Unit 8 Thinking, Language and Intelligence PDF

UNIT 9: Thinking, Language and Intelligence Learning Objectives: Understand the barriers to problem-solving. And the significance of finding a solution to the problem. Understand the meaning of language. Determined and language into different cultures. Understand...

UNIT 9: Thinking, Language and Intelligence Learning Objectives: Understand the barriers to problem-solving. And the significance of finding a solution to the problem. Understand the meaning of language. Determined and language into different cultures. Understand the meaning of Intelligence Analyze and evaluate the existing theories of intelligence. Determine the influences that affect intelligence Introduction In this chapter, we now turn to the subject of how the brains process language as well as how it uses information to solve problems and make decisions. Cognitive psychology is the branch of psychology that focuses on the study of higher mental processes including thinking, language, memory, problem solving, knowing, reasoning, judging, and decision making. Although the realm of cognitive psychology is broad, we will center on three major topics. The first topic we consider in this chapter is thinking, reasoning and the different strategies for approaching problems, means of generating solutions, and ways of making judgments about the usefulness and accuracy of solutions. Then we discuss how language is developed and acquired, its basic characteristics, and the relationship between language and thought. Finally, we will tackle intelligence, its theories and measurement. Thinking, Reasoning and Decision Making What are you thinking about at this moment? The mere ability to pose such a question underscores the distinctive nature of the human ability to think. No other species contemplates, analyzes, recollects, or plans the way humans do. Understanding what thinking is, however, goes beyond knowing what we think. Psychologists define thinking as brain activity in which we mentally manipulate information, including words, visual images, sounds, or other data. Thinking transforms information into new and different forms, allowing us to answer questions, make decisions, solve problems, or make plans. To understand more the fundamental elements involved in thinking, we begin by considering our use of mental images and concepts, the building blocks of thought. Mental Images Think of your best friend. Chances are that you “see” some kind of visual image when asked to think of her or him, or any other person or object, for that matter. To some cognitive psychologists, such mental images constitute a major part of thinking. Mental images are representations in the mind of an object or event. They are not just visual representations; our ability to “hear” a tune in our heads also relies on a mental image. In fact, every sensory modality may produce corresponding mental images (De Bini, Pazzaglia , & Gardini , 2007; Gardini et al., 2009; Koçak et al., 2011). Research suggests that our mental images also have the same properties of the actual stimuli they represent (i.e. scanning smaller objects take a shorter time than scanning larger objects just as the eyes takes shorter time to scan actual smaller objects than actual large objects). Similarly, mental images can be manipulated just as we are able in the real world. Try to mentally rotate one of each pair of patterns to see if it is the same as the other member of that pair. It’s likely that the farther you have to mentally rotate a pattern, the longer it will take to decide if the patterns match one another. Does this mean that it will take you longer to visualize a map of the world than a map of the United States? Why or why not? (Source: Adapted from Shepard & Metzler, 1971) Some experts see the production of mental images as a way to improve various skills. For instance, many athletes use mental imagery in their training. Basketball players may try to produce vivid and detailed images of the court, the basket, the ball, and the noisy crowd helping them to improve performance through a process they call “getting in the zone” (Fournier, et al., 2008; Moran, 2009; Velentzas, et al. 2011). Similarly, pianists who simply mentally rehearse an exercise show brain activity that is virtually identical to that of the people who actually practice the exercise manually. Apparently, carrying out the task involved the same network of brain cells as the network used in mentally rehearsing it (Pascual-Leone et al., 1995; Kensinger & Schacter, 2006; Sanders et al., 2008). Concepts If someone asks you what is in your refrigerator, you might answer with a detailed list of items (a jar of jam, eggs, tomatoes, frozen pork or fish, leftover dishes, bottles of water, etc.) Though, you may also categorize items in terms of “cooked” or “fresh ingredients.” Using such categories reflects the operation of concepts. Concepts are mental groupings of similar objects, events, or people. Concepts enable us to organize complex phenomena into cognitive categories that are easier to understand and remember (Murphy, 2005; Connolly, 2007; Kreppner et al., 2011). Other concepts— often those with the most relevance to our everyday lives are more ambiguous and difficult to define. When we consider these more ambiguous concepts, we usually think in terms of examples called prototypes. Prototypes are typical, highly representative examples of a concept that correspond to our mental image or best example of the concept. For instance, although a robin and an ostrich are both examples of birds, the robin is an example that comes to most people’s minds far more readily. ex. when we hear the word "gadget", phone comes to mind first Consequently, robin is a prototype of the concept “bird.” Similarly, when we think of the concept of a table, we’re likely to think of a coffee table before we think of a drafting table, making a coffee table closer to our prototype of a table. Culture shapes our recognition of prototypes as we will see on Figure 2. Prototypes are typical, highly representative examples of a concept. For instance, a highly typical prototype of the concept “furniture” is a chair, whereas a stove is not a good prototype. High agreement exists within a culture about which examples of a concept are prototypes. (Source: Adapted from Rosch & Mervis, 1975) Figure 2 will help you remember that prototypes represent “best” or most common examples of a particular concept. However, the above figure is limited to that of the Western culture and does not best represent us Filipinos. Relatively high agreement exists among people in a particular culture about which examples of a concept are prototypes as well as which examples are not. For instance, Filipinos, when asked to consider vehicles, jeepneys or tricycles are good examples, whereas sleds are not. (Sample Situation)Application: Conceptual Networks and Priming Try to think about one word and nothing else. It’s impossible. You can’t think about anything without relating it to something else. For example, when you think about bird, you link it to more specific terms, such as sparrow, more general terms, such as animals, and related terms, such as flight and eggs.  Figure 3 tricycles jeepneys bungalow We link each concept to a variety of other related concepts. Any stimulus that activates one of these concepts will also partly activate (or prime) the ones that are linked to it. (From “A spreading- activation theory of semantic processing” by A. M. Collins and E. F. Loftus in Psychological Review 1975, pp. 407–428. Reprinted by permission of Elizabeth Loftus.) We also link a word or concept to related concepts. Figure 3 shows a possible network of conceptual links that someone might have at a particular moment. Suppose this network describes your own concepts. Thinking about one of the concepts shown in this figure will activate, or prime, the concepts linked to it through a process called spreading activation (A. M. Collins & Loftus, 1975). For example, if you hear flower, you are primed to think of rose, violet, and other flowers. If you also hear red, the combination of flower and red strongly primes you to think of rose. You might think of the word spontaneously, and you would recognize it more easily than usual if it were flashed briefly on a screen or spoken very softly. The idea of priming a concept is analogous to priming a pump: If you put some water in the pump to get it started, you can continue using the pump to draw water from a well. Similarly, priming a concept gets it started. A small reminder of a concept makes it easier for someone to think of it. Reasoning Professors deciding when students’ assignments are due. An employer determining who to hire out of a pool of job applicants. The president concluding that it is necessary to send troops to a foreign nation. What do these three situations have in common? Each requires reasoning, the process by which information is used to draw conclusions and make decisions. Formal Reasoning When the (fictitious) Sherlock Holmes sought to solve a crime, he carefully observed the scene of the crime and then made informed guesses about what those observations meant. For example, in one story, the ever-observant Holmes noted that cuts on the side of a shoe suggested that a potential criminal must have had mud on his shoes that needed to be scraped off. ex. thinking of a research study The type of reasoning that Holmes used is known as deductive reasoning. Deductive reasoning is reasoning from the general to the specific. Psychologists, like all scientists, use deductive reasoning when they start with a general, broad theory, then derive a hypothesis from the theory, and ultimately test the hypothesis by collecting data to arrive at a conclusion. ex. generalizing a finding to the whole population The other major class of reasoning is inductive reasoning. Inductive reasoning is reasoning from the specific to the general. Inductive reasoning is data driven, in that we accumulate pieces of information and put them together to form a conclusion. That’s what psychologists do when they study a sample of participants (for instance, 20 color-blind college students), and then they use the information they observe to form a conclusion about the broader population from which the sample is drawn (all color-blind college students). If we consistently used deductive and inductive reasoning, we would make decisions and solve problems quite logically. However, as we’ll see next, that often doesn’t happen, leading to less-than-optimal results. Algorithms and Heuristics When faced with making a decision, we often turn to various kinds of cognitive shortcuts, known as algorithms and heuristics, to help us. An algorithm is a rule that, if applied appropriately, guarantees a solution to a problem. We can use an algorithm even if we cannot understand why it works. For example, you may know that you can find the area of a circle by using the formula 𝐴=𝜋𝑟2 although you may not have the foggiest notion of the mathematical principles behind the formula. On the other hand, a heuristic is a thinking strategy that may lead us to a solution to a problem or decision, but—unlike algorithms— may sometimes lead to errors. Heuristics increase the likelihood of success in coming to a solution, but, unlike algorithms, they cannot ensure it. For example, when I play tic-tac-toe, I follow the heuristic of placing an X in the center square when I start the game. This tactic doesn’t guarantee that I will win, but experience has taught me that it will increase my chances of success. Similarly, some students follow the heuristic of preparing for a test by ignoring the assigned textbook reading and only studying their lecture notes—a strategy that may or may not pay off. (Sample Situation) Application: Availability heuristic and familiarity heuristic Although heuristics often help people solve problems and make decisions, certain kinds of heuristics may lead to inaccurate conclusions. For example, the availability heuristic involves judging the likelihood of an event occurring on the basis of how easy it is to think of examples. According to this heuristic, we assume that events we remember easily are likely to have occurred more frequently in the past—and are more likely to occur in the future—than events that are harder to remember. For instance, the availability heuristic makes us more afraid of dying in a plane crash than in an auto accident, despite statistics clearly showing that airplane travel is much safer than auto travel. Similarly, although 10 times as many people die from falling out of bed than from lightning strikes, we’re more afraid of being hit by lightning. The reason is that plane crashes and lightning strikes receive far more publicity, and they are therefore more easily remembered (Oppenheimer, 2004; Fox, 2006; Kluger, 2006; Caruso, 2008). We also make use of a familiarity heuristic, in which familiar items are seen as superior to those that are unfamiliar. For example, suppose each time you went to a supermarket you had to ponder every type of yogurt to decide which you wanted—as well as every other item on your grocery list. Instead, you see the brand of yogurt you usually buy, and settle for it. Usually it’s a good rule of thumb, because it saves a lot of time. On the other hand, it’s not so good if you are an emergency room physician susceptible to the familiarity heuristic. If you simply settle on the first, most obvious diagnosis for a patient presenting particular symptoms (the ones that are most familiar to you), you may miss making a more accurate diagnosis (Herbert, 2011). Problem Solving Cognitive psychologists are interested in understanding how people solve complex, real life problems. Psychologists have found that problem solving typically involves three steps (See Figure 5): Preparing to create solutions, producing solutions and evaluating the generated solutions. Figure 5. Steps in Problem Solving. Step 1. Preparation: Understanding and Diagnosing Problems Typically a problem falls into one of the three categories. Arrangement problems require the problem solver to rearrange or recombine elements in a way that will satisfy a certain criterion. Just like in anagrams and jigsaw puzzles. In problems of inducing structure, a person must identify the existing relationships among the elements presented and then construct a new relationship among them. In such a problem, the problem solver must determine not only the relationships among the elements but also the structure and size of the elements involved. Figure 4. Exercise: The goal of the Tower of Hanoi puzzle is to move all three disks from the first post to the third and still preserve the original order of the disks, using the fewest number of moves possible while following the rules that only one disk at a time can be moved and no disk can cover a smaller one during a move. The Tower of Hanoi puzzle (as seen in Figure 4) represents the third kind of problem— transformation problems —that consist of an initial state, a goal state, and a method for changing the initial state into the goal state. In the Tower of Hanoi problem, the initial state is the original configuration, the goal state is to have the three disks on the third peg, and the method is the rules for moving the disks. Whether the problem is one of arrangement, inducing structure, or transformation, the preparation stage of understanding and diagnosing is critical in problem solving because it allows us to develop our own cognitive representation of the problem and to place it within a personal framework. We may divide the problem into subparts or ignore some information as we try to simplify the task. Winnowing out nonessential information is often a critical step in the preparation stage of problem solving. Step 2. Production: Generating Solutions After preparation, the next stage in problem solving is the production of possible solutions. If a problem is relatively simple, we may already have a direct solution stored in long-term memory, and all we need to do is retrieve the appropriate information. If we cannot retrieve or do not know the solution, we must generate possible solutions and compare them with information in long- and short-term memory. At the most basic level, we can solve problems through trial and error. Thomas Edison invented the lightbulb only because he tried thousands of different kinds of materials for a filament before he found one that worked (carbon). The difficulty with trial and error, of course, is that some problems are so complicated that it would take a lifetime to try out every possibility. In place of trial and error, complex problem solving often involves the use of heuristics, cognitive shortcuts that can generate solutions. Probably the most frequently applied heuristic in problem solving is a means-ends analysis, which involves repeated tests for differences between the desired outcome and what currently exists. Consider this simple example (Huber, et al., 2004; Chrysikou, 2006; Bosse et al., 2011): I want to take my son to preschool. What’s the difference between what I have and what I want? One of distance. What changes distance? My automobile. My automobile won’t work. What is needed to make it work? A new battery. What has new batteries? An auto repair shop.... In a means-end analysis, each step brings the problem solver closer to a resolution. Although this approach is often effective, if the problem requires indirect steps that temporarily increase the discrepancy between a current state and the solution, means-ends analysis can be counterproductive. For example, sometimes the fastest route to the summit of a mountain requires a mountain climber to backtrack temporarily; a means-end approach—that implies that the mountain climber should always forge ahead and upward—will be ineffective in such instances. Forming Subgoals Another heuristic commonly used to generate solutions is to divide a problem into intermediate steps, or subgoals. If solving a subgoal is a step toward the ultimate solution to a problem, identifying subgoals is an appropriate strategy. In some cases, however, forming subgoals is not all that helpful and may actually increase the time needed to find a solution. Insight: Sudden Awareness In a classic study the German psychologist Wolfgang Köhler examined learning and problem-solving processes in chimpanzees (Köhler, 1927). In his studies, Köhler exposed chimps to challenging situations in which the elements of the solution were all present; all the chimps needed to do was put them together. In one of Köhler’s studies, chimps were kept in a cage in which boxes and sticks were strewn about, and a bunch of tantalizing bananas hung from the ceiling, out of reach. Initially, the chimps made trial-and-error attempts to get to the bananas: They would throw the sticks at the bananas, jump from one of the boxes, or leap wildly from the ground. Frequently, they would seem to give up in frustration, leaving the bananas dangling temptingly overhead. But then, in what seemed like a sudden revelation, they would stop whatever they were doing and stand on a box to reach the bananas with a stick. Köhler called the cognitive process underlying the chimps’ new behaviour insight, a sudden awareness of the relationships among various elements that had previously appeared to be unrelated. para bang connecting the dots ng dating natutunan = insight Although Köhler emphasized the apparent suddenness of insightful solutions, subsequent research has shown that prior experience and trial-and-error practice in problem solving must precede “insight.” Consequently, the chimps’ behavior may simply represent the chaining together of previously learned responses, no different from the way a pigeon learns, by trial and error, to peck a key (Fields, 2011; Wen, et al., 2013). Step 3. Judgment: Evaluating Solutions The final stage in problem solving is judging the adequacy of a solution. Often this is a simple matter: If the solution is clear—as in the Tower of Hanoi problem—we will know immediately whether we have been successful (Varma, 2007). If the solution is less concrete or if there is no single correct solution, evaluating solutions becomes more difficult. In such instances, we must decide which alternative solution is best. Unfortunately, we often quite inaccurately estimate the quality of our own ideas. For instance, a team of drug researchers working for a particular company may consider their remedy for an illness to be superior to all others, overestimating the likelihood of their success and downplaying the approaches of competing drug companies (Eizenberg & Zaslavsky, 2004). Theoretically, if we rely on appropriate heuristics and valid information to make decisions, we can make accurate choices among alternative solutions. However, as we see next, several kinds of obstacles to and biases in problem solving affect the quality of the decisions and judgments we make. Impediments to Solutions What are the significant obstacles to problems that can exist? Although cognitive approaches to problem solving suggest that thinking proceeds along fairly rational, logical lines as a person confronts a problem and considers various solutions, several factors can hinder the development of creative, appropriate, and accurate solutions. (Sample Situation) restricting an object to one way of use lang Application: Functional fixedness and mental set Functional fixedness is the tendency to think of an object only in terms of its typical use. For instance, functional fixedness probably leads you to think of this book as only something to read instead of its potential use as a doorstop or as kindling for a fi re. Functional fixedness is an example of a broader phenomenon known as mental set, the tendency to approach a problem in a certain way because that method worked previously. Mental set can affect perceptions as well as patterns of problem solving. It can prevent you from seeing beyond the apparent constraints of a problem. ex. restarting the wifi ulit kasi gumana yon dati, tapos ngayon di Confirmation bias na effective Another impediment in problem solving is inaccurate evaluation of solutions. Confirmation bias happens when problem solvers prefer their first hypothesis and ignore contradictory information that supports alternative hypotheses or solutions. Even when we find evidence that contradicts a solution ex. type 2 error: failure to reject null hypothesis that is false we have chosen, we are apt to stick with our original hypothesis. Confirmation bias occurs for several reasons. For one thing, because rethinking a problem that appears to be solved already takes extra cognitive effort, we are apt to stick with our first solution. For another, we give greater weight to subsequent information that supports our initial position than to information that is not supportive of it (Rassin, 2008; Allen, 2011; Koslowski, B., 2013). Creativity and Problem Solving Despite obstacles to problem solving, many people adeptly discover creative solutions to problems. One enduring question that cognitive psychologists have sought to answer is what factors underlie creativity, the ability to generate original ideas or solve problems in novel ways. Highly creative individuals show divergent thinking, thinking that generates unusual, yet appropriate, responses to problems or questions. This type of thinking contrasts with convergent thinking, which is thinking in which a problem is viewed as having a single answer and which produces responses that are based primarily on knowledge and logic. For instance, someone relying on convergent thinking would answer “You read it” to the query “What can you do with a newspaper?” In contrast, “You use it as a dustpan” is a more divergent—and creative—response (Cropley, 2006; Schepers & van den Berg, 2007; Zeng, et al., 2011). Another aspect of creativity is its cognitive complexity, or preference for elaborate, intricate, and complex stimuli and thinking patterns. For instance, creative people often have a wider range of interests and are more independent and more interested in philosophical or abstract problems than are less creative individuals (Barron, 1990; Richards, 2006; Kaufman & Plucker , 2011). Language Our ability to make sense out of nonsense, if the nonsense follows typical rules of language, illustrates the complexity of both human language and the cognitive processes that underlie its development and use. The use of language —the communication of information through symbols arranged according to systematic rules—is a central cognitive ability, one that is indispensable for us to communicate with one another. Not only is language central to communication, it is also closely tied to the very way in which we think about and understand the world. Without language, our ability to transmit information, acquire knowledge, and cooperate with others would be tremendously hindered. No wonder psychologists have devoted considerable attention to studying language (Hoff, 2008; Reisberg , 2009; LaPointe , 2013). Grammar The basic structure of language rests on grammar, the system of rules that determine how our thoughts can be expressed. It deals with three major components of language: phonology, syntax, and semantics. Phonology - the study of the smallest units of speech, called phonemes. phonemes - the smallest units of speech that affect meaning, and of the way we use those sounds to form words and produce meaning. (e.g. the a sound in fat and the a sound in fate represent two different phonemes in English)  Syntax - refers to the rules that indicate how words and phrases can be combined to form sentences. It is also the rules that guide the order in which words may be strung together to communicate meaning (e.g. to understand the effect of syntax in English, consider the changes in meaning caused by the different word orders in the following three utterances: “John kidnapped the boy,” “John, the kidnapped boy,” and “The boy kidnapped John.” Semantics - the meanings of words and sentences. Semantic rules allow us to use words to convey the subtle nuances in meaning. (e.g. We might say “A truck hit Laura” and we can also say “Laura was hit by a truck” in other circumstances). LANGUAGE DEVELOPMENT: Developing a Way with Words To parents, the sounds of their infant babbling and cooing are music to their ears (except, perhaps, at 3 o’clock in the morning). These sounds also serve an important function. They mark the first step on the road to the development of language. BABBLING is a Meaningless speech like sounds made by children from around the age of 3 months through 1 year. While babbling, they may produce, at one time or another, any of the sounds found in all languages, not just the one to which they are exposed. Even deaf children display their own form of babbling, for infants who are unable to hear yet who are exposed to sign language from birth “babble” with their hands (Pettito , 1993; Majorano & D’Odorico , 2011; Shehata-Dieler et al., 2013). An infant’s babbling increasingly reflects the specific language being spoken in the infant’s environment, initially in terms of pitch and tone and eventually in terms of specific sounds. Young infants can distinguish among all 869 phonemes that have been identified across the world’s languages. However, after the age of 6 to 8 months, that ability begins to decline. Infants begin to “specialize” in the language to which they are exposed as neurons in their brains reorganize to respond to the particular phonemes infants routinely hear. Some theorists argue that a critical period exists for language development early in life in which a child is particularly sensitive to language cues and most easily acquires language. In fact, if children are not exposed to language during this critical period, later they will have great difficulty overcoming this deficit (Bates, 2005; Shafer & Garrido -Nag, 2007). PRODUCTION OF LANGUAGE By the time children are approximately 1 year old, they stop producing sounds that are not in the language to which they have been exposed. After the age of 1 year, children begin to learn more complicated forms of language. They produce two-word combinations, the building blocks of sentences, and sharply increase the number of different words they are able to use. By age 2, the average child has a vocabulary of more than 50 words. Just 6 months later, that vocabulary has grown to several hundred words. At that time, children can produce short sentences, although they use not grammatically correct telegraphic speech —sentences in which only essential words are used. Rather than saying, “I showed you the book,” a child using telegraphic speech may say, “I show book,” and “I am drawing a dog” may become “Drawing dog.” As children get older, of course, they use less telegraphic speech and produce increasingly complex sentences (Volterra et al., 2003; Pérez-Leroux , Pirvulescu , & Roberge , 2011). By age 3, children learn to make plurals by adding s to nouns and to form the past tense by adding - ed to verbs. This skill also leads to errors, since children tend to apply rules inflexibly. In such overgeneralization, children employ rules even when doing so results in an error. Thus, although it is correct to say “he walked” for the past tense of walk , the - ed rule doesn’t work quite so well when children say “he runned ” for the past tense of run (Howe, 2002; Rice et al., 2004; Gershkoff -Stowe, Connell, & Smith, 2006; Kidd & Lum , 2008). UNDERSTANDING LANGUAGE ACQUISITION: IDENTIFYING THE ROOTS OF LANGUAGE Anyone who spends even a little time with children will notice the enormous strides that they make in language development throughout childhood. However, the reasons for this rapid growth are far from obvious. Psychologists have offered three major explanations: one based on learning theory, one based on innate processes, and one that involves something of a combination of the two. Learning-Theory Approaches: Language as a Learned Skill. The learning- theory approach suggests that language acquisition follows the principles of reinforcement and conditioning discovered by psychologists who study learning. For example, a child who says “mama” receives hugs and praise from her mother, which reinforce the behavior of saying “mama” and make its repetition more likely. This view suggests that children first learn to speak by being rewarded for making sounds that approximate speech. Ultimately, through a process of shaping, language becomes more and more like adult speech (Skinner, 1957; Ornat & Gallo, 2004). Nativist Approaches: Language as an Innate Skill. Pointing to such problems with learning-theory approaches to language acquisition, linguist Noam Chomsky (1968, 1978, 1991) provided a groundbreaking alternative. Chomsky argued that humans are born with an innate linguistic capability that emerges primarily as a function of maturation. According to his nativist approach to language, humans are biologically pre-wired to learn language at certain times and in a particular way. Furthermore, he suggests that all the world’s languages share a common underlying structure that is pre-wired, biologically determined, and universal. The nativist approach argues that the human brain has an inherited neural system that lets us understand the structure language provides—a kind of universal grammar. These inborn capabilities give us strategies and techniques for learning the unique characteristics of our own native language (Lidz & Gleitman, 2004; McGilvray, 2004; White, 2007). Interactionist Approaches. To reconcile the differing views, many theorists take an interactionist approach to language development. The interactionist approach suggests that language development is produced through a combination of genetically determined predispositions and environmental circumstances that help teach language. Specifically, proponents of the interactionist approach suggest that the brain is hardwired for our acquisition of language, in essence providing the “hardware” that allows us to develop language. However, it is the exposure to language in our environment that allows us to develop the appropriate “software” to understand and produce language. The interactionist approach has many proponents. Still, the issue of how language is acquired remains hotly contested (Pinker & Jackendoff , 2005; Hoff, 2008; Waxman, 2009). Do Eskimos living in the frigid Arctic have a more expansive vocabulary for discussing snow than people living in warmer climates? It makes sense, and arguments that the Eskimo language has many more words than English for snow have been made since the early 1900s. At that time, linguist Benjamin Lee Whorf contended that because snow is so relevant to Eskimos’ lives, their language provides a particularly rich vocabulary to describe it—considerably larger than what we fi nd in other languages, such as English (Martin & Pullum , 1991; Pinker, 1994).  Linguistic-relativity hypothesis, the hypothesis that language shapes and may determine the way people perceive and understand the world. According to this view, language provides us with categories that we use to construct our view of others and events in the world around us. Consequently, language shapes and produces thought (Whorf, 1956; Casasanto, 2008; Tan et al., 2008). Let’s consider another possibility, however. Suppose that instead of language being the cause of certain ways of thinking, thought produces language. The only reason to expect that Eskimo language might have more words for snow than English does is that snow is considerably more relevant to Eskimos than it is to people in other cultures. INTELLIGENCE The definition of intelligence that psychologists employ contains some of the same elements found in the layperson’s conception. To psychologists, intelligence is the capacity to understand the world, think rationally, and use resources effectively when faced with challenges. This definition does not lay to rest a key question asked by psychologists: Is intelligence a unitary attribute, or are there different kinds of intelligence? We turn now to various theories of intelligence that address the issue. Theories of Intelligence NAHHHHHHHHHHHHHHHH G FACTOR The different ways in which people view their own talents mirror a question that psychologists have grappled with. Is intelligence a single, general ability, or is it multifaceted and related to specific abilities? Early psychologists interested in intelligence assumed that there was a single, general factor for mental ability, which they called g, or the g -factor. This assumption was based on the fact that different types of measures of intelligence, whether they focused on, say, mathematical expertise, verbal competency, or spatial visualization skills, all ranked test-takers in roughly the same order. People who were good on one test generally were good on others; those who did poorly on one test tended to do poorly on others. Given that there was a correlation between performance on the different types of tests, the assumption was that there was a general, global intellectual ability underlying performance on the various measures—the g-factor. This general intelligence factor was thought to underlie performance in every aspect of intelligence, and it was the g -factor that was presumably being measured on tests of intelligence (Spearman, 1927; Colom, Jung, & Haier, 2006; Haier et al., 2009; Major, Johnson, & Bouchard, 2011). FLUID AND CRYSTALLIZED INTELLIGENCE Some psychologists suggest that there are two different kinds of intelligence: fluid intelligence and crystallized intelligence. Fluid intelligence is the ability to reason abstractly. It reflects our ability to reason effectively, identify patterns, and recognize relationships between concepts. If we were asked to solve an analogy or group a series of letters according to some principle, we would be using fluid intelligence (Kane & Engle, 2002; Saggino, Perfetti, & Spitoni, 2006; Di Fabio & Palazzeschi, 2009). like logical reasoning ito (what louie does for math problems) In contrast, crystallized intelligence is the accumulation of information, knowledge, and skills that people have learned through experience and education. It reflects our ability to call up information from long-term memory. We would be likely to rely on crystallized intelligence, for instance, if we were asked to participate in a discussion about the solution to the causes of poverty, a task that allows us to draw on our own past experiences, education, and knowledge of the world. In contrast to fluid intelligence, which reflects a more general kind of intelligence, crystallized intelligence is more a reflection of the culture in which a person is raised. The differences between fluid intelligence and fluid = general in contrast to me, i rely on memorizing crystallized = unique to culture (not all the time ata) formulas to solve math problems  crystallized intelligence become especially evident in late adulthood, when people show declines in fluid, but not crystallized, intelligence (Buehner, Krumm, & Ziegler, 2006; Tranter & Koutstaal, 2008; Ackerman, 2011). GARDNER’S MULTIPLE INTELLIGENCES: THE MANY WAYS OF SHOWING INTELLIGENCE GOATTTT M B Psychologist Howard Gardner has taken an approach very different from traditional thinking about L intelligence. Gardner argues that rather than asking “How smart are you?” we should be asking a L S different question: “How are you smart?” In answering the latter question, Gardner has developed a I theory of multiple intelligences that has become quite influential (Gardner, 2000; Kaufman, I N Kaufman, & Plucker, 2013). Gardner argues that we have a minimum eight different forms of intelligence, each relatively independent of the others: musical, bodily kinesthetic, logical- mathematical, linguistic, spatial, interpersonal, intrapersonal, and naturalist. In Gardner’s view, each of the multiple intelligences is linked to an independent system in the brain. Furthermore, he suggests that there may be even more types of intelligence, such as existential intelligence, which involves identifying and thinking about the fundamental questions of human existence. For example, the Dalai Lama might exemplify this type of intelligence (Gardner, 1999, 2000). idk, mga philosophers siguro ganito STERNBERG’S TRIARCHIC THEORY of Intelligence Robert Sternberg (1985) attempted to go beyond this view by proposing a triarchic theory that deals with three aspects of intelligence: (a) cognitive processes, (b) identifying situations that require intelligence, and (c) using intelligence in the external world. He tried to analyze the cognitive processes into smaller components. For example, he suggested that when solving certain kinds of problems we go through several stages, including encoding the information, drawing inferences, mapping relationships, and applying the knowledge. If so, it might make sense for intelligence tests to measure each of these processes separately. The goal was an intelligence test that had some theoretical relationship to cognitive psychology. Unfortunately, when Sternberg tried to develop tests of encoding, inferring, mapping, and so forth, he found that all the measures correlated fairly highly with each other (Deary, 2002). In other words he had rediscovered g. Sternberg has explored other possible distinctions among types of intelligence. He has argued that we have at least three types of intelligence: analytical (or academic), creative (planning approaches to new problems), and practical (actually doing something). While criticizing standard IQ tests for concentrating only on analytical intelligence, Sternberg has tried to develop new tests that tap creative and practical aspects as well. Controversy persists regarding the status of creative and practical intelligence. On the one hand, we have all known people who seem high in academic intelligence but not in creativity or practical intelligence. On the other hand, thinking of examples is not enough; the question is whether in general analytical intelligence is strongly or weakly correlated with creativity and practical intelligence. Furthermore, measuring creativity and practical intelligence is easier said than done. ASSESSING INTELLIGENCE. Given the variety of approaches to the components of intelligence, it is not surprising that measuring intelligence has proved challenging. Psychologists who study intelligence have focused much of their attention on the development of intelligence tests that quantify a person’s level of intelligence. These tests have proved to be of great benefit in identifying students in need of special attention in school, diagnosing specific learning difficulties, and helping people make the best educational and vocational choices. At the same time, their use has proved controversial, raising important social and educational issues. BINET AND THE DEVELOPMENT OF IQ TESTS. The first real intelligence tests were developed by the French psychologist Alfred Binet (1857–1911). His tests followed from a simple premise: If performance on certain tasks or test items improved with chronological, or physical, age, performance could be used to distinguish more intelligent people from less intelligent ones within a particular age group. On the basis of this principle, Binet devised the first formal intelligence test, which was designed to identify the “dullest” students in the Paris school system in order to provide them with remedial aid. Binet began by presenting tasks to same-age students who had been labeled “bright” or “dull” by their teachers. If a task could be completed by the bright students but not by the dull ones, he retained that task as a proper test item; otherwise it was discarded. In the end he came up with a test that distinguished between the bright and dull groups, and—with further work—one that distinguished among children in different age groups (Binet & Simon, 1916; Sternberg & Jarvin, 2003). On the basis of the Binet test, children were assigned a score relating to their mental age, the age for which a given level of performance is average or typical. For example, if the average 8-year-old answered, say, 45 items correctly on a test, anyone who answered 45 items correctly would be assigned a mental age of 8 years. Consequently, whether the person taking the test was 20 years old or 5 years old, he or she would have the same mental age of 8 years (Cornell, 2006). A solution to the problem came in the form of the intelligence quotient (IQ), a measure of intelligence that takes into account an individual’s mental and chronological (physical) age. Historically, the first IQ scores employed the following formula in which MA stands for mental age and CA for chronological age: Using this formula, we can return to the earlier example of an 18-year-old performing at a mental age of 20 and calculate an IQ score of (20/18) 3 100 5 111. In contrast, the 5-year-old performing at a mental age of 7 comes out with a considerably higher IQ score: (7/5) 3 100 5 140. Contemporary IQ Tests: Gauging Intelligence Remnants of Binet’s original intelligence test are still with us, although the test has been revised in significant ways. Now in its fifth edition and called the Stanford- Binet Intelligence Scale, the test consists of a series of items that vary according to the age of the person being tested (Roid, Nellis, & McClellan, 2003). For example, young children are asked to copy figures or answer questions about everyday activities. Older people are asked to solve analogies, explain proverbs, and describe similarities that underlie sets of words. The test is administered orally and includes both verbal and nonverbal assessments. An examiner begins by finding a mental age level at which a person is able to answer all the questions correctly and then moves on to successively more difficult problems. When a mental age level is reached at which no items can be answered, the test is over. By studying the pattern of correct and incorrect responses, the examiner is able to compute an IQ score for the person being tested. In addition, the Stanford- Binet test yields separate sub-scores that provide clues to a test-taker’s particular strengths and weaknesses. The IQ tests most frequently used in the United States were devised by psychologist David Wechsler and are known as the Wechsler Adult Intelligence Scale–IV, or, more commonly, the WAIS-IV (for adults) and a children’s version, the Wechsler Intelligence Scale for Children–IV, or WISC-IV. Both the WAIS-IV and the WISC-IV measure verbal comprehension, perceptual reasoning, working memory, and processing speed (see sample WAIS-IV items in Figure 5). Because the Stanford-Binet, WAIS-IV, and WISC-IV all require individualized, one-on-one administration, they are relatively difficult to administer and score on a large-scale basis. Consequently, there are now a number of IQ tests that allow group administration. Rather than having one examiner ask one person at a time to respond to individual items, group IQ tests are strictly paper-and-pencil tests. The primary advantage of group tests is their ease of administration (Anastasi & Urbina, 1997; Danner et al., 2011). RELIABILITY AND VALIDITY: TAKING THE MEASURE OF TESTS When we use a ruler, we expect to find that it measures an inch in the same way it did the last time we used it. When we weigh ourselves on the bathroom scale, we hope that the variations we see on the scale are due to changes in our weight and not to errors on the part of the scale (unless the change in weight is in an unwanted direction!). In the same way, we hope that psychological tests have reliability—that they measure consistently what they are trying to measure. We need to be sure that each time we administer the test, a test-taker will achieve the same results—assuming that nothing about the person has changed relevant to what is being measured. But suppose your score changed hardly at all, and both times you received a score of about 400. You couldn’t complain about a lack of reliability. However, if you knew your verbal skills were above average, you might be concerned that the test did not adequately measure what it was supposed to measure. In sum, the question has now become one of validity rather than reliability. A test has validity when it actually measures what it is supposed to measure. Knowing that a test is reliable is no guarantee that it is also valid. For instance, Sir Francis Galton assumed that skull size is related to intelligence, and he was able to measure skull size with great reliability. However, the measure of skull size was not valid—it had nothing to do with intelligence. In this case, then, we have reliability without validity. However, if a test is unreliable, it cannot be valid. Assuming that all other factors— motivation to score well, knowledge of the material, health, and so forth—are similar, if a person scores high the first time he or she takes a specific test and low the second time, the test cannot be measuring what it is supposed to measure. Therefore, the test is both unreliable and not valid. Test validity and reliability are prerequisites for accurate assessment of intelligence— as well as for any other measurement task carried out by psychologists. Consequently, the measures of personality carried out by personality psychologists, clinical psychologists’ assessments of psychological disorders, and social psychologists’ measures of attitudes must meet the tests of validity and reliability for the results to be meaningful (Phelps, 2005;Yao, Zhour, & Jiang, 2006; Markus & Borsboom, 2013). Assuming that a test is both valid and reliable, one further step is necessary in order to interpret the meaning of a particular test-taker’s score: the establishment of norms. Norms are standards of test performance that permit the comparison of one person’s score on a test to the scores of others who have taken the same test. For example, a norm permits test-takers to know that they have scored, say, in the top 15% of those who have taken the test previously. Tests for which norms have been developed are known as standardized tests. ex. dates of enrollment in pup allows enrollees to know how well they performed INFLUENCES ON INTELLIGENCE Neuroscience in Your Life: What Makes a Child Intelligent? What makes one child more intelligent than another is a complex question that depends, in part, on how one defines intelligence. Researchers are, however, starting to understand how differences in the way the brain works are related to intelligence. One aspect of brain functioning that appears to be important is how well different areas of the brain are connected to one another. In the images below, we see areas that are related to nonverbal intelligence. Research finds that children with greater degrees of brain connectivity [here between right parietal and right frontal regions (A), and between right parietal and dorsal anterior cingulate regions (B)], demonstrate greater nonverbal intelligence. Keep in mind that this finding does not imply causality: we still don’t know if higher intelligence leads to greater brain connectivity, or if greater connectivity leads to greater intelligence (Langeslag et al., 2013). The Biological Basis of Intelligence Using brain-scanning methods, researchers have identified several areas of the brain that relate to intelligence. For example, the brains of people completing intelligence test questions in both verbal and spatial domains show activation in a similar location: the lateral prefrontal cortex. That area is above the outer edge of the eyebrow about where people rest their heads in the palms of their hands if they are thinking hard about a problem. This area of the brain is critical to juggling many pieces of information simultaneously and solving new problems. In addition, higher intelligence is related to the thickness of the cerebral cortex (Karama et al., 2009; Luders et al., 2009; Brant et al., 2013). Practical and Emotional Intelligence: Toward a More Intelligent View of Intelligence Consider the following situation: “An employee who reports to one of your subordinates has asked to talk with you about waste, poor management practices, and possible violations of both company policy and the law on the part of your subordinate. You have been in your present position only a year, but in that time you have had no indications of trouble about the subordinate in question. Neither you nor your company has an “open door” policy, so it is expected that employees should take their concerns to their immediate supervisors before bringing a matter to the attention of anyone else. The employee who wishes to meet with you has not discussed this matter with her supervisors because of its delicate nature (Sternberg, 1998).” Your response to this situation has a lot to do with your future success in a business career, according to psychologist Robert Sternberg. The question is one of a series designed to help give an indication of your intelligence. However, it is not traditional intelligence that the question is designed to tap but rather intelligence of a specific kind: practical intelligence. Practical intelligence is intelligence related to overall success in living (Sternberg, 2002; Muammar, 2007; Wagner, 2002, 2011). Noting that traditional tests were designed to relate to academic success, Sternberg points to evidence showing that most traditional measures of intelligence do not relate especially well to career success (McClelland, 1993). Specifically, although successful business executives usually score at least moderately well on intelligence tests, the rate at which they advance and their ultimate business achievements are only minimally associated with traditional measures of their intelligence. Sternberg argues that career success requires a very different type of intelligence from that required for academic success. Whereas academic success is based on knowledge of a specific information base obtained from reading and listening, practical intelligence is learned mainly through observation of others’ behavior. People who are high in practical intelligence are able to learn general norms and principles and apply them appropriately. Consequently, practical intelligence tests measure the ability to employ broad principles in solving everyday problems (Stemler & Sternberg, 2006; Stemler et al., 2009; Sternberg, 2013). In addition to practical intelligence, Sternberg argues there are two other basic, interrelated types of intelligence related to life success: analytical and creative. Analytical intelligence focuses on abstract but traditional types of problems measured on IQ tests, while creative intelligence involves the generation of novel ideas and products (Benderly, 2004; Sternberg, Kaufman, & Pretz, 2004; Sternberg, Grigorenko, & Kidd, 2005). Some psychologists broaden the concept of intelligence even further beyond the intellectual realm to include emotions. Emotional intelligence is the set of skills that underlie the accurate assessment, evaluation, expression, and regulation of emotions (Mayer, Salovey, & Caruso, 2004; Humphrey, Curran, & Morris, 2007; Mayer, Salovey, & Caruso, 2008). Emotional intelligence is the basis of empathy for others, self-awareness, and social skills. It encompasses the ability to get along well with others. It provides us with an understanding of what other people are feeling and experiencing, which permits us to respond appropriately to others’ needs. These abilities may help explain why people with only modest scores on traditional intelligence tests can be quite successful: the basis of their success may be a high emotional intelligence, which allows them to respond appropriately and quickly to others’ feelings. Although the notion of emotional intelligence makes sense, it has yet to be quantified in a rigorous manner. Furthermore, the view that emotional intelligence is so important that skills related to it should be taught in school has raised concerns among some educators. They suggest that the nurturance of emotional intelligence is best left to students’ families, especially because there is no well-specified set of criteria for what constitutes emotional intelligence (Becker, 2003; Vesely, Saklofske, & Leschied, 2013). Still, the notion of emotional intelligence reminds us that there are many ways to demonstrate intelligent behavior—just as there are multiple views of the nature of intelligence (Fox & Spector, 2000; Barrett & Salovey, 2002)