THE BUILDING BLOCKS OF INTELLIGENCE
Let’s pause to take stock. We’ve now seen that IQ scores are reliable predictors of many important life outcomes; critically, the outcomes most closely linked to IQ—success in school, for example, or success in complex jobs—are exactly the sorts of things that should be correlated with IQ if the IQ test is measuring what it’s intended to measure:intelligence.
These points prompted us to take a closer look at the IQ scores, and that’s what led us to separate general intelligence (g) from more specialized forms of mental ability. But can we take our analysis further? What is it, inside a person, that gives them more g, or less? Do smart people have certain skills that the rest of us don’t have? Do smart peo-ple have bigger brains, or brains with a different structure? Let’s look at the evidence relevant to these points.
Intelligence tests require complex mental processes: The test taker has to detect compli-cated patterns, work her way through multiple-step plans, and so on. Each of these processes takes some time, and this invites the proposal that the people we consider intelligent may just be those who are especially fast in these processes. This speed would allow them to perform intellectual tasks more quickly; it also would give them time for more steps in comparison with those of us who aren’t so quick.
One version of this hypothesis proposes that high-IQ people are faster in all mental steps, no matter what the steps involve (Eysenck, 1986; Nettelbeck, 2003; Vernon, 1987). A related hypothesis proposes that high-IQ people are faster not in all mental processes, but in just those needed for key mental operations such as memory retrieval (E. Hunt, 1976, 1985b). In either case, what could be the basis for this speed? One possibility is a greater degree of myelination of the neurons in the brains of high-IQ people (E. Miller, 1994; bear in mind that it’s the myelin wrappers around axons that allow fast transmission of the neural impulse; axons without these wrappers transmit the action potential much more slowly; for details). Alternatively, high-IQ people may have a greater availability of metabolic “fuel” for the neurons (Rae, Digney, McEwan, & Bates, 2003). But, no matter what the neural mechanism might be, what is the evidence linking intelligence scores to measures of speed?
A number of studies have measured simple reaction time, in which the participant merely responds as quickly as he can when a stimulus appears. Others have measured choice reaction time, in which the participant must again respond as quickly aspossible but now has to choose among several responses, depending on the stimulus presented. In such tasks, reaction times are in fact correlated with intelligence scores (note, though, that the correlation is negative, and that lower times—indicating greater speed—are correlated with higher IQ; see, for example, Jensen, 1987).
Other studies have focused on measures of inspection time—the time someone needs to make a simple discrimination between two stimuli (which of two lines is longer, or which of two tones is higher). These measures correlate around ".50 with intelligence scores (see, for example, T. Bates & Shieles, 2003; Dantiir, Roberts, Schulze, Wilhelm, 2005; Deary & Derr, 2005; Grudnik & Kranzler, 2001; Lohman, 2000; Petrill, Luo, Thompson, & Detterman, 2001; again, the correlation is negative because lower response times go with higher scores on intelligence tests).
The suggestion, then, is that intelligent people may literally have brains that operate more swiftly and more efficiently than the brains of less intelligent people. This idea finds further support in a classic study that examined the relationship between brain activity and someone’s ability to perform well on the Raven’s Matrices, often used as a measure of g (see Figure 11.4). PET scans showed robust negative correlations between scores on theRaven’s test and glucose metabolism in many areas distributed around the cortex (Haier et al., 1988). In other words, the data showed less energy consumption by the brains of people with higher IQs. This is certainly consistent with the idea that high g is somehow the product of more efficient brain function—as if smarter people were simultaneously getting more “horsepower” as well as better “fuel economy” out of their mental engine!
Mental speed is likely to be one contributor to intelligence, but there are other elements as well—including a central role for working memory capacity. To understand the point here, bear in mind that many mental tasks involve multiple bits of information, and you need to keep track of them as you proceed. In addition, many tasks involve multiple steps, and they demand that you shift your focus from one moment to the next—thinking about your overall goal for a second, to figure out what to do next; then focusing on that next step, to deal with its specific demands; then focusing once again on your goal, to choose the next step; and so on .
On this basis, perhaps the people we call “intelligent” are those who have particu-larly good working memories, so that they can hold onto the information they need for complex tasks. They may also have especially good control of their attention—so they’re able to coordinate their goals and priorities in an appropriate way, first by focusing here and then there, without getting lured off track by distraction.
To test this broad proposal, researchers have relied on measures that assess someone’s working memory capacity (WMC; e.g., Engle, Tuholski, Laughlin, & Conway, 1999). There are several varieties of these measures; but in one common procedure, the participant is asked to read aloud a brief series of sentences, such as
Due to his gross inadequacies, his position as director was terminated abruptly.
It is possible, of course, that life did not arise on the Earth at all.
Immediately after reading the sentences, the participant is asked to recall the final word in each one—in this case, abruptly and all. Participants are tested in this way with pairs of sentences (as in our example) and also with larger groups of sentences—as many as 6 or 7. The aim, of course, is to find each participant’s limit: What’s the largest group of sentences for which the participant can do this read-and-recall task?
This seemingly peculiar task provides a good measure of WMC because it involves storing some material (the final words of sentences) for later use in the recall test, while the person is simultaneously thinking about other material (the full sentences, which have to be read out loud). This juggling of processes, as we move from one part of the task to the next, is exactly how we use working memory and attention in everyday life. Thus, performance on this test is likely to reflect how efficiently a person’s working memory will operate in more natural settings. And if, as hypothesized, this efficiency is essential for intelligent performance, then these measurements of WMC should be cor-related with intelligence.
The data confirm this prediction. People with a larger WMC, measured as we’ve described, do have an advantage on many other tests. For example, people with a larger WMC do better on the verbal SAT, on tests of reasoning, on measures of reading com-prehension, and on tests specifically designed to measure g (A. Conway et al., 2005; Gathercole & Pickering, 2000a, 2000b; Daneman & Carpenter, 1980; Kane, Poole, Tuholski, & Engle, 2006; Lépine, Barrouillet, & Camos, 2005; Salthouse & Pink, 2008).
How exactly does a larger WMC improve intellectual performance? A number of interrelated proposals have been offered; one proposal focuses on the construction and maintenance of the task model needed to perform a task. This model provides the “mental agenda” that a person needs to carry out the task; the model is based on the person’s understanding of the task’s goals, rules, and requirements as well as their knowledge of the relevant facts. Once constructed, the model governs the person’s mental steps as he works his way through the task.
Tasks differ in the complexity of the model they require. The model will have to be more complicated (for example) if task performance involves either multiple goals or a sharp change in goals as certain cues come into view. Evidence suggests that the ability to handle this complexity is strongly linked to measures of g—so that higher-g individuals are able to maintain more complex task models, allowing them to out-perform lower-g people whenever such models are required (J. Duncan et al., 2008).
A different (but related) proposal is that measures of WMC are actually measures of each person’s executive control over her own thoughts. This term—which we first met—refers to the processes people use to launch mental actions, redirect their attention, or shift their strategies. From this perspective, the link between intelligence and WMC implies that smart people are literally in better control of their own thoughts than less intelligent people are.
What does executive control involve? Part of the answer lies in processes needed for goal maintenance—the mental activities that help us keep our goals in view, so that weconsistently direct our behavior toward that goal. As we discussed, these activities seem to depend on the frontmost part of the brain’s frontal lobe—the pre-frontal area (Figure 11.7). Damage to this brain site produces many problems, including goal neglect (in which the person fails to keep track of the goal) and perseveration (inwhich the person cannot make the necessary adjustment in behavior when a goal changes).
Executive control also requires other steps, rooted in other brain areas. For example, the anterior cingulate cortex seems to play a key role in detecting conflict between dif-ferent mental processes—including the conflict that will arise if one process is pulling toward one goal while another process pulls toward a different goal (e.g., Botvinick, Cohen, & Carter, 2004; also Banich, 2009; Buckley, Mansouri, Hoda, Mahboubi, Browning et al., 2009; Egner, 2008). Once these conflicts are detected, this informa-tion feeds back to other mechanisms (probably in the prefrontal area) that actually con-trol the flow of thoughts so that the conflict can be addressed. (For more on the frontal lobe, see Koechlin & Hyafil, 2007; for more on executive control, see J. Duncan, 1995; Gilbert & Shallice, 2002; Kane & Engle, 2003; Kimberg, D’Esposito, & Farah, 1998; Stuss & Levine, 2002.)
Notice that both of these proposals—one emphasizing task models, one emphasizing executive control—rely on claims about working memory (so that you can keep your task model or your goals in mind). They also rely on claims about attention,
so that you can focus on your task’s steps or on the relationship between those steps and your current goal. Both proposals are thus fully compatible with the so-called parieto-frontal integration theory (P-FIT) of intelligence suggested by R .Jung & Haier (2007; Figure 11.8). This theory grows out of neuroimaging studies that have compared the brains of individuals at differing levels of intelligence; the theory iden-tifies a network of brain sites that seem crucial for intellectual performance. As the theory’s name implies, some of these brain sites are in the parietal lobe—sites crucial for the control of attention; other sites are in the frontal lobe and are essential for working memory. Still other important sites seem to play an important role in language processing. The P-FIT conception emphasizes, though, that what really matters for intelligence is the integration of information from all of these sites, and thus the coordinated functioning of many cognitive components. This is, of course, a biologically based proposal that fits well with the functionally defined proposals emphasizing task models and executive control.
It seems, therefore, that one part of what makes someone “intelligent” is simply mental speed. This speed may make individual mental steps faster; it may also allow better communication among distinct brain areas. What’s more, someone’s working memory capacity and their ability to stay focused on a goal even in the face of inter-ference or distraction also matters for intelligence. Related, intelligence also may depend on the ability to construct and employ complex task models; this ability is linked in turn to a higher degree of executive control.
Beyond these points, though, other factors also matter for intellectual performance. For example, even if someone is slow, or has poor executive control, he can benefit from the knowledge and skills he has gained from life experience. This is, of course, the con-tribution from crystallized intelligence, which helps people solve problems, draw sensible conclusions, and make good decisions (also Ackerman & Beier, 2005; Hambrick, 2005).
The performance of intellectual tasks is also powerfully shaped by attributes that we might not think of as “intellectual” capacities. These include someone’s motivation, her attitude toward intellectual challenges, and her willingness to persevere when a prob-lem becomes frustratingly difficult. (Indeed, these factors will be crucial for us later, when we turn to differences among various groups in their average level of performance on these tests.)
Plainly, therefore, being intelligent requires a large and diverse set of attributes. If we choose, in light of these points, to represent someone’s intelligence with a single number—an IQ score—this seems both useful and potentially misleading. This score does summarize someone’s performance, so it can be useful in predicting how that per-son will perform in a wide range of other settings. At the same time, this single number blurs diverse constituents together; and so, if we wish to understand intelligence—and more important, if we want to find ways to improve someone’s intelligence—then we need to look past this single measurement and examine the many components contributing to that score.
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