Family resemblance, conceptual cohesiveness, and category construction

Douglas L. Medin*, William D. Wattenmaker, Sarah E. Hampson

*Corresponding author for this work

Research output: Contribution to journalArticle

253 Scopus citations

Abstract

Many people have argued that natural categories are organized in terms of a family resemblance principle. Members of family resemblance categories tend to share properties with each other but have no properties that are singly necessary and jointly sufficient (defining) for category membership. This paper reports seven experiments using a sorting task to evaluate the conditions under which people prefer to construct categories according to a family resemblance principle. The first set of studies followed the typical practice of defining family resemblance in terms of independent sets of matching and mismatching values. Across a variety of stimulus materials, instructions, procedures, and category structures, family resemblance sorting was almost never observed. Despite procedures designed to prevent it, participants persisted in sorting on the basis of a single dimension. The second set of studies explored the idea that interproperty relationships rather than independent features serve to organize categories. We found that people will abandon unidimensional sorting in favor of sorting by correlated properties, especially when they can be causally connected. In addition, when conceptual knowledge is added which makes interproperty relationships salient, family resemblance sorting becomes fairly common. Implications of the results for the development of family resemblance categories and the practice of treating properties or features as additive and independent are discussed.

Original languageEnglish (US)
Pages (from-to)242-279
Number of pages38
JournalCognitive Psychology
Volume19
Issue number2
DOIs
StatePublished - Apr 1987

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Artificial Intelligence

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