Predicting Features for Members of Natural Categories When Categorization Is Uncertain

Barbara C. Malt, Brian H. Ross, Gregory L. Murphy

Research output: Contribution to journalArticle

Abstract

An important function of concepts is to allow the prediction of unseen features. A Bayesian account of feature prediction suggests that people will consider all the categories an object could belong to when they judge the likelihood that the object has a feature. The judgment and decision literature suggests that they may instead use a simpler heuristic in which they consider only the most likely category. In 3 experiments, no evidence was found that participants took into account alternative categories as well as the most likely one when they judged feature probabilities for familiar objects in meaningful contexts. These results, in conjunction with those of Murphy and Ross (1994), suggest that although people may consider alternative categories in certain limited situations, they often do not. Reasons for why the use of alternative categories may be relatively rare are discussed, and conditions under which people may take alternative categories into account are outlined.

Original languageEnglish (US)
Pages (from-to)646-661
Number of pages16
JournalJournal of Experimental Psychology: Learning Memory and Cognition
Volume21
Issue number3
StatePublished - May 1995

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Heuristics
heuristics
experiment
evidence
Prediction
literature
Experiment

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Psychology(all)

Cite this

Predicting Features for Members of Natural Categories When Categorization Is Uncertain. / Malt, Barbara C.; Ross, Brian H.; Murphy, Gregory L.

In: Journal of Experimental Psychology: Learning Memory and Cognition, Vol. 21, No. 3, 05.1995, p. 646-661.

Research output: Contribution to journalArticle

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