Abstract
In this paper, we demonstrate that predicting stimulus co-occurrence patterns in a Bayes-optimal manner endogenously explains classical conditioning. Simulated experiments with a standard Bayesian implementation of this model show that it is capable of explaining a broader range of effects than any previous theory of classical conditioning. By simplifying the mathematical structure of statistical modelling of conditioning and demonstrating its ability to explain a large set of experimentally observed effects, our work advances Bayes-optimal inference about stimulus co-occurrence as a rational principle explaining classical conditioning.
| Original language | English (US) |
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| Title of host publication | Proceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014 |
| Publisher | The Cognitive Science Society |
| Pages | 1503-1508 |
| Number of pages | 6 |
| ISBN (Electronic) | 9780991196708 |
| State | Published - 2014 |
| Event | 36th Annual Meeting of the Cognitive Science Society, CogSci 2014 - Quebec City, Canada Duration: Jul 23 2014 → Jul 26 2014 |
Publication series
| Name | Proceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014 |
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Conference
| Conference | 36th Annual Meeting of the Cognitive Science Society, CogSci 2014 |
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| Country/Territory | Canada |
| City | Quebec City |
| Period | 7/23/14 → 7/26/14 |
Bibliographical note
Publisher Copyright:© 2014 Proceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014. All rights reserved.
Keywords
- Bayesian modelling
- computer simulation
- decision-making
- learning