CONCEPT I: Concepts

CONCEPT II: Problem Solving

Problem solving requires a knowledge of concepts and the relationship among them.

Problem solving can be conceptualized as a three-step process. First, the problem must be represented, or interpreted. For a difficult problem, often the best strategy is to try to examine it from several perspectives. Humans, however, have a tendency to cling to the initial representation of a problem, despite the fact that it may not be helpful in a particular situation. This is called functional fixedness—the tendency to view an object as being used only for its customary function. The use of schemas by experts increases their ability to represent problems in ways that make them easier to solve. Also, research aimed at helping people solve arithmetic word problems suggests that more effective problem representation strategies can be learned.

The second problem-solving step is to devise solution strategies. The limits of our short-term memory make such strategies necessary. An algorithmic strategy is a precisely stated set of rules that generally solves all problems of a particular type. A heuristic strategy, which depends on rules-of-thumb or best guesses on how to proceed, has the advantage of being faster than an algorithmic method. People use several general heuristics, such as subgoal analysis (dividing a complex problem into smaller component problems), means-end analysis (comparing present to desired positions and trying to find a way to reduce the difference), and backward search (starting at the end of the problem and working backward). Heuristics can be useful in overcoming the limits imposed by short-term memory, but they have the disadvantage of promoting the tendency toward repeating heuristic solutions that have worked in the past even when they clearly are not appropriate to the present situation. The inclination to use an old perspective in a new situation is called a mental set. One way to break out of an unproductive mental set is to get away from the problem for awhile.

The third and final step in problem solving is deciding when a satisfactory solution has been reached. Sometimes deciding on the best solution is difficult; pressures of time and limits of cognitive capacity may determine when a particular problem-solving effort ends.

Artificial intelligence involves programming computers to simulate human problem solving. An early problem-solving program, called the General Problem Solver (GPS), was programmed according to how humans solve problems when they think aloud. Newer programs, called expert systems, are based more on the "if-then" logic that experts use as they solve problems. SOAR, a new program, uses expert systems until an impasse is reached, at which time it reverts back to a generalistic mode to determine a new direction of logic to try. Humans are still better problem solvers than computers, having more expansive abilities to process many things at the same time, greater perceptual capabilities, and the human capacities for emotion, motivation, self-awareness, and creativity.

Creative solutions involve novel restructuring of information and must be workable solutions. Creative insights may occur in sudden and unpredictable ways; they may appear in the form of analogies or arise unconsciously. Creative thinkers tend to have strong motivation and persistence. Creative people are usually intelligent, but intelligent people are not always creative. People can be trained to increase the ability to perform divergent thinking, the ability to generate many different answers to a question. However, people high in divergent thinking are not always the most creative. Some research suggests that the most creative people are especially likely to take risks.

CONCEPT III: Decision Making

In making decisions, two sets of variables are considered: the value one places on the potential outcomes (utility), and the likelihood that each outcome will actually take place (probability). Although we seldom formally calculate utility and probability in making a decision, research demonstrates that we generally follow this model in decision making. Much of our problem in making complex decisions results from the limited capacity of our working memory.

People often rely on heuristics rather than statistical probability to solve problems. If the limitations of these heuristics are ignored, the strategies can lead to serious errors. The representativeness heuristic, for example, involves stereotyping people and attributing to them certain characteristics. Although this is a useful method of reducing the amount of information to be processed, people using it sometimes overlook objective information and base their judgments on inaccurate data.

The availability heuristic specifies that easy-to-remember events are more frequent than events that are difficult to remember. Although this information is generally useful, factors other than frequency of occurrence affect our ability to recall events. Media coverage, for instance, has a significant influence on how people will estimate causes of death. People overestimate the risk of death from highly publicized events and underestimate the risk of death from rarely reported causes.

Generally, however, people are better problem solvers in real life than in the laboratory, and misperceptions do not make heuristics useless.

CONCEPT IV: Language

Language is complex and central to human existence. It is our primary means of communication. It is extremely flexible and rich in the meanings it can convey.

Because language uses symbols to represent other objects and events, it allows us to communicate beyond the concrete and the present. Furthermore, the symbolism is arbitrary, agreed on only by convention. Language is based on a principle of combination in which components are arranged in a nearly infinite number of different patterns.

Humans have a remarkable facility for using and understanding language, usually with little or no effort. Yet language depends on very complex mental processes.

Language can be analyzed at different levels. For example, a phoneme is a class of slightly varying sounds that speakers of a language perceive as linguistically similar. Different languages use somewhat different phoneme classes. Most languages exclude some phonemes that may be included in other languages. Every language has its own morphological rules for combining phonemes into meaningful units. A morpheme is the smallest unit that has meaning. Phonemes may be processed by specialized circuitry in the brain which is separate from that used to identify nonspeech noises. Other psychologists take a different view, believing that we hear phonemes clearly because the brain anticipates the speech sounds we are apt to encounter.

Written language is processed differently, probably by a combination of extracting meaning from the sight of a word (how it looks) and from our transformation of the word to how it sounds.

When we are reading sentences, rather than words, we automatically dissect them into phrases, called constituents, that make sense when taken together. From these constituents, we extract propositions, or units of meaning, that the sentence contains. The syntactic approach argues that we understand written language by relying on its syntax, the ways in which words and their parts are combined according to rules. The semantic approach analyzes language according to its meaningful components rather than the order and pattern in which the words are combined. We use both the syntactic and semantic approaches in understanding language.

In addition to syntax and meaning, we also rely on a general understanding of the world in our interpretation of language. Part of this knowledge is in the form of scripts, the sequence of action that can be expected in a given situation. We also make assumptions about speech and what it refers to, and oftentimes these implied assumptions are remembered the same way we remember explicit information. The field called pragmatics studies the implicit understandings people have about how language should be used in particular social contexts.

Language and thought are intimately related, although the exact nature of this relationship is the subject of debate. For example, Benjamin Whorf argued that language shapes thought. Whorf argued that language forces us to perceive our environment in a manner shaped by and consistent with our language, a tendency called forced observation. This notion is called the linguistic relativity hypothesis.

Research on cultural differences in the perception of color reveals, however, that different cultures perceive the same colors, regardless of the number of basic color terms in a particular language. This evidence contradicts the linguistic relativity hypothesis. An alternative view is that thought processes shape language. Here, the attempt is to discover linguistic universals, features found in all languages. This view is supported by research showing that all cultures assign color names in the same order of importance.

An interesting question is whether language is a uniquely human ability. In the 1940s, early attempts to teach spoken language to chimpanzees failed, due to chimpanzees' lack of the physiological structures necessary for speech production. Beatrice and Allen Gardner repeated these experiments with a chimp named Washoe, this time using ASL, the sign language used by deaf humans. After four years of training, Washoe had learned 160 signs, could generalize a sign to unique situations, and could use several signs in combination. The Gardners felt that Washoe's "language" ability was roughly equivalent to that of a three-year-old child. David Premack had similar results with a chimp named Sarah, whom he taught a language based on plastic symbols that represented words. Another chimp, Lana, learned to use a special typewriter connected to a computer. She was able to create names for previously unnamed objects. From such research, some psychologists conclude that chimps have some capacity for understanding syntax.

Other psychologists (such as Herbert Terrace, who taught sign language to a chimp named Nim Chimpsky) question whether this capacity has been adequately demonstrated. They believe that chimps simply create sentences by word substitution. Also, chimps' language growth is extremely limited when compared to that of children, particularly with respect to asking for names of new objects. Savage-Rumbaugh and her colleagues, however, have had considerable success in teaching chimps to name tasks, thereby indicating that chimps do understand that words are not just discriminative stimuli signaling rewards but rather are symbols which stand for objects.

Researchers agree that chimps do not process language in the same way that humans do. Further research will probably be directed at establishing the limits of a chimp's ability to communicate, rather than simply determining whether or not chimps can talk.