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Triarchic Intelligence Components

Introduction

In the triarchic theory of human intelligence, information-processing components are applied to experience in order to adapt to, shape, and select environments (Sternberg, 1985, 1997, 1999). A common set of universal processes underlies all aspects of intelligence.

Metacomponents, or executive processes, plan what to do, monitor things as they are being done, and evaluate things after they are done. Performance components execute the instructions of the metacomponents. Knowledge-acquisition components learn how to solve problems or simply to acquire declarative knowledge in the first place.

Analytical intelligence is invoked when components are applied to fairly familiar kinds of problems abstracted from everyday life; creative intelligence when the components are applied to relatively novel kinds of tasks or situations; and practical intelligence when the components are applied to experience to adapt to, shape, and select environments.

Measuring Analytical Intelligence

Analytical kinds of problems, such as analogies or syllogisms, can be analysed componentially (Sternberg, 1983), with response times or error rates decomposed to yield their underlying information-processing components. Componential analysis reveals information-processing origins of individual differences in intelligence. The general strategy of such research is to (a) specify an information-processing model of task performance; (b) assign a mathematical parameter to each information-processing component; and (c) construct cognitive tasks administered in such a way that it is possible through mathematical modelling to isolate the parameters of the mathematical model. In this way, several sources of important individual or developmental differences are isolated: (a) the performance components used; (b) time to execute each component; (c) susceptibility of each component to error; (d) strategy for combining the components; (e) mental representations upon which the components act.

What are some results? Although children generally became quicker in information processing with age, not all components were executed more rapidly with age. The encoding component first shows a decrease in component time with age and then an increase. Apparently, older children realize that their best strategy is to spend more time in encoding the terms of a problem so that they later would be able to spend less time in operating on these encodings. Moreover, better reasoners spend relatively more time than do poorer reasoners in global, up-front metacomponential planning, when they solve difficult reasoning problems. Poorer reasoners, on the other hand, spend relatively more time in local planning. Presumably, better reasoners recognize it is better to invest more time up front so as to be able to process a problem more efficiently later on.

In one set of studies on knowledge-acquisition components, individuals figured out meanings of unknown words in sentences; for example, ‘The blen rises in the east and sets in the west’ (Sternberg, 1987). A componential model was able to predict word difficulty very well, and scores on the decontextualization task provided excellent prediction of individual differences in vocabulary skills.

Measuring Creative Intelligence

Creative intelligence is measured by convergent or divergent problems assessing how well people can cope with relative novelty.

In work with convergent problems, participants received novel kinds of reasoning problems that had a single best answer. For example, participants might be told that some objects are green and others blue; but still other objects might be grue, meaning green until the year 2000 and blue thereafter, or bleen, meaning blue until the year 2000 and green thereafter (Sternberg, 1982; Tetewsky & Sternberg, 1986). Their task was to predict future states from past states, given incomplete information. In another set of studies, 60 people were given more conventional kinds of inductive reasoning problems. But the problems had premises preceding them that were either conventional (dancers wear shoes) or novel (dancers eat shoes). The participants had to solve the problems as though the counterfactuals were true (Sternberg & Gastel, 1989a, b).

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