Categorization as casual reasoning

Research output: Contribution to journalArticle

Abstract

A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal-model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are directly connected by causal relationships. These expectations also included sensitivity to higher-order feature interactions that emerge from the asymmetries inherent in causal relationships. Quantitative fits of causal-model theory were superior to those obtained with extensions to traditional similarity-based models that represent causal knowledge either as higher-order relational features or "prior exemplars" stored in memory.

Original languageEnglish (US)
Pages (from-to)709-748
Number of pages40
JournalCognitive Science
Volume27
Issue number5
DOIs
StatePublished - Sep 2003

Keywords

  • Categorization
  • Causal knowledge
  • Causal models
  • Causal reasoning
  • Conceptual representation

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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