Analogy Just Looks Like High Level Perception: Why a Domain-General Approach to Analogical Mapping is Right

Abstract

Hofstadter and his colleagues have criticized current accounts of analogy, claiming that such accounts do not accurately capture interactions between processes of representation construction and processes of mapping. They suggest instead that analogy should be viewed as a form of high level perception that encompasses both representation building and mapping as indivisible operations within a single model. They argue specifically against SME, our model of analogical matching, on the grounds that it is modular, and offer instead programs like Mitchell & Hofstader's Copycat as examples of the high level perception approach. In this paper we argue against this position on two grounds. First, we demonstrate that most of their specific arguments involving SME and Copycat are incorrect. Second, we argue that the claim that analogy is high-level perception, while in some ways an attractive metaphor, is too vague to be useful as a technical proposal. We focus on five issues: (1) how perception relates to analogy, (2) how flexibility arises in analogical processing, (3) whether analogy is a domain-general process, (4) how should micro-worlds be used in the study of analogy, and (5) how best to assess the psychological plausibility of a model of analogy. We illustrate our discussion with examples taken from computer models embodying both views.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1998
Accession Number
ADA465997

Entities

People

  • Arthur B. Makman
  • Dedre Gentner
  • Ken Forbus
  • Ronald W. Ferguson

Organizations

  • Northwestern University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Cognition
  • Cognitive Science
  • Computational Science
  • Computer Programs
  • Computer Vision
  • Computers
  • Judgment
  • Language
  • Linguistics
  • New York
  • Perception
  • Physical Theories
  • Psychology
  • Reasoning
  • Simulations

Readers

  • Artificial Intelligence
  • Educational Psychology