Similarity and discrimination: A selective review and a connectionist model.
Abstract
The 1st part of this article evaluates the extent to which 2 elemental theories of conditioning, stimulus sampling theory and the Rescorla-Wagner (1972) theory, are able to account for the influence of similarity on discrimination learning. A number of findings are reviewed that are inconsistent with predictions derived from these theories, either in their present form or in various modified forms. The 2nd part of the article is concerned with developing an alternative, configural account for discrimination learning. In contrast to previous configural theories, the present version is set within the framework of a connectionist network.
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Cited by (20)
- Context and Behavioral Processes in Extinction: Table 1.Bouton, Mark E. · 2004 · Learning & Memory
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- Mechanisms of fear extinctionMyers, K M, Davis, M · 2007 · Molecular Psychiatry
- Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gamblingRedish, A. David, Jensen, Steve, Johnson, Adam, Kurth-Nelson, Zeb · 2007 · Psychological Review
- The Role of Associative History in Models of Associative Learning: A Selective Review and a Hybrid ModelLe Pelley, M. E. · 2004 · The Quarterly Journal of Experimental Psychology Section B
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Similarity and discrimination: A selective review and a connectionist model. https://app.cochat.ai/papers/10.1037//0033-295x.101.4.587
- The paper
- Pearce, J. M. (1994). Similarity and discrimination: A selective review and a connectionist model. Psychological Review. https://doi.org/10.1037//0033-295x.101.4.587
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.