008). Thus, there is a link between processing speed and individual differences (that is, idiographic differences) in intelligence. Theoretically, speed in encoding information, scanning and organizing it in working memory, and retrieving information from long-term memory contributes to the link between processing speed and intelligence (Vernon & Jensen, 1984). Neural maturation during childhood and adolescence underlies increases in processing speed for typically developing children (Ferrer et al., 2013). Consequently, faster processing impacts age-related changes in intelligence. In other words, developmental changes in processing speed are linked to normative changes in intelligence (Coyle, Pillow, Snyder, & Kochunov, 2011). By normative changes we mean that as children mature they answer more questions correctly—their raw scores improve on measures of intelligence. Processing speed, therefore, impacts both individual differences and age-related developmental differences. Crystallized and Fluid Intelligence Psychologists working from the psychometric approach examine performance on a variety of tasks to examine how intelligence is organized. Crystallized intelligence is the accumulated Theories of Intelligence Section 10.2 knowledge and ability generally acquired from one’s culture and from formal instruction (Cattell, 1987). Tasks that assess vocabulary, general knowledge, and comprehension tend to be interrelated (grouped) and comprise the crystallized intelligence factor. Crystalized intelligence is typically assessed with verbal items that measure one’s store of knowledge (for example, “What is the state capital of Idaho?,” “What does the word democracy mean?,” and so forth). Fluid intelligence, on the other hand, involves reasoning and problem solving, particularly on unfamiliar problems that require efficient and in-themoment inferences and solutions (Cattell, 1987). Completing patterns of matrices is a task that draws on fluid intelligence (see the right-hand column in Figure 10.1). Drawing an inference from a series of relationships— for instance, reasoning that the next number in the series 1, 2, 4, 8, 16 ___ is “32”— also illustrates fluid intelliage fotostock/SuperStock gence. The answer is derived by rea- Fluid intelligence involves problem solving and soning that the next number in the detecting relationships, skills children use to solve sequence is the result of multiplying puzzles. the previous number by 2 (1 × 2 = 2; 2 × 2 = 4; 4 × 2 = 8, etc.). Because measurements of fluid intelligence focus on novel problems, they are assumed to rely less on prior learning than do measures of crystallized intelligence (Nisbett et al., 2012). Consider an example that will illustrate both positive manifold and the different intellectual abilities we have just discussed. Imagine that William is a fourth grader who plays with puzzles and video games that require reasoning skills. He does reasonably well at these games. However, William enjoys reading about dinosaurs most of all. In fact, he is reading books on the topic that are well above his grade level. Based on the concept of positive manifold, we can predict that if William scores above average on tasks that assess reasoning (fluid intelligence), he will probably also score above average on other tasks that assess reading comprehension (crystallized intelligence). This is positive manifold. We would not be surprised, however, to also learn that given William’s enthusiasm for reading about dinosaurs, he scored very high on verbal tests that measure reading and vocabulary (crystallized intelligence) while scoring lower (but still above average) on the measurements of nonverbal reasoning (fluid intelligence). In other words, crystallized and fluid intelligence are related (positive manifold), but also distinct and separable. The psychometric approach has been refined in recent years by uncovering additional broad intellectual abilities that emerge from analyses of large data sets of intelligence test scores (Carroll, 1993; Horn & Blankson, 2005). Like crystallized and fluid intelligence, each broad category is made up of cognitive tasks that share more in common with each other than with tasks in the other categories. Section 10.2 Theories of Intelligence These domains of intellectual abilities are organized hierarchically in what is known as the three-stratum model of intelligence (Carroll, 1993). The highest level of the hierarchy, g, is the most general stratum (layer) of intelligence (see Figure 10.3). The next broadest stratum is characterized by eight intellectual abilities (see Table 10.2). The third and most narrow layer of intelligence includes a number of specific abilities; for instance, memory for sound patterns, foreign language proficiency, and reading comprehension (see Flanagan et al., 2013 for a review). Figure 10.3: The three-stratum model of intelligence In the three-stratum model of intelligence, general intelligence, g, is at the top of the hierarchy. Broad abilities related to g are at the second tier. The broad abilities are reflected in specific abilities at the third tier. General intelligence (g) Broad abilities Fluid intelligence Crystallized intelligence General memory and learning Broad visual perception Broad auditory perception Broad retrieval capacity Broad cognitive speediness Processing speed (Decision speed) Specific abilities: Examples Reasoning Vocabulary knowledge Memory span Detecting spatial relations Speechsound discrimination Creativity Rate of test-taking Reaction time Source: From “The Three-Stratum Theory of Cognitive Abilities” by J. B. Carroll. In D. B. Flanagan, J. L. Genshaft, and P. L. Harrison (Eds.), Contemporary Source: Adapted from Carroll, Theories, J. B. (1996). TheIssues. three-stratum of cognitive abilities. In D. with B. Flanagan, J. the L. Genshaft, Intellectual Assessment: Tests, and Copyright © theory 1996 by Guiford Publications, Inc. Adapted permission of publisher. & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues. New York: Guilford Press. Copyright © 1996 by Guilford Publications, Inc. The three-stratum model conceptualizes intelligence as unitary: The abilities classified beneath g are interrelated and, therefore, not considered independent intelligences. The broad ability at the top of the hierarchy, g, influences second-level abilities that are more specific (such as crystallized intelligence). At the third and most specific level, an even more specialized skill is impacted by the second-level ability (which is impacted by g). For the most widely used intelligence tests, items and subtests generally correspond to the abilities outlined in the three-stratum model (Flanagan et al., 2013; Gottfredson, 2004a). For instance, the Stanford-Binet (fifth edition) measures five broad abilities that correspond to abilities in the three-stratum model (Roid, 2003): fluid reasoning, crystallized knowledge, quantitative reasoning, visual-spatial reasoning, and working memory. Section 10.2 Theories of Intelligence Table 10.2: Eight broad intellectual abilities in the three-stratum model of intelligence Ability Description Fluid intelligence Using reasoning and inference to identify relationships and solve novel problems; includes quantitative reasoning General memory and learning The limited-capacity system for maintaining immediately relevant information during a short period Crystallized intelligence Broad visual perception Broad auditory perception Broad retrieval ability Broad cognitive speediness Processing speed Cultural knowledge pertaining to concepts, vocabulary, and other information typically derived from experience and education Transforming and spatially orienting shapes, figures, and other visual information Discriminating, analyzing, and manipulating patterns of sound (for example, music, speech). Consolidating new information and retrieving it from long-term memory when cued; for instance, fluently naming examples of a category or remembering ideas, names, and other facts Efficiency in performing familiar cognitive tasks that require attention (for example, reading) Reaction time when presented with simple tasks such as matching stimuli Source: Adapted from McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 1–10. Intelligence Outside of the Classroom As we indicated at the outset of this chapter, definitions of intelligence typically center on cognitive abilities associated with success in school, such as reasoning, comprehending complex ideas, and learning from experience. These cognitive abilities can also apply to situations and problems outside of formal schooling. For instance, as we discussed in the theory of mind chapter (Chapter 6), complex reasoning is employed when we infer the thoughts and intentions of others in social situations. Everyday phrases like “street smart” capture this broader notion of intelligence, extending it beyond school performance. In fact, adults often view social competence as an important aspect of intelligence (Silvera, Martinussen, & Dahl, 2001; Sternberg, Conway, Ketron, & Bernstein, 1981). Following from these observations, broader definitions more explicitly associate intelligence with abilities that are useful in contexts outside of the classroom. One prominent researcher on the subject defines successful intelligence as the “ability to choose and successfully work toward the attainment of one’s goals in life, within one’s cultural context or contexts” (Sternberg, 2014, p. 209). In this definition, intelligence is identified by behaviors and outcomes that are particularly adaptive and valued in one’s culture. Although basic cognitive processes like memory and attention may be important components of intelligence across different cultures, the type of problems that draw on those resources varies according to culture and context (Kranzler & Floyd, 2013; Sternberg, 2014). Theories of Intelligence Section 10.2 For instance, Luo children in a village in Kenya who were identified as particularly competent were actually removed from formal schooling in order to serve in an apprenticeship (Sternberg, 2014). Intelligence in that culture was not closely tied to the type of problem solving typically encountered in school. Practical knowledge valued in the community involved problems such as distinguishing among different herbal medicines to treat parasitic illnesses. If the skills this culture considered valuable and adaptive were measured in a written intelligence test, the questions might look like the following hypothetical item: Sample Item Measuring Successful Intelligence in a Rural Kenyan Village “A small child in your family has homa. She has a sore throat, headache, and fever. She has been sick for 3 days. Which of the following five Yadh nyaluo (Luo herbal medicines) can treat homa? i. Chamama. Take the leaf and fito (sniff medicine up the nose to sneeze out illness).∗ ii. Kaladali. Take the leaves, drink, and fito.∗ iii. Obuo. Take the leaves and fito.∗ iv. Ogaka. Take the roots, pound, and drink. v. Ahundo. Take the leaves and fito.” *Correct answers (Sternberg, 2014) Situating intelligence within a social context is related to social constructivist views of cognition. Recall that within a zone of proximal development, cognitive performance is measured as it unfolds in the context of social guidance (Vygotsky, 1978a). In contrast, intelligence testing in Western societies is typically decontextualized, meaning that the setting is neutral and strictly limits any influence from the tester. However, Vygotsky (1978a) claimed that guidance from a tester could help draw out intellectual potential that would otherwise remain hidden in a testing situation. In reaction to this concern, he engaged in dynamic testing, a flexible assessment in which feedback, social prompts, and support are provided to the child during testing (Murphy, 2011). To illustrate, Vygotsky wrote that testers “might run through an entire demonstration and ask the children to repeat it, others might initiate the solution and ask the child to finish it, or offer leading questions” (Vygotsky, 1978a, pp. 85–86). The interplay between tester and child is the strength of dynamic testing because it provides information about a child’s potential to benefit from experience. This interplay is also a weakness, however, from a practical standpoint. Drawbacks of dynamic testing include its time-intensive nature and its relatively high cost in terms of training the testers (Murphy, 2011). Consequently, it is used relatively infrequently (Fuchs, Compton, Fuchs, Bouton, & Caffrey, 2011). Section 10.2 Theories of Intelligence Dynamic testing is of theoretical interest, however, because it can provide direct evidence of the child’s ability to learn when provided with assistance (Sternberg & Grigorenko, 2006). Recall that learning from experience is a feature of intelligence as per our definition found at the outset of the chapter. Practically speaking, dynamic testing seems most compatible, at present, with attempts to assess children who are underperforming and who do not appear to be exhibiting their intellectual potential within more conventional ways of testing and assessment (Murphy, 2011). Also differing from the psychometric approach, Howard Gardner theorizes that intelligence is not a general ability captured by a single score. The theory of multiple intelligences proposes that separate core abilities and processes operate to “solve problems or create products that are of value in a culture” (Gardner, 1999, p. 34). In this theory there are many domains of intelligence extending beyond the analytical and problem-solving abilities typically associated with school performance. Instead, Gardner asks us to consider how intelligence operates in a wide variety of pursuits and contexts, ranging from activities like playing a musical instrument, competing in athletic competitions, or playing a game of chess (Gardner, 1983, 2006). By tying intelligence to culture and values, Gardner’s theory highlights the importance of opportunities in the environment to nurture intelligence (Gardner, 1999). If, for instance, virtuosity in music is not particularly valued in a culture, a child’s potential in that particular domain of intelligence will often go unrealized. Altogether, Gardner has identified eight separate intelligences (see Table 10.3). The intelligences are, to some extent, distinct from one another. This means we might expect unevenness in ability; for instance, a performing artist might exhibit exceptional musical and bodily-kinesthetic intelligence while other intelligences are much less advanced (Gardner & Moran, 2006). Table 10.3: Gardner’s theory of multiple intelligences Intelligence Characteristic core components Linguistic Sensitivity to language and effective use of it; the ability to learn a new language Spatial Recognizing and manipulating visual patterns Logical–mathematical Bodily–kinesthetic Musical Interpersonal Intrapersonal Naturalistic Source: Gardner, 1999. Carrying out mathematical and logical operations; scientific thinking Using one’s body for creative expression or problem solving Producing and appreciating musical components like pitch and rhythm Discerning mental states of others and responding by working effectively with others Apprehending one’s own thoughts and feelings to guide behavior; knowledge of one’s own strengths and weaknesses The ability to recognize and classify animate and inanimate objects Theories of Intelligence Section 10.2 Gardner’s theory is derived from evidence gathered from a variety of sources beyond the IQ and experimental tasks that make up the psychometric approach. For instance, he examined patterns of evidence from brain-damaged individuals indicating a particular cognitive faculty was localized in the brain (Gardner, 1999). The localization of function is evident when damage to a specific part of the brain impairs a specific cognitive function but spares other cognitive abilities. Damage to particular areas of the left hemisphere specifically impairs language, suggesting to Gardner that humans have a separate linguistic intelligence (Gardner, 1999). Essentially, evidence tying specific cognitive faculties to specific areas of the brain is counted as evidence for an intelligence. Evidence from prodigies also informs the theory of multiple intelligences. A prodigy is someone with exceptional, rare ability in a specific area of expertise, typically coupled with at least average general intellectual ability (Gardner, 1999). An example of a child prodigy’s accomplishments might be committing 100 pages of music to memory and playing at Carnegie Hall before kindergarten (Ruthsatz, & Urbach, 2012). The uniqueness of the extraordinary ability suggests it is independent from the other, more typical, intellectual abilities of the prodigy. A prodigy in an area like math or music allows us to observe “a particular intelligence in sharp relief” (Gardner, 1999, p. 39). Gardner’s theory reminds us that emphasis in school curricula on cognitive abilities like math and language may neglect cognitive capacities such as interpersonal and musical intelligences that are also valuable in society (Gardner, 1995). In addition, Gardner’s theory draws attention to the possibility that education might become more personalized and less uniform once we broaden our definition of intelligence (Gardner, 1999). In this view, accounting for differences in the ways children process information and learn—acknowledging there are multiple intelligences—calls for individualized approaches to teaching and assessment (Gardner, 2009). Although the theory of multiple intelligences is thought-provoking in expanding our ideas of intelligence, it has been criticized on empirical grounds. For instance, in a study with adults, many of the intelligences with higher order cognitive requirements (for example, linguistic, logical, interpersonal, spatial) were intercorrelated and appeared to reflect a common, g factor of intelligence (Visser, Ashton, & Vernon, 2006). This evidence raises the question of whether there are really multiple intelligences or just one g that impacts performance on multiple tasks. To date, theories that extend the concept of intelligence beyond traditional academic settings have not produced measurements that have the same practical utility as conventional intelligence tests that measure cognitive abilities closely linked to school performance (Gottfredson, 2004a). Abilities like bodily-kinesthetic, musical, and intrapersonal intelligence do not readily translate to the conventional approach, in which children answer questions that are clearly quantified as right or wrong. For instance, measuring musical intelligence might necessitate assessing activities, such as singing a new melody, that do not immediately lend themselves to an objective right or wrong response (Gardner, 1995). Heredity, Environment, and Intelligence Section 10.3 Questions to Consider 1. How do the three theoretical approaches that characterize intelligence—psychometric, multiple intelligences, and successful intelligence—compare and contrast to one another? 2. If you were given the opportunity to design an elementary school curriculum (including teaching materials, lesson plans, and content) that was influenced by Gardner’s theory of multiple intelligences, what are some possible differences that might emerge in comparison to traditional school curricula? 10.3 Heredity, Environment, and Intelligence The relationship of IQ to outcomes like school performance, job performance, and health highlight the relevance of asking why individual differences in intelligence emerge in the first place (Neisser et al., 1996). Ideally, answers to the question will provide information we can use to reduce disparities. A theme highlighted throughout this book is the role heredity (nature) and environment (nurture) jointly play in influencing cognitive development. This theme guides our discussion in this section. We will attempt to clarify how the contributions of genes to individual differences are quantified, how genes and environment interact to produce differences in intelligence, and how the nature/nurture theme applies to possible group differences in IQ test performance. Twin Studies Efforts to understand and quantify the different contributions of genes and environments to differences in intelligence are greatly advanced by studies of twins. In contemporary research, twin studies are sometimes very large scale and involve thousands of twin pairings (Haworth et al., 2010). In this section we will first address why researchers would go to such great lengths to study thousands of twins. What do twins tell us about the nature/nurture theme? Second, we will summarize the results of twin studies in the context of intelligence differences. Hopefully, the more we understand the sources of intelligence differences, the better we can design methods to intervene and optimize the growth of individuals’ intelligence. At the beginning of modern intelligence testing, Francis Galton, a half-cousin of Charles Darwin, traced the lineage of famous individuals in Great Britain (Galton, 1908). He observed that prominent, accomplished individuals were often genetically related to other prominent individuals. Many times we make similar observations in our own lives. Teachers, for instance, might observe that the classroom performance of younger siblings often bears some resemblance to their older siblings. Galton inferred that success “runs in families” because intelligence is inherited. An alternative explanation, of course, appeals to the similarities in cultural and economic background often shared by relatives. Psychological resemblances among relatives could, in short, be plausibly explained by genetic similarities and/or by social-environmental similarities. Heredity, Environment, and Intelligence Creatas Images/Creatas/Thinkstock Identical twins share 100 percent of their genes, whereas fraternal twins share only about 50 percent. Twin studies help researchers determine the extent that genetic similarities are related to intellectual similarities. Section 10.3 Modern twin studies originated in the 1920s and help clarify the influences of nature and nurture on intelligence (Rende, Plomin, & Vandenberg, 1990). They compare the correspondence between identical (monozygotic) twins with the correspondence between fraternal (dizygotic) twins on the same measurement. Monozygotic (MZ) twins are genetically identical while dizygotic (DZ) twins share, on average, 50% of their genes. These differences occur because DZ (fraternal) twins come from two separate fertilized eggs, whereas MZ (identical) twins develop from a single fertilized egg that has split in two. If identical twins are more closely related in intelligence than fraternal twins, a genetic influence is inferred. The greater relationship between identical twins is attributed to their greater genetic similarity. The results of a number of seminal twin studies indicate that IQ scores of identical twins are more closely related than are IQ scores of fraternal twins, which is evidence that IQ is influenced by genetic factors (Bouchard & McGue, 1981). Because of such results, the existence of a relationship between genetic and intellectual differences is generally considered well established (Nisbett et al., 2012). A common concern about twin studies is the possibility that identical twins are treated more similarly by others than their fraternal twin counterparts (Miller, 2012a). Perhaps the physical similarities of the identical twins leads to similar treatment, which then leads to stronger correlations compared to fraternal twins. One way to address this concern is to examine identical twins who were adopted into different homes. Many studies have shown that IQ scores are more strongly correlated for identical (MZ) twins reared in different homes compared to fraternal (DZ) twins reared together in the same home (Bouchard & McGue, 1981). This indicates that even when identical twins grow up in different homes, they are still related to one another in terms of their IQ scores. Because the magnitude of the correlation exceeds the correlation for fraternal twins raised in the same home, we again see evidence for the influence of nature on intellectual differences. Twins who share all of their genes but do not share a home are more closely related (in terms of IQ scores) than twins who share a home but do not share all of their genes. The genetic influence on intelligence reminds us that children bring to each environment their own unique combination of skills and interests. Although some children may thrive in a particular environment or under a particular method of instruction, other children may not benefit to the same extent. As we see in the next section, the influence of the environment depends on the influence of the genes (and vice versa). Heredity, Environment, and Intelligence Section 10.3 Heritability A teacher looking out on a classroom of students will notice a variety of differences in hair color, height, and weight. The children also, of course, differ psychologically. Heritability refers to the extent the differences among the children are related to their genetic differences. More formally, heritability is the proportion of the variance within a population that is related to genetic differences (Nisbett et al., 2012). In the context of cognitive development, heritability estimates the extent to which psychological differences within the group relate to genetic differences. Heritability is quantified through calculations yielding a coefficient ranging from 0 to 1. The higher the number, the more heritable is the trait being measured. Estimates of the heritability of IQ range between .4 and .8 across many studies (Nisbett et al., 2012). This means that some, but not all, of the observed differences in intelligence within a population are related to genetic differences. The heritability of intelligence is often misinterpreted to mean that differences in IQ are unalterably fixed (Block, 1995). This misinterpretation stems from confusing heritability with inheritability (Block, 1995; Keller, 2010; Moore, 2013b). The concepts are very different! Inheritance refers to an individual’s genetic material. Heritability is a measurement of differences within a population, a statistic that does not tell us anything about the DNA one inherits. An individual’s genetic material—inheritance—does not change because of the environment. However, the reasons why people within a group differ from one another—heritability—can change. In fact, intelligence can become more or less heritable depending on culture and circumstance. We can illustrate how heritability changes by discussing the association between heritability and a child’s age (Briley & Tucker-Drob, 2013). Specifically, the heritability of intelligence increases from infancy to young adulthood (Haworth et al., 2010). In other words, during development, individual differences in intelligence become more strongly related to genetic differences (based on the results of twin studies). A common explanation for this finding illustrates how environment and genes work together to influence intelligence. In particular, many theories propose that heritability increases with age because of the transactional nature of development (Haworth et al., 2010; Trzaskowski et al., 2013; Tucker-Drob, Briley, & Harden, 2013). This means that features related to one’s genes change one’s environment, and the environment impacts features that are influenced by genes (Sameroff, 2010). More succinctly, it means that nature affects nurture and nurture affects nature. Differences in the environment can impact differences in intelligence, even though intelligence is a heritable characteristic (Eisenberg, 2004). For instance, two children may possess slight genetic differences that are reflected in different IQ scores measured during their early childhood. By middle childhood, the differences in their environments may become magnified as the children choose different levels of academic challenges, different friends, different extracurricular activities, and so on (Briley & Tucker-Drob, 2013). From this perspective, children select their own environments following, at least in part, from their genetic propensities. As a consequence of these choices, the differences in intelligence test performance between the children become multiplied because of their different environmental experiences (Dickens & Flynn, 2001). Heredity, Environment, and Intelligence Section 10.3 An example from outside of intelligence testing will help clarify the transactional nature of development. Imagine two children differ in height, and that one is very tall for her age. These differences relate to the children’s genetic differences. Parents of the tall child respond by encouraging her to develop skills in basketball. The other child, in contrast, may have an interest in basketball but does not receive the same encouragement and training. These environmental differences magnify the differences in basketball skills between the two children. Nature (height) influenced nurture (environment), and then nurture amplified the differences in basketball skills. This discussion of heritability has implications for people who work with children. First and foremost, it helps us see that even though intelligence is heritable, it is not a fixed trait in the sense that eye color or other characteristics are fixed by DNA. Differences in intelligence are the product of both environmental and genetic differences that work together. Intelligence is not simply a characteristic that impacts how much a child will learn and succeed in school. It is also the product, or the outcome, of educational experiences both inside and outside of school (Martinez, 2000). Moreover, the transactional view of development reminds us that children are active participants in their learning as they choose and select specific opportunities and environments (Scarr & McCartney, 1983). It may be the case that individual differences, related to some extent to genetic differences, predispose children to pursue certain educational activities with greater interest than other activities (Krapohl et al., 2014). A uniform, one-size-fits-all approach to education is somewhat ill-suited to children’s individual differences. Perhaps with technological advances, such as educational software and individualized means of information delivery, education can become increasingly personalized to reflect children’s individual differences (Krapohl et al., 2014). Group Differences Differences in cognitive performance occur between the sexes and between races. Before discussing these differences, we point out that there is substantial overlap between groups on cognitive task performance. In other words, if group A has a higher average score than group B, it is still the case that many individuals in group B outperform many individuals in group A. Group membership is not a basis for inferring an individual’s capabilities. As we will see, stereotypes are not only unfair—they can also adversely impact cognitive performance. Race and IQ When IQ differences arise between groups, a question immediately arises: What do these differences mean? Put another way, how should we interpret group differences? Addressing these questions will help us understand, and critique, highly controversial proposals that public policy should be influenced by group differences in IQ test scores. For instance, when someone claims that immigration policy in the United States should restrict the admittance of groups with purportedly below-average IQ scores, an effective, informed response would include an understanding of what the data on group differences mean (and what they do not mean) (Richwine, 2009). Aside from the ethical problems associated with discriminating between groups, one would also need to be prepared to point out Heredity, Environment, and Intelligence Section 10.3 flawed interpretations of data. To foreshadow our discussion, the data do not support making decisions about groups based on group differences in IQ scores. Data indicate IQ scores of African Americans (AA) tend to be lower than the scores of European Americans (EA). Summaries of the data have placed the difference at about 15 points (Neisser et al., 1996; Rushton & Jensen, 2006). Others have found the 15 point gap is decreasing, with AA gains of about 4 to 7 points since the 1970s (Dickens & Flynn, 2006). We will use these AA and EA differences to address the question central to this section—how do we interpret group differences in IQ? Our first consideration when interpreting findings of racial differences is that for many scientists, the very concept of “race” is fuzzy. This is because most genetic variation among individuals exists within racial categories and not between them (Sapp, 2012; Sternberg, Girgonko, & Kidd, 2005). In essence, members of the same race are, genetically, very diverse on average. Generally speaking, people within racial categories are vastly different from one another and the biological boundaries between races are difficult to define and are subject to debate (Sternberg et al., 2005). However, the concept of race is a social reality in the United States, and related to differences in socioeconomic conditions (Fryer, 2011; Sapp, 2012). Given social realities, the topic of race differences in IQ continues to be studied, and the findings need to be properly interpreted. Consistent with our nature/nurture theme, researchers tend to explain the group differences by emphasizing either biological or environmental influences (Rushton & Jensen, 2006; Nisbett, 2009). Two extensive summaries of the research literature on intelligence have concluded that while the issue remains unsettled, the weight of evidence points to the importance of environmental differences in understanding the reasons for the gap in IQ scores (Neisser et al., 1996; Nisbett et al., 2012). Thus, we see a preliminary answer to our question; in particular, when racial differences occur, the evidence suggests we look to cultural and environmental differences for explanations. One clear strand of evidence showing the importance of the environment comes from the relationship between social change and IQ change for African Americans. The gains in IQ scores among African Americans since the 1970s indicate that some cultural/environmental factors (for example, progress in school funding that benefits minority groups) have positively impacted their intelligence test performance (Dickens & Flynn, 2006). We will see in the next section how environmental factors may produce positive change in IQ for other groups as well. Another strand of evidence occurs when we pinpoint counterproductive features in the environment that can artificially lower IQ scores. One adverse environmental factor is the influence of negative racial stereotypes on test performance among minorities (Walton & Spencer, 2009). Stereotype threat occurs when awareness of a negative stereotype (for example, “Black people are not intelligent”) causes anxiety and stress when completing a test or measurement related to the stereotype (Steele, 1997). These negative feelings in turn interfere with standardized test performance (Good, Aronson, & Inzlicht, 2003). Heredity, Environment, and Intelligence Section 10.3 Real-World Application: Reducing Stereotype Threat Scientific findings can helpfully close gaps in cognitive achievements between groups. The discovery of stereotype threat in the mid-1990s spurred interventions in the classroom. As the linked article indicates, results of these interventions are encouraging. http://www.apa.org/monitor/2011/09/achievement.aspx Critical-Thinking Question What other classroom exercises can you think of that might protect children from stereotype threat? Finally, and most directly, we can compare individuals of substantial, if not 100%, African ancestry, with individuals that possess a combination of African and European ancestry. In particular, we can examine whether IQ scores are associated with such racial differences. If race differences in IQ are genetic in origin, then IQ scores among African Americans should rise and fall depending on the degree of their European ancestry. Data indicate that the relationship between IQ and the degree of European ancestry among African Americans is negligible (Nisbett, 2005). Again, the weight of evidence indicates that we should look to environmental factors rather than genetic ones when interpreting race differences in IQ. Cognitive Sex Differences As with racial differences, the study of cognitive sex differences can help identify factors that potentially create inequalities, which in turn informs attempts to improve children’s cognitive performance (Halpern, 2013). As we investigate this topic, keep in mind that we are not investigating which sex is smarter or better—instead, research asks to what extent, and under what circumstances, differences might exist (Halpern, 2013). When IQ tests are constructed, items that differentiate between the sexes are eliminated so that there are no sex differences in average IQ scores (Halpern, 2013; Halpern & LaMay, 2000). However, as we discussed earlier, general intelligence consists of more specific cognitive abilities (such as verbal and nonverbal spatial reasoning subscales). Decades of psychological research suggests four cognitive sex differences at this more specific level of cognition (Miller & Halpern, 2014). These differences are listed in Table 10.4. Precise explanations that investigate the interplay of nature and nurture on these differences continue to be sought by investigators (Halpern, 2013; Miller & Halpern, 2014). In this section, we will discuss some preliminary evidence in the areas of math and spatial reasoning to provide a sense of the types of explanations and evidence being sought. Heredity, Environment, and Intelligence Section 10.3 Table 10.4: Cognitive sex differences suggested by research evidence Area Math Memory Spatial Verbal Findings • Males are more likely than females to score exceptionally high (for example, top 1%) on measures of mathematical ability. • Averaging across the entire range of scores on measures of mathematical ability, males have a small to negligible advantage. • Females outperform males in some areas of memory, such as face recognition and object location. • Males score higher than females on some measures of spatial reasoning, such as mental rotation tasks. • Females consistently outperform males on verbal measures (for example, reading achievement and writing). Source: Adapted from Miller, D. I., & Halpern, D. F. (2014). The new science of cognitive sex differences. Trends in Cognitive Sciences, 18(1), 37–45. The influence of the environment is evident because cognitive sex differences are changing in some societies. For instance, in the United States the ratio of males to females exhibiting exceptional mathematical abilities has decreased from 13:1 in the 1970s and 1980s to 4 (or less):1 in more recent years (Miller & Halpern, 2014). This means there is a greater representation of females among top performers than in previous decades. Cultural and educational changes that place a greater emphasis on gender equity are presumably responsible for females’ gains on assessments of mathematical ability (Kane & Mertz, 2012). For example, cross-cultural research indicates that providing equal access to education reduces sex differences in math performance. To illustrate, 8th grade boys and girls tended to perform similarly on a measurement of math achievement in nations where there was equal access to formal education, and less similarly in nations where females’ access was more limited (Else-Quest, Hyde, & Linn, 2010). As with racial differences, negative stereotypes (“girls are not good at math”) can interfere with performance on math assessments (Shapiro & Williams, 2012). In general, anxiety about cognitive tests, including IQ tests, can impair performance (Hembree, 1988; Hopko, Crittendon, Grant, & Wilson, 2005). Stereotyped beliefs may cause anxieties that in turn negatively impact performance (Maloney, Schaeffer, & Beilock, 2013). Math anxiety is anxiety that sets in when a person thinks about or performs math problems. A link between math anxiety and math performance is evident as early as first grade and can occur throughout schooling (Hembree, 1990; Ramirez, Gunderson, Levine, & Beilock, 2013). Both males and females experience math anxiety, although females tend to display higher levels in some studies (Beilock, Gunderson, Ramirez, & Levine, 2010; Else-Quest et al., 2010; Hembree, 1990). Math anxiety and stereotype threat may share the adverse effect of impairing working memory performance during testing (Maloney et al., 2013). Negative thoughts about doing poorly can take up working memory capacity that might otherwise be devoted to problem solving. Interventions to reduce intrusive thoughts that accompany anxiety can be helpful in this regard. Heredity, Environment, and Intelligence Section 10.3 For instance, in one study, the negative impact of test anxiety was diminished when 9th graders were given the opportunity to express their worries in writing about an upcoming high-stakes exam (Ramirez & Beilock, 2011). The writing intervention took place just minutes before the exam and afforded students an opportunity to express and process negative thoughts, possibly freeing working memory capacity during the exam (Ramirez & Beilock, 2011). The joint influence of nature and nurture is evident in research investigating sex differences in mental rotation. Explanations for sex differences include the effects of hormones, maturational rates of the brain, experiences, and task factors (such as time limits) that may be disadvantageous for females (Hyde, 2014; Moore & Johnson, 2008). To illustrate the interplay of nature and nurture in understanding sex differences in mental rotation, we look at studies of the sex hormone androgen, which stimulates and maintains the development of masculine characteristics. Before birth, androgen may play a role in brain organization (Wallen, 2009). Female adolescents and young adults (ages 16 to 30) who were exposed to higher than normal levels of androgen before birth (because of a genetic disorder) outperformed typically developing females on measures of mental rotation like the one illustrated in Figure 10.4 (Berenbaum, Bryk, & Beltz, 2012). However, research does not always find a link between elevated levels of prenatal androgen and spatial abilities, and the relationship continues to be investigated (Malouf, Migeon, Carson, Petrucci, & Wisniewski, 2005). Figure 10.4: A test of mental rotation skills On average, boys outperform girls on tests of mental rotation like the one shown here. Source: Adapted from Vandenberg, S. G., & Kuse, A. R. (1978). Mental rotations, a group test of three-dimensional spatial visualization. Perceptual and Motor Skills, 47(2), 599–604. Theories highlighting the transactional nature of development draw on findings that early excess androgen exposure in females is related to play behaviors like climbing and playing with tool sets, behaviors more commonly associated with males (Berenbaum et al., 2012; Pasterski et al., 2005). Theoretically, these behaviors positively impact spatial development by giving children enriched experiences with spatial information (Beltz, Swanson, & Berenbaum, 2011; Berenbaum et al., 2012; Golombok & Rust, 1993). We see in this example how nature and nurture can have reciprocal effects. IQ: Stability and Modifiability Section 10.4 Questions to Consider 1. In what ways could findings regarding group differences be applied to the classroom? 2. How might the transactional nature of development help explain the improvement in IQ scores among African Americans over successive generations? 10.4 IQ: Stability and Modifiability Returning to our case study that opened this chapter, if a young child scores poorly on an intelligence test, does this mean the child will continue to have low intelligence scores into adulthood? Or will intelligence change? This section considers these questions. When using the terms stability and change, we are referring to the position of one’s IQ scores relative to same-age peers over time. Our concerns in this section are (a) whether there is stability in IQ scores and (b) what environmental interventions might modify or change IQ over time. These concerns are, naturally, motivated by the desire to ensure that we optimize children’s development. To the extent change is possible, it is useful to know which interventions show particular promise. In addition, the stability of IQ is an important consideration for educators when IQ scores are used as evidence for a child’s placement in a particular educational program. For instance, if a young child scores poorly on an IQ test and receives special education services, can we assume the poor IQ score is sufficiently stable so that the child does not need to be retested at a later date? Development and Stability in IQ Recall that we distinguished earlier in the chapter between test reliability and the stability of intelligence. Test reliability is consistency in performance typically measured over a period of days or weeks. If intelligence is a stable characteristic of the individual, a person’s score on an intelligence test should remain about the same over a prolonged period. By “the same,” we mean that a person’s rank order in comparison to peers should be relatively stable. In other words, someone who is above or below average in early childhood would remain above or below average at later ages if IQ is stable. By “prolonged period” we are referring to a period measured in years. At the outset of this section we point out two generalizations with regard to stability and IQ. First, the shorter the interval between test and retest, the greater the stability in IQ scores (Schneider, Niklas, & Schmiedeler, 2014). For example, there is typically less change in IQ between ages 7 and 9 than there is between a longer interval like between ages 7 and 11. Second, the older the child when first tested, the more stability his or her scores exhibit upon retest (Neisser et al., 1996; Schneider et al., 2014; Moffitt, Caspi, Harkness, & Silva, 1993). We begin investigating stability in intelligence by reaching back to the very beginnings of development. In particular, we look at whether there are measureable differences IQ: Stability and Modifiability Monkeybusinessimages/iStock/Thinkstock Section 10.4 in intelligence in infancy and if those differences remain stable over time. The Bayley Scales of Infant and Toddler Development measure motor skills, language, and cognitive/perceptual abilities in infants and toddlers ages 1 to 42 months (Bayley, 2006). In the most recent version of the Bayley Scales, items include object and sound recognition, direction following, and fine and gross motor skills such as sitting upright or grasping objects. Because the latest version of the assessment is relatively new, findings about long-term stability in intelligence involve earlier versions of the measurement. For instance, an earlier version of the Bayley Scales was administered to 3-month-olds and was positively related to later IQ at age 6 (Wilson, 1978). In general, however, evidence that individual differences on the Bayley Scales predict later IQ differences is mixed (Colombo, 1993; Hack et al., 2005; Luttikhuizen dos Santos, de Kieviet, Königs, Van Elburg, & Oosterlaan, 2013). IQ test scores tend to be more stable (a) the shorter the time between the first test and retest and (b) the older the child at the time of the first test. More conclusive are those studies that measure infant intelligence by drawing on IP theory. In IP theory the amount of time it takes an infant to habituate to a stimulus is used as a measure of early intelligence (Bornstein, Hahn, & Wolke, 2013; Rose & Feldman, 1995). The reasoning is that infants who quickly and efficiently process information will need fewer trials than other infants to recognize that a stimulus is familiar and habituate to it. Remember that IP theory emphasizes continuity in development. Consequently, the ability to efficiently and accurately encode information into memory should continue to influence cognitive performance after infancy (McCall & Carriger, 1993). For example, in one study, the efficiency with which infants habituated to a stimulus was moderately correlated eleven years later with performance on many subtests of the Wechsler test (Rose & Feldman, 1995). Other studies find continuity between infant information processing and IQ extending into adulthood (Fagan, Holland, & Wheeler, 2007). Such findings indicate long-term continuity and indicate that infant cognition contributes to later intelligence test performance (Rose, Feldman, Jankowski, & Van Rossem, 2012). It is important to note that these correlations are modest in magnitude, and it would be a mistake to assume that individual differences in intelligence do not change from infancy onward. The correlations are noteworthy, however, given the amount of time they encompass and the differences between measuring intelligence with looking time and with paper-and-pencil IQ assessments. Generally, beginning around age 6 or 7, IQ scores begin to stabilize and correlate more substantially with later assessments taken in childhood and adolescence (Keage et al., 2015; Schneider et al., 2014). To illustrate, in one study intelligence was measured at ages 2, 4, 7, and 11 to 13. Age 7 was the youngest age at which IQ scores predicted the number of years IQ: Stability and Modifiability Section 10.4 of schooling eventually completed by the participants (Keage et al., 2015). Long-term studies show that childhood IQ remains somewhat stable even after a nearly 70-year gap between tests (Deary, Whiteman, Starr, Whalley, & Fox, 2004). In our case study at the outset of this chapter, Michael’s age (6 years) is young enough that it should give us pause and lessen our confidence that his IQ score is an exceptionally strong indicator of future IQ scores. An additional reason for caution is evidence that IQ scores sometimes substantially change over time. To illustrate, in one study children who were tested for possible placement in special education services with the WISC-IV were tested again approximately 3 years later. Although the overall stability, encompassing the entire group of children, was relatively high, a closer look revealed occasional instability in individual scores. Approximately 25% of the children’s scores differed by 10 or more points between test sessions (Watkins & Smith, 2013). Comparable changes in IQ scores over time were also observed in a nonclinical sample of elementary school children ages 7 to 12 tested approximately 2 years apart (Kieng, Favez, Jérôme, Geistlich, & Lecerf, 2014). In another study, a subset of preschoolers were initially classified with low intellectual abilities but their scores were well above average by age 17 (Schneider et al., 2014). Thus, on average, the long-term stability for intelligence is relatively high, but we can also expect some individual exceptions. For this reason, clinicians and educators must be cautious in assuming long-term stability. Some researchers question the exclusive reliance on early IQ scores to warrant children’s continued placement in special education, and recommend readministering IQ tests at a later date (Watkins & Smith, 2013). Interventions The apparent relationship between IQ scores and outcomes like academic achievement, job performance, and socioeconomic status motivates significant societal and scientific interest in whether interventions can positively impact intelligence (Gottfredson, 2004b). Evidence for the effectiveness of interventions supports the view that nurture plays a role in individual differences in intelligence. Recall from Chapter 1 that plasticity of the brain means neural pathways can be reorganized and new neural connections can form in response to experiences. Researchers are attempting to identify the types of experiences that can produce significant and long-lasting changes in intelligence. One approach is to target domain-general mechanisms like working memory that correlate with IQ (Kane & Engle, 2002). Working memory (discussed in Chapter 3) is the ability to hold information in mind while working on cognitive tasks. It is related to general intelligence in children (Cornoldi, Orsini, Cianci, Giofrè, & Pezzuti, 2013; Giofrè, Mammarella, & Cornoldi, 2013). There are a variety of commercial programs available that claim to boost intelligence through a combination of cognitive activities that include working memory practice (a quick Internet search with keywords “brain training”, “IQ”, and “working memory” will reveal some popular programs). Researchers have investigated whether working memory training can actually improve children’s intelligence. As discussed in Chapter 3, training typically involves repeated practice on IQ: Stability and Modifiability Section 10.4 working memory tasks over a period of several weeks. To date, evidence for the effectiveness of working memory training on intelligence is mixed. For instance, in one study, elementary and middle school children completed working memory sessions that involved remembering whether a stimulus had appeared on a computer screen in the same location as on previous trials (Jaeggi, Buschkuehl, Jonides, & Shah, 2011). Training lasted for a month, and three months later a positive effect on fluid intelligence was still evident. However, such findings are not consistently replicated (Shipstead, Hicks, & Engle, 2012). A number of variables could impact the relationship between working memory training and intelligence, such as length and type of training as well as the age, background, and ability of the children being trained. Research continues on the question, and at this point caution is warranted when considering whether working memory training can produce noticeable long-term gains in intelligence (Shipstead, Redick, & Engle, 2012). For now, we can conclude that working memory training has the potential to positively impact intelligence, but more research is needed to identify how and when that potential is realized and how enduring and impactful such improvements might be in children’s lives. Spotlight on Research: Music Training and IQ in Children In addition to working memory, another well-researched intervention attempt is music training (Schellenberg, 2011). In one study, children ages 4 to 6 years received either visual art or music training via computerized programs (Moreno et al., 2011). The visual art group served as the control in the experiment—these children learned about concepts like shape, color, and dimension. Children receiving music training heard lessons that emphasized musical concepts like rhythm, pitch, and melody. Each training session was administered twice a day for 4 weeks. Only the children who received the music training showed significant improvement on a test of verbal IQ assessing vocabulary. Also, children in the music training group showed a difference in neural activity when completing an EF task compared to children in the visual art group. The distinctive pattern of neural activity is one that is associated with higher-order cognitive processing (Moreno et al., 2011). Thus, one possibility is that music training impacted inhibitory control, attention, and/or memory skills, which in turn led to better performance on the verbal assessment (Moreno et al., 2011). The study did not assess whether the gains in IQ were long-lasting. And while other studies have also demonstrated cognitive enhancement resulting from music training (Fujioka, Ross, Kakigi, Pantev, & Trainor, 2006; Ho, Cheung, & Chan, 2003; Kaviani, Mirbaha, Pournaseh, & Sagen, 2014) sometimes other studies find no benefits (Mehr, Schachner, Katz, & Spelke, 2013). Overall, the present finding suggests that intelligence is potentially modifiable by experience. Critical-Thinking Question Does the effect of musical training on intelligence better support the psychometric theories that posit a single intelligence (g) or Gardner’s theory of multiple intelligences? Why? There are a variety of other intriguing findings that intelligence in young children is malleable (Protzko, Aronson, & Blair, 2013). Supplementing a pregnant mother’s diet or an infant’s formula with long-chain polyunsaturated fatty acids (omega-3 fatty acids) may result in the IQ: Stability and Modifiability Section 10.4 young child’s IQ score being about 3.5 points higher than it would be otherwise. Omega-3 fatty acids support brain development, which is the hypothesized basis for the effect (Innis, 2009; Protzko et al., 2013). To illustrate, in one study pregnant mothers were randomly assigned to receive the omega-3 supplement or a placebo. The positive effect on intelligence was evident when the children were 4 years old (Helland, Smith, Saarem, Saugstad, & Drevon, 2003). However, in another study, there was no effect by age 6, and the long-term benefits of omega-3 supplements on intelligence continue to be investigated (Gould, Smithers, & Makrides, 2013; Willatts et al., 2013). Another finding is that preschool enrollment of children from low-income homes can raise IQ scores by as many as 7 points, particularly if the preschool focuses on enhancing language skills (Protzko et al., 2013). More generally, school attendance positively impacts IQ throughout childhood (Becker, Lüdtke, Trautwein, Köller, & Baumert 2012; Ceci, 1991). As evidence of this, IQ scores tend to decline during summer months when children are away from school, particularly when children have little or no involvement in enriching academic activities over the summer (Ceci & Williams, 1997; Nisbett et al., 2012). Moreover, dropping out of school is associated with a subsequent decline in IQ scores (Ceci & Williams, 1997). Explanations for the positive effects of schooling include improving children’s vocabulary and their store of factual content information as well as engaging them in complex problem solving (Protzko et al., 2013). Additional evidence that education has an impact on IQ scores comes from the Flynn effect. The Flynn effect refers to the finding that in many parts of the world, intelligence scores have risen by perhaps 15 or more points since the 1940s (Flynn, 1999; Trahan, Stuebing, Fletcher, & Hiscock, 2014). The effect is evident from the changes in performance that have occurred in normative samples. Recall that intelligence tests are administered to large samples in order to obtain norms (that is, average scores and standard deviations). In successive generations normative samples have shown increased intelligence test performance compared to the preceding generation. This means their raw scores are higher than the previous generation’s scores. These changes necessitate adjustments of the scoring scale of IQ tests so that the average remains 100. Common explanations for the advancement in intelligence include better nutrition and increased access to formal schooling and college (Liu, Yang, Li, Chen, & Lynn 2012; Trahan et al., 2014). Not only has access to schooling increased in many countries, but the type of material taught in schools has also evolved. An increasing emphasis in schools in the United States on the type of problem-solving skills underlying fluid intelligence may be one reason for gains in IQ (Blair, Gamson, Thorne, & Baker, 2005). For example, at the beginning of the 20th century, math education in grade school emphasized rote learning and memorization (Blair et al., 2005). By the 1950s and 1960s, math education began to emphasize problem solving such as detecting visual-spatial relationships like those depicted in Figure 10.1. Today an emphasis on recognizing patterns and relationships fundamentally related to geometry is found in first- and second-grade school curricula. Similar concepts would not have been addressed until seventh or eighth grade in the 1950s (Blair et al., 2005). Section 10.4 IQ: Stability and Modifiability IQ and Poverty Over the years, a number of studies have found a relationship between particular characteristics of children’s home life and intelligence (Totsika & Sylva, 2004). Researchers commonly assess these characteristics with the Home Observation for Measurement of the Enviro...
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