07 - Cognition and Mental Abilities
ENDURING ISSUES IN COGNITION AND MENTAL ABILITIES
We will encounter the diversity-universality theme when we explore the differences and similarities in the way people process information and again when we discuss exceptional abilities. We make two additional references to the enduring issues as we discuss the stability-change of intelligence test scores over time, and again when we explore how measures of intelligence and performance sometimes vary as a function of expectations and situations (Person-Situation).
BUILDING BLOCKS OF THOUGHT
When you think about a close friend, you may have in mind complex statements about her, such as "I'd like to talk to her soon" or "I wish I could be more like her." You may also have an image of her - probably her face, but perhaps the sound of her voice as well. Or you may think of your friend by using various concepts or categories such as woman, kind, strong, dynamic, and gentle. When we think, we make use of all these things - language, images, and concepts - often simultaneously. These are the three most important building blocks of thought.
Language
Human language is a flexible system of symbols that enables us to communicate our ideas, thoughts, and feelings. Deaf children have great difficulty communicating because they know no language. Although all animals communicate with each other, language is unique to humans (MacWhinney, 2005).
One way to understand language is to consider its basic structure. Spoken language is based on units of sound called phonemes. The sounds of t, th, and k, for instance, are all phonemes in English. By themselves, phonemes are meaningless and seldom play an important role in helping us to think. But phonemes can be grouped together to form words, prefixes (such as un- and pre-), and suffixes (such as -ed and -ing). These meaningful combinations of phonemes are known as morphemes - the smallest meaningful units in a language. Unlike phonemes, morphemes play a key role in human thought. They can represent important ideas such as "red" or "calm" or "hot." The suffix -ed captures the idea of "in the past" (as in visited or liked). The prefix pre- conveys the idea of "before" or "prior to" (as in preview or predetermined).
We can combine morphemes to create words that represent complex ideas, such as pre-exist-ing, un-excell-ed, psycho-logy. In turn, words can be arranged to form sentences according to the rules of grammar. The two major components of grammar are syntax and semantics. Syntax is the system of rules that governs how we combine words to form meaningful phrases and sentences. For example, in English and many other languages, the meaning of a sentence is often determined by word order. "Sally hit the car" means one thing; "The car hit Sally" means something quite different; and "Hit Sally car the" is meaningless.
Semantics describes how we assign meaning to morphemes, words, phrases, and sentences - in other words, the content of language. When we are thinking about something - say, the ocean - our ideas often consist of phrases and sentences, such as "The ocean is unusually calm tonight." Sentences have both a surface structure - the particular words and phrases - and a deep structure - the underlying meaning. The same deep structure can be conveyed by different surface structures:
The ocean is unusually calm tonight.
Tonight the ocean is particularly calm.
Compared with most other nights, tonight the ocean is calm.
Alternatively, the same surface structure can convey different meanings or deep structures, but a knowledge of language permits one to know what is meant within a given context.
Syntax and semantics enables speakers and listeners to perform what linguist Noam Chomsky calls transformations between surface structure and deep structure. According to Chomsky (1957; Chomsky, Place, & Schoneberger, 2000), when you want to communicate an idea, you start with a thought, then choose words and phrases that will express the idea, and finally, produce the speech sounds that make up those words and phrases. When you want to understand a sentence, your task is reversed. You must start with speech sounds and work your way up to the meaning of those sounds.
Our remarkable ability to perform these transformations becomes clear when you attempt to comprehend the following sentence: when lettres wihtin wrods are jubmled or trnasposed (as they are in this sentence), raeding speed is redcued, though not as much as you might expect (approximately 11%-26%). However, it is much more difficult to extract the meaning of a sentence when letter substitutions are made (such as "qroblem" or "problnc") (Rayner, White, Johnson, & Liversedge, 2006).
Images
Using language is not the only way to think about things. Think for a moment about Abraham Lincoln. Your thoughts of Lincoln may have included such phrases as "wrote the Gettysburg Address" and "president during the Civil War." But you probably also had some mental images about him: bearded face, lanky body, or log cabin. An image is a mental representation of some sensory experience, and it can be used to think about things. Images also allow us to use concrete forms to represent complex or abstract ideas, as when newspapers use pie charts and graphs to illustrate how people voted in an election.
Concepts
Concepts are mental categories for classifying specific people, things, or events. Dogs, books, fast, beautiful, and interesting are all concepts. When you think about a specific thing - say, Mt. Everest - you may think of facts. You may also have an image of it. But you are also likely to think of the concepts that apply to it, such as mountain, highest, dangerous, and snow-covered. Concepts help us to think efficiently about things and how they relate to one another. They also give meaning to new experiences and allow us to organize our experiences. For example, most children soon develop a concept of fish that allows them to recognize, think about, and understand new kinds of fish when they see them for the first time.
Although it is tempting to think of concepts as simple and clear-cut, most of the concepts that we use are rather "fuzzy": They overlap one another and are often poorly defined. For example, most people can tell a mouse from a rat, but listing the critical differences between the two is difficult (Rosch, 2002). If we cannot explain the difference between a mouse and a rat, how can we use these fuzzy concepts in our thinking? It turns out that we often construct a prototype (or model) of a representative mouse and one of a representative rat, and then use those prototypes in our thinking (Rosch, 2002; Voorspoels, Vanpaemel, & Storms, 2008). For example, when thinking about birds, most of us have a prototype in mind - such as a robin or a sparrow - that captures for us the essence of bird. When we encounter new objects, we compare them with this prototype to determine whether they are, in fact, birds. And when we think about birds, we usually think about our prototypical bird.
Concepts, then, like words and images, help us to think. But human cognition involves more than just passively thinking about things. It also involves actively using words, images, and concepts to fashion an understanding of the world, solve problems, and to make decisions.
LANGUAGE, THOUGHT, AND CULTURE
Do people from different cultures perceive and think about the world in different ways? A series of controlled experiments suggests they do. In one experiment (Nisbett, Peng, Choi, & Norenzayan, 2001), American and Japanese students were shown an underwater scene and asked to describe what they saw. Most Japanese participants described the scene as a whole, beginning with the background; by contrast, most American participants described the biggest, brightest, fastest fish. Similar differences exist between Eastern Europeans and Americans and between Russians and Germans (Varnum, Grossmann, Kitayama, & Nisbett, 2010). These studies demonstrate fundamental, qualitative differences in how people in different cultures perceive and think about the world that are not due to genetic differences or differences in language.
As we have seen, language is one of the building blocks of thought. Can language influence how we think and what we can think about? Benjamin Whorf (1956) strongly believed that it does. According to Whorf's linguistic relativity hypothesis, the language we speak determines the pattern of our thinking and our view of the world - a position known more generally as linguistic determinism. For Whorf, if a language lacks a particular expression, the corresponding thought will probably not occur to speakers of that language. For example, the Hopi of the southwestern United States have only two nouns for things that fly. One noun refers to birds; the other is used for everything else. A plane and a dragonfly, for instance, are both referred to with the same noun. According to Whorf, Hopi speakers would not see as great a difference between planes and dragonflies as we do, because their language labels the two similarly.
The linguistic relativity hypothesis has intuitive appeal - it makes sense to think that limits of language will produce limits in thinking. However, research indicates that language doesn't restrict thinking to the extent that some linguistic determinists believed. For example, English has only three words for lightness: white (or light), black (or dark), and gray. Yet English speakers can discriminate hundreds of levels of visual intensity (Baddeley & Attewell, 2009). Moreover, experience and thought actually influence language. For example, the growth of personal computers and the Internet has inspired a vocabulary of its own, such as gigabyte, CPU, smartphone, and blogs. In short, people create new words when they need them.
Psychologists have not dismissed the Whorf hypothesis altogether, but rather have softened it, recognizing that language, thought, and culture, are intertwined (Chiu, Leung, & Kwan, 2007). Experience shapes language; and language, in turn, affects subsequent experience (K. Fiedler, 2008). This realization has caused us to examine our use of language more carefully.
Is Language Male Dominated?
The English language has traditionally used masculine terms such as man and he to refer to all people - female as well as male. Several studies suggest that this affects the way English speakers think. Hyde (1984) discovered that the use of "he" or "she" to describe a factory worker affected how children assessed the performance of male and female workers. Children who heard workers described by the masculine pronoun "he" rated female workers poorly; those who heard workers identified by the pronoun "she" judged female workers most positively; and the ratings of children who heard gender-neutral descriptions of workers fell in between those of the two other groups.
More recent research has focused on the unconscious, automatic nature of gender stereotyping and language (Palomares, 2004; Parks & Roberton, 2004). In an experiment requiring men and women to respond rapidly to gender-neutral and gender-specific pronouns, both sexes responded more quickly to stimuli containing traditional gender stereotypes (e.g., nurse/she) than to stimuli containing nontraditional ones (e.g., nurse/he). This occurred even among participants who were explicitly opposed to gender stereotyping (Banaji & Hardin, 1996).
As we have seen, language, cognition, and culture are interrelated in a complex fashion, each contributing to how people communicate, think, and behave. However, non-humans do communicate with one another.
NON-HUMAN LANGUAGE AND THOUGHT
The Question of Language
The forms of animal communication vary widely. Honeybees enact an intricate waggle dance that tells their hive mates not only exactly where to find pollen, but also the quality of that pollen (Biesmeijer & Seeley, 2005). Humpback whales perform long, haunting solos ranging from deep bass rumblings to high soprano squeaks. The technical term for such messages is signs, general or global statements about the animal's current state. But fixed, stereotyped signs don't constitute a language. The distinguishing features of language are meaningfulness (or semantics), displacement (talking or thinking about the past or the future), and productivity (the ability to produce and understand new and unique words and expressions). Using these criteria, as far as we know, no other species has its own language.
For more than two decades, however, Francine Patterson (Bonvillian & Patterson, 1997; F. G. Patterson, 1981) used American Sign Language with a lowland gorilla named Koko. By age 5, Koko had a working vocabulary of 500 signs - similar to a 5-year-old deaf child using sign language, though far lower than a hearing, speaking child's vocabulary of 1,000-5,000 words. In her mid-20s, Koko signed about her own companions' happy, sad, or angry emotions. Most interesting, Koko referred to the past and the future (displacement). Using signs before and later, yesterday and tomorrow appropriately, she mourned the death of her pet kitten and expressed a desire to become a mother. More recently, John Pilley has taught a border collie named "Chaser" to understand more than 1,000 words. Chaser also understands simple sentences such as "to ball take Frisbee" and recognizes that command means something different from "to Frisbee take ball." (Pilley, 2013; Pilley & Hinzman, 2013; Pilley & Reid, 2011).
Critics suggest that researchers such as Patterson may be reading meaning and intentions into simple gestures. To reduce the ambiguity of hand signs, other researchers have used computer keyboards to teach and record communications with apes (Rumbaugh, 1977; Rumbaugh & Savage-Rumbaugh, 1978); to document behavior with and without humans on camera; and also to study another ape species, bonobos. Most impressive - and surprising - was a bonobo named Kanzi (Savage-Rumbaugh & Lewin, 1994). Initially in the lab, Kanzi was adopted by an older female who lacked keyboard skills. Some months later, Kanzi, who had been accompanying his "mother" to lessons but who was not receiving formal training, was learning keyboard symbols and spoken English on his own - much as children do.
That non-human great apes can learn signs without intensive training or rewards from human trainers is clear. Whether they can grasp the deep structure of language is less clear. Moreover, at best, apes have reached the linguistic level of a 2- to 2-1/2-year-old child. Critics see this as evidence of severe limitations, whereas others view it as an extraordinary accomplishment.
Animal Cognition
Language is only one of the building blocks of thought. Without language, can non-humans nonetheless think? The question is particularly difficult to answer because psychologists have only recently developed techniques for learning how other animals use their brains for identifying the similarities and differences between human and non-human thought.
Numerous studies indicate that other animals have some humanlike cognitive capacities (Herrmann, Hernandez-Lloreda, Call, Haer, & Tomasello, 2010; Kluger, 2010; Patton, 2008-2009; Tomasello & Herrmann, 2010).
Parrots, for example, are exceptionally good vocal mimics. But do parrots know what they are saying? According to Irene Pepperberg (2000, 2007), Alex, and African gray parrot, did. Alex could count to 6; identify more than 50 different objects; and classify objects according to color, shape, material, and relative size. Pepperberg contends that rather than demonstrating simple mimicry, the parrot's actions reflected reasoning, choice, and, to some extent, thinking.
Other researchers have taught dolphins to select which of two objects is identical to a sample object - the basis of the concepts same and different (Herman, Uyeyama, & Pack, 2008) - and to respond accurately to numerical concepts such as more or less (Jaakkola, Fellner, Erb, Rodriguez, & Guarino, 2005). What's more, rhesus and capuchin monkeys can learn the concept of numeration, or the capacity to use numbers, and serialization, or the ability to place objects in a specific order based on a concept (Terrace, Son, & Brannon, 2003; A. A. Wright & Katz, 2007). In short, humans are not unique in their ability to form concepts, one of the building blocks of thought.
But do chimps, dolphins, and parrots know what they know? Do non-human animals have a sense of self? George Gallup (1985, 1998) noticed that after a few days' exposure, captive chimpanzees began making faces in front of a mirror and used it to examine and groom parts of their bodies they had never seen before. To test whether the animals understood that they were seeing themselves, Gallup anesthetized them and painted a bright red mark above the eyebrow ridge and on top of one ear. The first time the chimps looked at the mirror after awakening, they reached up and touched the red marks, presumably recognizing themselves.
Since Gallup's initial study, hundreds of researchers have used the mirror test and more recently live video displays with many other animals. So far, only seven non-human species - chimpanzees, bonobos (formerly called "pygmy chimpanzees"), orangutans, dolphins, elephants, magpies, and less frequently, gorillas - have been shown to have self-awareness (Bard, Todd, Bernier, Love & Leavens, 2006; Boysen & Himes, 1999; Gallup, 1985; Heschl & Burkart, 2006; Prior, Schwarz, & Gunturkun, 2008; Vauclair, 1996). For that matter, even human infants do not demonstrate mirror-recognition until 18 to 24 months of age.
If chimpanzees possess self-awareness, do they understand that others have information, thoughts, and emotions that may differ from their own? Observational studies suggest they do have at least a limited sense of other-awareness. One measure of other-awareness is deception. For example, if a chimpanzee discovers a hidden store of food and another chimpanzee happens along, the first may begin idly grooming himself. Presumably, the first chimpanzee recognizes that the second (a) is equally interested in food, and (b) will interpret the grooming behavior as meaning there is nothing interesting nearby. Both in the wild and in captive colonies, chimpanzees frequently practice deception in matters of food, receptive females, and power or dominance.
So far, we have been talking about what humans and non-humans think about. Cognitive psychologists are equally interested in how people use thinking to solve problems and make decisions.
PROBLEM SOLVING
Problem 1
You have three measuring spoons. One is filled with 8 teaspoons of salt; the other two are empty, but have a capacity of two teaspoons each. Divide the salt among the spoons so that only 4 teaspoons of salt remain in the largest spoon.
Most people find this problem easy.
Problem 2
You have three measuring spoons. One (spoon A) is filled with 8 teaspoons of salt. The second and third spoons are both empty. The second spoon (spoon B) can hold 5 teaspoons, and the third (spoon C) can hold 3 teaspoons. Divide the salt among the spoons so that spoon A and spoon B each have exactly 4 teaspoons of salt and spoon C is empty.
Most people find this problem much more difficult than the first one. Why? The answer lies in interpretation, strategy, and evaluation. Problem 1 is considered trivial because interpreting what is needed is easy, the strategies for solving it are simple, and the steps required to move closer to a solution can be verified effortlessly. Problem 2, by contrast, requires some thought to interpret what is needed; the strategies for solving it are not immediately apparent; and the steps required to see actual progress toward the goal are harder to evaluate. These three aspects of problem solving - interpretation, strategy, and evaluation - provide a useful framework for investigating this topic.
Interpreting Problems
The first step in solving a problem is called problem representation, which means interpreting or defining the problem. It is tempting to leap ahead and try to solve a problem just as it is presented, but this impulse often leads to poor solutions. For example, if your business is losing money, you might define the problem as deciphering how to cut costs. But by defining the problem so narrowly, you have ruled out other options. A better representation of this problem would be to figure out ways to boost profits - by cutting costs, by increasing income, or both. Problems that have no single correct solution and that require a flexible, inventive approach call for divergent thinking - or thinking that involves generating many different possible answers. In contrast, convergent thinking is thinking that narrows its focus in a particular direction, assuming that there is only one solution (or at most a limited number of right solutions).
To see the importance of problem representation, consider the next two problems.
Problem 3
You have four pieces of chain, each of which is made up of three links. All links are closed at the beginning of the problem. It costs 2 cents to open a link and 3 cents to close a link. How can you join all 12 links together into a single, continuous circle without paying more than 15 cents?
Problem 3 is difficult because people assume that the best way to proceed is to open and close the end links on the pieces of chain. As long as they persist with this "conceptual block," they will be unable to solve the problem. If the problem is represented differently, the solution is obvious almost immediately.
Problem 4
A monk wishes to get to a retreat at the top of a mountain. He starts climbing the mountain at sunrise and arrives at the top at sunset of the same day. During the course of his ascent, he travels at various speeds and stops often to rest. He spends the night engaged in meditation. The next day, he starts his descent at sunrise, following the same narrow path that he used to climb the mountain. As before, he travels at various speeds and stops often to rest. Because he takes great care not to trip and fall on the way down, the descent takes as long as the ascent, and he does not arrive at the bottom until sunset. Prove that there is one place on the path that the monk passes at exactly the same time of day on the ascent and on the descent.
This problem is extremely difficult to solve if it is represented verbally or mathematically. It is considerably easier to solve if it is represented visually. Interestingly, Albert Einstein relied heavily on his powers of visualization to understand phenomena that he would later describe by using complex mathematical formulas. This great thinker believed his extraordinary genius resulted in part from his skill in representing problems visually.
Another aspect of successfully representing a problem is deciding to which category the problem belongs. In fact, gaining expertise in any field consists primarily of increasing your ability to represent and categorize problems so that they can be solved quickly and effectively. Star chess players, for example, can readily categorize a game situation by comparing it with various standard situations stored in their long-term memories (Huffman, Matthews, & Gagne, 2001; A. J. Waters, Gobet, & Leyden, 2002). This strategy helps them interpret the current pattern of chess pieces with greater speed and precision than a novice chess player can.
Implementing Strategies and Evaluating Progress
Once you have properly interpreted a problem, the next steps are to select a solution strategy and evaluate progress toward your goal. A solution strategy can be anything from simple trial and error, to information retrieval based on similar problems, to a set of step-by-step procedures guaranteed to work (called an algorithm), to rule-of-thumb approaches known as heuristics.
Trial and Error
Trial and error is a strategy that works best when choices are limited. For example, if you have only three or four keys to choose from, trial and error is the best way to find out which one unlocks your friend's front door. In most cases, however, trial and error wastes time because there are many different options to test.
Information Retrieval
On approach is to retrieve information from long-term memory about how such a problem was solved in the past. Information retrieval is an especially important option when a solution is needed quickly. For example, pilots simply memorize the slowest speed at which a particular airplane can fly before it stalls.
Algorithms
Complex problems require complex strategies. An algorithm is a problem-solving method that guarantees a solution if it is appropriate for the problem and is properly carried out. For example, to calculate the product of 323 and 546, we multiply the numbers according to the rules of multiplication (the algorithm). If we do it accurately, we are guaranteed to get the right answer.
Heuristics
Because we don't have algorithms for every kind of problem, we often turn to heuristics, or rules of thumb. Heuristics do not guarantee a solution, but they may bring it within reach.
A very simple heuristic is hill climbing: We try to move continually closer to our goal without going backward. At each step, we evaluate how far "up the hill" we have come, how far we still have to go, and precisely what the next step should be. On a multiple-choice test, for example, one useful hill-climbing strategy is first to eliminate the alternatives that are obviously incorrect.
Another problem-solving heuristic is to create subgoals, which involves breaking a problem into smaller, more manageable pieces that are easier to solve individually than the problem as a whole. Consider the problem of the Hobbits and the Orcs.
Problem 5
Three Hobbits and three Orcs are on the bank of a river. They all want to get to the other side, but their boat will carry only two creatures at a time. Moreover, if any of the Orcs outnumber the Hobbits, the Orcs will attack the Hobbits. How can all the creatures get across the river without danger to the Hobbits?
You can find the solution to this problem by thinking of it in terms of a series of subgoals. What has to be done to get just one or two creatures across the river safely, temporarily leaving aside the main goal of getting everyone across? We could first send two of the Orcs across and have one of them return. That gets one Orc across the river. Now we can think about the next trip. It's clear that we can't then send a single Hobbit across with an Orc, because the Hobbit would be outnumbered as soon as the boat landed. Therefore, we have to send either two Hobbits or two Orcs. By working on the problem in this fashion - concentrating on subgoals - we can eventually get everyone across.
Problem 6
This problem is identical to Problem 5, except that there are five Hobbits and five Orcs, and the boat can carry only three creatures at a time.
Subgoals are often helpful in solving a variety of everyday problems. For example, a student whose goal is to write a term paper might set subgoals by breaking the project into a series of separate tasks: choosing a topic, doing research, writing the first draft, editing, and so on. Even the subgoals can sometimes be broken down into separate tasks: Writing the first draft might break down into the subgoals of writing the introduction, describing the position to be taken, supporting the position with evidence, drawing conclusions, writing a summary, and writing a bibliography. Subgoals make problem solving more manageable because they free us from the burden of having to "get to the other side of the river" all at once.
One of the most frequently used heuristics, called means-end analysis, combines hill climbing and subgoals. Like hill climbing, means-end analysis involves analyzing the difference between the current situation and the desired end, and then doing something to reduce that difference. But in contrast to hill climbing - which does not permit detours away from the final goal in order to solve the problem - means-end analysis takes into account the entire problem situation. It formulates subgoals in such a way as to allow us temporarily to take a step that appears to be backward in order to reach our goal in the end. One example is the pitcher's strategy in a baseball game when confronted with the best batter in the league. The pitcher might opt to walk this batter intentionally even though doing so moves away from the major subgoal of keeping runners off base. Intentional walking might enable the pitcher to keep a run from scoring and so contribute to the ultimate goal of winning the game. This flexibility in thinking is a major benefit of means-end analysis.
But means-end analysis also poses the danger of straying so far from the end goal that the goal disappears altogether. One way of avoiding this situation is to use the heuristic of working backward. Start at the end goal and work backwards through the steps of the solution.
Obstacles to Solving Problems
Many factors can either help or hinder problem solving. One factor is a person's level of motivation, or emotional arousal. Generally, we must generate a certain surge of excitement to motivate ourselves to solve a problem, yet too much arousal can hamper our ability to find a solution.
Another factor that can either help or hinder problem solving is mental set - our tendency to perceive and to approach problems in certain ways. A mental set can be helpful if we have learned operations that can legitimately be applied to the present situation. In fact, much of our formal education involves learning useful mental sets. But sets can also create obstacles, especially when a novel approach is needed. The most successful problem solvers can choose from many different mental sets and can also judge when to change sets or when to abandon them entirely.
One type of mental set that can seriously hinder problem solving is called functional fixedness. The more you use an object in only one way, the harder it is to see new uses for it and to realize that an object can be used for an entirely different purpose.
Because creative problem solving requires generating original ideas, deliberate strategies don't always help. Solutions to many problems rely on insight, often a seemingly arbitrary flash "out of the blue." Psychologists have only recently begun to investigate such spontaneous problem-solving processes as insight and intuition, but research indicates that such "mental breakthroughs" are likely to occur only when we widen our scope of attention from a few obvious but incorrect alternatives to more diverse possible solutions (B. Bower, 2008). This conclusion is supported by neuroimaging, which reveals that insight is generally preceded by periods of increased electrical activity in the frontal regions of the brain involved in suppressing unwanted thoughts (Kounios et al., 2008; Qiu Li, Jou, Wu, & Zhang, 2008).
The value of looking for new ways to represent a difficult problem cannot be overstressed. Be open to potential solutions that at first seem unproductive. The solution may turn out to be more effective, or it may suggest related solutions that will work. This is the rationale behind the technique called brainstorming: When solving a problem, generate a lot of ideas before you review and evaluate them.
DECISION MAKING
Decision making is a special kind of problem solving in which we already know all the possible solutions or choices. The task is not to come up with new solutions or choices, but rather to identify the best available one. This process might sound fairly simple, but sometimes we have to juggle a large and complex set of criteria as well as many possible options. For example, suppose that you are looking for an apartment among hundreds available. A reasonable rent is important to you, but so are good neighbors, a good location, a low noise level, and cleanliness. If you find an inexpensive, noisy apartment with undesirable neighbors, should you take it? Is it a better choice than a more expensive, less noisy apartment in a better location? How can you make the best choice?
Compensatory Decision Making
The logical way to make a decision is to rate each of the available choices on all the criteria you are using, arriving at some overall measure of the extent to which each choice matches your criteria. For each choice, the attractive features can offset or compensate for the unattractive features. This approach to decision making is therefore called a compensatory model.
Decision-Making Heuristics
Research has identified a number of common heuristics that people use to make decisions. We use the representativeness heuristic whenever we make a decision on the basis of certain information that matches our model of the typical member of a category.
Another common heuristic is availability. In the absence of full and accurate information, we often base decisions on whatever information is most readily available, even though this information may not be accurate or complete.
Yet another heuristic, closely related to availability, is confirmation bias - the tendency to notice and remember evidence that supports our beliefs and ignore evidence that contradicts them (Bower, 2013). For example, individuals who believe that AIDS is something that happens to "other people" (homosexual men and intravenous drug users, not middle-class heterosexuals) are more likely to remember articles about rates of HIV infection in these groups or in third-world countries than articles about AIDS cases among people like themselves (Fischhoff & Downs, 1997). Convinced that HIV is not something that they personally need to worry about, they ignore evidence to the contrary.
A related phenomenon is our tendency to see connections or patterns of cause and effect where none exist. For example, many parents strongly believe that sugar can cause hyperactivity in children and that arthritis pain is related to weather - despite research evidence to the contrary. The list of commonsense beliefs that persist in the face of contrary evidence is long (Redelmeier & Tversky, 2004).
Framing
Numerous studies have shown that subtle changes in the way information is presented can dramatically affect the final decision. A classic study (McNeil, Pauker, Sox, & Tversky, 1982) illustrates how framing can influence a medical decision. In this study, experimented participants were asked to choose between surgery and radiation therapy to treat lung cancer. However, the framing of the information they were provided was manipulated. In the survival frame, participants were given the statistical outcomes of both procedures in the form of survival statistics, thus emphasizing the 1- and 5-year survival rates after treatment. In the mortality frame, the participants were given the same information, presented (or framed) according to death rates after 1 year and after 5 years. Although the actual number of deaths and survivors associated with each procedure was identical in both the survival and mortality frames, the percentage of participants who chose one procedure over another varied dramatically depending on how the information was framed. Probably most surprising was that this framing effect was found even when 424 experienced radiologists served as the research participants!
Explaining Our Decisions
Hindsight
Whether a choice is exceptionally good, extraordinarily foolish, or somewhere in between, most people think about their decisions after the fact. The term hindsight bias refers to the tendency to view outcomes as inevitable and predictable after we know the outcome, and to believe that we could have predicted what happened, or perhaps that we did. For example, physicians remember being more confident about their diagnoses when they learn that they were correct than they were at the time of the actual diagnoses (Roese & Vohs, 2012). However, this can lead to overconfidence, which in turn can lead to poor decision making in the future (Arkes, 2013).
"If Only"
At times, everyone imagines alternatives to reality and mentally plays out the consequences. Psychologists refer to such thoughts about things that never happened as counterfactual thinking - in which thoughts are counter to the facts. Counterfactual thinking often takes the form of "If only" constructions, in which we mentally revise the events or actions that led to a particular outcome: "If only I had studied harder"; "If only I had said no"; "If only I had driven straight home." It is tempting to think that such imaginary, after-the-fact thinking, is of no value. However, research shows that under some circumstances counterfactual thinking can play a constructive role in helping one to regulate behavior, learn from mistakes, and improve future performance (Epstude & Roese, 2008).
MULTITASKING
With the advent of the digital age, multitasking has become a way of life. We listen to iPods while jogging, program our TiVo while watching a movie, e-mail and surf the Web simultaneously, and follow the directions of a GPS while driving and talking to a passenger in a car. Fortunately, our brains appear reasonably well equipped for at least some multitasking. The prefrontal cortex, which governs goal-directed behavior and suppresses impulses, also enables us to mentally toggle between separate tasks with relative ease (Jancke, Brunner, & Esslen, 2008; Modirrousta & Fellows, 2008).
Is multitasking really efficient? Research indicates that if the tasks are dissimilar and the person is an experienced multitasker and is intelligent, multitasking can be effective up to a point. But in general, research has shown that multitasking often slows down thinking, decreases accuracy, and in some cases increases stress (Buhner, Konig, Pick, & Krumm, 2006; Clay, 2009; Kinney, 2008; Mark, Gudith & Klocke, 2008; J. S. Rubinstein, Meyer, & Evans, 2001). Moreover, despite a commonly held belief that young people are more adept at multitasking than older adults, research that compared 18- to 21-year-olds to 35- to 39-year-olds found the negative effects of multitasking were generally more pronounced in the younger group (Westwell, 2007).
Perhaps nowhere is the impact of multitasking more important than when driving a car (Strayer & Drews, 2007). It makes no difference if the conversations are hands-free or hands-on (Pogue, 2013). The mental challenge of carrying on a conversation has been shown to cause drivers to miss seeing much of what is around them (Strayer et al., 2013). For example, while talking on a "hands-free" cell phone, braking time is slowed and attention to events in the peripheral visual field is reduced. Even when the participants in one study were specifically instructed to give more attention to driving than the extraneous task, or were well practiced at multitasking, driving performance was adversely affected by multitasking (J. Levy & Pashler, 2008; J. Levy, Pashler, & Boer, 2006).
Texting while driving is even worse. One British study using 17- to 24-year-old participants found that texting while driving reduced braking time by 35%, which was much worse than the effect of alcohol or marijuana. Steering control while texting was reduced 91%, compared to a 35% reduction under the influence of marijuana (RAC Foundation, 2008). Once again it makes no difference if you are texting by hand or hands-free by voice. Mental distraction is the same in both cases and response times increase equally (Yager, 2013). Research such as this has prompted Professor David Meyer, a noted researcher in the area of multitasking, to conclude that "If you're driving while cell-phoning, then your performance is going to be as poor as if you were legally drunk" (Hamilton, 2008).
INTELLIGENCE AND MENTAL ABILITIES
In many societies, one of the nicest things you can say is "You're smart"; and one of the most insulting is "You're stupid." Intelligence is so basic to our view of human nature that any characterization of a person that neglects to mention that person's intelligence is likely to be considered incomplete. Although psychologists have studied intelligence almost since psychology emerged as a science, they still struggle to understand this complex and elusive concept. The following are some questions intended to measure intelligence:
Describe the difference between laziness and idleness.
Which direction would you have to face so that your right ear would be facing north?
What does obliterate mean?
In what way are an hour and a week alike?
If three pencils cost 25 cents, how many pencils can you buy for 75 cents?
Select the lettered pair that best expresses a relationship similar to that expressed in the original pair: Crutch: Locomotion:
(A) paddle; canoe
(B) hero; worship
(C) horse; carriage
(D) spectacles; vision
(E) statement; contention
These questions were taken from various tests of intelligence, or general mental ability. We will first consider some historical and contemporary theories of intelligence.
Theories of Intelligence
For more than a century, one of the most basic questions addressed by psychologists has been whether intelligence is a single, general mental ability or whether it is composed of many separate abilities.
Early Theorists
Charles Spearman, an early 20th-century British psychologist, maintained that intelligence is quite general - that people who are bright in one area are usually bright in other areas as well. The American psychologist L. L. Thurstone disagreed with Spearman. Thurstone argued that intelligence is composed of seven distinct kinds of mental abilities (Thurstone, 1938): spatial ability, memory, perceptual speed, word fluency, numerical ability, reasoning, and verbal meaning. Unlike Spearman, Thurstone believed that these abilities are relatively independent of one another. Thus, a person with exceptional spatial ability (the ability to perceive distance, recognize shapes, and so on) might lack word fluency.
Contemporary Theorists
Contemporary psychologists have considerably broadened the concept of intelligence and how it can be best measured. For example, Robert Sternberg (2009) has proposed a triarchic theory of intelligence. Sternberg's theory holds that intelligence involves mental skills (analytical intelligence), insight and creative adaptability (creative intelligence), and environmental responsiveness (practical intelligence).
Another contemporary theory of intelligence is the theory of multiple intelligences advanced by Howard Gardner and his associates at Harvard (J. Q. Chen, Moran, & Gardner, 2009). Gardner, like Thurstone, believes that intelligence is made up of several distinct abilities, each of which is relatively independent of the others. These intelligences include verbal, logical-mathematical, musical, visual-spatial, movement, interpersonal, intrapersonal, existential, and natural.
Finally, Daniel Goleman (1997) has proposed a theory of emotional intelligence, which refers to how effectively people perceive and understand their own emotions and the emotions of others and can manage their emotional behavior. Five traits are generally recognized as contributing to emotional intelligence
Knowing one's own emotions. The ability to monitor and recognize our own feelings. This is of central importance to self-awareness and all other dimensions of emotional intelligence.
Managing one's emotions. The ability to control impulses, to cope effectively with sadness, depression, and minor setbacks, as well as to control how long emotions last.
Using emotions to motivate oneself. The capacity to marshal emotions toward achieving personal goals.
Recognizing the emotions of other people. The ability to read subtle, nonverbal cues that reveal what other people really want and need.
Managing relationships. The ability to accurately acknowledge and display one's own emotions, as well as being sensitive to the emotions of others.
Intelligence Tests
The Stanford-Binet Intelligence Scale
The first test developed to measure intelligence was designed by two Frenchmen, Alfred Binet and Theodore Simon. The test, first used in Paris in 1905, was designed to identify children who might have difficulty in school.
The first Binet-Simon Scale consisted of 30 tests arranged in order of increasing difficulty. With each child, the examiner started with the easiest tests and worked down the list until the child could no longer answer questions. A well-known adaptation of the Binet-Simon Scale, the Stanford-Binet Intelligence Scale, was prepared at Stanford University by L. M. Terman, first published in 1916 and updated repeatedly since then. The current Stanford-Binet Intelligence Scale is designed to measure four virtually universal abilities related to traditional views of intelligence: verbal reasoning, abstract/visual reasoning, quantitative reasoning, and short-term memory. The Stanford-Binet is best suited for children, adolescents, and very young adults.
Terman also introduced the now famous term intelligence quotient (IQ) to establish a numerical value of intelligence, setting the score of 100 for a person of average intelligence.
The Wechsler Intelligence Scales
The most commonly used individual test of intelligence for adults is the Wechsler Adult Intelligence Scale - Fourth Edition (WAIS-IV), originally developed in the late 1930s by psychologist David Wechsler. The Stanford-Binet emphasizes verbal skills, but Wechsler believed adult intelligence consists more of the ability to handle life situations than to solve verbal and abstract problems.
The WAIS-IV assesses verbal comprehension, perceptual reasoning, working memory, and processing speed. Scores on all four of those indices can be combined to give a Full-Scale IQ. Scores on just the first two indices can be combined to give a General Ability Index. Like the WAIS-IV, the Wechsler Intelligence Scale for Children - Fourth Edition (WISC-IV) yields a Full-Scale IQ score as well as scores for verbal comprehension, perceptual reasoning, working memory, and processing speed.
Group Tests
With the Stanford-Binet, the WAIS-IV, and the WISC-IV, an examiner takes a single person to an isolated room, spreads the materials on a table, and spends from 60 to 90 minutes administering the test. The examiner may then take another hour or so to score the test according to detailed instructions in a manual. This is a time-consuming, costly operation. Moreover, under some circumstances the examiner's behavior can influence the score. For these reasons, test makers have devised group tests, which a single examiner can administer to many people at once. Instead of sitting across the table from a person who asks you questions, you receive a test booklet that contains questions for you to answer in writing within a certain amount of time.
Group tests have some distinct advantages over individualized tests. They eliminate bias on the part of the examiner, answer sheets can be scored quickly and objectively, and it is possible to collect data from large numbers of test takers. But group tests also have some distinct disadvantages. The examiner is less likely to notice whether a person is tired, ill, or confused by the directions. People who are not used to being tested tend to do less well on group tests than on individual tests.
Performance and Culture-Fair Tests
To perform well on the intelligence tests that we have discussed, people must be proficient in the language in which the test is given. How, then, can we test non-native English speakers in English-speaking countries? Psychologists have designed two general forms of tests for such situations: performance tests and culture-fair tests.
Performance tests consist of problems that minimize or eliminate the use of words. One of the earliest performance tests, the Seguin Form Board, is essentially a puzzle. The examiner removes specifically designed cutouts, stacks them in a predetermined order, and asks the person to replace them as quickly as possible. A more recent performance test, the Porteus Maze, consists of a series of increasingly difficult printed mazes. People trace their way through the maze without lifting the pencil from the paper. Such tests require the test taker to pay close attention to a task for an extended period and continuously to plan ahead in order to make the correct choices.
Culture-fair tests, like performance tests, minimize or eliminate the use of language. But they also try to downplay skills and values - such as the need for speed - that vary from culture to culture. In the Goodenough-Harris Drawing Test, for example, people are asked to draw the best picture of a person that they can. Drawings are scored for proportions, correct and complete representation of the parts of the body, detail in clothing, and so on.
Biological Measures of Intelligence
Numerous efforts have been made to assess intelligence using biological measures (Deary, Penke, & Johnson, 2010; Tang et al., 2010). Beginning early in the 20th century, psychologists attempted to correlate brain size with intelligence. The correlations were very weak but always positive, suggesting a slight relation between the two. More recently, investigators have compared the sizes and metabolic functioning of such brain structures as the cerebellum and hippocampus, revealing small but significant differences among the brains of people with different forms of intellectual disability (Lawrence, Lott, & Haier, 2005). Other researchers have found modest relationships between intelligence and the electrical response of brain cells to stimulation (Stelmack, Knott, & Beauchamp, 2003).
To date, no known biological measure of intelligence approaches the accuracy of psychological tests, but findings such as these suggest that measures of intelligence may someday involve a biological component.
What Makes a Good Test?
How can we tell whether intelligence tests will produce consistent results no matter when they are given? And how can we tell whether they really measure what they claim to measure? Psychologists address these questions by referring to a test's reliability and validity. Issues of reliability and validity apply equally to all psychological tests, not just to tests of mental abilities.
Reliability
By reliability, psychologists mean the dependability and consistency of the scores that a test yields. How do we know whether a test is reliable? The simplest way to find out is to give the test to a group and then, after a while, give the same people the same test again. If they obtain similar scores each time, the test is said to have high test-retest reliability.
To avoid the possibility that those being tested simply remembered the answers from the first test, psychologists prefer to give two equivalent tests, both designed to measure the same thing. If people score the same on both forms, the tests are considered reliable. One way to create alternate forms is to split a single test into two parts - for example, to assign odd-numbered items to one part and even-numbered items to the other. If scores on the two halves agree, the test has split-half reliability.
These methods of testing reliability can be very effective. But psychological science demands more precise descriptions than "very reliable" or "fairly reliable." Psychologists express reliability in terms of correlation coefficients, which measure the relation between two sets of scores. If test scores on one occasion are absolutely consistent with those on another occasion, the correlation coefficient is 1.0. If there is no relationship between the scores, the correlation coefficient is zero.
How reliable are intelligence tests? In general, people's IQ scores on most intelligence tests are quite stable (Meyer et al., 2001). Performance and culture-fair tests are somewhat less reliable. However, scores on even the best tests vary somewhat from one day to another.
Validity
Do intelligence tests really measure "intelligence"? When psychologists ask this question, they are concerned with test validity. Validity refers to a test's ability to measure what it has been designed to measure. How do we know whether a given test actually measures what it claims to measure?
One measure of validity is known as content validity - whether the test contains an adequate sample of the skills or the knowledge that it is supposed to measure. Most widely used intelligence tests seem to measure at least some of the mental abilities that we think of as part of intelligence. These include planning, memory, understanding, reasoning, concentration, and the use of language. Although they may not adequately sample all aspects of intelligence equally well, they at least seem to have some content validity.
Another way to measure a test's validity is to see whether a person's score on that test closely matches his or her score on another test designed to measure the same thing. The two different scores should be very similar if they are both measures of the same ability. Most intelligence tests do this well: Despite differences in test content, people who score high on one test tend to score high on others. However, this outcome doesn't necessarily mean that the two tests actually measure intelligence. Conceivably, they could both be measuring the same thing, but that thing might not be intelligence. To demonstrate that the tests are valid measures of intelligence, we need an independent measure of intelligence against which to compare test scores. Determining test validity in this way is called criterion-related validity. Ever since Binet invented the intelligence test, the main criterion against which intelligence test scores have been compared has been school achievement. Even the strongest critics agree that IQ tests predict school achievement very well (Groth-Marnat, 2009).
Criticisms of IQ Tests
What is it about IQ tests, then, that makes them controversial? One source of disagreement and criticism concerns their content. Since psychologists disagree on the very nature of intelligence, it follows that they will disagree on the merits of particular tests of intelligence.
That said, there is general agreement among psychologists that at the least, intelligence tests measure the ability to take tests. This fact could explain why people who do well on one IQ test also tend to do well on other tests. And it could also explain why intelligence test scores correlate so closely with school performance since academic grades also depend heavily on test-taking ability.
Apart from predicting academic grades, how useful are intelligence tests? IQ tests also tend to predict success after people finish their schooling. People with high IQ scores tend to enter high-status occupations: Physicians and lawyers tend to have higher IQs than truck drivers and janitors. Critics point out, however, that this pattern can be explained in various ways. For one thing, because people with higher IQs tend to do better in school, they stay in school longer and earn advanced degrees, thereby opening the door to high-status jobs. Moreover, children from wealthy families generally grow up in environments that encourage academic success and reward good performance on tests (Blum, 1979; Ceci & Williams, 1997). In addition, they are more likely to have financial resources for postgraduate education or advanced occupational training, as well as family connections that pave the way to occupational success. Still, higher grades and intelligence test scores do predict occupational success and performance on the job (Kuncel, Hezlett, & Ones, 2004; Mcquillan, 2007; Ree & Earles, 1992).
Goleman's concept of emotional intelligence is specifically intended to predict success in the real world. Since this is a relatively new concept, researchers have only begun to evaluate it. However, some studies have shown promising results. For example, one study found that students with higher emotional intelligence scores adapted better socially and academically at school (Mestre, Guil, Lopes, Salovey, & Gil-Olarte, 2006). As you might expect, the ability to manage and regulate one's emotions is also important to success in the workplace (Cherniss & Goleman, 2001; Druskat, Sala, & Mount, 2006).
Though some investigators argue that emotional intelligence is no different from abilities that are already assessed by more traditional measures of intelligence and personality (M. Davies, Stankov, & Roberts, 1998; Waterhouse, 2006), the theory of emotional intelligence continues to gain support from psychological research (Mayer, Salovey, & Caruso, 2008). It has captured the attention of managers and others responsible for hiring, promoting, and predicting the performance of people in the workplace (Salovey, 2006; Yu & Yuan, 2008). In addition, recent research on emotional intelligence is advancing our understanding of the factors that contribute to the development of some forms of mental illness (Malterer, Glass, & Newman, 2008).
Another major criticism of intelligence tests is that their content and administration do not take into account cultural variations and, in fact, discriminate against minorities. High scores on most IQ tests require considerable mastery of standard English, thus biasing the tests in favor of middle- and upper-class White people. Moreover, White middle-class examiners may not be familiar with the speech patterns of lower income African American children or children from homes in which English is not the primary language, a complication that may hamper good test performance (Sattler, 2005). In addition, certain questions may have very different meanings for children of different social classes.
Although some investigators argue that the most widely used and thoroughly studied intelligence tests are not unfairly biased against minorities (Gottfredson, 2009), others contend that a proper study of cultural bias has yet to be made (E. Hunt & Carlson, 2007). Clearly, the issue of whether tests are unfair to minorities will be with us for some time.
HEREDITY, ENVIRONMENT, AND INTELLIGENCE
To what extent is intelligence inherited and to what extent is it the product of the environment? Sorting out the importance of each factor as it contributes to intelligence is a complex task.
Heredity
Scientists can use studies of identical twins to measure the effects of heredity in humans. Twin studies of intelligence begin by comparing the IQ scores of identical twins who have been raised together. The correlation between their IQ scores is very high. In addition to identical genes, however, these twins grew up in very similar environments: They shared parents, home, teachers, vacations, and probably friends, too. These common experiences could explain their similar IQ scores. To check this possibility, researchers have tested identical twins who were separated early in life - generally before they were 6 months old - and raised in different families. Even when identical twins are raised in different families, they tend to have very similar intelligence test scores; in fact, the similarity is much greater than that between non-twin siblings who grow up in the same environment.
These findings make a strong case for heritability of intelligence, though twin studies do not constitute "final proof." However, other evidence also demonstrates the role of heredity (Deary, Johnson, & Houlihan, 2009; Kovas et al., 2013; Plomin et al., 2013). For example, adopted children have been found to have IQ scores that are more similar to those of their biological mothers than those of the mothers who are raising them. Do psychologists, then, conclude that intelligence is an inherited trait and that environment plays little, if any, role?
Environment
Probably no psychologist denies that genes play a role in determining intelligence, but most believe that genes provide only a base or starting point. Each of us inherits a certain body build from our parents, but our actual weight is greatly determined by what we eat and how much we exercise. Similarly, although we inherit certain mental capacities, their development depends on what we see around us as infants, how our parents respond to our first attempts to talk, what schools we attend, which books we read, which television programs we watch - even what we eat (Sternberg & Grigorenko, 2001; Nisbett, 2009). For example, recent evidence indicates that the role of heredity varies with social economic status: In impoverished families, it appears to have little or no bearing on intelligence; in affluent families, its influence appears to be stronger (Bates, Lewis, & Weiss, 2013; Tucker-Drob, Briley, & Harden, 2013). Evidence also shows that the effect of heredity increases from early childhood to middle childhood and into adulthood (Brant et al., 2013; Briley & Tucker-Drob, 2013; Lyons et al., 2009). One explanation for these facts is that bright people and people from more affluent backgrounds are more likely to seek out positive learning experiences, and those experiences in turn stimulate their cognitive development (Kan, Wicherts, Dolan, & van der Maas, 2013; Tucker-Drob, Briley, & Harden, 2013).
Environment affects children even before birth, such as through prenatal nutrition (Protzko, Aronson, & Blair, 2013). During infancy, malnutrition can lower IQ scores by an average of 20 points (Stock & Smythe, 1963). Conversely, vitamin supplements can increase young children's IQ scores, possibly even among well-nourished children (D. Benton & Roberts, 1988; Schoenthaler, Amos, Eysenck, Peritz, & Yudkin, 1991).
Quite by chance, psychologist H. M. Skeels found evidence in the 1930s that IQ scores among children also depend on environmental stimulation. While investigating orphanages for the state of Iowa, Skeels observed that the children lived in very overcrowded wards and that the few adults there had almost no time to play with the children, to talk to them, or to read them stories. Many of these children were classified as "subnormal" in intelligence. Skeels followed the cases of two girls who, after 18 months in an orphanage, were sent to a ward for women with severe intellectual disabilities. Originally, the girls' IQs were in the range of disability, but after a year on the adult ward, as if by magic, their IQs had risen to normal (Skeels, 1938). Skeels regarded this fact as quite remarkable - after all, the women with whom the girls had lived with themselves were severely retarded. When he placed 13 other "slow" children as houseguests in such adult wards, within 18 months their mean IQ rose from 64 to 92 (within the normal range) - all apparently because they had had someone (even someone of below-normal intelligence) to play with them, to read to them, to cheer them on when they took their first steps, and to encourage them to talk (Skeels, 1942). During the same period the mean IQ of a group of children who had been left in orphanages dropped from 86 to 61. Thirty years later, Skeels found that all 13 of the children raised in adult wards were self-supporting, their occupations ranging from waiting on tables to real-estate sales. Of the contrasting group, half were unemployed, four were still in institutions, and all of those who had jobs were dishwashers (Skeels, 1966). Later studies have reinforced Skeel's findings on the importance of intellectually stimulating surroundings as well as the importance of good nutrition (Capron & Duyme, 1989).
Intervention Programs: How Much Can We Boost IQ?
In 1961, the Milwaukee Project set out to learn whether intervening in a child's family life could offset the negative effects of cultural and socioeconomic deprivation on IQ scores (Garber & Heber, 1982; Heber, Garber, Harrington, & Hoffman, 1972). The average score of the 40 pregnant women in the study was less than 75 on the Wechsler scale. Women in the control group received no special education or training; those in the experimental group were sent to school, given job training, and instructed in child care, household management, and personal relationships.
After the babies were born, the research team shifted their focus to them. For 6 years, the children whose mothers received special training spent most of each day in an infant-education center, where they were fed, taught, and cared for by paraprofessionals. The children whose mothers received no special training did not attend the center. Ultimately the children in the experimental group achieved an average IQ score of 126, 51 points higher than their mothers' average scores. In contrast, the average score of the children in the control group was 94. Thus, this landmark study supported the notion that intervention may indeed counter the negative effects of cultural and socioeconomic deprivation on IQ scores.
Head Start, the nation's largest intervention program, began in 1965. Since its inception, Head Start has provided comprehensive services to more than 25 million children and their families through child care, education, health, nutrition, and family support (National Head Start Association, 2008). Focusing on preschoolers between the ages of 3 and 5 from low-income families, the program has two key goals: to provide children with educational and social skills before they go to school, and to provide information about nutrition and health to both the children and their families. Head Start involves parents in all its aspects, from daily activities to administration of the program itself.
Some studies evaluating the long-term effects of Head Start have found that it boosts cognitive and language abilities (W. S. Barnett, 1998; Wasik, Bond, & Hindman, 2006; Zhai, 2008; Zigler & Styfco, 2008). However, the congressionally mandated Head Start Impact Study found much more modest benefits (Puma, Bell, Cook, & Hyde, 2010). Specifically, that study concluded that access to Head Start does have a positive impact on children's preschool experiences in some areas but almost all of those advantages faded by the end of first grade. Thus, it is questionable whether Head Start provides any appreciable, long-term, practical benefits.
Overall, the effectiveness of early intervention appears to depend on the quality of the particular programs (S. L. Ramey, 1999; C. T. Ramey, 2007; Zigler & Styfco, 1993). Intervention programs that have clearly defined goals - that include reading interactively with the children, that explicitly teach such basic skills as counting, naming colors, language development, and writing the alphabet; and that take into account the broad context of human development, including health care and other social services - achieve the biggest and most durable gains (Protzko, Aronson, & Blair, 2013).
The IQ Debate: A Useful Model
Both heredity and environment have important effects on individual differences in intelligence, but is one of these factors more important than the other? A useful analogy comes from studies of plants. Suppose that you grow one group of randomly assigned plants in enriched soil, and another group in poor soil. The enriched group will grow to be taller and stronger than the nonenriched group; the difference between the two groups in this case is due entirely to differences in their environment. Within each group of plants, however, differences among individual plants are likely to be primarily due to genetics, because all plants in the same group share essentially the same environment. Thus, the height and strength of any single plant reflects both heredity and environment.
Similarly, group differences in IQ scores might be due to environmental factors, but differences among people within groups could be due primarily to genetics. At the same time, the IQ scores of particular people would reflect the effects of both heredity and environment. Robert Plomin, an influential researcher in the field of human intelligence, concludes that "the world's literature suggests that about half of the total variance in IQ scores can be accounted for by genetic variance" (Plomin, 1997, p. 89). This finding means that the environment accounts for the other half.
The Flynn Effect
An interesting side note to this discussion is the fact that IQ scores have gone up in the population as a whole. Because James Flynn (1984, 1987) of the University of Otago in New Zealand was the first to report this finding, it is often called the Flynn Effect. In his original research, Professor Flynn gathered evidence showing that, between 1932 and 1978, intelligence test scores rose about three points per decade. More recently, by pulling together data from five nations (Britain, Netherlands, Israel, Norway, and Belgium) Flynn (1999, 2012) has shown that the average increase in IQ may be as high as six points per decade. Consistent with this result is a finding by Flieller (1999) that children today between the ages of 10 and 15 years display significant cognitive advancement compared with children of the same age tested 20 and 30 years ago. And, as Neisser (1998) points out, accompanying this general increase in IQ scores is a decrease in the difference in intelligence scores between Blacks and Whites.
Although the Flynn Effect has many possible explanations, none of them seem to account entirely for the magnitude of the effect (Sundet, Borren, & Tambs, 2008). Rather than getting smarter, maybe people are simply getting better at taking tests. Environmental factors, such as improved nutrition and health care, may also contribute to this trend (Teasdale & Owen, 2005). Some psychologists have suggested that the sheer complexity of the modern world is responsible (Schooler, 1998). For example, the proliferation of televisions, computers, and video games could be contributing to the rise in IQ scores (Greenfield, 1998; Neisser, 1998).
Mental Abilities and Human Diversity: Gender and Culture
Gender
In 1974, psychologists Eleanor Maccoby and Carol Jacklin published a review of psychological research on gender differences. They found no differences between males and females in most of the studies they examined. However, a few differences did appear in cognitive abilities: Girls tended to display greater verbal ability, and boys tended to exhibit stronger spatial and mathematical abilities. Largely as a result of this research, gender differences in verbal, spatial, and mathematical abilities become so widely accepted that they were often cited as one of the established facts of psychological research.
A closer examination of the research literature, including more recent work, indicates that while gender differences in some math and verbal skills exist, they are relatively small and often concentrated in very specific skills. For example, an analysis of 242 studies involving more than a million people showed no difference between men and women in mathematical ability (Lindberg, Hyde, Petersen, & Linn, 2010). While girls do appear to display stronger verbal skills than boys, female superiority is generally only found when the assessment of verbal skill includes writing. Conversely, boys tend to outperform girls primarily on measures of visual-spatial skill (Halpern et al., 2007). Interestingly, the advantage males have over females in visual-spatial ability has been detected in infants as young as 3-5 months (D. S. Moore & Johnson, 2008; Quinn & Liben, 2008). Men also differ from women in another way: They are more likely than women to fall at the extremes of the mathematical intelligence range (Ceci & Williams, 2010; Halpern et al., 2007; Wai, Cacchio, Putallaz, & Makel, 2010). Conversely, women outnumber men at the very high end of the scale when it comes to verbal reasoning and writing ability (Wai et al., 2010).
What should we conclude from these findings? First, cognitive differences between males and females appear to be restricted to specific cognitive skills. Scores on tests such as the Stanford-Binet or the WAIS reveal no overall gender differences in general intelligence (Halpern, 1992). Second, gender differences typically are small (Skaalvik & Rankin, 1994). Third, we do not know whether the differences that do exist are a result of biological or cultural factors (Hyde & Mezulis, 2002). Finally, one extensive review of the literature concluded that "There is no single factor by itself that has been shown to determine sex differences in science and math. Early experience, biological constraints, educational policy, and cultural context each have effects, and these effects add and interact in complex and sometimes unpredictable ways" (Halpern et al., 2007, p. 41).
Culture
For years, U.S. media have been reporting an achievement gap, especially in math, between American and Asian students. Recent media reports suggest even broader differences.
Psychological research tells us something about the causes of these achievement gaps. Two decades ago, a team of researchers led by the late Harold Stevenson (1924-2005) began to study the performance of first- and fifth-grade children in American, Chinese, and Japanese elementary schools (Stevenson, Lee, & Stigler, 1986). At that time, the American students at both grade levels lagged far behind the other two countries in math and came in second in reading. A decade later, when the study was repeated with a new group of fifth-graders, the researchers discovered that the American students performed even worse than they had earlier. In 1990, the research team also studied the original first-graders from all three cultures, now in the eleventh grade. The result? The American students retained their low standing in mathematics compared with the Asian students (Stevenson, 1992, 1993; Stevenson, Chen, & Lee, 1993).
The next question was, Why? Stevenson's team wondered whether cultural attitudes toward ability and effort might, in part, explain the differences. To test this hypothesis, the researchers asked students, their parents, and their teachers in all three countries whether they thought that effort or ability had a greater impact on academic performance. From first through eleventh grade, American students on the whole disagreed with the statement that "everyone in my class has about the same natural ability in math." In other words, the Americans thought that "studying hard" has little to do with performance. Their responses appear to reflect a belief that mathematical skill is primarily a function of innate ability. American mothers expressed a similar view. Moreover, 41% of the American eleventh-grade teachers thought "innate intelligence" is the most important factor in mathematics performance. By contrast, Asian students, parents, and teachers believed that effort and "studying hard" determine success in math.
Such culturally influenced views of the relative importance of effort and innate ability may have profound consequences for the way that children, their parents, and their teachers approach the task of learning. Students who believe that learning is based on natural ability see little value in working hard to learn a difficult subject. By contrast, students who believe that academic success comes from studying are more likely to work hard. Indeed, even the brightest students will not get far without making an effort. Although many Americans no doubt believe in the value of effort and hard work, our widespread perception that innate ability is the key to academic successes may be affecting the performance of U.S. students.
In short, while Stevenson's research confirms the existence of significant differences in student performance across various cultures, the evidence suggests that these differences reflect cultural attitudes toward the importance of ability and effort, rather than an underlying difference in intelligence across the cultures.
Hunt (2012, 2013) has investigated a much broader range of countries. He has compared cognitive ability in modern industrial and post-industrial societies (such as the United States, Canada, and Japan) versus pre-industrial societies (such as countries in sub-Saharan Africa). His research has shown that there are substantial differences in cognitive skills between those two kinds of countries and that the differences can be attributed in large part to the same environmental factors that affect cognitive abilities within a country: nutrition, pollution, home environment, educational opportunities, attitudes, and motivation.
Extremes of Intelligence
The average IQ score on intelligence tests is 100. Nearly 70% of all people have IQs between 85 and 115, and all but 5% of the population have IQs between 70 and 130. In this section, we focus on people who score at the two extremes of intelligence - those with an intellectual disability and those who are intellectually gifted.
Intellectual Disability
Intellectual disability (previously known as mental retardation) encompasses a vast array of mental deficits with a wide variety of causes, treatments, and outcomes. The American Psychiatric Association (2013) defines intellectual disability as a significant deficit in general intellectual functioning, accompanied by significant limitations in adaptive behavior. The definition also specifies that the onset of symptoms must originate during the developmental period. (A person of any age may be diagnosed with intellectual disability, but their symptoms must have originated during the developmental period.) There are also various degrees of intellectual disability (mild, moderate, severe, and profound), based principally on the severity of the individual's limitations in adaptive functioning.
It is important to recognize that a low IQ is not sufficient for diagnosing intellectual disability. The person must also be unable to perform the daily tasks needed to function independently (Rust & Wallace, 2004). A person who is able to live independently, for example, is not considered to have an intellectual disability even if his or her IQ may be extremely low. To fully assess individuals and to place them in appropriate treatment and educational programs, mental health professionals need information on physical health and on emotional and social adjustment (Borthwick-Duffy, 2007).
Some people with intellectual disabilities exhibit remarkable abilities in highly specialized areas, such as numerical computation, memory, art, or music (Pring, Woolf, & Tadic, 2008; Treffert & Wallace, 2002). Probably the most dramatic and intriguing examples involve savant performance (Boelte, Uhlig, & Poustka, 2002; L. K. Miller, 2005). Savant performances include mentally calculating large numbers almost instantly, determining the day of the week for any date over many centuries, and playing back a long musical composition after hearing it played only once.
What causes intellectual disability? In most cases, the causes are unknown - especially in cases of mild intellectual disability, which account for nearly 90% of all intellectual disability. When causes can be identified, most often them stem from a wide variety of genetic, environmental, social, nutritional, and other risk factors (A. A. Baumeister & Baumeister, 2000; Moser, 2004).
About 25% of cases - especially the more severe forms of intellectual disability - appear to involve genetic or biological disorders. Scientists have identified more than 100 forms of intellectual disability caused by single defective genes (Plomin, 1997). One is the genetically based disease phenylketonuria, or PKU, which occurs in about one person out of 25,000. In people suffering from PKU, the liver fails to produce an enzyme necessary fro early brain development. Fortunately, placing a PKU baby on a special diet can prevent intellectual disability from developing (Merrick, Aspler, & Schwarz, 2005; Widaman, 2009). In the disorder known as Down syndrome, which affects 1 in 600 newborns, an extra 21st chromosome is the cause. Down syndrome, named for the physician who first described its symptoms, is marked by mild to severe intellectual disability.
Biologically caused intellectual disability can be moderated through education and training (C. T. Ramey, Ramey, & Lanzi, 2001). The prognosis for those with no underlying physical causes is even better. People whose intellectual disability is due to a history of social and educational deprivation can respond dramatically to appropriate interventions. Today, the majority of children with physical or intellectual disabilities are educated in local school systems (Dore, Wagner, Dore, & Brunet, 2002), in inclusion arrangements (Kavale, 2002) (previously known as mainstreaming), which help these students to socialize with their nondisabled peers. The principle of mainstreaming has also been applied successfully to adults with intellectual disability, by taking them out of large, impersonal institutions and placing them in smaller community homes that provide more normal life experiences (I. Brown, Buell, Birkan, & Percy, 2007).
Giftedness
At the other extreme of the intelligence scale are "the gifted" - those with exceptional mental abilities, as measured by scores on standard intelligence tests. As with intellectual disability, the causes of giftedness are largely unknown.
The first and now-classic study of giftedness was begun by Lewis Terman and his colleagues in the early 1920s. The defined giftedness in terms of academic talent and measured it by an IQ score in the top 2 percentile (Terman, 1925). The study involved 1,528 children whose average IQ score was 151. More recently, some experts have sought to broaden the definition of giftedness beyond that of simply high IQ (L. J. Coleman & Cross, 2001; Csikszentmihalyi, Rathunde, & Whalen, 1993; Subotnik & Arnold, 1994). One view is that giftedness is often an interaction of above-average general intelligence, exceptional creativity, and high levels of commitment. Various criteria can identify gifted students, including scores on intelligence tests, teacher recommendations, and achievement test results. School systems generally use diagnostic testing, interviews, and evaluation of academic and creative work (Sattler, 1992). These selection methods can identify students with a broad range of talent, but they can miss students with specific abilities, such as a talent for mathematics or music (Cramond & Kim, 2008). This is important because research suggests that most gifted individuals display special abilities in only a few areas. "Globally" gifted people are rare (Achter, Lubinski, & Benbow, 1996; Lubinski & Benbow, 2000; Olzewski-Kubilius, 2003; Winner, 1998, 2000).
A common view of gifted people is that they have poor social skills and are emotionally maladjusted. However, research does not support this stereotype (J. Richards, Encel, & Shute, 2003; Robinson & Clinkenbeard, 1998). Indeed, one review (Janos & Robinson, 1985) concluded that "being intellectually gifted, at least at moderate levels of ability, is clearly an asset in terms of psychosocial adjustment in most situations" (p. 181). many people who are profoundly gifted (the top 0.01% in mathematical and verbal reasoning) in childhood have been shown to go on to occupy critical leadership positions and to make extraordinary contributions to society throughout early adulthood (Kell, Lubinski, & Benbow, 2013).
Any discussion of giftedness inevitably leads to the topic of creativity. The two topics are, indeed, closely related.
CREATIVITY
Creativity is the ability to produce novel and socially valued ideas or objects ranging from philosophy to painting, from music to mousetraps (Sternberg, 2012). Sternberg included creativity and insight as important elements in human intelligence. Most IQ tests, however, do not measure creativity, and many researchers would argue that intelligence and creativity are not the same thing.
Intelligence and Creativity
It is tempting to conclude that creativity and intelligence are related. However, early studies typically found little or no relationship between creativity and intelligence (for example, Getzels & Jackson, 1962; Wing, 1969), but these studies were concerned only with bright students. Perhaps creativity and intelligence are indeed linked, but only until IQ reaches a certain threshold level, after which higher intelligence isn't associated with higher creativity. There is some evidence for this threshold theory (Barron, 1963; Yamamoto & Chimbidis, 1966). However, other studies have failed to provide support (Preckel, Holling, & Wiese, 2006) finding instead that the relationship between intelligence and creativity is best understood only when the individual facets of intelligence and creativity (such as musical or artistic) are considered (K. H. Kim, 2008; Sligh, Conners, & Roskos-Ewoldsen, 2005). Creative people are often perceived as being more intelligent than less creative people who have equivalent IQ scores. But this may be the result of other characteristics that creative people share. For instance, research has shown that creative people also tend to score high on measures of extraversion - a personality trait reflecting gregariousness, assertiveness, and excitement seeking (Furnham & Bachtiar, 2008; Furnham, Batey, Anand, & Manfield, 2008).
In general, creative people are problem finders as well as problem solvers. The more creative people are, the more they like to work on problems that they have set for themselves. Creative scientists (such as Charles Darwin and Albert Einstein) often work for years on a problem that has sprung from their own curiosity (Gruber & Wallace, 2001). Also, "greatness" rests not just on "talent" or "genius"; such people also have intense dedication, ambition, and perseverance (Stokes, 2006).
Recent research has confirmed what many of us have experienced: when solving a problem that requires creative thinking, it pays to focus on an undemanding task that lets your mind wander. For reasons that are not yet understood, the irrelevant thoughts that occur as your mind wanders serve to facilitate creative problem solving (Baird et al., 2012). Finally, creativity is also influenced by external factors. One line of research has shown that individuals are more creative when put into disorderly, "messy" environments (Vohs, Redden, & Rahinel, 2013).
Creativity Tests
Measuring creativity poses special problems (Cramond & Kim, 2008; Naglieri & Kaufman, 2001; Runco, 2008). Because creativity involves original responses to situations, questions that can be answered true or false or a or b are not good measures. More open-ended tests are better. Instead of asking for one predetermined answer to a problem, the examiner asks the test takers to let their imaginations run free. Scores are based on the originality of a person's answers and often on the number of responses as well.
In one such test, the Torrance Test of Creative Thinking, people must explain what is happening in a picture, how the scene came about, and what its consequences are likely to be. In the Christensen-Guilford Test, they are to list as many words containing a given letter as possible, to name things belonging to a certain category (such as "liquids that will burn"), and to write four-word sentences beginning with the letters RDLS - "Rainy days look sad, Red dogs like soup, Renaissance dramas lack symmetry." One of the most widely used creativity tests, S. A. Mednick's (1962) Remote Associates Test (RAT), asks people to relate three apparently unrelated words. For example, a test taker might relate the stimulus words poke; go, and molasses using the word slow: "Slowpoke, go slow, slow as molasses." In the newer Wallach and Kogan Creative Battery, people form associative groupings. For instance, children are asked to "name all the round things you can think of" and to find similarities between objects, such as between a potato and a carrot.
Although people who do not have high IQs can score well on the Wallach and Kogan test, the Torrance test seems to require a reasonably high IQ for adequate performance. This finding raises the question of which of these tests is a valid measure of creativity. In general, current tests of creativity do not show a high degree of validity (Baer, 2008; Clapham, 2004), so measurements derived from them must be interpreted with caution.
As with intelligence, there is considerable interest in identifying the neural mechanisms that underlie creative thinking, but to date the results have been discouraging. One recent, comprehensive review of the literature concludes that creativity as a whole is not clearly associated with any particular brain area. The authors point out that "It is hard to believe that creative behavior, in all its manifestations, from carrying out exquisitely choreographed dance moves, to scientific discovery, constructing poems, and coming up with ingenious ideas of what to do with a brick, engages a common set of brain areas or depends on a limited set of mental processes" (Dietrich & Kanso, 2010, p. 845). Whether there are brain areas associated with specific kinds of creativity remains to be seen.