Introduction to Psychological Science: Integrating Behavioral, Neuroscience and Evolutionary Perspectives - William J. Ray 2021
Cognition, Problem-Solving, and Intelligence
✵ 9.1 Describe the research on thinking processes, including decision making and behavioral economics.
✵ 9.2 Discuss the processes involved in solving problems.
✵ 9.3 Describe the different ways that intelligence is measured.
✵ 9.4 Identify five cognitive domains that are associated with general intelligence.
✵ 9.5 Discuss the genetic factors associated with intelligence.
Many of you have heard about the flock of Canadian geese hitting the US Airways airplane three minutes after takeoff from a New York airport on January 15, 2009. The plane lost power. As the plane lost altitude, the pilots considered flying to another airport, but without power that was not possible. The pilots looked for a place to land that would minimize the loss of life. The captain Chesley Sullenberger (Sully) was able to land the plane without its engines working. He landed the plane on the Hudson River and everyone on board escaped. This event came to be known as the “Miracle on the Hudson” and was made into a movie (Sully) in 2016.
You may not know the story of another flight that ended in a very different way. Air France Flight 447 left Rio de Janeiro, Brazil for Paris on June 1, 2009. At some point when the flight was over the Atlantic Ocean, the airspeed indicators, which are small tubes, began to collect ice crystals. This gave the pilots incorrect readings. Without accurate readings to run the equipment, the pilots disengaged the autopilot and began to fly the plane manually. Without autopilot, the plane rolled during turbulence and the cockpit recorders later suggested that the pilots overcompensated. The plane began to lose altitude.
One of the pilots sought to correct for this by pulling the nose of the plane upward. This placed the plane into a stall. The necessary reaction for a stall is to place the nose of the plane down to increase its speed and regain control. The pilot, on the other hand, kept the plane pointed upward in an attempt to regain altitude. After three and a half minutes, the plane hit the Atlantic Ocean and everyone on board was killed.
How we make decisions is complicated. Sometimes we carefully consider our choices and make a decision. Other times, we quickly make a decision. Some of our responses, as in the two airline crises above, are made under great pressure. Feeling under pressure or fatigued causes us to consider fewer alternatives or solutions to a problem. Sometimes, we have a hard time deciding. In these times, we go back and forth trying to decide. This chapter will focus on how we solve problems and make decisions and the individual differences in these abilities, which we refer to as intelligence.
How We Think
Although we tend to use the word “thinking” to represent a single process, that is not the case. In this chapter, you will come to understand different types of thinking and different ways we approach problem solving and decision making. This is commonly referred to as reasoning or creating explanations. In later chapters, you will learn about problems in decision making as seen as those with schizophrenia and other disorders.
Fast and Slow Thinking
Psychologists have noted that some decisions we make are performed quickly and some require more time and effort. These different types of thinking have been described by different terms such as automatic versus controlled, unconscious versus conscious, implicit versus explicit, or System 1 versus System 2 (Stanovich & Toplak, 2012). The psychologist Daniel Kahneman, who won the Nobel Prize in 2002 for his decision-making research in behavioral economics, refers to these two systems as “fast” and “slow” thinking. His ideas were brought to the general public in his 2011 book, Thinking, Fast and Slow.
Fast thinking or decision-making occurs quickly with little or no effort, and little sense of you being actively involved in the process. When you speak with a person you know or walk or drive home, you just do it. There is no feeling of effort or constantly thinking about what to do next. Much of our daily life involves this type of processing. We read words on a sign, judge the emotion in someone’s face, recall the phone number we’ve had for years, turn when we hear an unexpected sound, and interact with our friends. All the time we are (or rather our brain is) constantly making decisions.
Slow thinking requires more effort, and we experience ourselves as a part of the thinking process. Its characteristics include the feeling of making a choice, of being involved in the decision-making process, and requiring concentration and effort. A hallmark of the slow system is attention. If you don’t pay attention it is hard to make a decision or solve a problem. When considering which of two trips you should go on during Spring Break, you typically use the slow way of thinking. You also use the slow system when you fill out an application for a job and decide how to describe yourself. And, of course, when you take an essay test, you must make a number of decisions about what to say.
As you will see throughout this book, the fast system is seen in a number of contexts. Walking in the woods and jumping when you mistake a stick for a snake is one such context. Recognizing the emotional expression on a face is another. Experiencing an optical illusion as you saw in the chapter on sensation and perception is also part of the fast system. Having a conversation or reading signs on the road are other contexts.
For years economists suggested that humans were rational and used logic for making decisions. However, as psychologists became involved in determining how people make decisions, it became clear that we as humans are not rational (Evans & Over, 2013). We make predictions about what to expect. In making a prediction, we rely on some types of information differently than other types of information. This results in our judgments being biased. In fact, there are a number of biases that we employ when making a choice. One of these is to rely on fast thinking.
Solve this problem as quickly as you can:
A ball and bat together cost $1.10.
A bat costs one dollar more than the ball.
How much does the ball cost?
Many people found that their first thought was that the ball costs 10¢. In fact, many thought this was the correct answer (Frederick, 2005). You mean 10¢ is not the correct answer? Think about it: if 10¢ was the correct answer, then the bat would cost $1.10 plus the 10¢ for the ball or $1.20. The correct answer is the ball costs 5¢ and the bat $1.05. Shane Frederick (2005) suggests that because the problem seems easy at first, it is System I or fast thinking that we use to save effort. Slow thinking, which requires attention and effort, would have probably given you the correct answer.
Want to try another problem?
If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?
Again, if you let the fast system answer this problem, you probably said 100 minutes. However, if you were to reason with System 2 or slow thinking, you would realize that 1 machine can make 1 widget in 5 minutes. Thus, 100 machines can make 100 widgets in 5 minutes. Having realized your error, if you had one, in the first problem, you probably changed to slow thinking for solving the second problem.
Concepts and Categories
A concept is an abstract idea or representation. We tend to use categories to organize concepts that have shared features. Although there are a number of types of cats, for example, we know the features of the general category, such as four legs, a tail, a certain shaped face and ears, makes a meowing sound, and so forth. Although the category of dogs shares some of these features, we make a distinction between dogs and cats, but we also consider them both in the category of animals. We also make distinctions between nonliving objects such as chairs, bicycles, cars, and other such objects. Further, we distinguish between nouns and verbs as parts of speech. Overall, we use our concepts and categories to help us make decisions. Our language influences how we consider and talk about what we perceive and experience in terms of concepts and categories (Goldstone, Kersten, & Carvalho, 2012).
If you try to define a concept or category, you will discover that it is not easy. You could define the category of birds but then a penguin would probably upset your definition. The philosopher Ludwig Wittgenstein (1953) pointed out the difficulty of finding an exact definition for concepts. As an alternative he introduced the idea of family resemblance. As the name implies, a family resemblance points to features that appear to be characteristic of a category. However, as in a family photo, the family members may look alike but not every member of the family has every characteristic.
In psychological research, the family resemblance approach was developed by Eleanor Rosch (1975; Rosch & Mervis, 1975). Rosch suggested the categories that form naturally are based on a prototype. A prototype is generally the best example of a category. You can image what you think of when you hear the category “dog,” “bird,” “chair,” or “game.” Of course, where you grew up would influence your concept of a “bird.” Penguins and toucans would not be included in most of our prototypes of a bird.
An alternative model for understanding how we place items in a category is referred to as the exemplar approach (Medin & Schaffer, 1978). This approach suggests that you categorize an object by comparing it to similar items in your memory. If you are walking in the woods and see something move, you would compare it to your memory of other animals. Is it a house cat, a fox, a weasel, or some other animal? You would compare the actual animal to those in your memory, and realize that it is a fox. Although at first the exemplar may seem like the prototype approach, the difference is that the exemplar approach is a comparison with your memories, whereas the prototype approach is an abstraction of your experiences.
Brain research suggests that we use both the exemplar and the prototype approach, depending on the condition. As would be expected, the exemplar approach involves those areas of the brain involved in vision, whereas the prototype approach uses areas more involved in conceptualization such as the frontal lobes. In addition, individuals with brain damage have shown specific types of problems with certain categories. For example, with one type of damage, people were able to describe living things and foods but were unable to recognize human-made objects. Other individuals with different types of brain damage were able to recognize human-made objects but were unable to recognize living things and foods (Martin & Caramazza, 2003). This suggests that various brain networks at particular locations in the brain are involved in organizing different types of categorizations.
In watching humans make decisions, researchers often use the term bounded rationality (Simon, 1982). What this means is that our decisions as humans are limited or bounded by a number of factors, as seen in the two airplane situations at the beginning of the chapter. Three information-limiting factors are the amount of information we have, our cognitive abilities, and the amount of time available to make the decision or judgment. For example, if we saw someone driving fast, we might come to a different conclusion if we did not know the person was taking someone to the hospital. This section will examine some of the ways that psychologists have come to view our cognitions and decision-making processes. As you will see, we as humans are often not logical in how we reason and make our decisions. In fact, we are often biased when we reason. Let’s see how.
Napoleon is to brandy as Caesar is to ____________? How would you answer this question, which is an analogy? You might begin by saying what is common to Napoleon and Caesar? One answer is that they were both military leaders. What is the relationship of Napoleon with brandy, which is something people consume? It is simply a name or descriptor. What is consumed that uses the descriptor Caesar? One answer is salad. Thus, Napoleon is to brandy as Caesar is to salad.
An analogy is nothing more than a comparison of one item with another. The item can be a behavior, an experience, a function, or a relationship. When you learned that imprinting in ducks worked like a lock and key, you were given an analogy. In this way an analogy is a way to understand a process. Often, scientists, when they initially study a process, use analogies to help others understand how something works. Usually, the unknown process, such as genetic replication or the functioning of the brain, is compared to something that is known. At one time people said the brain was like a telephone switchboard or that deoxyribonucleic acid (DNA) worked like a blueprint. As such, analogies give a general understanding of one process in terms of another. Although useful in many cases, analogies are limited in terms of psychological definitions.
As you learned in the chapter on research methods, science is based on logic and observation. When we begin with a statement and arrive at its logical consequences, this is called deductive reasoning. For example, we use deductive reasoning when saying, “If it is true that schizophrenia is genetically determined, then we should find greater similarity in the presence or absence of the disorder between twins than between strangers.” On the other hand, when we begin with an observation and figure out a general rule that covers it, this is called inductive reasoning. For example, inductive reasoning might be of the form, “I just saw a monkey use sign language to ask me for food; therefore, it is true that monkeys can communicate with humans.” In summary, deductive reasoning goes from theory (the premise) to data (the conclusion), whereas inductive reasoning goes from data to theory.
Suppose a friend said to you, “You know, all textbooks are really dull.” You might respond, “That’s not true; I am reading one right now that is really interesting.” (Well, what did you expect a textbook author would have you say?) This is a logical way to disprove the statement, “All textbooks are really dull.” By finding an exception to a statement, you can show it to be false. This is an important use of logic where we look for an exception to disprove the rule. Thus, if you suggest that all swans are white, then a black swan will disprove the rule.
However, humans, especially those trying to sell you something on late night TV, use an invalid type of propositional logic. For example, you may be told that your brain changes as you age and that some people who take certain pills do not age as fast. Therefore, it is the pill that prevents aging. Is this proposition valid?
All roses are flowers.
Some flowers fade quickly.
Therefore, some roses fade quickly.
Even college students will see this as valid. However, it is not, since logically being a flower does not lead to the conclusion that roses fade quickly. There could be no roses that fade quickly.
Better yet, remember the story of our friend from Boston in the research methods chapter who got up every morning, went outside his house, walked around in a circle three times, and yelled at the top of his voice. His neighbor, being somewhat curious after days of this ritual, asked for the purpose behind his strange behavior. The man answered that the purpose was to keep away tigers. “But,” the neighbor replied, “there are no tigers within thousands of miles of here.” To which our friend replied, “Works quite well, doesn’t it?”
Formally, the logic would be as follows:
Yelling keeps away tigers.
There are no tigers in Boston.
Therefore, yelling was the factor that kept the tigers away.
How could we demonstrate to our friend that his yelling is not causally related to the absence of tigers? One strategy might be to point out that the absence of tigers might have come about for other reasons, including the fact that there are no tigers roaming in the greater Boston area. In technical terms, we would say that yelling could be a necessary condition but not a sufficient condition for the absence of tigers. Our friend’s reasoning was incorrect because it overlooked many other plausible explanations for the obvious absence of tigers. Although our friend sought to infer a relationship between his yelling and the absence of tigers, his inference was weak.
Here is a problem. A father and his son were riding in a car that was involved in a terrible accident. The father was killed, and the son was immediately taken to the hospital. The surgeon looked at the boy and said, “I cannot operate on this boy. He is my son.” How could this be?
When you first hear the question—you might say to yourself, is there a way the boy could have two fathers? Did the surgeon remarry? The answer of course is that the surgeon was his mother. If you initially did not consider this possibility, it is because we tend to reason and solve problems in ways that do not consider all the logical possibilities. We look for ways to make problem solving easy on ourselves.
In solving problems, we as humans tend to make decisions in a manner that requires less effort rather than more. One of the ways we do this is to adopt strategies that guide our thinking. These approaches are referred to as a heuristic or bias. Heuristics have been extensively studied by Daniel Kahneman and Amos Tversky (e.g., Tversky & Kahneman, 1973, 1974; Kahneman, 2011). Heuristics place more value on some types of information than on other types. In order to speed up our processing, we actually ignore some types of information.
It is assumed that these heuristics developed over our long evolutionary history. It is also not surprising that humans are not the only species to use heuristics (Gigerenzer, 2008). An important area of study for biologists has been the rules of thumb that animals use for choosing mates, finding food, and choosing where to nest. As noted, the female peahen looks at the male peacock’s tail for choosing a mate. Those heuristics that involve relationships with other people will be discussed in Chapter 12 on social psychology.
From the evolutionary perspective, there is an adaptive value to using heuristics that helped us to live our lives. We also know we use different types of heuristics in different domains. We make decisions about social relationships differently than decisions about buying a computer. Further, different areas of our brain influence different types of heuristics. Overall, these heuristics allow us to use fast thinking with less effort, although we also use the slower form of thinking once we have our “facts” and decide what to focus on.
One of the common heuristics we use is referred to as the availability heuristic or availability bias. As the name implies, this bias is simply our tendency to use information that we can quickly access in our minds. Do you think it is more common for words to begin with the letter L or to have L as the third letter of the word? Most of us will say it is more common for words to begin with L since those are easiest to remember. Actually, there are more words that have an L as the third letter. You can try this same question with the letters R, N, and V. All of these letters appear in the third space of words more often than the first. It is just easier to remember words in which they appear in the first position of a word.
Let’s try another example. Do you think more humans are killed by sharks or cows? Since you hear on TV or on the Internet that sharks harm people, you would probably say sharks. You rarely hear about cows harming people unless perhaps you are a bull rider. However, it is cows that actually kill more people according to the US Centers for Disease Control (https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5829a2.htm). Again, the availability bias influences our reasoning.
Those ideas that come easiest to mind are the ones we are more likely to initially consider as the correct answer. For example, one study showed that medical students in training are more likely to diagnose new cases with a similar diagnosis to that of cases they have recently seen (Mamede et al., 2010). On a personal level, seeing airplane crashes in the news will result in a perception that airline travel is more dangerous than car travel, which it is not. Since we can never consider all the information available for solving a problem, we use that which is available to us first.
Another common bias is the confirmation bias. This was demonstrated by our friend from Boston who yelled to keep tigers away. That is, he only searched for evidence that supported his hypothesis. If we have a strongly held belief, we are more likely to see information that supports our views. Lawyers in a court trial will only present evidence that supports their hypothesis to help the jury not engage in the slower process of looking for contradictions to the evidence. Research has shown that we engage in the confirmation bias even when we perform a simple search task to find certain letters or colors (Rajsic, Wilson, & Pratt, 2015).
Most of the times when we make a decision, we are just going about our normal day-to-day activities. However, as Paul Slovic and his colleagues have shown in a number of studies, decision-making can be greatly influenced by our emotional feelings at that moment (Slovic & Peters, 2006). In these studies, Slovic was not interested in extreme emotions such as anger or fear, but everyday feelings. This is referred to as the affect heuristic. The basic idea is that people make a decision based not only on what they think but also on what they feel. Your emotional reaction to the use of pesticides, nuclear energy, and genetically modified foods will influence how you reason about their use.
One of the clear examples of the affect heuristic involves risk. In one of his early studies, Slovic and his colleagues asked participants in a survey to estimate which events would more likely result in death and by what proportion. This type of study illustrates both the availability bias since floods, lightning, tornadoes, and diseases are often carried on the local news, and the affect bias since we all have an affective reaction to the risk of death. Some 80% of the people in the survey judged accidental death to be more likely than strokes, although the health data at that time showed the opposite to be true. Likewise, tornadoes were seen as more likely to kill you than asthma, although the data show a 20-to-1 ratio in favor of asthma. In addition, death by disease is 18 times more likely than accidental death, but the individuals in the survey thought the two were equal.
If you ask one group of people to quickly tell you the answer to this multiplication problem in five seconds—8 × 7 × 6 × 5 × 4 × 3 × 2 × 1—they will give you a larger answer than if asked to guess the answer to the same problem presented in the opposite order—1 × 2 × 3 × 4 × 5 × 6 × 7 × 8. In fact, when these problems were given to high-school students, they guessed the answer to the first problem would be 2,250, whereas the answer to the second problem was only 512. Kahneman and Tversky referred to this heuristic as anchoring (Tversky & Kahneman, 1974). That is, if you need to answer a question quickly, what you see first can influence your judgment. By the way, 40,320 is the actual answer to the multiplication problem.
Another critical factor in terms of how we make decisions is the way it is presented or framed. Let’s begin with this problem:
New York faces an outbreak of an unusual disease that is expected to kill 600 people. Two programs have been suggested.
1. Program A: 200 people will be saved.
2. Program B: 33% probability that 600 people will be saved and 66% probability that no one will be saved.
Which would you recommend?
Let us now look at two more programs:
1. Program C: 400 people will die.
2. Program D: 33% that no one will die and 66% that 600 will die.
Which of these two would you recommend?
In one study, the participants in the first choice condition chose A by 72% (Tversky & Kahneman, 1981). In the second condition, 78% chose D. What is interesting is that program A and program C are the same and programs B and D are the same. How the problem is presented makes all the difference. In these examples, they chose the programs that initially sounded more positive.
Based on a psychological understanding of how humans make decisions, a new field of study has developed referred to as behavioral economics (or neuroeconomics when brain-imaging techniques are used) (Bossaerts & Murawski, 2015; Camerer, 2014; Loewenstein, Rick, & Cohen, 2008; Thaler, 2015). A primary focus of economics is the consideration of decision and choice. Unlike traditional economics, which suggests that humans optimize their choice in a rational manner, behavioral economics uses behavioral and neuroscience research to understand the factors involved. That is, people do not make decisions in isolation, but in relation to other choices as well how these choices are framed and the effort required. For example, more people accept deductions from their pay checks if they do not have to make a decision about them. That is, if given a choice to opt-in (agree to a deduction) or opt-out (agree to not take the deduction), most people will go with the alternative that does not require them to decide. Behavioral economics approaches would also include the heuristics just described.
Let’s look at one example:
When you arrive at the movie theater, you get out your wallet to pay for the $10 ticket. Although you have enough money to pay for the ticket, you realize you are missing a $10 bill. Would you still buy the ticket?
Most people say they would (Tversky & Kahneman, 1981). What if the story is changed by a few details?
You get to the theater and buy a ticket for $10. You then go back to your car to make sure it is locked. When you return to the theater, you realize you have lost your movie ticket. Would you buy a new ticket?
In this second case, just over half of the people say they would not buy a second ticket. However, in both cases you would have $20 less than when you began the story. What Tversky and Kahneman discovered is that individuals frame the second story differently. That is, they see the cost of the movie as $20 in the second situation and $10 in the first. Thus, they felt that $20 was too much to pay for a movie.
Following the behavioral framing effect described by Kahneman and Tversky, other researchers have asked how the brain is involved in making these decisions (DeMartino, Kumaran, Seymour, & Dolan, 2006). Benedetto De Martino and his colleagues used a framing situation that offered participants to receive money for sure or to gamble for additional money. The participants were initially given $50. The sure condition was framed as either they would keep $20 of the initial $50 or lose $30 of the original $50. As can be seen, both frames left the person with the same amount of money, but one presented it as a gain and the other as a loss. Following this, the participants were given the opportunity to gamble their original $50. The gamble option was the same for both of the frames and the probability presented as a pie chart (see Figure 9-1). As can be seen in the chart, there was a slightly larger chance to lose all the money but exact numbers were not given.
Figure 9-1 Probability pie chart shown in the study.
In looking at the behavioral data, framing played an important role. If told they could keep $20 of the $50, participants were less likely to choose the gamble option. However, when presented as they would lose $30 of the original $50, they were more likely to gamble. These data are presented in Figure 9-2.
Figure 9-2 Behavioral data in framing.
Figure 9-3 Gain and loss framing and its influence on amygdala activity.
Does your brain respond differently to the gain and loss framing? The answer is yes. Overall, activity in the amygdala was related to how the information was framed. In the gain frame, amygdala activity increased if the person did not gamble, but decreased if they did. In the loss frame, amygdala activity increased if the participants gambled, and decreased if they did not. Since the amygdala is associated with personal emotional material including threat, its activity reflects the behavioral patterns in terms of choice. That is, stay conservative in the sure condition and take a risk in the loss condition. This is shown in Figure 9-3. than gaining money. Amygdala activity is more associated with loss than gain (Charpentier, DeMartino, Sim, Sharot, & Roiser, 2015). Other research shown that individuals who have damage to the amygdala show a dramatic reduction in loss aversion (DeMartino, Camerer, & Adolphs, 2010). Although these individuals understood risk, they no longer had the same emotional reaction to loss. The amygdala also has connections with areas of the prefrontal cortex. If the prefrontal cortex is damaged, then people show problems with making decisions. Areas of the prefrontal cortex also are involved when we compare items in terms of value and our confidence in our decision (DeMartino, Fleming, Garrett, & Dolan, 2013). Overall, this suggests that the amygdala is involved in the affect heuristic that emotionally influences our decisions related to gains and losses. Perfect logic is not the nature of humans as described in the box: Myths and Misconceptions: Human Beings Are Rational and Logical.
Myths and Misconceptions: Human Beings Are Rational and Logical
If you read one of the early issues of Scientific American in 1846, you would read about the rationality of men, especially those in business. The rationality of humans continued to be emphasized over the next 150 years. Economists and even some cognitive psychologists suggested for years that humans were rational and made decisions always in their own self-interest. At many economic and scientific meetings, there would be big debates concerning whether humans were rational or irrational. However, by the beginning of the 21st century it became clear that to view humans as logical and rational was a myth (Ariely, 2009; Thaler, 2015; Ubel, 2009). This is also described in a TED Talk (https://www.ted.com/talks/dan_ariely_asks_are_we_in_control_of_our_own_decisions?language=en). Further, as you have read in this chapter, this is a myth that no longer can be supported scientifically.
However, our manner of reasoning has benefited us in many situations. To understand this and how we reason, we need to remind ourselves that we as humans have a long history of interacting with our environment, others, and ourselves. The manner in which we make judgments and decisions has developed during this period and reflects decisions that are adaptive in specific situations. For example, as you learned previously, if you eat a food that makes you sick, you will avoid that food, sometimes even for years. Given the care and handling of foods today, it may not seem logical to not eat a particular food just because a one-time consumption made you sick. However, it does make sense if you obtain all of your food from unknown sources in the environment.
From this evolutionary perspective, we can also understand there is not just one type of reasoning. Over time, we have developed different heuristics for different situations. Protecting ourselves, choosing a mate, and having social relationships all require different types of decisions. Thus, it is not surprising that our decision-making processes are influenced by cognitive, emotional, and bodily experiences that are going on within us at the moment. We buy more food at the grocery store when we are hungry and buy things we do not need when we feel emotional, especially if we think there are only a few of the item.
There are ways we can use our knowledge of how we make decisions to help us make smarter decisions. For example, if people are asked to be part of a retirement system in their workplace, not everyone will join even though it is in their best interest. However, if you automatically sign everyone up for the plan with an option to opt out, fewer people will opt out. Another example is organ donation (Johnson & Goldstein, 2003). France and some other European countries require people to opt out and they have a larger percentage of people who are organ donors than does the US where people must opt in. Figure 9-4 shows the differences in Europe of those countries that require an opt in for organ donation and those that require an opt out. The US has a rate of 28% even though some states offer the ability to sign up for organ donation when one’s driver’s license is obtained or renewed (Gigerenzer, 2008).
Figure 9-4 The differences in Europe of those countries that require an opt-in for organ donation and those that require an opt-out.
Source: Johnson and Goldstein (2003).
Part of the task for psychologists is to understand the nature of our irrationality. This includes both situations in which our decision-making processes are helpful to us as well as those that are hurtful to us. We can then construct environments such as an automatic opt in for retirement or organ donation that benefit society as a whole.
Thought Question: How has evolution impacted human decision-making processes?
1. What are fast thinking and slow thinking and what are the conditions when you would use each of them?
2. How are the following terms, which help us think about and talk about what we perceive and experience, related to one another?
c. Family resemblance
3. What is an analogy and what are the conditions in which you would use it?
4. What are deductive reasoning and inductive reasoning and what are the conditions when you would use each of them?
5. We use heuristics all of the time in our thinking. What is the definition of each of these heuristics and when are they likely to occur?
a. Availability heuristic
b. Confirmation bias
c. Affect heuristic
6. How are traditional economics and behavioral economics different in terms of gain and loss framing?
In the last section, you learn about how we reason and make decisions. In doing this, a number of factors were considered, including the effort involved in coming to a conclusion, the types of heuristics that can influence our decisions, and the way in which fast and slow thinking can influence our conclusions. Much of this discussion emphasized how we reason and make conclusions. This section extends that discussion to another type of problem—those that have a possible or correct solution. Many people do crossword puzzles or Sudoku each day. These are referred to as well-defined problems since a solution can be determined given that there are known constraints with a specific outcome. This type of problem became the basis for measuring differences between humans, historically referred to as intelligence.
Let’s begin with a simple-looking problem, as shown in Figure 9-5:
Figure 9-5 Nine-dot problem.
Your task is to connect all nine dots using four straight lines. That is, you are to take your pencil and draw four lines that will connect all the dots without taking your pencil off the paper. Can you do it? One reason the task is difficult for many of us is based on the Gestalt principle discussed in the chapter 5 on perception. That is, we see the nine dots as if they form a box. Further, we want our solution to stay within the box. Of course, with this constraint, it cannot be done. To solve the problem, we literally must think outside the box. You will see one solution to the problem later in this chapter.
There are other similar problems. For example, imagine you are hanging strings of lights on your back porch. You have a tack hammer and some tacks to attach them to the ceiling. Once you finish, you see the ends of the strings hanging down (Figure 9-6). However, they are too far apart for you to reach them at the same time and plug one into the other. What do you do?
Figure 9-6 Hanging strings of lights problem.
To solve the problem, you must use your tools in a different way. That is, you must tie the tack hammer to the end of one of the light strings and swing it back and forth. In that way, you will be able to reach both of the light strings (see Figure 9-7).
Figure 9-7 Hanging strings of lights solution.
This type of problem was originally studied by Norman Maier in the 1930s (Maier, 1930, 1931). Maier placed subjects in a room with two strings hanging down and told them their task was to tie the two strings together. In the room were tables, chairs, poles, clamps, extension cords, and pliers. Without any help almost 40% solved the problem, but 60% did not. If they didn’t solve problem, then the experimenter would accidentally walk by one of the strings so that it began to swing. With this help another 38% of the people solved the problem. At the conclusion of the experiment Maier asked those who solved the problem how they did it. What he reported was that usually the solution appeared suddenly and as a complete idea (Maier, 1931). That is, they had an insight.
Another common insight problem is the candle problem (Duncker, 1945). On the table in front of you is a book of matches, a box of tacks, and a candle. On the wall is a bulletin board. You task is to attach the candle to the bulletin board in a manner that allows it to be lit and burn normally. How would you do this?
To solve the candle problem, you must use the objects you have in a different way. That is, you must first use the tacks to hold the box on the bulletin board (see Figure 9-8). By attaching the box horizontally to the floor, the candle can be placed inside of the box and burn normally. This can be a difficult problem to solve. If the problem is presented with the tacks being on the table and the box empty, individuals find it easier to solve (Glucksberg, 1962). Figure 9-9 shows the answer to the nine-dot problem.
Figure 9-8 Candle problem.
Figure 9-9 Answer to nine-dot problem.
Nature of Insight
The types of problems you have just attempted are referred to as insight problems since the solution often comes suddenly. An insight problem is a problem that requires a person to shift his or her perspective and view the problem in a different way. This has been referred to as the “aha” or “eureka” moment (Knoblich & Oellinger, 2006; Kounios & Beeman, 2009, 2015; Sprugnoli et al., 2017). Suddenly, you know the answer. The answer is often a reorganization of the information available and seeing the situation in a new light. Although not necessary, the realization of the answer to an insight problem may come with an emotional feeling. The solution of insight problems is not unlike seeing a hidden image in a perceptual scene that all of a sudden pops out at you. From the last century, insight problems have been studied from a psychological perspective and, with the advent of brain imaging, from a cognitive neuroscience perspective (Kounios & Beeman, 2014; Sprugnoli et al., 2017).
Another aspect is that the insight breaks an impasse since the person is fixated on an incorrect solution to the problem. Karl Duncker (1945) devised the candle problem and noticed that individuals would want to use the tacks to fix the candle to the bulletin board. Others tried to use the wax from the burning candle to fix it to the board. As with the nine-dot problem, they had a hard time seeing that the box that held the tacks could be used to hold the candle. They could not break their mental set that the box was to hold tacks but not the candle. To be fixed on a limited view of how an object is used is referred to as functional fixedness.
Although functional fixedness may hurt our ability to solve insight problems, it does have the evolutionary value of helping us limit our alternatives. This saves us valuable energy and time. If you were planning to build a house, it would not be useful to consider every possible building material such as mud, cork, straw, and even ice. It is our frontal lobes that help limit our considerations and make traditional problem-solving more efficient. What if there is damage to someone’s frontal lobes, would it make solving insight problems easier? The surprising answer is yes.
Carlo Reverberi and his colleagues gave insight problems to a group of individuals who had damage to their lateral frontal cortex and to a group without any frontal damage (Reverberi, Toraldo, D’Aostini, & Skrap, 2005). They used the matchstick problem, which will be described shortly. What they found was that with a difficult version of the problem, only 43% of the normal controls could solve the problems, whereas 82% of those with frontal lobe damage could. The finding that brain damage could actually help you solve insight problems is surprising. This suggests that executive control may limit our consideration of alternative solutions. It also suggests that solving insight problems uses areas of the brain different from the frontal lobes.
One of these areas is the right hemisphere, especially the temporal lobes. Previously, you learned that when you recognize an image that pops out at you (the dalmatian dog), your brain produces a burst of EEG gamma band (40 Hz) activity. The same is true when you solve an insight problem (Jung-Beeman et al., 2004). Also, the EEG gamma band activity is preceded by a burst of EEG alpha band (8—13 Hz) activity. These changes are seen in the right temporal lobe. Some researchers see this burst of EEG alpha followed by EEG gamma activity as like the brain resetting itself, which they refer to as a brain blink (Kounios & Beeman, 2009). Likewise, fMRI activity shows changes in blood flow in similar areas of the brain when solving similar types of problems. The EEG changes were not seen when the participants solved normal problems that did not require an insight to solve.
Another way to approach problem-solving is to ask whether it is unique to humans or also found in other species. At this point, you know it has an evolutionary history in that it is seen in other species such as Thorndike’s cats solving their way out of the box to gain food. Another one of the early set of studies to examine insight problems was performed by the Gestalt psychologist, Wolfgang Köhler (1887—1967). Köhler (1925) studied chimpanzees on the Canary Island of Tenerife off the coast of Africa. In one study, a banana was placed just outside the chimp’s cage but beyond the reach of the chimp. If a stick was placed near the banana, the chimp would use the stick to pull the banana into reach. However, if the stick was placed in the cage behind the chimp, the chimp would not solve the problem as quickly. Köhler suggested that the perceptual field as seen by the chimp was critical to solving the problem.
Another version of the banana problem was to place two bamboo sticks in the cage, neither of which was long enough to reach the banana. To solve this problem, the chimp needed to realize that one stick could be inserted into the other in order to reach the banana. Other studies required that the animals place one box on top of another to reach the food. In his report of these studies, there was a focus on the insight that took place and allowed the animal to solve the problem (Köhler, 1925). Köhler also noted which of the chimpanzees were better able to solve the problems, which he referred to as clever. Ways in which insight can be improved is described in the box: Applying Psychological Science: Improving Insight.
Applying Psychological Science: Improving Insight
What can you do to improve solving insight problems? A number of studies have shown that inducing positive emotions, letting one’s mind wander, and mindfulness meditation help to improve the solving of insight problems (Kounios & Beeman, 2009; Ostafin & Kassman, 2012). Although the solving of insight problems was helped, these procedures did not improve the solving of traditional types of problems.
Researchers have asked if you stimulate or inhibit different parts of the brain, would that also improve solving insight problems? That is, could you create a brain blink that would allow for information to be organized in a different way? To answer this question, researchers used a technique that places a battery-powered low-level current across one’s scalp. This is referred to as transcranial direct current stimulation (tDCS). tDCS has been shown to be able to increase or decrease cortical excitability as well as change blood flow in areas of the brain below the electrodes.
Richard Chi and Allan Snyder used tDCS to stimulate the right temporal lobe and inhibit the left temporal lobe (Chi & Snyder, 2011, 2012). As you learned previously, the left hemisphere processes information in a more serial manner, whereas the right hemisphere organizes information in a more holistic manner. The idea was that by inhibiting the left hemisphere and stimulating the right, it would be easier for the person to reorganize the information and solve the insight problem. The right temporal area of the brain is also associated with making connections with other parts of the brain during problem-solving.
In the first study, they used the matchstick task that required participants to rearrange matchsticks such that the Roman numeral equations would be correct. Examples of this problem are presented in Figure 9-10.
Figure 9-10 Roman numeral insight problem used by Chi and Snyder (2011). The first false statement shows III = IX — I (3 = 9—1). To make a correct solution, the X needs to be changed to a V, thus III = IV-I (3 = 4—1).
They found that, in a control condition, 20% of the participants could solve the problem. With tDCS stimulation, three times as many participants could solve the problem. In the second study, they used the nine-dot task. In this case, the results were even more dramatic. Whereas in the control condition no one was able to solve the nine-dot problem, in the tDCS condition, more than 40% of the participants were able to solve the problem.
Overall, activities that help us think in different ways help to solve insight problems. Some of the ways of doing this are to use humor, to relax, to meditate, or to use brain stimulation. These techniques can help us see the world in a new way and thus solve insight problems.
Thought Question: Being in school is all about problem-solving. In your school-work have you been given any insight problems to solve? If so, what approaches did you use to solve them? What might you try in the future after reading the information in this feature?
1. What is an insight problem and how is it different from traditional problem-solving?
2. What is functional fixedness?
a. What role does it play in solving insight problems?
b. What role does it play in traditional problem-solving?
3. Which areas of the brain are involved in solving insight problems?
4. What techniques can you use to improve your ability to solve insight problems?
From Problem-Solving to Measuring Intelligence
It has been noted that some people seem able to solve problems better than other people (Deary, 2014). Some people are also able to work faster or come up with just the right thing to say. Others seem to learn new information quickly and understand how to apply it in very different situations. How might we understand these differences? Is it a general ability associated with the individual, or are certain people better at some particular tasks than others? Of course, you can think of examples of both.
In the 1800s, Sir Francis Galton (1822—1911) in London became interested in the question of individual differences. Partly influenced by his cousin, Charles Darwin, Galton began to study heredity. Like Darwin, one of Galton’s ideas was that there is variation among members of a species and that inheritance plays a role in this variation. Galton published his book Hereditary Genius in 1869.
Based on following the lineage of well-known individuals in Europe, Galton came to the conclusion that mental ability was inherited in a way similar to the physical characteristics of plants and animals described by Darwin. As such, natural abilities according to Galton would center on a mean on a normal distribution. Galton’s main data for assuming a genetic basis of natural abilities was that these abilities appeared to run in families. Of course, at that time in history, one prediction of what children would become later in life was related to the occupations of the family into which they were born. Children of bread makers tended to become bread makers and children of lawyers and doctors tended to follow the occupations of their families.
Galton had been trained in mathematics at Trinity University and invented the correlation and regression procedures as a means of understanding the relationships between human abilities. In 1884 at an international exhibition in London and for a few years thereafter, he measured some 17 different aspects of more than 9,000 people. Actually, he managed to have these people pay to take his tests. These tests included physical measures such as height and mental abilities such as speed of response and memory. He found little relationship between the different measures, although he did suggest that such measures would form a normal distribution.
Understanding the differential abilities of others and the nature of these differences is an important part of the study of intelligence (Mackintosh, 2011). Intelligence is one of the best predictors of such important life outcomes as education, occupation, mental and physical health, illness, and mortality (Plomin & Deary, 2014; Plomin, 2018). However, these are correlational relationships. That is, such factors as bad nutrition or difficult early life experiences could influence both IQ and later health.
How Do We Measure Intelligence?
Overall, intelligence is defined as the ability to learn from and adapt to our environment by solving problems and predicting what might happen next. This is a broad understanding of intelligence. There is also a narrower definition that refers to how we perform on tests of intelligence and compare to others. By using the larger definition, we consider a number of different ways humans adapt and interact with one another. When we use the narrower definition, we tend to define intelligence as a single term with only a few subcategories.
The study of intelligence is in some ways different from other psychological concepts, such as memory or learning, which you have studied thus far. Traditionally, scientists seek to understand a concept by dividing it into its component parts. Memory, for example, is divided into short-term memory, long-term memory, and so forth. Also, you learned about particular areas of the brain involved with each type of memory. The concept of intelligence is in some ways the opposite of this. In fact, the history of the study of intelligence has emphasized the concept as a whole. Further, there is no one brain area that is associated with differences in intelligence.
Scientists have studied what this larger concept of intelligence is related to, and what factors are involved in its expression. As you will see, a historical question has been the manner in which genetic makeup is important and the manner in which the environment in which you live contributes to a person’s intelligence level. Environmental factors have included your schooling, the nutrition that was available to you, the stress you experienced, and other such factors. Lead levels in water, paint, and leaded gasoline in a person’s environment have been shown to reduce cognitive functioning and IQ over a long period of time (Reuben et al., 2017). Thus, it is not surprising that each underlying factor that contributes to intelligence can be influenced by a number of variables. What is somewhat amazing is that intelligence as measured on tests is a relatively stable individual difference that remains stable over a number of years.
The study of intelligence also has, at its core, prediction. The Han dynasty of China in 206 BC gave tests to determine who would make the best administrators. Civil servants in a number of countries have been selected by intelligence tests. In the United States, college students are partly selected by the SAT (Scholastic Achievement Test), ACT (American College Testing), and the GRE (Graduate Record Examinations) for graduate study, which show a positive correlation with standard intelligence tests. But we are getting ahead of the story.
At the beginning of the 1900s, the French government sought to solve a practical problem in terms of which children would benefit from normal schooling. As they began to expand public schooling, the French Government needed to know who would and would not benefit from normal schooling. To answer this question, they needed a way to measure mental abilities. Alfred Binet (1857—1911) and his associate Théodore Simon developed a measure of mental abilities, the Binet—Simon Intelligence Scale, in 1905. In choosing items for the scale, Binet began by asking teachers what types of tasks children of different ages could perform. The tasks were largely related to practical tasks such as telling time from a clock, naming parts of their body, knowing the definition of words, repeating a series of numbers, and copying geometric figures. In doing this, Binet helped establish the modern tradition of viewing intelligence as a composite of different abilities that relate to learning about and solving problems related to one’s environment.
In developing the scale and his measure of intelligence, Binet made two assumptions. The first assumption was that one’s abilities improve with age. This is the assumption that a child can perform better in different tasks in the sixth grade than in the first grade. What this means is that a child can be compared to other children at a given age. That is, Binet could say that a 10-year-old child should be able to perform a specific set of tasks. This allowed for the concept of mental age. Mental age is based on the set of tasks that children of a certain age can perform. However, if an 8-year-old child could successfully perform tasks that 10-year-olds usually perform, then this child would have a mental age of 10 years.
Binet’s second assumption was that the child’s ability in comparison to other children remains constant. That is, if one had a mental age of 10 when he was 8 years old, then it was assumed he would have a mental age of 12 when he was 10 years old. This second assumption has been supported by research over the last 100 years. That is, after the teenage years, scores on intelligence measures remain relatively stable for the rest of the person’s life in comparison to his or her peers. In addition to the two assumptions about a child’s intelligence level, an additional assumption was that a test could be constructed and given in a relatively short period of a few hours that would measure mental age. Binet’s test and basic ideas were brought to the United States. It was modified by Lewis Terman at Stanford University in 1916 and became known as the Stanford—Binet Intelligence Scales.
Whereas Binet used the concept of mental age, William Stern (1871—1938) turned it into a ratio by dividing mental age by chronological age. The concept of IQ or intelligence quotient is calculated by the following:
Intelligence Quotient (IQ) = mental age divided by chronological age times 100
Thus, a 12-year-old with a mental age of 16 would have an IQ of 125 (16/12 × 100 = 125). With this formula, a child whose mental age and chronological age are the same would have an IQ score of 100. An IQ score above 100 would be above average and one below 100 would be below average.
By the 1920s, intelligence tests were being administered in the United States, England, France, and other European countries. They were not only administered in schools but also in the armed services as a way to classify individuals and assign them to jobs. When the United States entered World War I, the army asked psychologists to help determine which men should be officers. This test was known as the Army Alpha. Another version of the test, the Army Beta, was designed for individuals who could not read.
Today, the most common intelligence measures are the Wechsler Adult Intelligence Scale (WAIS) and the Wechsler Intelligence Scale for Children (WISC). The initial WAIS was released in 1939 and the current version, referred to as WAIS-IV, was released in 2008. The 2008 version of the test was initially given to 2,200 individuals who were chosen in terms of age, sex, education level, ethnicity, and region. The test structure of the WAIS is directed at four major abilities. These are verbal comprehension, perceptual reasoning, working memory, and processing speed. The structure of the WAIS can be seen in Figure 9-11.
Figure 9-11 The structure of the WAIS.
Verbal comprehension includes subtests in terms of:
Similarities: How are a hammer and a screwdriver alike?
Vocabulary: What does ambivalent mean?
Information: What is the capital of France?
Perceptual reasoning includes:
Block design: Using nine blocks (each block had two sides all red, two sides all white, while the other two sides had diagonals of red and white) copy a design such as Figure 9-12.
Figure 9-12 Block design.
Matrix reasoning: The person is shown a number of figures and determines what would be the shape of the missing one from a list. A matrix problem is shown in Figure 9-13.
Figure 9-13 This is an example of a matrices test.
Visual puzzles: Choose which three of six pieces go together to make a single figure (which is shown in the test).
Working memory includes:
Arithmetic: If 2 people can do a job in 1 hour, how long would it take 8 people to do the job? Digit span: Repeat the digits read to you (for example, 8, 2, 4, 7, 9, 3, 1).
Processing speed includes:
Symbol search: Determine as fast as possible whether a particular symbol appeared in a previous list.
Coding: Symbols are paired with numbers. A list of numbers is shown and the task is to add the symbol that was associated with that number as fast as possible.
The items in each subscale range from easy to difficult so that the same test can be given to individuals who vary in mental abilities. One problem with measuring intelligence in terms of mental and chronological age is that chronological age continues to change, whereas mental age does not. Thus, if you wanted to compare adults, a different measure was needed. Rather than using mental and chronological age, the Wechsler test determined intelligence level by comparing a person’s score to others in his or her age group. To do this a normal curve was used. Initially, the scores on the test and their standard deviation were determined. Using a normal curve (see Figure 9-14) the mean score of the group on the test was set at 100. The vertical lines in the graph represent a standard deviation and the percentage shown would be the number of individuals who fall within that deviation. For example, 98% of all individuals have an IQ of 130 or less.
Figure 9-14 WAIS IQ normal curve.
Since IQ is determined by comparing a person’s performance with his or her peers, the same number of items correctly answered would result in a different IQ for different age groups.
Psychometric Theories of Intelligence
During the 1900s when intelligence tests were being developed, Charles Spearman began to explore the structure of intelligence. One of his early studies was to ask teachers and older students about the cleverness of a particular student. He also measured performance on three sensory measures such as judging the experience of weight, light, and sound. He would compare musical pitch discrimination with ability in French class, for example. Overall, he had reports from teachers, older students, and sensory measures. Spearman found that the average correlation between these three measures was.55 (Spearman, 1904). He also found a strong relationship between academic subjects such as Latin and other areas such as music. This suggested to him that there must be an overall factor in intelligence.
Spearman’s research led to his development of a two-factor theory of intelligence. The first factor is related to the specific test itself, such as the ability to do math or language. The second factor, based on the correlation between measures, is a global factor. Spearman referred to this general cognitive ability as “g” or general intelligence. Spearman actually saw “g” as an energy or power that was available to the entire brain (Spearman, 1923). In essence, “g” refers to the situation in which someone who does well in one domain tends to perform well in others.
Raymond Cattell suggested that general intelligence is made up of two separate factors (Cattell, 1971). The first factor is referred to as fluid intelligence. Fluid intelligence reflects the ability to perceive relationships with previous specific experience. One early idea was that fluid intelligence reflects one’s native abilities. Such measures as the Raven Matrices Test (see Figure 9-15) are seen to reflect this ability. The Raven Matrices Test shows a person eight figures and he or she needs to determine what would be the shape of the ninth. The first two shapes in each row, if combined, result in the third shape. Likewise, the first two shapes from the top in each column result in the bottom one.
Figure 9-15 Raven Matrices Test.
The second factor of intelligence for Cattell was referred to as crystallized intelligence. This type of intelligence reflects the ability of a person to acquire knowledge available in his or her culture. For example, the ability to use tools, know the meaning of words, or understand cultural practices such as having a driver’s license all reflect what one has learned from the culture. An interesting question is whether a group of people can be more correct than one individual. This question is described in the box: The World Is Your Laboratory: Wisdom of the Crowd.
The World Is Your Laboratory: Wisdom of the Crowd
In 1907 Francis Galton published a paper in the journal Nature examining the wisdom of crowds. He described a contest he created at a local fair to guess the weight of an ox. What he found was that based on the guesses of more than 700 people, the collective estimate of the group was correct within 1% of the actual weight. Since that time, prediction markets, as they are currently called, have been able to predict a variety of topics including election outcomes and the use of health care.
A more recent version of Galton’s demonstration was performed at a state-owned casino in the Netherlands (van Dolder & van den Assem, 2018). On three separate occasions, individuals guessed via computer the number of pearls, diamonds, or casino chips in large glass containers. The winner received € 100,000. Using more than 1.2 million guesses, it was shown that the collective estimate was indeed an accurate representation of the true value.
Figure 9-16 Scores of the group as a whole compared to that of individual measures.
Source: Woolley, Chabris, Pentland, Hashmi, and Malone (2010).
Psychologists define intelligence in terms of a number of subtests that measure different abilities. For some, there is a global ability referred to as general intelligence or “g,” which is seen to be measured by intelligence tests. Recently, psychologists have asked if you could measure the global intelligence of a number of people (Woolley, Chabris, Pentland, Hashmi, & Malone, 2010). That is, is there a wisdom of the crowd and can it be measured? In order to answer this question, Anita Woolley and her colleagues randomly assigned individuals to three-person groups and asked them to perform a variety of tasks. The tasks included visual puzzles, brainstorming, making collective moral judgments, and negotiating over limited resources. Before the group session, the ability of each individual was determined. In a second experiment, 152 groups of two to five members were given a broader range of tasks. Overall, these researchers found that there was a collective performance measure that could not be explained by the individual intelligence of the group members alone. Further, two factors were related to the overall group score. The first was social sensitivity. The more a person was able to understand others in his or her group, the higher the group score was. And, second, in groups where a few people dominated the problem-solving, the overall group score was lower. Using this information, these authors suggest that for problem-solving it would be easier to raise the intelligence level of the group than the intelligence level of the individual (see Figure 11-21).
Thought Question: From what you’ve read here, what measures might you take to improve the intelligence level and performance of your next project team?
Most of the items seen on intelligence measures lack one aspect—context. In our daily lives, we solve problems and understand others that reflect abilities not seen in traditional intelligence tests. Even when we need to know the definition of a particular word, it is often clarified for us in reading or in our discussions with others. In fact, it is suggested that development of intelligence as a species came from our interactions with others over evolutionary time. It is in these interactions with others that our ability to think abstractly and manipulate symbols becomes critical.
Thinking of mental abilities in a broader sense leads one to conceptualize intelligence in a different way than that of an intelligence test. That is to say, intelligence can be understood in relation to solving problems in the context of our culture (Gardner, Kornhaber, & Wake, 1996; Davis, Christodoulou, Seider, & Gardner, 2011). Our experience in culture also shows that a particular person may be very successful in one domain such as sports and not in another such as language skills. This would suggest that abilities can be separate from one another.
Howard Gardner sought to define intelligence within the context of different domains with his theory of multiple intelligences (Gardner, 1993). Unlike the psychometric approach that examined the statistical relationship between mental abilities, Gardner looked to normal individuals who were particularly talented in a particular ability such as playing chess, music, politics, athletics, and entrepreneurship.
He also examined individuals who had suffered some type of brain damage through accidents or disorders. Following the brain damage, an individual who once could find his or her way through any city may now feel lost in his or her own home. However, even with problems in spatial ability, other abilities such as speech and language remained intact. Likewise, as noted previously, the ability of savants to be able to reproduce in great detail a drawing of a city seen for only a short time or to know the exact date that the first Monday in November will fall on in 20 years suggest separate abilities.
This led Howard Gardner to the idea that there exist separate intelligences. Unlike the concept of “g” or general intelligence, a person can be excellent in one ability such as dancing or music and less proficient in another such as mathematics. Gardner suggested there are eight separate intelligences:
Linguistic intelligence involves sensitivity to spoken and written language, the ability to learn languages, and the capacity to use language to accomplish certain goals. This intelligence includes the ability to effectively use language to express oneself rhetorically or poetically as well as using language as a means to remember information. Writers, poets, lawyers, and speakers are among those whom Howard Gardner sees as having high linguistic intelligence.
Logical-mathematical intelligence consists of the capacity to analyze problems logically, carry out mathematical operations, and investigate issues scientifically. In Howard Gardner’s terms, it entails the ability to detect patterns, reason deductively, and think logically. This intelligence is most often associated with scientific and mathematical thinking.
Musical intelligence involves skill in the performance, composition, and appreciation of musical patterns. It encompasses the capacity to recognize and compose musical pitches, tones, and rhythms. According to Howard Gardner, musical intelligence runs in an almost structural parallel to linguistic intelligence.
Bodily kinesthetic intelligence entails the potential of using one’s whole body or parts of the body to solve problems. It is the ability to use mental abilities to coordinate bodily movements. Howard Gardner considers mental and physical activity to be related.
Spatial intelligence involves the potential to recognize and use the patterns of wide space as well as more confined areas. Some people can drive through a city once and be able to navigate the same city at a later time.
Interpersonal intelligence is concerned with the capacity to understand the intentions, motivations, and desires of other people. It allows people to work effectively with others. Educators, salespeople, religious and political leaders, and counselors all need a well-developed interpersonal intelligence.
Intrapersonal intelligence entails the capacity to understand oneself and to appreciate one’s feelings, fears, and motivations. In Howard Gardner’s view, it involves having an effective working model of ourselves and to be able to use such information to regulate our lives.
Naturalistic intelligence entails the ability to identify and distinguish aspects of the natural world. This would include weather patterns, types of plants, animals, or rock structure. Experts in meteorology, botany, and zoology are professionals who demonstrate high levels of naturalistic intelligence.
1. What is the broad definition of intelligence? What is the narrow definition of intelligence?
2. What contribution did Alfred Binet and his associate Théodore Simon make to the measurement of mental abilities? What are three assumptions they adopted in developing their measurements?
3. Define the concept of mental age. How is it related to IQ (intelligence quotient)?
4. What are the four major abilities covered by the Wechsler Adult Intelligence Scale (WAIS)? What is an example of each of those abilities?
5. One problem with measuring intelligence in terms of mental and chronological age is that chronological age continues to change, whereas mental age does not. How did Wechsler solve this problem in comparing adults?
6. Describe the theories of intelligence proposed by the following researchers: Charles Spearman, Raymond Cattell, and Howard Gardner. What unique contribution did each make to our overall understanding of intelligence?
Relationship of “g” to Cognitive Factors
A number of studies have sought to determine the relationship of a person’s performance on different cognitive domains. Timothy Salthouse combined the data from 33 separate studies involving 6,832 people ranging in age from 18 to 95 (Salthouse, 2004). He found that a number of domains could be combined into five larger categories based on their relationship with one another. These five domains are reasoning (such as the Raven Matrices Test), spatial ability, memory, processing speed, and vocabulary. Figure 9-17 shows the relationship of each of these domains with a general factor of intelligence. This figure shows it is possible to find different measures of a particular domain such as reasoning, which are highly correlated with one another. In turn, the five domains are correlated with the general intelligence factor called “g.”
Figure 9-17 This figure shows the correlation between the general measure of intelligence (g) and five domains. Specific tests of each domain are numbered 1 to 12.
Source: Deary, Penke, and Johnson (2010).
Cognitive abilities do change as one ages. Timothy Salthouse not only looked at the relationship of specific domains with “g” but also how these change with age (Salthouse, 2004, 2011). As can be seen in Figure 9-18, as one grows older, there is a decrease in processing speed (digit symbol coding) and memory (digit span). Reasoning and spatial ability tend not to change, whereas one’s vocabulary improves. It is also the case that language comprehension remains stable although language production such as finding the right word becomes more difficult (Shafto & Tyler, 2014).
Figure 9-18 Changes in cognitive abilities with age.
One implication of this is that any intelligence measure such as digit symbol coding that emphasizes speed will show decline over time, whereas those that emphasize vocabulary will not. That is, measures of crystallized intelligence with its emphasis on knowledge and vocabulary will not show the decreases that would be seen in fluid intelligence with its emphasis on speeded reasoning. However, since IQ is based on how a person compares with his or her peers, their IQ would not change as they age.
Although memory is often seen as a problem of aging, other abilities are also affected. As can be seen in the graphs, there is a consistent picture of change over the lifespan for each category with the exceptions of vocabulary ability, which does not decrease over the lifespan, and perceptual speed, which shows a consistent decrease over the lifespan (Salthouse, 2011). Overall, except for vocabulary and perceptual speed, cognitive processing remains constant until the person is in his or her 60s. After that age, there is a decrease in certain cognitive abilities, but decision-making competences can be retained (Bruine de Bruin, Parker, & Fischhoff, 2007.
Brain Change with Age
Two consistent findings are that older adults show changes in brain structure and that they use their brains in different ways from younger adults (see Park & Reuter-Lorenz, 2009; Reuter-Lorenz & Park, 2010 for overviews). In terms of brain volume, volume reduction is seen in the hippocampus, cerebellum, prefrontal cortex, and caudate nucleus, which are areas related to executive control and memory. Problems with the hippocampus are associated with a reduction in declarative memory, whereas problems in the prefrontal cortex are related to working memory (Morrison & Baxter, 2012). However, volume reduction alone cannot explain the decline of cognitive functioning (Salthouse, 2011). On the other hand, the visual cortex and the entorhinal cortex show little reduction in volume with age. The entorhinal cortex is located in the temporal lobe and serves as a hub that connects the hippocampus and the neocortex. It is involved in memory and spatial navigation. It is one of the first areas affected in Alzheimer’s.
In order to solve problems, older individuals use their brains differently. Even when younger and older adults both perform a memory task successfully, older adults recruit more brain regions than do younger adults. One interpretation is that older adults need additional executive resources to perform the same task. This is referred to as compensation. That is, in order to optimize their performance, older adults perform the same task using additional neural circuitry. When older adults use just the brain areas that are activated in younger individuals, they do not perform the tasks as well as younger adults. If younger adults are given more difficult tasks, then they also recruit additional areas.
When you are sitting and doing nothing, the default network in your brain turns on. When you start performing a task, more task-related networks are activated, and the default network is inhibited. In younger individuals, the same pattern of activity in the frontal lobes, the parietal lobes, the temporal lobes, and the cingulate is seen across a variety of tasks in numerous studies (see Beason-Held, Hohman, Venkatraman, An, & Resneck, 2017 for an overview). In older individuals, the number of brain areas involved in the default network is larger, especially in the frontal lobes. Older adults also have a more difficult time turning off the default network. It is assumed that this is related to the problems some older adults have in shifting cortical resources to new tasks. However, it should also be noted some older individuals show the same levels of performance as younger individuals.
The Scottish Mental Survey Study
On June 4, 1947, more than 70,000 11-year-olds across Scotland were given an intelligence test that sought to determine who would benefit from school (Deary, Whalley, & Starr, 2009; Underwood, 2014). This was known as the Scottish Mental Survey and was a follow-up to an earlier 1932 testing session. What was unique about the 1932 and 1947 test sessions was that they included almost everyone born in Scotland in 1921 and 1936. Some years later, Ian Deary and his colleagues at the University of Edinburgh were able to have 1,000 individuals in the Lothian region of Scotland near Edinburgh who were in the 1932 testing session retake the test as well as be part of brain-imaging studies with an MRI.
This interesting study followed this group of individuals who took the IQ test at age 11 and then again at age 79 (www.lothianbirthcohort.ed.ac.uk/). Beginning at age 79, they were also followed until they were 87 using a logical memory task. The website lists more than 250 publications that have resulted from these data. One general conclusion from this program of research is that an individual’s score on the test at age 11 can predict about 50% of the variance in their IQ at age 77 (Deary, 2014; Deary, Whiteman, Starr, Whalley, & Fox, 2004). Figure 9-19 shows a scatter plot of these data. Each dot in the figure represents a person and shows his or her score at age 11 and at age 80 on an intelligence measure. It was also shown that intelligence level at age 11 was a predictor for health and longevity (Gottfredson & Deary, 2004). This study strongly supports the idea that intelligence can remain constant across one’s life. Further, these authors suggested that intelligence enhances individuals’ care of their own health because it represents learning, reasoning, and problem-solving skills useful in preventing chronic disease and accidental injury and in adhering to complex treatment regimens.
Figure 9-19 Scattergram of age corrected Moray House Test (MHT) scores at age 11 and age 80. Note: each dot represents one person.
Source: Deary, Whiteman, Starr, Whalley, and Fox (2004).
Genetic Factors Associated with Intelligence
By this point, you have realized that genes and the environment work together in very complicated ways. For example, having genes associated with seeking excitement and adventure may put you in different environments such as climbing mountains than those who do not show the expression of these genes. These environments in turn can affect one’s experiences.
Other environments, such as those without hygiene or sufficient food, may result in psychological or physical conditions separate from one’s genetics. One common analogy is to point out that seeds with the same genetic components when planted in poor soil with insufficient water develop very differently than when planted in good soil with sufficient sun and water (Lewontin, 1976). However, if there were differences in the seeds planted in the same container that received the same environmental conditions, then one should consider genetic differences.
Those who grow up in impoverished areas may not show the same physical and mental achievement as those who have their needs met. Safe neighborhoods with friends and challenges offer greater physical and mental development opportunities.
Human height is considered to be 80% heritable. However, like intelligence, height has increased over the past 150 years. Clearly, environmental factors such as nutrition, health care, and sanitation have increased during this period and play a part in the increase in height. How could genes play a role in height and intelligence if it has changed over time?
The answer is that heritability is about individual differences not group means. That is, in each generation, genetic factors would result in some individuals being taller than others, and these individuals would have come from the same family. In the next generation, that family would also have even taller children as compared to others.
The technical estimate of heritability is a statistic that describes the proportion of a given trait’s variation within a group of people that is attributable to variations in the genes. If the trait or phenotype is completely determined by genetic variation, then the heritability factor would be 1.0. If genes have no influence, then the heritability would be 0.0. Some physical traits such as hair color or blood type have very high heritability. Most psychological traits show heritability of.4 to.6, which would suggest both genes and environment contribute to the trait.
Overall, genetic factors are related to how one individual compares with another based on a specific trait. In this manner, environmental factors can increase the IQ of all individuals, whereas genetic factors influence the rank order of where the person falls in the group. Further, epigenetic factors based on the environment may result in behaviors seen in some individuals that is different from the behaviors of others. When we turn to an understanding of intelligence, there is a great opportunity for numerous factors to have an impact. For example, in one study of 11,000 pairs of twins from both the US and Europe, individuals with a high IQ show that environmental factors influence IQ into adolescence, whereas individuals with a low IQ show that heritability factors influence IQ in adolescence (Brant et al., 2013).
To remind you of what you learned previously in terms of behavioral genetics, there are three types of twin studies. The first was based on the study of how one individual was related to another. Monozygotic (MZ) twins are identical twins resulting from the zygote (fertilized egg) dividing during the first two weeks of gestation; they share 100% of their genes. Dizygotic (DZ) twins, on the other hand, result from the situation in which two different eggs are fertilized by two different spermatozoa, and they share on average 50% of their genes. In terms of genes, MZ twins are more alike than DZ twins. These individuals are called fraternal twins since their shared genes are approximately 50%, which is the same as that between any two siblings. DZ twins can be either same sex or opposite sex, whereas MZ twins must always be the same sex. The basic idea is that if genes influence cognitive abilities, then you would see a higher correlation between IQ scores, for example, in MZ twins as compared to DZ twins.
Research over the past 50 years has consistently shown that genes influence cognitive abilities. In terms of IQ, MZ twins show a higher correlation of IQ scores between the two twins (.86) than DZ twins do (.60). In one study, both types of twins were reared in the same household (Segal & Johnson (2009). Figure 9-20 shows these data. Like DZ twins, parents and their children as well as siblings show the same amount of genetic similarity, which is.5. If raised in the same home, they show a correlation between IQ scores of about.42.
Figure 9-20 The figure shows similarity of MZ and DZ twins reared together.
Source: Segal and Johnson (2009).
The second type of twin study is the adoption study. This is the situation where DZ and MZ twin pairs were split up and have been raised apart by different parents. Since the children were raised in different environments, this made it possible to better determine the environmental and genetic influences. However, genetic factors may have led each twin to seek similar experiences although in different environments. In this way, genetic factors could make different environments more similar.
As seen in Figure 9-21, MZ twins who were adopted and raised in different environments show less correlation in their IQ scores than those who were raised together in the same environment.
Figure 9-21 IQ correlations for twins varying in genetic and environmental relatedness. Old and new MZ twins represent different samples.
Source: Segal and Johnson (2009).
A third type of study emphasizes molecular genetics. This examines generations of families and looks for the association between particular DNA marker alleles and particular traits. Although individual differences in intelligence are highly heritable, it has been difficult to find single genes involved, even in the small percentage of individuals with very high intelligence (Spain et al., 2016). This has led researchers to suggest that a large number of genes make small contributions to the overall intelligence score. What has been suggested is that individual genes are more likely to result in problems in intellectual functioning such as the identification of genes associated with disorders of cognitive abilities such as dyslexia (Mascheretti et al., 2015).
Overall, genetic factors have been associated with intelligence. In fact, intelligence is one of the most heritable behavioral traits (Plomin & Deary, 2014; Plomin, 2018). Physical traits such as height will show a higher correlation, but behavioral traits do not show relationships much over.50. In terms of fluid intelligence, such as the Raven Matrices Test, heritability has been estimated at 51% (Deary, 2012). For crystallized intelligence, which includes what one learns in life, heritability is associated at 40%. Both of these estimates suggest that intelligence is influenced by environmental factors at least as much as by genetic factors. These environmental factors can include the quality of the foods you eat, the availability of health care, whether you exercise, your educational experiences, and other such factors.
As discussed, much of the research on intelligence has come from studies of MZ and DZ twins who vary in genetic similarity. One general finding suggests that there are genetic components that are related to “g” (Plomin & Spinath, 2002; Knopik, Neiderhiser, DeFries, & Plomin, 2017). Robert Plomin and Ian Deary have summarized this research in terms of five critical findings (Plomin & Deary, 2014).
Figure 9-22 A meta-analysis of 11,000 pairs of twins shows that the heritability of intelligence increases significantly from childhood (age 9) to adolescence (age 12) and to young adulthood (age 17).
Heritability increases dramatically from infancy through adulthood: The effect of genes on intelligence is about 20% in infancy, about 40% in adolescence, and about 60% in adulthood. There is some evidence to suggest that this increases to 80% in later life. It is likely that different genes play a role in intelligence at different times of life.
2. Intelligence reflects genetic effects on diverse cognitive and learning abilities: In essence what this suggests is that genetic effects are general rather specific. That is, there is not a set of genes for each ability but rather a number of genes affect general intellectual ability.
3. Assortative mating is greater for intelligence in comparison to personality and psychopathology: Assortative mating suggests that humans do not randomly choose partners but choose partners who are like themselves on some selected trait. Intelligence level is a critical behavioral factor for choosing a mate. This means that spouses are more genetically similar than that found in two individuals chosen at random.
4. Intelligence is normally distributed: Research has shown that the top section of the distribution is just as heritable as the other parts of the distribution.
5. Intelligence is associated with education and social class: This suggests that the genes that influence intelligence also influence education level and social class, and in turn influence health outcomes and mortality.
APOE Gene and Intelligence
Each of your parents contributes to your genetic makeup, which means you can have one variant (allele) of a gene you receive from your father and the same or a different one from your mother. A particular gene related to brain processes is the APOE gene named after the protein it produces. The APOE 4 variant is associated with dementia and Alzheimer’s disease. The risk of dementia is increased in those who have one APOE 4 allele and even more for those who have two APOE 4 alleles (Glorioso et al., 2019).
The Scottish Mental Study of intelligence discovered that those individuals with the APOE 4 allele showed more cognitive decline even in the absence of a neurocognitive disorder (Harris & Deary, 2011). Figure 9-23 shows that at age 11, there was little difference in the IQ scores of those with and without the APOE4 allele. However, by age 79 those with the APOE 4 allele showed a lower IQ score, whereas those without this allele showed a similar IQ score to that seen when they were age 11. On the logical memory task, those with the APOE4 allele showed a decline over the next eight years.
Figure 9-23 Presence of the APOE 4 allele is associated with cognitive decline in individuals without a neurocognitive disorder.
Maintaining Intelligence throughout Life
Maintaining intellectual functioning throughout life has become a popular topic in the media. Numerous brain game systems are available that claim to improve performance and prevent cognitive decline with age. However, the data to support these claims are not definitive (Slagter, 2012). Other techniques that include increased effort may be effective (Tang & Posner, 2014).
What is known is that physical exercise has a profound effect on brain function in both humans and animals (Voss, Vivar, Kramer, & van Praag, 2013). Even more, physical exercise of an hour a day reduced the negative effects of APOE 4 (Schuit et al., 2001). Exercise protects and restores the brain through increased blood flow and other mechanisms. This results in functional and structural changes throughout the brain. The hippocampus, involved in learning and memory, is one specific structure that benefits from exercise. In fact, exercise increases the production of new neurons in the hippocampus. Exercise also plays an important role in aging by promoting healthy cardiovascular function. That is, exercise increases blood flow to the entire brain.
In a review of literature from different areas, Kramer and Erickson (2007) suggest that exercise provides multiple routes to enhancing cognitive vitality across the lifespan. These include both the reduction of disease risk as well as improvement in molecular and cellular structures of the brain. This in turn increases brain function. Further, it is suggested that aerobic exercise affects executive function more than other cognitive processes. Exercise has also been shown to slow the expression of Alzheimer’s-like disorders in a mouse model.
It was first noticed that not all individuals showed the same cognitive changes to aging, neurocognitive disorders, or brain injury. From this observation, the concept of reserve was developed. That is, high-functioning individuals tend to show less loss of cognitive abilities in relation to aging and neurocognitive disorders. Being able to speak more than one language has also been shown to protect against cognitive decline in older adults and seen to be related to reserve (Bialystok, Craik, & Luk, 2012). The concept of reserve suggests that the brain can compensate for problems in neural functioning. This is illustrated by the case in which the brains of older individuals use larger areas of the brain to solve problems than younger individuals. High-functioning or intelligence is often associated with greater reserve.
Following more than 700 older individuals without neurocognitive disorders for several years, Aron Buchman and his colleagues found that daily physical activity slowed cognitive decline (Buchman et al., 2012). Exercise was also associated with a lower risk for developing Alzheimer’s disease. Although performing various types of cognitive tasks such as crossword puzzles or speaking a second language are also associated with successful aging, these brain effects appear to be more localized in those areas of the brain related to the specific task.
In order to better help articulate the causal role of exercise, Lindsay Nagamatsu and her colleagues randomly assigned older individuals who were beginning to show mild cognitive impairment to one of three groups (Nagamatsu et al., 2013). The first group received resistance training and lifted weights. The second group received aerobic training and walked outdoors at a level that increased their heart rate. The third group received balance and stretching exercises. The third group served as the control group. After six months of twice-weekly exercise, the first two groups showed improvement in memory functions. This was seen more strongly on a difficult spatial memory test. The aerobic group also improved performance on the verbal memory test. The important point of this study is that six months of exercise can improve memory in 70-year-olds.
Social support has also been associated with a reduced risk for neurocognitive disorders. Two of these factors are the size of one’s network of friends and whether one is married or not. As suggested in studies of the social brain, understanding and maintaining networks of friends requires a variety of cognitive resources that, in turn, offer a reserve for dealing with brain pathologies. One study followed 16,638 individuals over the age of 50 for six years. Those individuals who were more socially integrated and active showed less memory loss during the six-year period (Ertel, Glymour, & Berkman, 2008). With greater conceptualization of intelligence, we begin to see it as a complex measure, as noted in the box: Myths and Misconceptions: IQ Is a Single Measure, Stable, and Caused by a Single Factor and Can Be Applied to Education.
Myths and Misconceptions: IQ Is a Single Measure, Stable, and Caused by a Single Factor and Can Be Applied to Education
While it is true that intelligence has a strong genetic influence, this can lead to erroneous conclusions and myths about genetic influences. For example, it is a myth that genetic influences on intelligence represent a simple relationship and can be applied directly in schools. In fact, if the environment of the school is changed, the genetic effects that normally result in performance differences can be reduced (Thomas, Kovas, Meaburn, & Tolmie, 2015). In a similar way, being involved in team sports protects teenagers from becoming regular smokers, even if they have genes associated with smoking. However, the opposite is also the case. If the environment remains stable for all, genetic effects will be more apparent. In the United Kingdom, where classrooms and content are similar across the country, genetic influences on exam performance were shown to be highly heritable across 13,306 16-year-old twins (Krapohl et al., 2014).
Adding results from psychological domains with a genetic influence including self-efficacy, personality, well-being, and behavior problems increased the effect of herit-ability on the results. It should also be noted that gender differences accounted for less than 1% of the variance. Students may create their own educational environments even in structured environments by approaching the school material in different ways.
Another myth is that IQ or “g” is stable across one’s lifetime. If you were to examine preschool children, you would not find the 60% differences in terms of genetic influence on performance that is found in later school years. What you would find is that genes can only explain about 20 to 30% of the differences between preschool children. In fact, the environment, typically the home at this point, can account for about 60% of the individual differences in cognitive abilities (Asbury & Plomin, 2013). The children who are being read to, talked to, shown how to play with age-appropriate toys, and introduced to the world in an interesting manner do better than those who are not treated in this way.
As you think about what has been presented thus far, it becomes clear that it is a myth to see genetic influence on intelligence as deterministic. However, this myth has been stressed throughout history. In the 1930s there were Nazi programs in Germany to increase what was seen as desirable traits, including intelligence, by controlling mating patterns and availability of partners. The idea that large groups of people, often described in terms of race, differ in intellectual ability continued in a number of countries including the United States and at times included forced sterilization.
In 1969 Arthur Jensen published a paper in the Harvard Education Review that stated that programs such as Head Start, which were designed to improve performance in children failed because intelligence has a strong genetic component. Jensen estimated that about 80% of the variance in intelligence was genetic. This set off a debate in terms of racial differences in IQ. Some 25 years later, a book was published, The Bell Curve, which continued this debate. The authors, Richard Herrnstein and Charles Murray, said that historically there has been a difference in the intelligence scores of black and white individuals of about 15 IQ points. Further, they suggested that these results were not the result of test bias, but reflected differences in cognitive functioning. As you can imagine, these conclusions created great debate.
What was missed in these debates was that IQ and achievement are not the same thing, although they may be related. In one study performed in the 1960s, 3-year-old and 4-year-old African American children considered to be at risk for school failure went through a special two-year program (Heckman, 2006). By age 10 the IQ scores of these children were no higher than a control group. However, their achievement scores in school were significantly higher. Further, these children at age 40 were shown to have higher rates of high-school graduation, higher salaries, more home ownership, as well as receiving less welfare and having fewer criminal charges than controls (Schweinhart et al., 2005). Clearly, society benefited from the initial money spent on the program. Thus, although related, IQ and achievement do not always go together.
How might society use genetic research on IQ to improve school achievement? Some researchers suggest that one way to improve the educational experience is by an emphasis on individualized learning procedures (Asbury & Plomin, 2013). Specifically, three types of proposals for educators and policymakers can be emphasized: (1) embrace genetic variation in abilities; (2) tailor educational curricula to allow maximization of children’s different genetic potential and encourage children to play a role in this process; and (3) invest in alleviating the limiting effects of deprived backgrounds early in development.
Thought Question: The title of this feature is “IQ Is a Single Measure, Stable, and Caused by a Single Factor and Can Be Applied to Education.” If this sentence is a myth, or at least a concept that needs to be reconsidered, how would you finish the sentence “IQ is…”?
1. Timothy Salthouse identified five different cognitive domains that are associated with general intelligence. For each of these domains:
a. Give a brief description of the domain and an example.
b. Trace the typical change in an individual’s ability level across the lifespan.
2. What are the two primary ways in which the brains of older adults are different from the brains of younger adults?
3. Describe the Scottish Mental Survey. What were two major conclusions it made about intelligence over the individual’s lifespan?
4. What conclusions can we draw from the following types of research studies about the roles of genetics and environment in developing cognitive ability:
a. Twin studies comparing monozygotic (MZ) and dizygotic (DZ) twins?
b. Twin adoption studies comparing MZ and DZ twins who were raised apart in different environments?
c. Molecular genetics studies examining generations of families?
5. What are the five critical findings identified by Robert Plomin and colleagues in the genetic components related to general intelligence?
6. What are three strategies that have been shown to be important in maintaining intellectual functioning throughout life? What kinds of benefits does each of these strategies provide?
One advantage of using a normal distribution for determining IQ is that an individual can be compared with other people of the same age range. In fact, IQ is currently determined by comparing one individual with others of a similar age. If we did not do this, we would have to confront some surprising results. For example, IQ has changed in different groups of individuals over time. If you were to look at just the score that a group of people received on IQ tests across generations, you would see an increase in IQ. That is, people today perform much better on IQ tests than did those of earlier generations. Soldiers, for example, showed increases in intelligence measures between World War I and World War II. This effect has been found across the world and in all ethnic/racial groups. This has been referred to as the Flynn Effect after James Flynn who studied this effect using the major IQ tests in the United States (Flynn, 2011). Figure 9-24 shows this effect for the years 1909 to 2013. This research report looked at a number of individual studies involving 31 countries and almost 4 million individuals (Pietschnig & Voracek, 2015). As can be seen in the graph, IQ changes using a variety of IQ tests show changes over the past 100 years.
Figure 9-24 IQ gain over time for 31 different countries and almost 4 million individuals. The positive change is referred to as the Flynn Effect.
Source: Pietschnig and Voracek (2015).
Why might this have happened? A number of factors have been suggested. Our health and public hygiene have changed greatly during this time. Water supplies and food sources are safer and more available. Health agencies also are more available. It is also true that individuals today are more involved in technology, which requires a different type of interaction with the world. However, this alone would not explain the changes seen between World War I and World War II. At this point no single answer to the Flynn Effect has been demonstrated. There is some debate as to whether it could still be demonstrated. For example, a study of 10,000 adolescents from 1989 and 2003 did not show the effect (Platt, Keyes, McLaughlin, & Kaufman, 2019).
Group Differences in Intellectual Ability
One of the most debated topics in relation to IQ is group differences. These groups include differences in socioeconomic level (SES), race, and gender. These debates are usually caught up in political and societal debates concerning how education should work, who should receive special opportunities, what is the role of national and local government, and how should cultural differences be understood. For most countries, education is the second-largest cost to society following health care.
For the individual, assumptions about factors related to IQ and achievement can follow throughout one’s life and determine future possibilities. However, it is important to remember that genetic research related to human abilities is about correlations and the relationship of one person with another (Turkheimer, 2016). At this point there is very little that can be said about causation of intellectual abilities in groups. However, it is important to consider possible relationships between variables.
One intriguing psychological factor related to group differences in cognitive performance is stereotype bias. Stereotype bias reflects negative stereotypes about one’s group (Steele & Aronson, 1995). Claude Steele studied stereotype bias experienced by both females and African Americans as a faculty member at the University of Michigan and Stanford University. Although initially his focus was on why certain groups were underrepresented in certain areas of college, stereotype bias can have an impact on any group—from white males, to liberals, to hillbillies. The basic idea is that a negative stereotype can influence one’s performance, as he describes in a Radiolab broadcast (https://www.wnycstudios.org/story/stereothreat).
One study had black and white college students take a 30-minute test composed of difficult items from the Graduate Record Exam (GRE) (Steele, 1997). In the stereotype threat condition, the test was described as being diagnostic of intellectual ability. In the other condition, the test was described as a laboratory problem-solving task that did not reflect ability. A third condition described the test as a challenge. Thus, everyone took the same test with the only difference being how it was described.
As can be seen in Figure 9-25, just telling the black college students that it was an IQ test resulted in a lower score. Clearly, it was not the student’s ability since the only difference was what the students were told.
Figure 9-25 Mean scores on the GRE test by group.
Source: Steele and Aronson (1995).
Another stereotype exists that suggests that females do not do as well as males in topics requiring mathematical abilities. Similar to the earlier study, one group of college students was told that the difficult math problems that they were to solve had been chosen to reflect gender differences (Aronson, Quinn, & Spencer, 1998; Spencer, Steele, & Quinn, 1999). The second group was told these problems were just being tested in the lab. To ensure that the male and female participants in the study were indeed good at math, they were required to have scored above the 85th percentile on the math subsection of the SAT or ACT when they applied to college. Also, all participants had taken at least one semester of calculus. Again, just describing the test as reflecting gender differences resulted in the female students not performing as well.
Figure 9-26 Performance on the difficult math test depended on how it was described.
Source: Spencer, Steele, and Quinn (1999).
These studies of stereotype threat point to the complexity of understanding group differences in IQ. Stereotype threat by itself cannot explain all of the group differences found in IQ (Sackett, Hardison, & Cullen, 2005). However, it is important to understand that as one is learning to solve verbal, spatial, and mathematical problems as found on IQ tests, the individual is also learning about his or her culture and the abilities attributed to him or her by stereotype bias.
SES and Achievement and IQ
Socioeconomic level (SES) in educational settings is typically defined in terms of the educational level of a child’s parents and the type of work that the parents perform. As such, SES is an environmental factor that can influence the development of a child. It is a factor that has been found to predict reading ability and vocabulary size in children in Head Start programs (Scheffner-Hammer, Farkas, & Maczuga, 2010).
On the surface, it is easy to suggest that low-income parents may be working a number of jobs and not have the time to interact with their children in activities such as reading. The home environment may also contain fewer books or media devices. The family may also lack the money for music lessons, educational trips, and sports equipment. Overcrowded homes may also lead to stress throughout the family and the lack of quiet space to do homework or reading. As noted in the previous Box, Myths and Misconceptions, programs such as Head Start can influence future achievement while still not influencing IQ levels.
Interestingly, SES itself can be influenced by genetic factors (Asbuty & Plomin, 2013). In one study of more than 500 mother—child pairs, the IQ of the mother was related to SES (Ronfani et al., 2015). The IQ of the mother was also related to the child’s development at 18 months and this was influenced by the home environment. This suggests that parent IQ factors and home environment contribute to a child’s development. What is not known is how SES is influenced by genetic factors and how this in turn influences the child. Clearly, some children from low SES conditions show high IQs and achievement, which would suggest a number of factors are involved and it is not a simple relationship.
Racial Differences in IQ
The question of race in IQ is a difficult one. From a technical standpoint, race does not exist. That is, there is no single factor or set of factors that distinguishes one race from another. Most of what we consider to be race involves particular physical factors of the face and body and the groups that one identifies with. Although there are genetic differences that reflect where in the world your ancestors came from, in most cultures, especially in the United States, there is a genetic combining of cultural origin. Racial differences are also connected with cultural differences, which makes it difficult to distinguish environmental practices such as parental expectations and SES from those related to genetics.
The question that has intrigued researchers is why IQ differences exist around the world (Burhan et al., 2017). In one measure of cognitive development given around the world (the Programme for International Student Assessment (PISA)), people in Asian countries such as South Korea and Japan show the highest scores. Next are countries in Europe and the United States and then the countries of South America. In this study, the educational level of the parents did not have a direct impact on the children’s scores. In the United States, IQ measures show higher mean scores for whites, then Hispanic individuals, and then African Americans. However, the distributions of IQ measures of these groups overlap. Further, over the 30 years from 1972 to 2002, the gap between the IQ scores of whites and African Americans in the US was cut by 4 to 7 IQ points (Dickens & Flynn, 2006). Since this was little changed in who is included in the population of African-American individuals, this suggests an important role for the environment.
Gender Differences in IQ and Achievement
Gender differences in intellectual abilities has been a source of debate throughout history often along very simple lines. Today, the study of gender differences in intellectual abilities has been shown to be much more complex (Miller & Halpern, 2014). In particular, better research in terms of infant abilities, sex hormones, brain differences, culture, and stereotyping lead to different conclusions than those suggested in the last century.
One summary of the literature suggests little differences on IQ tests between the genders (Halpern, Beninger, & Straight, 2011). Also, it should be noted that items on IQ tests are chosen to minimize differences between males and females. However, the opposite is the case in terms of researchers who study differences outside of the context of intelligence. That is, these researchers seek to find tasks such as rotating an image in your mind in which one gender performs better than the other. Gender differences have been reported in specific types of tasks. Males, for example, show better performance on tasks that require mental rotation of geometric objects. Females, on the other hand, show better performance on tasks requiring writing and language abilities. Males and females show similar levels of abilities on tests assessing subjects learned in high school.
Some of the studies that have found gender differences in intelligence have not been based on complete populations. For example, one longitudinal study found that higher IQ females who took the test at age 10 were more likely to return for future testing at 26 and 30 years of age than males (Dykiert, Gale, & Deary, 2009). Another possible problem with group differences in IQ is that large groups of individuals may be missing. For example, not everyone applies to college and thus SAT or ACT scores could be biased in terms of who did not apply to college.
Figure 9-27 Number and percentage of boys and girls found within each IQ score band of the age 11 students.
Source: Deary, Thorpe, Wilson, Starr, and Whalley (2003).
One study that did include an entire population was the Scottish Mental Survey of 1932 described previously (Deary, Thorpe, Wilson, Starr, & Whalley, 2003). As you remember, this test was given to almost everyone in Scotland born in 1921. The total IQ score was 100.64 for girls and 100.48 for boys, which clearly represents no difference. However, what they did find were real differences in males and females at the extreme low and extreme high end of the IQ scale. As can be seen in Figure 9-27, there is an increase in the number of males who score below an IQ of 90 and above that of 120. Other studies have also reported that males show a greater variation in IQ scores at the lower and higher end of the IQ range (Halpern, Beninger, & Straight, 2011).
Another large-scale longitudinal study in the United Kingdom showed that girls at 7 and 11 years of age show IQ differences of about 1 point over boys (Lynn & Kanazawa, 2011). At age 16, this finding was reversed, with boys showing a slight advantage of less than 2 IQ points. The authors considered this to be related to the timing of maturity in boys and girls. Other studies have also reported changes in verbal and non-verbal IQ in the teenage years (Ramsden et al., 2011). A combination of structural and functional imaging showed that verbal IQ changed with gray matter in a region that was activated by speech, whereas non-verbal IQ changed with gray matter in a region that was activated by finger movements. This suggests that a teen’s IQ can increase or decrease during the teenage years.
As noted with the work of Claude Steele and others, stereotyping both by society and individuals themselves can influence the intellectual abilities of males and females. One study published in the journal Science suggests that these gender stereotypes emerge early in a girl’s life (Bian, Leslie, & Cimpian, 2017). In this study, both boys and girls at age 5 associated brilliance with their own gender. By age 7, girls compared to boys were less likely to associate their own gender with brilliance. Even more troubling was that girls at this age began to avoid activities said to be for children who were “really, really smart.” This, in turn, would limit their exposure to experiences that would increase their abilities.
Every society also has stereotypes related to gender differences. At one point the debate concerning gender and ability centered on what was observed in society. For example, in 1970 no female had held these occupations in America:
✵ Episcopalian minister
✵ Attorney general
✵ Armed forces general
✵ Secretary of state
✵ Supreme Court justice
Of course, today all of these occupations have been held by both females and males. However, our stereotypes can be out of awareness. For example, many museums have shown murals of hunter gatherers who lived before farming developed some 10,000 years ago. In these depictions, only males are shown being hunters. However, anthropological research suggests that females were also respected hunters, and their burials reflect this (Haas et al., 2020).
Today, it has also been noted that fewer women are involved in fields related to STEM (science, technology, engineering, and math) than men. This has led to a number of initiatives to try to understand these differences and support women who seek to go into these fields. In 2003, 41% of those employed as a “biological or life scientist” were female. The percentage increased to 46.9% in 2011. In engineering, these numbers were lower with 10.4% being female in 2003 and 11.7% in 2011. These and other differences related to gender differences can be found at the National Academy of Sciences website (https://www.nationalacademies.org/cwsem/women-in-science-and-engineering-statistics). Although a number of factors are involved in gender differences, the current consensus is that IQ is not directly related to being a male or female.
1. What is the Flynn Effect?
2. What evidence can you cite to show that stereotype bias impacts cognitive performance?
3. What can we say about group differences related to achievement and IQ for each of the following groups:
a. Socioeconomic level (SES)?
Learning Objective 1: Describe the concepts and categories involved in making decisions.
We commonly use our concepts and categories to help us make decisions. A concept is an abstract idea or representation. We tend to use categories to organize concepts that have shared features.
Researchers often use the term bounded rationality in watching humans make decisions. What this means is that our decisions are limited or bounded by a number of factors. Three information-limiting factors are (1) the amount of information we have, (2) our cognitive abilities, and (3) the amount of time available to make the decision. Fast thinking or decision-making occurs quickly with little or no effort, and little sense of you being actively involved in the process. Slow thinking requires more effort, and we experience ourselves as a part of the thinking process.
Behavioral economics uses behavioral and neuroscience research to understand how humans make decisions. People do not make decisions in isolation, but in relation to other choices as well as how these choices are framed and the effort required. For example, more people accept deductions from their pay checks if they do not have to make a decision about them. That is, if given a choice to opt-in (agree to a deduction) or opt-out (agree to not take the deduction), most people will go with the alternative that does not require them to make a decision.
Learning Objective 2: Discuss the processes involved in solving problems.
An insight problem is a problem that requires a person to shift her perspective and view the problem in a different way. The solution often comes suddenly. This has been referred to as the “aha” or “eureka” moment. Suddenly, you know the answer. The answer is often a reorganization of the information available and seeing the situation in a new light.
To be fixed on a limited view of how an object is used is referred to as functional fixedness. Although functional fixedness may hurt our ability to solve insight problems, it does have the evolutionary value of helping us limit our alternatives. This saves us valuable energy and time.
Learning Objective 3: Describe the different ways that intelligence is measured.
Intelligence is defined as the ability to learn from and adapt to our environment by solving problems and predicting what might happen next. This is a broad understanding of intelligence. There is also a narrower definition that refers to how we perform on tests of intelligence and compare to others.
Alfred Binet (1857—1911) and his associate Théodore Simon developed a measure of mental abilities, the Binet—Simon Intelligence Scale, in 1905. In choosing items for the scale, Binet selected tasks such as telling time from a clock, naming parts of their body, knowing the definition of words, repeating a series of numbers, and copying geometric figures. Binet helped establish the modern tradition of viewing intelligence as a composite of different abilities that relate to learning about and solving problems related to one’s environment. Binet’s test and basic ideas were brought to the United States. It was modified by Lewis Terman at Stanford University in 1916 and became known as the Stanford—Binet Intelligence Scales.
William Stern (1871—1938) turned the concept of mental age into a ratio by dividing mental age by chronological age. The concept of IQ or intelligence quotient is calculated by the following: Intelligence Quotient (IQ) = mental age divided by chronological age times 10.
Today, the most common intelligence measures are the Wechsler Adult Intelligence Scale (WAIS) and the Wechsler Intelligence Scale for Children (WISC). The test structure of the WAIS is directed at four major abilities. These are verbal comprehension, perceptual reasoning, working memory, and processing speed.
Charles Spearman’s research led to his development of a two-factor theory of intelligence. He suggested that each measure of intelligence is composed of two factors. The first factor is related to the specific test itself, such as the ability to do math or language. The second factor, based on the correlation between measures, is a global factor. Spearman referred to this general cognitive ability as “g” or general intelligence.
Howard Gardner sought to define intelligence within the context of different domains with his theory of multiple intelligences. Gardner suggested there are eight separate intelligences: linguistic, logical-mathematical, musical, bodily kinesthetic, spatial, interpersonal, intrapersonal, and naturalistic.
Learning Objective 4: Identify five cognitive domains that are associated with general intelligence.
Timothy Salthouse identified five different cognitive domains that are associated with general intelligence. These five domains are reasoning, spatial ability, memory, processing speed, and vocabulary. Salthouse proposed that it is possible to find different measures of a particular domain such as reasoning that are highly correlated with one another. In turn, the five domains are correlated with the general intelligence factor called “g.”
Salthouse not only looked at the relationship of specific domains with “g” but also how these change with age. As one grows older, there is a decrease in processing speed (digit symbol coding) and memory (digit span). Reasoning and spatial ability tend not to change, whereas one’s vocabulary improves. It is also the case that language comprehension remains stable although language production, such as finding the right word, becomes more difficult.
Learning Objective 5: Discuss the genetic factors associated with intelligence.
Genetic factors have been associated with intelligence. In fact, intelligence is one of the most heritable behavioral traits. Research over the past 50 years has consistently shown that genes influence cognitive abilities. In terms of IQ, MZ twins show a higher correlation of IQ scores between the two twins (.86) than DZ twins do (.60). In this study, both types of twins were reared in the same household. Like DZ twins, parents and their children, as well as siblings, show the same amount of genetic similarity, which is.5. If raised in the same home, they show a correlation between IQ scores of about.42.
One particular genetic factor found in the Scottish Study of intelligence was the APOE gene. The researchers discovered that those individuals with a variant of the APOE gene, referred to as the APOE 4 allele, showed more cognitive decline even in the absence of a neurocognitive disorder.
Maintaining intellectual functioning throughout life has become a popular topic in the media. What is known is that physical exercise has a profound effect on brain function in both humans and animals. Exercise protects and restores the brain through increased blood flow and other mechanisms. This results in functional and structural changes throughout the brain.
People today perform much better on IQ tests than did those of earlier generations. Soldiers, for example, showed increases in intelligence measures between World War I and World War II. This effect has been found across the world and in all ethnic/racial groups. This has been referred to as the Flynn Effect after James Flynn who studied this effect using the major IQ tests in the United States. One of the most debated topics in relation to IQ is group differences. These groups include differences in socioeconomic level (SES), race, and gender.
1. Make the evolutionary argument that having the ability to solve well-defined problems as well as the ability to solve insight problems provides benefit to individuals and species. What characteristics of different environments will favor one type of problem-solving over another?
2. This chapter has presented a number of scientists who have developed methods for measuring intelligence, for example, Alfred Binet and Théodore Simon, and David Wechsler. For each of these researchers, from looking at what and how they tested, how would you describe their underlying theory of intelligence?
3. This chapter has presented a number of scientists who have proposed theories of intelligence, for example, Charles Spearman, Raymond Cattell, and Howard Gardner. For each of these researchers, from looking at their theories of intelligence, what methods would they develop for measuring intelligence?
4. Research on intelligence typically focuses on the individual. Is there such a thing as a “collective intelligence”? If so, how would you characterize it? What kinds of research questions would you propose to increase our understanding of the phenomenon?
5. Genetics and the environment both play important roles in determining intelligence. How would you plot the changing impact of these two factors over an individual’s lifespan?
6. Historically, focusing on group differences in intelligence on the basis of groups such as socioeconomic level (SES), race, and gender has sometimes been used to negatively stereotype members of certain groups. What benefits are gained from studying group differences? What limitations do we need to keep in mind when extending group results to individual group members?
For Further Reading
✵ Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
✵ Kounios, J., & Beeman, M. (2015). The Eureka Factor: AHA Moments, Creative, and the Brain. New York: Random House.
✵ Lewis, M. (2017). The Undoing Project. New York: W.W. Norton.
✵ Humans are not rational—https://www.ted.com/talks/dan_ariely_are_we_in_control_of_our_own_decisions
✵ Scottish Mental Study—https://www.ed.ac.uk/lothian-birth-cohorts
✵ Claude Steele—https://www.wnycstudios.org/story/stereothreat
✵ NAS gender differences—(https://www.nationalacademies.org/cwsem/women-in-science-and-engineering-statistics).
Key Terms and Concepts
bodily kinesthetic intelligence
“g” or general intelligence
theory of multiple intelligences