The Neuroscience of Intelligence

by Richard J Haier

  1.10.3. Everyday Life

  The importance of general intelligence in everyday life often is not obvious but it is profound. As Professor Earl Hunt has pointed out, if you are a college-educated person, it is highly likely that most of your friends and acquaintances are as well. When is the last time you invited someone to your home for dinner that was not college-educated? Professor Hunt calls this cognitive segregation and it is powerful in fostering the erroneous belief that everyone has a similar capacity or potential for reasoning about daily problems and issues. Most people with high g cannot easily imagine what daily life is like for a person with low g.

  The complexity of everyday life is often quite challenging, especially when a non-routine or novel problem presents itself. Professor Linda Gottfredson summarizes this with a simple statement: “Life is a long mental test battery.” This was true as early humans navigated unforgiving natural environments and solved continuous problems of finding food, water, shelter, and safety. It was true as early civilizations developed and great thinkers (likely with high g) solved even more complex problems (e.g. just how does one build a seaworthy ship or a pyramid?). And it is still true today as we grapple with connecting our new television sets and audio systems with HDMI cables or using all the functions in our word processor or on our “smart” phones or digital cameras beyond the auto mode. Do you know how to use the scanners in the self-checkout lines at the supermarket or do you wait in a long slow line for a human cashier? How much do you understand about money management and investing in stocks, bonds, and mutual funds? Do you do your own taxes? Many people grapple daily with the challenges of navigating nearly impenetrable systems for healthcare, social support, or justice. Poverty presents a myriad of daily problems to solve. It could be said that in the modern world, nothing is simple for anyone all the time.

  Consider some statistics comparing low and high IQ groups (low = 75–90; high = 110–125) on relative risk of several life events. For example, the odds of being a high school dropout are 133 times more likely if you’re in the low group. People in the low group are 10 times more at risk for being a chronic welfare recipient. The risk is 7.5 times greater in the low group for incarceration, and 6.2 times more for living in poverty. Unemployment and even divorce are a bit more likely in the low group. IQ even predicts traffic accidents. In the high IQ group, the death rate from traffic accidents is about 51 per 10,000 drivers, but in the low IQ group, this almost triples to about 147. This may be telling us that people with lower IQ, on average, have a poorer ability to assess risk and may take more chances when driving or performing other activities (Gottfredson, 2002; 2003b).

  Textbox 1.2: Functional literacy

  Another way to look at the role of thinking skills in everyday life is based on functional literacy data. Functional literacy is assessed by the complexity of everyday tasks that a person can complete. Like IQ scores, functional literacy scores are meaningful relative to other people, but they provide more concrete examples of ability. The last comprehensive US national survey of functional literacy was done in 1992.

  Table 1.1 is from that survey. On the left side, we have five levels of functional literacy: 1 is the lowest, 5 is the highest. In the middle we have the percentage of people who are in each category, and on the right we have some sample tasks that people in each category can complete successfully. Let’s look at the top row. If you’re like me, you will be quite surprised to see that only 4% of the white population is in the top category and can complete tasks like using a calculator to figure out the cost of carpeting a room. This requires determining the area, converting to square yards, and multiplying by the price. In the next row down, 21% of people are at level 4 of functional literacy. They can calculate social security benefits from a table and understand basic issues of how employee benefits work. Thirty-six percent are in the middle category. They can calculate miles per gallon from a chart, and they can write a letter explaining a credit card error. Twenty-five percent are in category 2. They can determine price differences between two tickets, and they can locate an intersection on a map. Fourteen percent are in the lowest category. They can accomplish tasks like filling out a bank deposit slip, but more complex tasks, like locating an intersection on a map, would present difficulty.

  Table 1.1 Everyday literacy levels from the National Adult Literacy Survey along with sample problems from each level (The Intelligent Brain, copyright 2013 The Teaching Company, LLC. Reproduced with permission of The Teaching Company, LLC, www.thegreatcourses.com).

  NALS level% Population (white)Everyday simulated tasks

  5 4 Use calculator to determine cost of carpet for a room

  Use table of information to compare 2 credit cards

  4 21 Use eligibility pamphlet to calculate Supplementary Security Income (SSI) benefits

  Explain difference between 2 types of employee benefit

  3 36 Calculate miles per gallon from mileage record chart

  Write brief letter explaining error on credit card bill

  2 25 Determine difference in price between 2 show tickets

  Locate intersection on a street map

  1 14 Total bank deposit entry

  Locate expiration date on driver’s license

  The examples in Textbox 1.2 and Table 1.1 demonstrate that intelligence helps us navigate the problems of everyday life. It’s really not a shocking idea, but this is easy to take for granted, especially if you are navigating reasonably well and most of the people you spend time with are like you. The key point here is that functional literacy is another indicator of intelligence, and you can see from the functional literacy data that intelligence matters for daily tasks. But, of course, the g-factor does not predict many other important things like being a nice or likeable person. No intelligence researcher has ever asserted otherwise.

  Let’s talk for a moment about a controversial book from 1994 that explored the role of intelligence in social policy, The Bell Curve by Richard Herrnstein and Charles Murray (Herrnstein & Murray, 1994). The main theme was that modern society increasingly requires and rewards people with the best reasoning skills. This is to say people with high intelligence. Therefore, people in the bottom part of the normal distribution of IQ (a normal distribution is also called a bell curve because of its shape) will be at a serious disadvantage for succeeding, especially in school and some vocations. Herrnstein had introduced this theme in an earlier book, IQ in The Meritocracy (Herrnstein, 1973) that also generated considerable acrimony. Read the detailed description of hostility on the Harvard campus recounted in the Preface to get a sense of the times; a few years later another Harvard professor, Edward O. Wilson, encountered similar outrage when he proposed the concept of sociobiology (Wilson, 1975). The Bell Curve continued the argument with over 900 pages of data and statistical analyses mostly comparing high- and low-intelligence groups, but the one chapter that discussed black/white IQ differences aroused the fiercest controversy (please note that the terms “black” and “white” are used here because most of the research, from America and other countries, uses these terms). This issue of group differences haunts all intelligence research and I refer the reader to in-depth accounts of the complexities involved (see Further Reading).

  My point about The Bell Curve is whether public policy discussions benefit by recognizing that people with low g may need help navigating life, irrespective of race, background, or why they might have low g. This is a fundamental issue today in politics although the role of intelligence is hardly mentioned as explicitly as it was in The Bell Curve. Most researchers would agree that research data on intelligence can only inform policy decisions, but the goals of the policy need to be determined through democratic means; we return to this issue in Section 6.6. Unfortunately, psychometric research on intelligence has often been portrayed as damaging to a progressive social agenda because there are substantial average test score differences among some racial and ethnic groups. These relative average group differences often motivate a general
disregard for empirical research on intelligence although neuroscience approaches are advancing the field, as the next chapters discuss. Before we get to those, let’s continue with more data about IQ scores and what they mean.

  1.10.4. Longitudinal Studies of IQ and Talent

  The predictive power of a single test score in childhood also is demonstrated dramatically in three classic longitudinal studies. Each one starts with children and tests their mental abilities and life successes at various intervals over decades. One study started in California the 1920s, one started in Scotland in the 1930s, and one started in Baltimore in the 1970s.

  Study 1.

  Professor Lewis Terman at Stanford University initiated a long-term study of high-IQ individuals in the 1920s. This is the same Lewis Terman who brought Binet’s IQ test to the USA and revised it into the Stanford–Binet intelligence test. Terman designed a straightforward study. It started by testing many school children with the Stanford–Binet test. Children with very high IQ scores were selected and then studied extensively for decades. Terman’s study had two goals: to find the traits that characterized high-IQ children, and to see what kind of adults they would become. The common stereotype of intelligent adults was not so different then as it is now. Francis Galton, for example, wrote in his 1884 book, Hereditary Genius (Galton & Prinzmetal, 1884): “There is a prevalent belief that men of genius are unhealthy, puny beings – all brain and no muscle – weak-sighted, and generally of poor constitutions …” (reprinted in Galton, 2006).

  Here’s how Terman’s project started (Terman, 1925): In 1920–1921, 1,470 children with IQs of 135–196 were selected from over 250,000 children in California public schools and they were retested and interviewed every 7 years. Their average IQ was about 150, and 80 children had IQs over 170 (these were in the top 0.1%). This entire group became known unofficially as the Termites. They completed extensive medical tests, physical measurements, achievement tests, character and interest tests, trait ratings, and both parents and teachers supplied additional information. A control group with average IQ scores was also tested. The results of Terman’s study were published over time in five volumes. The data were quite extensive.

  Here’s a summary of key findings about the lives of the Termites. Overall, they completely refute the stereotypes both for children and adults. The negative, nerdy attributes were basically unfounded. They were not odd or puny. On average, they actually were physically quite robust and more physically and emotionally mature than their age-mates. On average, the Termites were happier and better-adjusted than the controls over the course of the study. Although they had their share of life problems, follow-up studies showed considerable achievement with respect to publishing books, scientific papers, short stories and poems, musical compositions, television and movie scripts, and patents (Terman, 1954). However, further follow-up indicated that high IQ alone did not necessarily predict life success. Motivation was also important, and Terman believed that while genes played an important role in high IQ, he also believed that exceptional ability required exceptional education to maximize a student’s potential. This may not sound so radical, but even today there is a debate about whether any education resources at all should be allocated to the most gifted students to develop their high ability.

  Terman’s project also demonstrated the predictive validity of the IQ score. That is, one IQ score in childhood can identify individuals who will excel in later life. Like all studies, however, there were some major flaws – here, two: (1) Terman intervened in the lives of these “subjects” and helped them with letters of reference for college and for employment; (2) Strong sex bias in education and employment resulted in female Termites mostly becoming housewives, so valid male/female comparisons were not possible. Similarly, there are no data about minorities. Do these problems invalidate the main findings? Not likely. Overall, the level of success and the achievement of these very high-IQ individuals stand on their own. However, fortunately, we have more data from a newer study that modified Terman’s approach.

  Study 2. The second longitudinal study is The Study of Mathematically & Scientifically Precocious Youth at Johns Hopkins University. This was an ambitious project initiated by Professor Julian Stanley in 1971 (Stanley et al., 1974). Dr. Stanley repeated Terman’s approach, but instead of IQ scores he used extremely high-SAT math (SAT-M) scores obtained by junior high school students aged 11–13 in special testing sessions called talent searches. So instead of general intelligence, Stanley focused on a very specific mental ability. This project also had two major goals. First, identify precocious students early, and second, foster their special talent.

  I started graduate school at Hopkins in 1971 and I worked on this study in its early years. I must say that this experience was an early influence on my interest in intelligence, and Dr. Stanley was one of the most important and interesting mentors I had at Hopkins.

  This project had its origins in the late 1960s. Dr. Stanley started working with a precocious student, and after he gave the student a battery of psychometric tests, Dr. Stanley helped the student get into Hopkins at the early age of 13. Dr. Stanley subsequently referred to this young man as the first “Radical Accelerant,” identified as Joseph B. In his first year at Hopkins, at age 13, Joseph took honors calculus, sophomore physics, and computer science, and his grade point average was 3.69 out of 4.0. He lived at home during this time but he also made friends with other college students and adjusted well to his accelerated studies. In four years, he received a BA and an MS degree in computer science. He began a PhD program in computer science at Cornell before he was 18 years old, and Joseph went on to have a productive career.

  From the beginning, a main goal for Dr. Stanley was to not only identify and follow such precocious students, but also to select the best individuals for education acceleration, including early college admission. So was born the idea of using the SAT-Math test for screening junior high school students to find precocious individuals with talent for math and science. The Spencer Foundation provided multiple year funding to Dr. Stanley beginning in 1971 and the first talent search was in 1972. For that search, junior high school students in the Baltimore area had to be nominated by their math teachers to participate. Actual SAT-Math tests were given in the standard way. In that first search, 396 7th and 8th grade students took the SAT-Math. Here are two fascinating results of that first talent search. Twenty-two of the 396 scored at least 660, which was higher than the average Hopkins freshman at the time. And, all of these 22 were boys and none of the 173 girls scored over 600.

  The male/female ratio has improved considerably over the years, but at the time, this huge disparity was surprising. And, what about the 22 boys who scored higher than Hopkins freshmen? What were they like? The early data analyses confirmed Terman’s results with respect to stereotype. These mathematically precocious students were more physically and emotionally mature than age-peers. One of my first research projects was to give this precocious group some standardized tests of personality. On average, they scored more like college students than their age-peers (Weiss et al., 1974).

  Professor Stanley believed that enriched classes were not as productive as actual college classes, so he helped many of these very talented students go to college early. Over the years, many of the most precocious students did get early admission, usually living at home. And, there was no evidence that they suffered any emotional harm from an accelerated program. Like the Termites, many went on to have successful and very productive careers.

  The original talent searches have evolved dramatically and now include many programs for enrichment in addition to early college admission, including summer camps that emphasize math and science experiences. You can find out more details about these programs using Google. Actually one of the students associated with the talent searches co-founded Google; that was Sergey Brin. Mark Zuckerberg of Facebook also was identified in a talent search and so was Lady Gaga. Seriously. Look it up.

  There are now d
etailed follow-up studies of thousands of the students who participated in several of the original searches. Follow-up results show that many of these mathematically precocious children, as determined by a single test score when they were in their early teens, became exceptionally successful in terms of occupational and life success (Lubinski et al., 1996, 2006, 2014; Robertson et al., 2010; Wai et al., 2005). Figure 1.6 shows professional achievement based on a 25-year follow-up study of the top 1% of the original searches that included 2,385 students (Robertson et al., 2010). All these students are divided into quartiles, Q1, Q2, Q3, and Q4, based on their SAT-Math score at age 13. On the x-axis, we have SAT-Math score at age 13. On the y-axis, we have the proportion of the quartile with an outcome like getting a PhD or a JD or an MD. Another outcome is having any peer-reviewed publications. Another would be getting a PhD and tenure in a STEM field, which includes science, technology, engineering, or math. Patents are another outcome and so is high income (defined as being in the 95th percentile).

  Figure 1.6 SAT-Math scores at age 13 predict adult outcomes of academic success.

  Reprinted with permission, Robertson et al. (2010).

  What we see in this chart is that for students with age 13 SAT-Math scores in the 400–500 range, which is in the top 1% for 13-year-olds but the lowest quartile 1 for this sample, about 15% got a doctorate in any field and this percentage increases with higher scores. In the top SAT-Math quartile 4, the percentage of advanced degrees is about 35%. This is all shown in the line with black dots at the top of the chart. You see this same trend for all the other outcomes.

  The OR after each outcome stands for odds ratio and compares the top quartile proportion to the bottom quartile for each outcome. For example, the greatest disparity is 18.2 for getting a doctorate in a STEM field. This means the upper quartile within the top 1% were 18 times more likely to get a STEM doctorate than the bottom quartile within the top 1%. So even in this rarified group of the top 1%, the individuals with the highest scores did the best based on these outcomes.