SUSTAIN: A Network Model of Category Learning

Bradley C. Love*, Douglas L. Medin, Todd M. Gureckis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

395 Scopus citations

Abstract

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. 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's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

Original languageEnglish (US)
Pages (from-to)309-332
Number of pages24
JournalPsychological Review
Volume111
Issue number2
DOIs
StatePublished - Apr 2004

ASJC Scopus subject areas

  • Psychology(all)

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