Reasoning with uncertain categories

Gregory L. Murphy, Stephanie Y. Chen, Brian H. Ross

Research output: Contribution to journalArticlepeer-review

Abstract

Five experiments investigated how people use categories to make inductions about objects whose categorisation is uncertain. Normatively, they should consider all the categories the object might be in and use a weighted combination of information from all the categories: bet-hedging. The experiments presented people with simple, artificial categories and asked them to make an induction about a new object that was most likely in one category but possibly in another. The results showed that the majority of people focused on the most likely category in making inductions, although there was a group of consistently normative responders who used information from both categories (about 25% of our college population). Across experiments the overall pattern of results suggests that performance in the task is improved not by understanding the underlying principles of bet-hedging but by increasing the likelihood that multiple categories are in working memory at the time of the induction. We discuss implications for improving everyday inductions.

Original languageEnglish (US)
Pages (from-to)81-117
Number of pages37
JournalThinking and Reasoning
Volume18
Issue number1
DOIs
StatePublished - Feb 2012

Keywords

  • Bayesian processes
  • Categories
  • Induction
  • Reasoning

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Philosophy
  • Psychology (miscellaneous)

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