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Connectionist simulation of attitude learning: Asymmetries in the acquisition of positive and negative evaluations

Eiser, J.R., Fazio, R.H., Stafford, T. and Prescott, T.J. (2003) Connectionist simulation of attitude learning: Asymmetries in the acquisition of positive and negative evaluations. Personality and Social Psychology Bulletin, 29 (10). pp. 1221-1235. ISSN 0146-1672

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Abstract

Connectionist computer simulation was employed to explore the notion that, if attitudes guide approach and avoidance behaviors, false negative beliefs are likely to remain uncorrected for longer than false positive beliefs. In Study 1, the authors trained a three-layer neural network to discriminate "good" and "bad" inputs distributed across a two-dimensional space. "Full feedback" training, whereby connection weights were modified to reduce error after every trial, resulted in perfect discrimination. "Contingent feedback," whereby connection weights were only updated following outputs representing approach behavior, led to several false negative errors (good inputs misclassified as bad). In Study 2, the network was redesigned to distinguish a system for learning evaluations from a mechanism for selecting actions. Biasing action selection toward approach eliminated the asymmetry between learning of good and bad inputs under contingent feedback. Implications for various attitudinal phenomena and biases in social cognition are discussed.

Item Type: Article
Copyright, Publisher and Additional Information: © 2003 Society for Personality and Social Psychology, Inc. This is an author produced version of a paper published in Personality and Social Psychology Bulletin.
Keywords: attitude, connectionism, learning, simulation
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield)
Depositing User: Repository Officer
Date Deposited: 06 Apr 2006
Last Modified: 05 Jun 2014 14:31
Published Version: http://psp.sagepub.com/cgi/content/abstract/29/10/...
Status: Published
Publisher: Sage Publications
Refereed: Yes
Identification Number: 10.1177/0146167203254605
URI: http://eprints.whiterose.ac.uk/id/eprint/1137

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