Causal-based property generalization

Research output: Contribution to journalArticle

Abstract

A central question in cognitive research concerns how new properties are generalized to categories. This article introduces a model of how generalizations involve a process of causal inference in which people estimate the likely presence of the new property in individual category exemplars and then the prevalence of the property among all category members. Evidence in favor of this causal-based generalization (CBG) view included effects of an existing feature's base rate (Experiment 1), the direction of the causal relations (Experiments 2 and 4), the number of those relations (Experiment 3), and the distribution of features among category members (Experiments 4 and 5). The results provided no support for an alternative view that generalizations are promoted by the centrality of the to-be-generalized feature. However, there was evidence that a minority of participants based their judgments on simpler associative reasoning processes.

Original languageEnglish (US)
Pages (from-to)301-344
Number of pages44
JournalCognitive Science
Volume33
Issue number3
DOIs
StatePublished - May 2009

Keywords

  • Causal reasoning
  • Causal-based induction
  • Generalization

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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