Human unsupervised and supervised learning as a quantitative distinction

Todd M. Gureckis, Bradley C. Love

Research output: Contribution to journalArticlepeer-review

Abstract

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) 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 to account for both supervised and unsupervised learning data through a common mechanism. The modified model, uSUSTAIN (unified SUSTAIN), is successfully applied to human learning data that compares unsupervised and supervised learning performances.

Original languageEnglish (US)
Pages (from-to)885-901
Number of pages17
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume17
Issue number5
DOIs
StatePublished - Aug 2003

Keywords

  • Category
  • Learning
  • Psychology
  • Supervised
  • Unsupervised

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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