A symbolic-connectionist theory of relational inference and generalization.

Full Abstract

The authors present a theory of how relational inference and generalization can be accomplished within a cognitive architecture that is psychologically and neurally realistic. Their proposal is a form of symbolic connectionism:
a connectionist system based on distributed representations of concept meanings, using temporal synchrony to bind fillers and roles into relational structures. The authors present a specific instantiation of their theory in the form of a computer simulation model, Learning and Inference with Schemas and Analogies (LISA). By using a kind of self-supervised learning, LISA can make specific inferences and form new relational generalizations and can hence acquire new schemas by induction from examples. The authors demonstrate the sufficiency of the model by using it to simulate a body of empirical phenomena concerning analogical inference and relational generalization.

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Author information

Author/s: Hummel, John E (JE); Holyoak, Keith J (KJ);

Affiliation: Department of Psychology, University of California, Los Angeles 90095-1563, USA. jhummel@lifesci.ucla.edu

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.

Journal: Psychological review (Psychol Rev), published in United States. (Language: eng)

Reference: 2003-Apr; vol 110 (issue 2) : pp 220-64

Dates: Created 2003/05/15; Completed 2003/06/18; Revised 2006/11/15;

PMID: 12747523, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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