Finding the commonalities: The role of analogical mapping during similarity judgments
Markman, Arthur Brian
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https://hdl.handle.net/2142/21275
Description
Title
Finding the commonalities: The role of analogical mapping during similarity judgments
Author(s)
Markman, Arthur Brian
Issue Date
1992
Doctoral Committee Chair(s)
Gentner, Dedre
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Psychology, Experimental
Language
eng
Abstract
Similarity plays a central role in cognitive theories. Much research has been devoted to understanding what makes a pair of objects similar. This research has demonstrated that the similarity of a pair increases with its commonalities and decreases with its differences. These common and distinctive elements can take the form of parts of objects, relations between parts or properties of whole objects. However, previous work has been unable to reconcile this variety of information within a single framework. We suggest that structural alignment, like that proposed to mediate analogical reasoning, provides a sufficiently powerful process for determining the commonalities and differences of complex representations. In order to make this suggestion more concrete, we present two simulations of the similarity comparison process using the Structure Mapping Engine (Falkenhainer, Forbus and Gentner, 1989), a computer implementation of Gentner's (1983) structure-mapping theory of analogy. The simulations indicate that salient object commonalities yield a preference for mappings based on object similarities, while salient relational commonalities yield a preference for mappings based on relational similarities. We test these predictions in five experiments using the one-shot mapping technique which places local and global similarities in opposition. The results support the general conjecture that similarity comparisons involve structural alignment, as well as the more detailed predictions derived from the simulations. These results pose a difficulty for models using representations that do not encode the binding between relations and their arguments.
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