Summary
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chapter 13 |
Problem Solving and Intelligence |
General Problem-Solving Methods People solve problems all the time. Some problems are pragmatic ("I want to Tom borrowed my car. How can I get there?"). Others are social ("I really want Amy to notice me; how should I arrange it?"). Others are academic ("Im trying to prove this theorem. How can I do it, starting from these axioms?"). What these situations share, though, is the desire to figure out how to reach some goal-a configuration that defines what we call problem solving. How do people solve go to the store, but problems? Problem Solving as Search Researchers compare problem solving to a process of search, as though you were navigating through a maze, seeking a path toward your goal (see Newe & Simon, 1972; also Bassok & Novick, 2012 Mayer, 2012). To make this point concrete, consider the Hobbits and Orcs problem in Figure 13.1. For this problem, you have choices for the various moves you can make (transporting creatures back and forth), but you're limited by the size of the boat and the requirement that Hobbits can never be outnumbered (lest they be eaten). This situation leaves you with a set of options shown graphically in Figure 13.2. The figure shows the moves available early in the solution and depicts the options as a tree. with each step leading to more branches. All the branches together form the problem space- that is, the set of all states that can be reached in solving the problem.
To solve this problem, one strategy would be to trace through the entire problem space, exploring each branch in turn. This would be like exploring every possible corridor in a maze, an approach that would guarantee that you'd eventually find the solution. For most problems, however, this approach would be hopeless. Consider the game of chess. In chess, which move is best at any point in the game depends on what your opponent will be able to do in response to your move, and then what you'll do next. To make sure you're choosing the best move, therefore, you need to think ahead through a few cycles of play, so that you can select as your current move the one that will lead to the best sequence. Let's imagine, therefore, that you decide to look ahead just three cycles of play-three of your moves and three of your opponent's. Some calculation, however, tells us that for three cycles of chess play there are roughly 700 million possibilities for how the game could go; this number immediately rules out the option of considering every possibility. If you could evaluate 10 sequences per second, you'd still need more than 2 years, on a 24/7 schedule, to evaluate the full set of options for each move. And, of course, there's nothing special here about chess, because most real-life problems offer so many options that you couldn't possibly explore every one. Plainly, then, you somehow need to narrow your search through a problem space, and specifically, what you need is a problem-solving heuristic. As we've discussed in other chapters. heuristics are strategies that are efficient but at the cost of occasional errors. In the domain of problem solving, a heuristic is a strategy that narrows your search through the problem space-but (you hope) in a way that still leads to the problem's solution. General Problem-Solving Heuristics One commonly used heuristic is called the hill-climbing strategy. To understand this term, imagine that you're hiking through the woods and trying to figure out which trail leads to the mountaintop. You obviously need to climb uphill to reach the top, so whenever you come to a fork in the trail, you select the path that's going uphill. The problem-solving strategy works the same way: At each point you choose the option that moves you in the direction of your goal. This strategy is of limited use, however, because many problems require that you briefly move away from your goal; only then, from this new position, can the problem be solved. For instance, if you want Mingus to notice you more, it might help if you go away for a while; that way, he'll be more likely to notice you when you come back. You would never discover this ploy, though, if you relied on the hill-climbing strategy. Even so, people often rely on this heuristic. As a result, they have difficulties whenever a problem requires them to "move backward in order to go forward." Often, at these points, people drop their current plan and seek some other solution to the problem: "This must be the wrong strategy; I'm going the wrong way" (See, e.g., Jeffries, Polson, Razran, & Atwood, 1977; Thomas, 1974.) Fortunately, people have other heuristics available to them. For example, people often rely on means-end analysis. In this strategy, you compare your current state to the goal state and you ask "What means do I have to make these more alike?" Figure 13.3 offers a commonsense example. Pictures and Diagrams People have other options in their mental toolkit. For example, it's often helpful to translate a problem into concrete terms, relying on a mental image or a picture. As an illustration, consider the problem in Figure 13.4. Most people try an algebraic solution to this problem (width of each volume multiplied by the number of volumes, divided by the worm's eating rate) and end up with the wrong People generally get this problem right, though, if they start by visualizing the arrangement. Now, they can see the actual positions of the worm's starting point and end point, and this usually answer. takes them to the correct answer. (See Figure 13.5; also see Anderson, 1993; Anderson & Helstrup, 1993; Reed, 1993; Verstijnen, Hennessey, van Leeuwen, Hamel, & Goldschmidt, 1998.)
Drawing on Experience Where do these points leave us with regard to the questions with which we began-and, in particular, the ways in which people differ from one another in their mental abilities? There's actually little difference from one person to the next in the use of strategies like hill climbing or means-end analysis-most people can and do use these strategies. People do differ, of course, in their drawing ability and in their imagery prowess (see Chapter 11), but these points are relevant only for some problems. Where, then, do the broader differences in problem-solving skill arise? Problem Solving via Analogy Often, a problem reminds you of other problems you've solved in the past, and so you can rely on your past experience in tackling the current challenge. In other words, you solve the current problem by means of an analogy with other, already solved, problems. It's easy to show that analogies are helpful (Chan, Paletz, & Schunn, 2012; Donnelly & McDaniel, 1993; Gentner & Smith, 2012; Holyoak, 2012), but it's also plain that people under-use analogies. Consider the tumor problem (see Figure 13.6A). This problem is difficult, but people generally solve it if they use an analogy. Gick and Holyoak (1980) first had their participants read about a related situation (see Figure 13.6B) and then presented them with the tumor problem. When participants were encouraged to use this hint, 75% were able to solve the tumor problem. Without the hint, only 10% solved the problem. Note, though, that Gick and Holyoak had another group of participants read the "general and fortress" story, but these participants weren't told that this story was relevant to the tumor problem. problem (see Figure 13.7). (Also see Kubricht, Lu, & Holyoak, Only 30% of this group solved the tumor 2017.) Apparently, then, uninstructed use of analogies is rare, and one reason lies in how people search through memory when seeking an analogy. In solving the tumor problem, people seem to ask themselves: "What else do I know about tumors?" This search will help them remember other situations in which they thought about tumors, but it won't lead them to the "general and fortress" problem. This (potential) analogue will therefore lie dormant in memory and provide no help. (See e.g., Bassok, 1996; Cummins, 1992; Hahn, Prat-Sala, Pothos, & Brumby, 2010; Wharton, Holyoak, Downing, & Lange, 1994.) To locate helpful analogies in memory, you generally need to look beyond the superficial features of the problem and think instead about the principles governing the problem-focusing on what's sometimes called the problem's "deep structure." As a related point, you'll be able to use an analogy only if you figure out how to map the prior case onto the problem now being solved-only if you realize, for example, that converging groups of soldiers correspond to converging rays and that a fortress-to-be-captured corresponds to a tumor-to-be-destroyed. This mapping process can be difficult (Holyoak, 2012; Reed, 2017), and failures to figure out the mapping are another reason people regularly fail to find and use analogies. Strategies to Make Analogy Use More Likely Perhaps, then, we have our first suggestion about why people differ in their problem-solving ability Perhaps the people who are better problem solvers are those who make better use of analogies- plausibly, because they pay attention to a problem's deep structure rather than its superficial traits. Consistent with these claims, it turns out that we can improve problem solving by encouraging people to pay attention to the problems' underlying dynamic. For example, Cummins (1992) instructed participants in one group to analyze a series of algebra problems one by one. Participants in a second group were asked to compare the problems to one another, describing what the problems had in common. The latter instruction forced participants to think about the problems underlying structure; guided by this perspective, the participants were more likely, later on, to use the training problems as a basis for forming and using analogies. (Also see Catrambone, Craig, & Nersessian, 2006; Kurtz & Loewenstein, 2007; Lane & Schooler, 2004; Pedrone, Hummel, & Holyoak 2001.) Expert Problem Solvers How far can we go with these points? Can we use these simple ideas to explain the difference between ordinary problem solvers and genuine experts? To some extent, we can. We just suggested, for example, that it's helpful to think about problems in terms of their deep structure, and this is, it seems, the way experts think about problems. In one study, participants were asked to categorize simple physics problems (Chi, Feltovich, & Glaser, 1981). Novices tended to place together all the problems involving river currents., all the problems involving springs, and so on, in each case focusing on the surface form of the problem. In contrast, experts (Ph.D. students in physics) ignored these details of the problems and, instead, sorted according to the physical principles relevant to the problems' solution. (For more on expertise, see Ericsson & Towne, 2012.) We've also claimed that attention to a problem's deep structure promotes analogy use, so if experts are more attentive to this structure, they should be more likely to use analogies-and they are (e.g., Bassok & Novick, 2012). Experts' reliance on analogies is evident both in the laboratory (e.g., Novick and Holyoak, 1991) and in real-world settings. Christensen and Schunn (2005) recorded work meetings of a group of engineers trying to create new products for the medical world. As the engineers discussed their options, analogy use was frequent-with an analogy being offered in the discussion every 5 minutes! Setting Subgoals Experts also have other advantages. For example, for many problems, it's helpful to break a problem into subproblems so that the overall problem can be solved part by part rather than all at once. This, too, is a technique that experts often use. Classic evidence on this point comes from studies of chess experts (de Groot, 1965, 1966; also see Chase & Simon, 1973). The data show that these experts are particularly skilled in organizing a chess game-in seeing the structure of the game, understanding its parts, and perceiving how the parts are related to one another. This skill can be revealed in many ways, including how chess masters remember board positions. In one procedure, chess masters were able to remember the positions of 20 pieces after viewing the board for just 5 seconds; novices remembered many fewer (see Figure 13.8). In addition, there was a clear pattern to the experts' recollection: In recalling the layout of the board, the experts would place four or five pieces in their proper positions, then pause, then recall another group, then pause, and so on. In each case, the group of pieces was one that made "tactical sense"-for example, the pieces involved in a "forked" attack, a chain of mutually defending pieces, and the like. (For similar data with other forms of expertise, see Tuffiash, Roring, & Ericsson, 2007 also see Sala & Gobet, 2017.)
It seems, then, that the masters-experts in chess-memorize the board in terms of higher-order units, defined by their strategic function within the game. This perception of higher-order units helps to organize the experts' thinking. By focusing on the units and how they're related to one another, the experts keep track of broad strategies without getting bogged down in the details. Likewise, these units set subgoals for the experts. Having perceived a group of pieces as a coordinated attack, an expert sets the subgoal of preparing for the attack. Having perceived another group of pieces as the early deevelopment of a pin (a situation in which a player cannot move without exposing a more valuable piece to an attack), the expert creates the subgoal of avoiding the pin. It turns out, though, that experts also have other advantages, including the simple fact that they know much more about their domains of expertise than novices do. Experts also organize their knowledge more effectively than novices. In particular, studies indicate that experts' knowledge is heavily cross-referenced, so that each bit of information has associations to many other bits (e.g., Bédard & Chi, 1992; Bransford, Brown & Cocking, 1999; Reed, 2017). As a result, experts have better access to what they know. It's clear, therefore, that there are multiple factors separating novices from experts, but these factors all hinge on the processes we've already discussed-with an emphasis subproblems, and memory search. Apparently, then, we can use our theorizing so far to describe on analogies, how people (in particular, novices and experts) differ from one another. e. Demonstration 13.1: Analogies Analogies are a powerful help in solving problems, and they are also an excellent way to convey new information. Imagine that you're a teacher, trying to explain some points about astronomy. Which of the following explanations do you think would be more effective?
Literal Version
Collapsing stars spin faster and faster as they fold in on themselves and their size decreases. This principle called phenomenon of spinning faster as the star's size shrinks occurs because of a "conservation of angular momentum."
Analogy Version
Collapsing stars spin faster as their size shrinks. Stars are thus like ice skaters, who pirouette faster as they pull in their arms. Both stars and skaters operate by a principle called "conservation of angular momentum"
Which version of the explanations would make it easier for students to answer a question like the following one?
What would happen if a star "expanded" instead of collapsing?
a) Its rate of rotation would increase.
b) Its rate of rotation would decrease.
c) Its orbital speed would increase.
d) Its orbital speed would decrease.
Does your intuition tell you that the analogy version would be better as a teaching tool? If so, then your intuition is in line with the data! Participants in one study analogy version. Later, they were asked questions about these were presented with materials just like these, in either a literal or an materials, and those instructed via analogy reliably did better. Do you think your teachers make effective use of analogy? Can you think of ways they can improve their use of analogy? Defining the Problem Experts, we've said, define problems in their area of expertise in terms of the problems' underlying dynamic. As a result, the experts are more likely to break a problem into meaningful parts, more likely to realize what other problems are analogous to the current problem, and so more likely to benefit from analogies. Clearly, then, there are better and worse ways to define a problem-ways that will lead to a problem? And what solution and ways that will obstruct it. But what does it mean to "define" a determines how people define the problems they encounter? III-Defined and Well-Defined Problems For many problems, the goal and the options for solving the problems are clearly stated at the start: Get all the Hobbits to the other side of the river, using the boat. Solve the math problem, using the axioms stated. Many problems, though, are rather different. For example, we all hope for peace in the world, but what will this goal involve? There will be no fighting, of course, but what other traits will the goal have? Will the nations currently on the map still be in place? How will disputes be settled? It's also unclear what steps should be tried in an effort toward reaching this goal. Would diplomatic negotiations work? Or would economic measures be more effective? Problems like this one are said to be ill-defined, with no clear statement at the outset of how the goal should be characterized or what operations might serve to reach that goal. Other examples of ill-defined problems include "having a good time while on vacation" and "saving money for college (Halpern, 1984; Kahney, 1986; Schraw, Dunkle, &Bendixen, 1995) When confronting ill-defined problems, your best bet is often to create subgoals, because many ill-defined problems have reasonably well-defined parts, and by solving each of these you can move toward solving the overall problem. A different strategy is to add some structure to the problem by including extra constraints or extra assumptions. In this way, the problem becomes well-defined instead of ill-defined-perhaps with a narrower set of options in how you might approach it, but with a clearly specified goal state and, eventually, a manageable set of operations to try. Functional Fixedness Even for well-defined problems, there's usually more than one way to understand the problem. Consider the problem in Figure 13.9. To solve it, you need to cease thinking of the box as a container and instead think of it as a potential platform. Thus, your chances of solving the problem depend on how you represent the box in your thoughts, and we can show this by encouraging one representation or another. In a classic study, participants were given the equipment shown in Figure 13.9A: some matches, a box of tacks, and a candle. This configuration (implicitly) underscored the box's conventional function. As a result, the configuration increased functional fixedness-the tendency to be rigid in how one thinks about an object's function. With fixedness in place, the problem was rarely solved (Duncker, 1945; Fleck & Weisberg, 2004).
Other participants were given the same tools, but configured differently. They were given some matches, a pile of tacks, the box (now empty), and a candle. In this setting, the participants were less they to solve the problem (Duncker, 1945). (Also see were less likely to think of the box likely to think of the box as a container for the tacks, and so likely as a container. As a result, they were more Figure 13.10; for more on fixedness, see McCaffrey, 2012.)
"Thinking outside the Box" A related obstacle derives from someone's problem-solving set-the collection of beliefs and assumptions a person makes about a problem. One often-discussed example involves the nine-dot problem (see Figure 13.11). People routinely fail to solve this problem, because-according to some interpretations-they (mistakenly) defined by the dots. In fact, this problem is probably the source of the cliché "You need to think assume that the lines they draw need to stay inside the "square" outside the box."
Ironically, though, this cliché may be misleading. In one study, participants were told explicitly that to solve the problem their lines would need to go outside the square. The hint provided little participants still failed to find the solution (Weisberg & Alba, 1981). Apparently, benefit, and most beliefs about "the box" aren't the obstacle. Even when we eliminate these beliefs, performance remains poor. Nonetheless, the expression "think outside the box" does get the broad idea right, because to most people assume solve this problem people do need to jettison their initial approach. Specifically, begin and end on dots. People also have the idea that they'll need to that the lines they draw must maximize the number of dots "canceled" with each move; as a result, they seek solutions in which each line cancels a full row or column of dots. It turns out, though, that these assumptions are guided by these mistaken beliefs, people find this problem quite hard. (See Kershaw & Ohlsson, 2004; MacGregor, Ormerod, & Chronicle, 2001; also Öllinger, Jones, Faber, & Knoblich, wrong; and so, 2013.) In the nine-dot problem, people seem to be victims of their own problem-solving set; to find the solution, they need change that set. This phrasing, however, makes it sound like a set is a bad thing, blocking the discovery of a solution. Let's emphasize, though, that sets also provide a benefit. as you seek most problems offer a huge number of options This is because (as we mentioned earlier) the solution-an enormous number of moves you might try or approaches you might consider. A problem-solving set helps you, therefore, by narrowing your options, which in turn eases the search for a solution. Thus, in solving the nine-dot problem, you didn't waste any time wondering whether e holding the pencil between your toes or whether the problem you should try drawing the lines sitting down while you worked on it instead of standing up. These are was hard because you were foolish ideas, so you brushed past them. But what identifies them as foolish? It's your problem- plausible, which ones are solving set, which tells you, among other things, which options physically possible, and so on. are In this way, a set can blind you to important options and thus be an obstacle. But a set can also blind you to a wide range of futile strategies, and this is a good thing: It enables you to focus, much more productively, on options that are likely to work out. Indeed, without a set, you might be so distracted by silly notions that even the simplest problem would become insoluble. e. Demonstration 13.2: Verbalization and Problem Solving Research on problem solving has attempted to determine what factors help problem solving (making it faster or more effective) and what factors hinder problem solving. Some of these factors are surprising. You'll need a clock or a timer for this one. Read the first problem below, and give yourself 2 minutes to find the solution. If you haven't found the solution in this time, take a break for 90 seconds; during that break, turn away from the problem and try to say out loud, in as much detail as possible, everything you can remember about how you've been trying to solve the problem. Provide information about your approach, your strategies, any solutions you tried, and so on. Then, go back and try working on the problem for another 2 minutes.
Problem 1: The drawing below shows 10 pennies. Can you make the triangle point downward by moving only 3 of the pennies?
Now, do the same for the next problem-2 minutes of trying a solution, 90 seconds of describing out loud everything you've thought of so far in working on the problem, and then another 2 minutes of working on the problem.
Problem 2: Nine sheep are kept together in a square pen. Build two more square enclosures that will isolate each sheep, so that each is in a pen all by itself.
Do you think the time spent describing your efforts so far helped you, perhaps by allowing you to organize your thinking, or hurt you? In fact, in several studies, this sort of "time off, to describe your efforts so far" makes it less likely that people will solve these problems. In one study, participants solved 36% of the problems if they had verbalized their efforts so far, but 46% of the problems if they didn't go through the verbalization step. Does this fit with your experience? Why might verbalization interfere with this form of problem solving? One likely explanation is that verbalization focuses your attention on steps you've already tried, and this may make it more difficult to abandon those steps and try new approaches. The verbalization will also focus your attention on the sorts of strategies that are conscious, deliberate, and easily described in words; this focus might interfere with strategies that are unconscious, not deliberate, and not easily articulated. In all cases, though, the pattern makes it clear that sometimes "thinking out loud" and trying to communicate your ideas to others can actually be counterproductive! Exactly why this is, and what this implies about problem solving, is a topic in need of further research. Demonstration adapted from Schooler, J., Ohlsson, S., & Brooks, K. (1993). Thoughts beyond words: When language overshadows insight. Journal of Experimental Psychology: General, 122, 166-183. Expand the bar below for the solutions to Demonstration 13.2. Creativity There is no question, though, that efforts toward a problem solution are sometimes hindered by someone's set, and this observation points us toward another way in which people differ. Some people are remarkably flexible in their approaches to problems; they seem easily able to "think outside the box." Other people, in contrast, seem far too ready to rely on routine, so they're more vulnerable to the obstacles we've just described. How should we think about these differences? Why do some people reliably produce novel and unexpected solutions, while other people offer only familiar solutions? This is, in effect, a question of why some people are creative and others aren't-a question that forces us to ask: What is creativity? Case Studies of Creativity One approach to this issue focuses on individuals who've been enormously creative-artists like Pablo Picasso and Johann Sebastian Bach, or scientists like Charles Darwin and Marie Curie. By studying these giants, perhaps we can draw hints about the nature of creativity when it arises, on a much smaller scale, in day-to-day life-when, for example, you find a creative way to begin a conversation or to repair a damaged friendship. As some researchers put it, we may be able to learn about "little-c creativity" (the everyday sort) by studying "Big-C Creativity" (the sort shown by people we count as scientific or artistic geniuses-Simonton & Damian, 2012). Research suggests, in fact, that highly creative people like Bach and Curie tend to have certain things in common, and we can think of these elements as "prerequisites" for creativity (e.g., Hennessey & Amabile, 2010). These individuals, first of all, generally have great knowledge and skills in their domain. (This point can't be surprising: If you don't know a lot of chemistry, you can't be a creative chemist. If you're not a skilled storyteller, you can't be a great novelist.) Second, to be creative, you need certain personality traits: a willingness to take risks, a willingness to ignore criticism, an ability to tolerate ambiguous findings or situations, and an inclination not to "foliow the crowd" Third, highly creative people tend to be motivated by the pleasure of their work rather than by the promise of external rewards. With this, highly creative people tend to work extremely hard on their endeavors and to produce a lot of their product, whether these products are poems, paintings, or scientific papers. Fourth, these highly creative people have generally been "in the right place at the right time"-that is, in environments that allowed them freedom, provided them with the appropriate supports, and offered them problems "ripe" for solution with the resources available. Notice that these observations highlight the contribution of factors outside the person, as well as the person's own capacities and skills. The external environment, for example, is the source of crucial knowledge and resources, and it often defines the problem itself. This is why many authors have suggested that we need a systematic "sociocultural approach" to creativity-one that considers the social and historical context, as well as the processes unfolding inside the creative individual's mind (e.g., Sawyer, 2006). We still need to ask, however: What does go on in a creative mind? If a person has all the prerequisites just listed, what happens next to produce the creative step forward? Actually, there is wide disagreement on these points (see Hennessey & Amabile, 2010; Mumford & Antes, 2007). It will be instructive, though, to examine a proposal offered years ago by Wallas (1926). His notion fits well with some commonsense ideas about creativity, and this is one of the reasons his framework continues to guide modern research. Nonetheless, the evidence forces us to question several of Wallas's claims. The Moment of Illumination According to Wallas, creative thought proceeds through four stages. In the first stage, preparation the problem solver gathers information and does some work on the problem, but with little progress. In the second stage, incubation, the problem solver sets the problem aside and seems not to be working on it. Wallas argued, though, that the problem solver continues to work on the problem unconsciously during this stage, so actually the problem's solution is continuing to develop, unseen. This development leads to the third stage, illumination, in which a key insight or new idea emerges, paving the way for the fourth stage, verification, in which the person confirms that the new idea really does lead to a solution and works out the details. Historical evidence suggests, however, that many creative discoveries don't include the steps Wallas described- or, if they do, they include these steps in a complex, back-and-forth sequence (Weisberg, 1986). Likewise, the moment of illumination celebrated in Wallas's proposal may be more myth than reality. When we examine creative discoveries in science or art, we usually find that the new ideas emerged, not from some glorious and abrupt leap forward, but instead from a succession of "mini-insights," each moving the process forward in some small way (Klein, 2013; Sawyer, 2006). And when people do have the "Aha!" experience that, for Wallas, signified illumination, what does this involve? Metcalfe (1986; Metcalfe & Weibe, 1987) gave her participants a series of "insight problems" like those shown in Figure 13.12A. As participants worked on each problem, they rated their progress by using a judgment of "warmth" ("Im getting and these ratings did capture the "moment of insight." Initially, the participants didn't have a clue how to proceed and gave warmth ratings of 1 or 2; then, abruptly, they saw a way forward, and at that instant their warmth ratings shot up to the top of the scale. warmer..., I'm getting warmer To understand this pattern, though, we need to look separately at those participants who subsequently announced the correct solution to the problem and those who announced an incorrect solution. Remarkably, the pattern is the same for both groups (see Figure 13.12B). In other words, some participants abruptly announced that they were getting "hot" and, moments later, solved the problem. Other participants made the same announcement and, moments later, slammed into a dead end. It seems, therefore, that when you say "Aha!" it means only that you've discovered a new approach, one that you've not yet considered. This is important, because often a new approach is just what you need. But there's nothing magical about the "moment of illumination." This moment doesn't signal that you've at last discovered a path leading to the solution. Instead, it means only that you've discovered something new to try, with no guarantee that this "something new" will be helpful. (For more on procedures that can promote "Aha!" moments, see Patrick, Ahmed, Smy, Seeby & Sambrooks, 2015; also Patrick & Ahmed, 2014. For more on the insight process overall, see Bassok & Novick, 2012; Smith & Ward, 2012; van Steenburgh, Fleck, Beeman, & Kounios, 2012. For discussion of neural mechanisms underlying insight, see Kounios & Beeman, 2014, 2015, and also Figure 13.13. For an alternative view, though, see Chuderski & Jastrze bski, 2018.)
Incubation What about Wallas's second stage, incubation? His claim here fits well with a common experience: You're working on a problem but getting nowhere with it. You give up and turn to other matters. Sometime later, when you're thinking about something altogether different, the problem's solution pops into your thoughts. What has happened? According to Wallas, your time away from the problem allowed incubation to proceed-unconscious work on the problem that allowed considerable progress. Systematic studies, however, tell us that this pattern is (at best) unreliable. In these studies participants are given a problem to solve. Some participants work on the problem continuously some are interrupted for a while. The prediction, based on Wallas's proposal, is that we'll observe better performance in the latter group-the group that can benefit from incubation. The data, however, are mixed. Some studies do show that time away from a problem helps in finding the problem's solution, but many studies find no effect at all. (See Baird et al., 2012; Dodds Ward, & Smith, 2007, 2012; Gilhooly, Georgiou, Garrison, Reston, & Sirota, 2012; Hélie & Sun, 2010; Sio, Kotovsky, & Cagan, 2017.) The explanation for this mixed pattern isn't clear. Some researchers argue that incubation is disrupted if you're under pressure to solve the problem. Other authors focus on how you spend your time during the incubation period, with the suggestion that incubation takes place only if the circumstances allow your thoughts to "wander" during this period. (For reviews of this literature, see Baird et al., 2012; Gilhooly et al., 2012; Kounios & Beeman, 2015.) Why should "mind wandering" be relevant here? Recall that in Chapter 9 we described the process of spreading activation through which one memory can activate related memories. It seems likely that when you're carefully working on a problem, you try to direct this flow of activation-and perhaps end up directing it in unproductive ways. When you simply allow your thoughts to wander, though, the activation can flow wherever the memory connections take it, and this may lead to new ideas being activated. (See Ash & Wiley, 2006; Bowden, Jung-Beeman, Fleck, & Kounios, 2005; Kounios & Beeman, 2015; Radel, Davaranche, Fournier, & Dietrick, 2015; Smith & Ward, 2012.) This process provides no guarantee that helpful (and perhaps unanticipated) ideas will be activated. In this way, incubation is like illumination-a or productive ideas will come to mind, only that more source of new possibilities that may or may not pay off Other authors, however, offer a more mundane explanation of incubation effects. They note that your early efforts with a problem may have been tiring or frustrating, and if so, the interruption simply provides an opportunity for the frustration or fatigue to dissipate. Likewise, your early efforts with a problem may have been dominated by a particular approach, a particular set. If you put the problem aside for a while, it's possible you'll forget about these earlier tactics, freeing you to explore other, more productive avenues. (See Smith & Blankenship, 1989, 1991; Storm, Angello, & Bjork, 2011: Storm & Patel, 2014; Vul & Pashler, 2007.) For the moment, there is no consensus about which of these proposals is best, so it's unclear why time away from a problem sometimes helps and sometimes doesn't. To put the matter bluntly sometimes when you're stymied by a problem, it does help to walk away from it for a while. But sometimes your best bet is just to keep plugging away, doggedly trying to move forward. For now research provides less guidance than we'd like in choosing which of these is the better plan.
The Nature of Creativity Where does this discussion leave us with regard to our overarching question-the question of how people differ in their cognitive abilities? In discussing expertise, have a stack of advantages: a tendency to think about problems in terms of their structure rather than their surface form, a broad knowledge base deriving from their experience in a field, heavily cross-referenced memories, and so on. Now, in discussing creativity, we've seen a similar pattern: Highly creative people tend to have certain personality traits (e.g., a willingness to take risks), a lot of knowledge, intense motivation, and some amount of luck. But what mental processes do they rely on? When we examine Darwin's notebooks or Picasso's early sketches, we discover that these creative giants relied on analogies, hints, heuristics, and a lot of hard work, just as the rest of us do (Gruber, 1981; Sawyer, 2006; Weisberg, 1986). In some cases, creativity may even depend on blind trial and error (e.g., Klein, 2013; Simonton, 2011); with this sort of process, the great artist or great we saw that experts in a domain inventor is simply someone who's highly discriminating-and thus able to discern which of the randomly produced products actually have value. Research also suggests that creative people may have advantages in how they search through memory. Some authors emphasize the skill of convergent thinking-an ability to spot ways in which seemingly distinct ideas might be interconnected. This ability is sometimes measured through the Remote Associates Test (Mednick, 1962; Mednick & Mednick, 1967; also Smith, Huber, & Vul, 2013; Figure 13.14). In this test, you're given a trio of words, and you need to find one more word that fits with each of the three. For example, you might be given the trio cross, rain, and tie; the correct answer is bow (as in crossbow, rainbow, and bowtie)
Other authors emphasize the skill of divergent thinking -an ability to move one's thoughts novel, unanticipated directions. (See Guilford, 1967, 1979; also see Beaty, Silvia, Nusbaum, Jauk, & Benedek, 2014; Hass, 2017; Vartanian, Martindale, & Matthews, 2009; see Figure 13.15. For a related in idea, see Zabelina, Saporta, & Beeman, 2016.) Here, there's no "right answer" Instead, success in divergent thinking is reflected in an ability to come up with a large number of new ideas-ideas that can then be evaluated to see if they're of any value.
Overall, though, what is it that allowed Darwin or Picasso, Rachel Carson or achieve their monumental creativity? Research provides no reason to think these giants possessed Georgia O'Keeffe, to some special "creativity mechanism." (See DeHaan, 2011; Goldenberg, Mazursky, & Solomon, 1999; Klahr & Simon, 2001; Simonton, 2003, 2009; Simonton & Damian, 2012; Weisberg, 2006.) However, there is one way in which these individuals surely were distinctive. Many of us are smart or particularly skillful in memory search; many of us are willing to take risks and to ignore criticism; many of us live in a cultural setting that might support a new discovery. What distinguishes creative geniuses, though, is that they are the special people who have all of these ingredients-the right intellectual tools, the right personality characteristics, the good fortune to be living in the right context, and so on. It's a rare individual who has all of these elements, and it's probably the combination of all these elements that provides the recipe for extraordinary creativity. e. Demonstration 13.3: Incubation
Many people believe that an "incubation" period aids problem solving. In other words, spending time away from the problem helps you to find the solution, because during the time away, it's claimed, you are unconsciously continuing to work on the problem. Is this belief warranted? Is it an accurate reflection of how problem solving truly proceeds? To find out, psychologists study problem solving in the lab, using problems like these. (Similar problems are presented in the chapter.)
Each of the figures shown here represents a familiar word or phrase. Can you decipher thein? (To get you started, the first figure represents the common phrase "Forgive and forget." We'll give you the other solutions in a moment.) In one study, participants were more likely to solve these puzzles if they worked on them for a while, then took a break, then returned to the puzzles. This result seems to indicate an incubation benefit, but the explanation for the result actually may not involve any sort of unconscious problem solving. Instead, the break may have helped simply because it allowed the to lose track of the bad strategies they'd been trying so far, and this in turn allowed participants them to get a fresh start on the problem when they returned to it. Support for this interpretation comes from the fact that in this study, a helpful clue was provided for some of the figures (e.g., the sixth picture shown here might be accompanied by the word "first"). For other figures, the clue was actually misleading (e.g., the first picture here might be accompanied by the clue "opposites"). The participants were helped much more by the break if they'd been misled at the start. This pattern is just what we'd expect if the break allowed the participants to set aside (and maybe even to forget) the misleading clue, so that they were no longer derailed by it. On this basis, the break didn't allow unconscious problem solving; it instead allowed something simpler: a bit of rest, a bit of forgetting, and hence a fresh start. The remaining solutions, by the way, are "you fell out of touch," "just between you and me "backward glance," "three degrees below zero," and "first aid." e. Demonstration 13.4: Remote Associates Can creativity be measured? Some researchers think it can, and they design tests that (they believe) tap into the processes that are essential for creativity. For example, the chapter mentions that some researchers believe creativity depends on the ability to detect unanticipated, perhaps distant, relationships among ideas. One test designed to measure this ability is the Remote Associates Test. In this test, you're given three words, and you need to come up with a word that can be combined with each of the three. Thus, you might be given "cottage, Swiss, cake," and you need to come up with "cheese" (as in "cottage cheese," "Swiss cheese," and "cheesecake"). The chapter provides some examples of remote associates. Below you'll find a number of additional test items. If you wish, time yourself, allowing 2 seconds to work on each item; how many can you solve?
Expand the table below to see the answers and the percentage of research participants who, in one study, were able to solve each problem within 2 seconds of trying. Intelligence We all admire people who seem wonderfully intelligent, and we express concerns about (and try to help) those we consider intellectually dull. But what is "intelligence"? Is there a way to measure it? And should we be focused on intelligence (singular) or intelligences (plural)? In other words, is there such a thing as "intelligence-in-general," so that someone who has this resource will be better off in all mental endeavors? Or should we instead talk about different types of intelligence, so that someone might be "smart" in some domains but less so in others? Measuring Intelligence Scholars disagree about how the word "intelligence" should be defined. Remarkably, though, early efforts toward measuring intelligence proceeded without a clear definition. Instead, Alfred Binet (1857-1911) and his colleagues began with a simple idea-namely, that intelligence is a capacity that matters for many aspects of cognitive functioning. They therefore created a test that included a range of tasks: copying a drawing, repeating arithmetic, and so on. Performance was then assessed with a composite score, summing across string of digits, understanding a story, doing these various tasks. In its original form, the test score was computed as a ratio between someone's "mental age" (the level of development reflected in the test performance) and his or her chronological age. (The ratio was then multiplied by 100 to get the final score.) This ratio-or quotient-was the source of the test's name: The test evaluated a person's "intelligence quotient," or IQ.. Modern forms of the test no longer calculate this ratio, but they're still called "IQ tests." One commonly used test is the Wechsler Intelligence Scale for Children, or WISC (Wechsler, 2003). Similarly, adult intelligence is often evaluated with the Wechsler Adult Intelligence Scale, or WAIS. Like Binet's original test, though, these modern tests rely on numerous subtests. In the WAIS, for example, there are tests to assess general knowledge, vocabulary, and comprehension (see Figure 13.16A), and a perceptual-reasoning scale includes visual puzzles like the one shown in Figure 13.16B. Separate subtests assess working memory and speed of intellectual processing.
Other intelligence tests have different formats. The Raven's Progressive Matrices Test (Figure 13.16C; Raven & Raven, 2008) hinges entirely on a person's ability to analyze figures and detect patterns. This test presents the test taker with a series of grids (these are the "matrices"), and he or she must select an option that completes the pattern in each grid. This test is designed to minimize influence from verbal skills or background knowledge. Reliability and Validity Whenever we design a test-to assess intelligence, personality, or anything else-we need to determine whether the test is reliable and valid. Reliability refers to how consistent a measure is, and one aspect of reliability is consistency from one occasion to another: If we give you a test, wait a while, and then give it again, do we get the same outcome? The issue here is test-retest reliability. Intelligence tests have strong test-retest reliability. There is, for example, a high correlation between measurements of someone's IQ at age 6 and measurements of IQ when she's 18. For that matter, if we know someone's IQ at age 11, we can accurately predict what her IQ will be at age 80. (See, e.g., Deary, 2014; Deary, Pattie, & Starr, 2013; Plomin & Spinath, 2004.) It's important, though, that IQ scores can change-especially if there's a substantial change in the person's environment. (We'll return to this point later in the chapter; also see Ramsden et al., 2011.) Nonetheless, if the environment is reasonably stable, the data show remarkable constancy in IQ scores, even over spans as long as 70 or 80 years. What about the validity of the IQ test? In general, the term validity refers to whether a test actually measures what it is intended to measure, and one way to approach this issue is via an assessment of predictive validity. The logic behind this assessment is straightforward. If intelligence tests truly measure what they're supposed to, then someone's score on the test should enable us to predict how well that person will do in settings that require intelligence. And here, too, the results are impressive. For example, there's at least a .50 correlation between a person's IQ and measures of academic performance, such as grade-point average. (See Arneson, Sackett, & Beatty, 2011; Deary 2012; Kuncel, Hezlett, & Ones, 2004; Strenze, 2007.) IQ scores are also correlated with performance outside of the academic world. For example, an IQ score is a strong predictor of how someone will perform on the job, although-sensibly-the data indicate that IQ matters more for some jobs than for others (Sackett, Borneman, & Connelly, 2008; Schmidt & Hunter, 1998, 2004). Jobs of low complexity require relatively little intelligence, correlation between IQ and job performance is small (although still positive) for such jobs-for example, a correlation of .20 between IQ and performance on an assembly line. As jobs become more so the complex, intelligence matters more, so the correlation between IQ and performance gets stronger (Gottfredson, 1997). For example, we find correlations between .50 and .60 when we look at IO scores and people's success as accountants or shop managers. IQ scores are also correlated with other life outcomes. People with higher IQ's tend to end up with higher-prestige careers and are less likely to suffer various life problems (and so are less likely to end up in jail, less likely to become pregnant as teens, and more). Higher-IQ people even live longer-with various mechanisms contributing to this longevity. Among other considerations, higher-IQ individuals are less likely to die in automobile accidents (see Table 13.1) and less likely to have difficulty following doctors' instructions. (See Deary, Weiss, & Batty, 2010; Gottfredson, 2004; Kuncel et al., 2004; Lubinski, 2004; Murray, Pattie, Starr, & Deary, 2012.)
Let's emphasize, though, that no matter what the setting, IQ scores are never a perfect predictor of life outcomes. (Notice that all of these correlations are appreciably less than +1.00.) This cannot be a surprise, however, because of course other factors beyond intelligence matter in all of these domains. For example, how well you'll do in school also depends on your motivation, whether you stay healthy, whether your friends encourage you to study or instead go to parties, and dozens of other factors. These points make it inevitable that intelligence levels won't be a perfect predictor of your academic achievement. More bluntly, these points make it inevitable that some high-IQ people will perform poorly in school, while some low-IQ people will excel. (Clearly, therefore, an IQ score doesn't define your destiny.) Even so, there's no question that there's a strong correlation between IQ and many important life outcomes-academic or job performance, longevity, and more. In fact, in many domains IQ is a better predictor of performance than any other factor. It seems plain, then, that IQ tests do have impressive predictive validity-with the clear implication that these tests are measuring something interesting, important, and consequential. General versus Specialized Intelligence If-as it seems-IQ tests are measuring something important, what is this "something"? This question is often framed in terms of two options. One proposal is that the tests measure a singular ability that can apply to any content. The idea is that your score on an IQ test reveals your general intelligence, a capacity that provides an advantage on virtually any mental task. (See Spearman, 1904, 1927; for more recent discussion, see Kaufman, Kaufman, & Plucker, 2012.) In contrast, some authors argue that there's no such thing as being intelligent in a general way. Instead, they claim, each person has a collection of more specific talents-and so you might be "math smart" but not strong with language, or "highly verbal" but not strong with tasks requiring visualization. From this perspective, if we represent your capacities with a single number-an IQ score-this is only a crude summary of what you can do, because it averages together the things you're good at and the things you're not. Which proposal is correct? One way to find out relies on the fact that, as we've said, many intelligence tests include numerous subtests. It's therefore instructive to compare a person's score on each subtest with his or her scores on other subtests. In this way, we can ask: If someone does well on one portion of the test, is she likely to do well across the board? If someone does poorly on one subtest, will he do poorly on other subtests as well? If we observe these patterns, this would indicate that there is such a thing as intelligence-in-general, a cognitive capacity that shapes how well someone does no matter what the specific task might be. More than a century ago, Charles Spearman developed a statistical procedure that enables us to pursue this issue in a precise, quantitative manner. The procedure is called factor analysis, and, as the name implies, this procedure looks for common factors-elements that contribute to multiple subtests and therefore link those subtests. Factor analysis confirms that there is a common element shared by all the components of the IQ test. (See Carroll, 1993; Deary, 2012; Johnson, Carothers, & Deary, 2008; Watkins, Wilson, Kotz, Carbone, & Babula, 2006.) Some subtests (e.g., comprehension of a simple story) depend heavily on this general factor; others (e.g., the ability to recall a string of digits) depend less on the factor. Nonetheless, this general factor matters across the board, and that's why scores on all the subtests end up correlated with one another. Spearman named this common element general intelligence, usually abbreviated with the letter g. Spearman (1927) argued that people with a high level of g will have an advantage in virtually every intellectual endeavor. Conversely, if someone has a low level of g, that person will do poorly on a wide range of tasks. A Hierarchical Model of Intelligence The data tell us, though, that g isn't the whole story, because people also have more specialized skills. One of these skills involves aptitude for verbal tasks, so that performance on (say) a reading comprehension test depends on how much g a person has and also on the strength of these verbal skills. A second specialized ability involves quantitative or numerical aptitude, so performance on an arithmetic test depends on how much g a person has and also the strength of these skills. Putting these pieces together, we can think of intellectual performance as having a hierarchical structure as shown in Figure 13.17. At the top of the hierarchy is g, contributing to all tasks. At the next level down are the abilities we just mentioned-linguistic and numerical-and several more including spatial skill, an ability to handle fast-paced mental tasks, and an ability to learn new and unfamiliar materials. Then. at the next level are more specific capacities, each useful for a narrow and specialized set of tasks. (See Carroll, 1993; Flanagan, McGrew, & Ortiz, 2000; Johnson, Nijenhuis, & Bouchard, 2007: McGrew, 2009; Snow, 1994, 1996.)
This hierarchical conception leads to a prediction. If we choose tasks from two different categories-say, a verbal task and one requiring arithmetic-we should still find a correlation in performance, because no matter how different these tasks seem, they do have something in common: They both draw on g. If we choose tasks from the same category, though-say, two verbal tasks or two quantitative tasks-we should find a higher correlation because these tasks have two things in common: They both draw on g, and they both draw on the more specialized capacity needed for just that category. The data confirm both of these predictions-moderately strong correlations among all of the IQ test's subtests, and even stronger correlations among subtests in the same category It seems, then, that both of the broad hypotheses we introduced earlier are correct. There is some sort of general capacity that is useful for all mental endeavors, but there are also various forms of more specialized intelligence. Each person has some amount of the general capacity and draws on it in all tasks; this is why there's an overall consistency in each person's performance. At the same time, the consistency isn't perfect, because each task also requires more specialized abilities. Each of us has our own profile of strengths and weaknesses for these skills, with the result that there are things we do relatively well and things we do less well. Fluid and Crystallized Intelligence
It's also important to distinguish between fluid intelligence and crystallized intelligence (Carroll, 2005; Horn, 1985; Horn & Blankson, 2005). Fluid intelligence involves the ability to deal with novel problems. It's the form of intelligence you need when you have no well-practiced routines you can bring to bear on a problem. Crystallized intelligence, in contrast, involves your acquired knowledge, including your verbal knowledge and your repertoire of skills-skills useful for dealing with problems similar to those already encountered. Fluid and crystallized intelligence are highly correlated (e.g., if you have a lot of one, you're likely to have a lot of the other). Nonetheless, these two aspects of intelligence differ in important ways. Crystallized intelligence usually increases with age, but fluid intelligence reaches its peak in early adulthood and then declines across the lifespan (see Figure 13.18; Horn, 1985; Horn & Noll, 1994; Salthouse, 2004, 2012). Similarly, many factors-including alcohol consumption, fatigue, and depression-cause more impairment in tasks requiring fluid intelligence than in those dependent on crystallized intelligence (Duncan, 1994; Hunt, 1995). Therefore, someone who is tired will probably perform adequately on tests involving familiar routines and familiar facts. The same individual, however, may be markedly impaired if the test requires quick thinking or a novel approach- earmarks of fluid intelligence.
The Building Blocks of Intelligence It's clear, then, that intelligence has many components. However, one component-g-is crucial, because this aspect of intelligence is relevant to virtually all mental activities. But what is g? What gives a person more g or less? One proposal is simple. Mental processes are quick but do take time, and perhaps the people we consider intelligent are those who are especially fast in these processes. This speed would enable quickly; it also would give them time for more steps in them to perform intellectual tasks more comparison with those of us who aren't so quick. (See Coyle, Pillow, Snyder, & Kochunov, 2011; Deary, 2012; Nettelbeck, 2003; Sheppard, 2008; Vernon, 1987.) As a variant of this proposal, some researchers argue that intelligence is created by faster processing, not throughout the brain, but in particular neural pathways-for example, the pathways linking temporal and parietal areas. (See Schubert, Hagemann & Frischkom, 2017; for a related idea, see Figure 13.19.) Support for these ideas comes from measures of inspection time-the time a person needs to decide which of two lines is longer or which of two tones is higher. These measures correlate .30 with intelligence scores. (See Bates & Shieles, 2003; Danthiir, Roberts, Schulze, & around Wilhelm, 2005; Deary & Derr, 2005; Ravenzwaaij, Brown, & Wagenmakers, 2011.) The correlation is negative because lower response times go with higher scores on intelligence tests. A different proposal about g centers on the notion of working-memory capacity (WMC). We first met this notion in Chapter 6, where we saw that WMC is actually a measure of executive control-and so it is a measure of how well people can monitor and direct their own thought processes. We mentioned in the earlier chapter that people with a larger WMC do better on many intellectual tasks, specifically designed to measure g. (See Burgess, Braver, Conway, & including, we now add, tests Gray, 2011; Engel & Kane, 2004; Fukuda, Vogel, Mayr, & Awh, 2011; Jewsbury, Bowden, & Strauss, 2016; Redick et al., 2016.) Perhaps, therefore, the people we consider intelligent are those who literally so they can coordinate their priorities in an appropriate have better control of their own thoughts, way, override errant impulses, and in general proceed in a deliberate manner when making judgments or solving problems. (For debate on how exactly WMC contributes to intelligence, see Bleckley, Foster & Engle, 2015; Gonthier & Thomassin, 2015; Harrison, Shipstead, & Engle, 2015; Kanerva & Kalakoski, 2016; Redick et al., 2013.) e. Demonstration 13.5: Defining Intelligence More than a century ago, Alfred Binet and his colleagues developed a measure of intelligence. They were guided, however, largely by their intuitions about intelligence, rather than by any sort of well- developed theory. That invites us to ask: What are your intuitions about intelligence? First, take a piece of paper and list 8 to 10 activities that can be performed especially well by someone who is intelligent. (If you prefer, you can turn this around: Name 8 to 10 activities that, if someone does them well, indicate that the person is intelligent.) Second, now do the reverse: List 8 to 10 activities that, in your view, have little to do with intelligence-so that a highly intelligent person will have no advantage on these activities in comparison to a less intelligent person. Third, our language includes many terms used to describe people with high levels of intellectual ability. The terms include "smart," "clever," and many others. Can you list 8 to 10 words that describe either a higher level of intellectual ability or perhaps a lower level ("slow," "dull," etc.)? Fourth, can we combine your first list (activities that involve intelligence) and your third list (words used to describe intellectual ability)? Do some of the terms apply to some activities but not to others? Do some of the activities reflect one type of "being smart" but not others? Finally, in light of what you've said in the first four steps here, can you offer a possible definition of intelligence? How do you think this term should be defined? Intelligence beyond the IQ Test We've now seen that IQ tests do measure something important, and we've gained important insights into what this "something" is. But, stepping away from what the IQ test does measure, we can also ask what it doesn't measure. Are there types of intelligence not included in conventional intelligence tests? Practical Intelligence Some people seem to be "street-smart"-that is, capable of sophisticated reasoning in day-to-day settings-even though they seem to lack the sort of analytical skill needed in a classroom. Psychologist Robert Sternberg has studied this sort of practical intelligence, with some of his research focused on whether teaching is more effective when instruction is matched to students abilities (with different forms of instruction for students high in practical ability, students high in analytical ability, and students high in creative ability). Evidence suggests that "tuning" the curriculum in this way can be helpful. (See Grigorenko, Jarvin, & Sternberg, 2002: Sternberg & Grigorenko, 2004; also see Sternberg, Kaufman, & Grigorenko, 2008; Wagner, 2000. For concerns though, see Gottfredson, 2003.) Measures of Rationality A different sort of complexity has been highlighted by Keith Stanovich. He reminds us that we all know people who are smart according to their test scores, but who nonetheless ignore facts, are overconfident in their judgment, are insensitive to inconsistencies in their views, and more. It's people like these who lead us to ask: "How could someone that smart be so stupid?" In light of such cases (and much other evidence), Stanovich argues that we need separate measures of intelligence and rationality (Stanovich, 2009; Stanovich, West, & Toplak, 2016). He defines the latter term as the capacity for critically assessing information as it is gathered in the natural environment. Other Types of Intelligence Still other authors highlight a capacity they call emotional intelligence-the ability to understand one's own emotions and others, as well as the ability to control one's emotions when appropriate (Mayer, Roberts, & Barsade, 2008; Salovey & Mayer, 1990). Tests have been constructed to measure this capacity, and people who score well on these tests are judged to create a more positive atmosphere in the workplace and to have more leadership potential (Grewal & Salovey, 2005; Lopes Salovey, Côté, & Beers, 2005). Likewise, college students who score well on these tests are rated by their friends as being more caring and more supportive; they're also less likely to experience conflict with peers (Brackett & Mayer, 2003; Mayer et al., 2008). Perhaps the best-known challenge to IQ testing, however, comes from Howard Gardrner's theory of multiple intelligences. Gardner (1983, 2006) argues for eight types of intelligence. Three of these linguistic intelligence, logical-mathematical intelligence, and are assessed in standard IQ tests: spatial intelligence. But Gardner also argues that we should acknowledge musical intelligence, bodily-kinesthetic intelligence (the ability to learn and create complex patterns of movement), interpersonal intelligence (the ability to understand ourselves), and naturalistic intelligence (the ability to understand other people), intrapersonal intelligence (the to understand patterns in ability nature). Some of Gardner's evidence comes from the study of people with so-called savant syndrome- including people like Stephen Wiltshire, mentioned at the start of this chapter. These individuals are profoundly disabled, with IQ scores as low as 40 or 50, but each of them has a stunning level of specialized talent. Some are incredible artists (see Figure 13.20). Others are "calendar calculators." immediately when asked questions like "What day of the week was March 19 in the able to answer vear 1642?" Still others have remarkable musical skills and can effortlessly memorize lengthy musical works (Hill. 1978: Miller. 1999). Apparently, then, it's possible to have extreme talent that is separate from intelligence as it's measured on IQ tests. But beyond this intriguing point, how should we think about Gardner's claims? First, his proposal reminds us that a broad range of human achievements are of enormous value, and surely we should celebrate the skill displayed by an artist at her canvas, a skilled dancer in the ballet, or an empathetic clergyman in a hospital room. These are certainly talents to be acknowledged and, as much as possible, nurtured and developed. But let's also be clear that Gardner's conception shouldn't be understood as a challenge to conventional intelligence testing. IQ tests were never designed to measure all human talents-so it's no surprise that these tests tell us little about, say, musical intelligence or bodily-kinesthetic intelligence. These other talents are important, but that takes nothing away from the importance of the capacities measured by IQ tests-capacities that (as we've seen) are needed for many aspects of life. Finally, there's room for debate about whether the capacities showcased by Gardner (or, for that matter, the capacity labeled "emotional intelligence") should be thought of as forms of intelligence. In ordinary conversation, example, we mean something different if we talk about an "intelligent" athlete in contrast to a "talented" athlete. Likewise, various television shows seek performers with talent rather than we make a distinction between "intelligence" and "talent"-and so, for performers with intelligence. Gardner's proposal invites us to step away from this distinction, and the motivation is clear: His use of the term "intelligence" does encourage us to give these other pursuits the esteem they surely deserve. At the same time, his terminology may lead us to ignore distinctions we might otherwise need. (For more on Gardner's claims, see Cowan & Carney, 2006 Deary, 2012; Thioux, Stark, Klaiman, & Shultz, 2006; Visser, Ashton, & Vernon, 2006; White 2008.) e. Demonstration 13.6: IQ Testing Many students are curious to know their own IQ scores, but we will not provide a full and formal IQ test. There are several reasons, including the fact that IQ tests, to be valid, need to be administered under carefully set up, carefully standardized circumstances. Nonetheless, we can at least point you toward some websites that offer reasonable tests that will provide you with an approximation of your IQ score. First, though, let's be clear that many of the intelligence tests available on the Internet have no validity: They aren't standardized tests; there is no evidence indicating we should take these tests seriously. However, other tests on the Internet do resemble the tests actually used by researchers and educators. For example, the chapter mentions the Raven's Progressive Matrices as a nonverbal intelligence test that (supposedly) has no cultural bias because it involves no prior knowledge and no language skills. (This claim of "no bias" must be viewed skeptically, though, because the Raven's test certainly requires familiarity with the idea of putting things into a "table," in the appropriate rows and columns, and this idea is by no means universally shared across cultures.) The Raven's test is, in any case, often used and does have some validity. You can, if you wish, enter these words into an Internet search engine: "Raven's Progressive Matrices Online Test." This step will bring you to several websites offering approximations of the Raven's test, and this will give you one rough estimate (and we emphasize a rough estimate) of your IQ score. In interpreting this score, though, you'll want to bear in mind that IQ scores-while generally stable across the lifespan-can change and, in fact, can change by as much as 15 points as the years go by. If your score is high, therefore, don't decide you can relax in your efforts to get a good education; and if your score is low, don't decide that all is lost. (Indeed, the chapter emphasizes at several points why your IQ score does not define your destiny!) Other intelligence tests take very different forms. For example, the chapter mentions that the standard IQ tests usually involve many subtests and that some of these subtests rely heavily on how much g the person has, while other subtests rely less on g. Verbal analogies, for example, depend strongly on g and also depend on the person's verbal skills. If you're curious to see how well you do with analogies, therefore, you can search online for "Miller Analogies Practice Test" The Miller test is not designed as an intelligence test; it is instead designed as a test of "high-level analytical reasoning" and is used by some graduate schools as part of their admissions screening. Even so, this test will give you a glimpse of what analogy problems look like and how well you do on them. Once again, though, let's be clear: Your IQ score is an important fact about you, but it does not define your destiny. The Roots of Intelligence Differences in intelligence, we've suggested, may be the result of variations in mental speed or in the functioning of executive control. But what causes those differences? Why does one person end up with a high level of intelligence, while other people end up with a lower level? Discussions of these questions often frame the issue as if it were a choice between two alternatives-"nature versus nurture" In other words, should our explanation emphasize genetics or should we focus on learning and the environment? This framing of the issue, and heredity however, is misleading: Both types of influence-one rooted in genetics, one rooted in experience play an important role in shaping intelligence. Moreover, these influences aren't separate; instead, the two types of influence actually depend on each other. We can see the impact of genetic influences on intelligence in the fact that people who resemble each other genetically also resemble each other in their IQ scores. This resemblance is in place even if the individuals grow up in different environments. For example, identical (i.e., monozygotic) twins tend to have highly similar IQ scores even if the twins are reared in different households. (See Figure 13.21; also see Bouchard, Lykken, McGue, Segal, & Tellegen, 1990; McGue, Bouchard, Iacono, & Lykken, 1993; Plomin & Spinath, 2004. For research examining which genes, and exactly which DNA patterns, contribute to individual differences in IQ, see Benyamin et al., 2013; Payton, 2009; Plomin et al., 2013; Rizzi & Posthuma, 2012.)
There's no question, though, that environmental factors also matter for intelligence. For example, we've known for many years that living in poverty impedes intellectual development, and the effect is cumulative: The longer the child remains in such an environment, the greater the harm. This point emerges in the data as a negative correlation between IQ and age. That is, the older the child (the longer she had been in the impoverished environment), the lower her IQ. (See Asher, 1935; Gordon 1923; for more recent data, see Heckman, 2006. For discussion of why poverty undermines intellectual development, see Nisbett, 2009; Protzko, Aronson, & Blair, 2013.) A related-and more optimistic-finding is that improving the environment can increase IQ. In one study, researchers focused biological parents because of abuse or neglect (Duyme, Dumaret, & Tomkiewicz, 1999). The cases in which the government had removed children from their researchers compared the children's "pre-adoption IQ" (when the children were living in a high-risk environment) with their IQ in adolescence-after years of living with adoptive families. The data showed substantial improvements in the children's scores, thanks to this environmental change (see Figure 13.22). (Also see Diamond & Lee, 2011; Nisbett et al., 2012a, b; Protzko, Aronson, & Blair. 2013.)