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Evaluating the TD model of classical conditioning

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Ludvig, Elliot Andrew, Sutton, Richard S. and Kehoe, E. James (2012) Evaluating the TD model of classical conditioning. Learning & Behavior, Volume 40 (Number 3). pp. 305-319. doi:10.3758/s13420-012-0082-6 ISSN 1543-4494.

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Official URL: http://dx.doi.org/10.3758/s13420-012-0082-6

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Abstract

The temporal-difference (TD) algorithm from reinforcement learning provides a simple method for incrementally learning predictions of upcoming events. Applied to classical conditioning, TD models suppose that animals learn a real-time prediction of the unconditioned stimulus (US) on the basis of all available conditioned stimuli (CSs). In the TD model, similar to other error-correction models, learning is driven by prediction errors—the difference between the change in US prediction and the actual US. With the TD model, however, learning occurs continuously from moment to moment and is not artificially constrained to occur in trials. Accordingly, a key feature of any TD model is the assumption about the representation of a CS on a moment-to-moment basis. Here, we evaluate the performance of the TD model with a heretofore unexplored range of classical conditioning tasks. To do so, we consider three stimulus representations that vary in their degree of temporal generalization and evaluate how the representation influences the performance of the TD model on these conditioning tasks.

Item Type: Journal Article
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Divisions: Faculty of Science, Engineering and Medicine > Science > Psychology
Library of Congress Subject Headings (LCSH): Classical conditioning, Association of ideas, Learning, Psychology of, Reinforcement learning, Algorithms
Journal or Publication Title: Learning & Behavior
Publisher: Springer New York LLC
ISSN: 1543-4494
Official Date: October 2012
Dates:
DateEvent
October 2012Published
Volume: Volume 40
Number: Number 3
Page Range: pp. 305-319
DOI: 10.3758/s13420-012-0082-6
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Alberta Innovates - Technology Futures , Natural Sciences and Engineering Research Council of Canada (NSERC)

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