A Knowledge-resonance (KRES) model of category learning

Bob Rehder, Gregory L. Murphy

Research output: Contribution to journalReview articlepeer-review

Abstract

This article introduces a connectionist model of category learning that takes into account the prior knowledge that people bring to new learning situations. In contrast to connectionist learning models that assume a feedforward network and learn by the delta rule or backpropagation, this model, the knowledge-resonance model, or KRES, employs a recurrent network with bidirectional symmetric connection whose weights are updated according to a contrastive Hebbian learning rule. We demonstrate that when prior knowledge is represented in the network, KRES accounts for a considerable range of empirical results regarding the effects of prior knowledge on category learning, including (1) the accelerated learning that occurs in the presence of knowledge, (2) the better learning in the presence of knowledge of category features that are not related to prior knowledge, (3) the reinterpretation of features with ambiguous interpretations in light of error-corrective feedback, and (4) the unlearning of prior knowledge when that knowledge is inappropriate in the context of a particular category.

Original languageEnglish (US)
Pages (from-to)759-784
Number of pages26
JournalPsychonomic Bulletin and Review
Volume10
Issue number4
DOIs
StatePublished - Dec 2003

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)

Fingerprint Dive into the research topics of 'A Knowledge-resonance (KRES) model of category learning'. Together they form a unique fingerprint.

Cite this