Expanding the search for a linear separability constraint on category learning

Mark Blair, Donald Homa

Research output: Contribution to journalArticle

37 Scopus citations

Abstract

Formal models of categorization make different predictions about the theoretical importance of linear separability. Prior research, most of which has failed to find support for a linear separability constraint on category learning, has been conducted using tasks that involve learning two categories with a small number of members. The present experiment used four categories with three or nine patterns per category that were either linearly separable or not linearly separable. With overall category structure equivalent across category types, the linearly separable categories were found to be easier to learn than the not linearly separable categories. An analysis of individual participants' data showed that there were more participants operating under a linear separability constraint when learning large categories than when learning small ones. Formal modeling showed that an exemplar model could not account for many of these data. These results are taken to support the existence of multiple processes in categorization.

Original languageEnglish (US)
Pages (from-to)1153-1164
Number of pages12
JournalMemory and Cognition
Volume29
Issue number8
DOIs
StatePublished - Jan 1 2001

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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