Towards a unified account of supervised and unsupervised category learning

Todd M. Gureckis, Bradley C. Love

Research output: Contribution to journalReview article

Abstract

(Supervised and Unsupervised STratified Adaptive IncrementalNetwork) is a network model of human category learning. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g. it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes/attractors/rules. SUSTAIN has expanded the scope of findings that models of human category learning can address. This paper extends SUSTAIN so that it can be used to account for both supervised and unsupervised learning data through a common mechanism. A modified recruitment rule is introduced that creates new conceptual clusters in response to surprising events during learning. The new formulation of the model is called uSUSTAIN for 'unified SUSTAIN.' The implications of using a unified recruitment method for both supervised and unsupervised learning are discussed.

Original languageEnglish (US)
Pages (from-to)1-24
Number of pages24
JournalJournal of Experimental and Theoretical Artificial Intelligence
Volume15
Issue number1
DOIs
StatePublished - Jan 2003

Keywords

  • Category
  • Connectionist
  • Learning
  • Psychology
  • Supervised
  • Unsupervised

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence

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