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UT INF 385P - THINKING

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P1: FXZNovember 11, 2000 10:46 Annual Reviews AR120-09Annu. Rev. Psychol. 2001. 52:223–47Copyrightc 2001 by Annual Reviews. All rights reservedTHINKINGArthur B. MarkmanDepartment of Psychology, University of Texas, Austin, Texas 78712;e-mail: [email protected] GentnerDepartment of Psychology, Northwestern University, Evanston, Illinois 60208;e-mail: [email protected] Words reasoning, analogy, domain-specific processes,mental models, learning■ Abstract Reasoning processes allow the human cognitive system to go beyondthe information readily available in the environment. This review focuses on the pro-cessesofhumanthinking, including deductivereasoning, induction, mentalsimulation,and analogy. We survey recent trends across several areas, including categorization,mental models, cognitive development, and decision making. Our chief organizingprinciple is the contrast between traditional approaches that focus on abstract logicalreasoning and a number of current approaches that posit domain-specific, knowledge-intensivecognition.We suggestthat some instancesofdomain-specific cognition resultfromdomain-generalprocesses operatingon domain-specificrepresentations. Anothertheme is the link between reasoning and learning. We suggest that learning typicallyoccurs as a byproduct of reasoning, rather than as an end in itself.CONTENTSINTRODUCTION ................................................ 224STRUCTURAL ALIGNMENT AND MAPPING.......................... 226Learning and Reasoning: The Shift from Active Processing to Storage.........228MENTAL MODELS.............................................. 228Logical Mental Models........................................... 229Causal Mental Models........................................... 229Relating Mental Models to Theories................................. 231The Development of Theories and Mental Models.......................233CATEGORIZATION.............................................. 235Similarity and Categorization...................................... 235Inference from Categories......................................... 237Feature Centrality............................................... 238Domain-Specific Approaches to Categorization......................... 239SUMMARY AND CONCLUSIONS................................... 2410066-4308/01/0201-0223$14.00223Annu. Rev. Psychol. 2001.52:223-247. Downloaded from arjournals.annualreviews.orgby University of Texas - Austin on 01/22/09. For personal use only.P1: FXZNovember 11, 2000 10:46 Annual Reviews AR120-09224 MARKMAN¥GENTNERINTRODUCTIONWe have called this chapter “Thinking” rather than using the more traditional title“Reasoning” because we think it important to go beyond the traditional deductivelogic focus of the term reasoning and include other ways of arriving at new con-clusions, including induction and analogy. In this paper we survey recent trends inthe field across several areas, including categorization, mental models, cognitivedevelopment, and decision making. A theme that emerges frequently in recentresearch is an interest in going beyond lab-oriented paradigms to investigationsof real-life cognitive activities. A related theme is the relation between abstractlogical reasoning and concrete domain-specific reasoning—or to put it anotherway, between abstract and knowledge-based approaches to reasoning. The idea ofreasoning is often equated with the notion of purely logical processes that operateindependent of content. In popular imagery, icons such as Mr. Spock and HALdraw their intellectual power from pure logic. Likewise, within psychology thestudy of reasoning has focused largely on the use of content-independent logicalrules (e.g. Johnson-Laird & Byrne 1991, Rips 1994). However, other researchshows that the content being reasoned about influences people’s reasoning ability,even for tasks to which logical rules are applicable (e.g. Cheng & Holyoak 1985,Cosmides 1989, Wason & Johnson-Laird 1972). Partly because of these findings,there has been considerable recent interest in how people learn and use richdomainrepresentations such as theories and mental models.As an example of the kinds of phenomena that need explanation, Bassok et al(1998) asked college undergraduates to write addition and division word prob-lems. Presumably, college students are experts at addition and division; it shouldbe straightforward for them to think abstractly about simple arithmetic. However,the content of the problem influenced the form of the word problem written. Whenasked to write a problem involving two members of the same category (e.g. applesand pears), the students found it easier to write an addition problem than a divi-sion problem. In contrast, when given members of thematically related categories(e.g. apples and baskets), they found it easier to write a division problem than anaddition problem. Thus, even in the seemingly abstract domain of mathematics,cognitive performance is affected by domain content.Such effects have led some researchers to suggest that content and contextare fundamental to reasoning (Newell & Simon 1972). Some theorists assert thathuman learning is conservative, with representations that are tied to the initiallearning situation (Gentner 1989, Medin & Ross 1989). Indeed, some have takenthe extreme opposite of the logicist view, arguing that there is no utility to a gen-eral notion of representation or process. One such view is the situated cognitionapproach that assumes all thinking is fundamentally context-governed (Suchman1987). A related position is the embodied cognition view, according to whichcognitive processes are optimized to mesh with particular sensorimotor activi-ties (Glenberg 1997, Pfeifer & Scheier 1999). For example, Glenberg reviewedevidence that some spatial reasoning tasks are facilitated by specific motormovements consistent with route-following.Annu. Rev. Psychol. 2001.52:223-247. Downloaded from arjournals.annualreviews.orgby University of Texas - Austin on 01/22/09. For personal use only.P1: FXZNovember 11, 2000 10:46 Annual Reviews AR120-09THINKING 225Another approach to domain-specific reasoning is modularity theory, whichassumes that human learning and development requires innate modules for cer-tain domains such as physical causality or psychology (e.g. Hirschfeld & Gelman1994). Finally, another approach that postulates domain-specific reasoning is evo-lutionary psychology, which posits domain-specific modules selected by


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