SUSTAIN: A model of human category learning

Bradley C. Love*, Douglas L Medin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. SUSTAIN is a three layer model where learning between the first two layers is unsupervised, while learning between the top two layers is supervised. SUSTAIN clusters inputs in an unsupervised fashion until it groups input patterns inappropriately (as signaled by the supervised portion of the network). When such an error occurs, SUSTAIN alters its architecture, recruiting a new unit that is tuned to correctly classify the exception. Units recruited to capture exceptions can evolve into prototypes/attractors/rules in their own right. SUSTAIN's adaptive architecture allows it to master simple classification problems quickly, while still retaining the capacity to learn difficult mappings. SUSTAIN also adjusts its sensitivity to input dimensions during the course of learning, paying more attention to dimensions relevant to the classification task. Shepard, Hovland, and Jenkins's (1961) challenging category learning data is fit successfully by SUSTAIN. Other applications of SUSTAIN are discussed. SUSTAIN is compared to other classification models.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Editors Anon
PublisherAAAI
Pages671-676
Number of pages6
StatePublished - Jan 1 1998
EventProceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI - Madison, WI, USA
Duration: Jul 26 1998Jul 30 1998

Other

OtherProceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI
CityMadison, WI, USA
Period7/26/987/30/98

ASJC Scopus subject areas

  • Software

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