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  Contingency and Correlation in Reversal Learning

Pletras, B., Stalnaker, T., Yu, T.-L., Schoenbaum, G., & Dayan, P. (2015). Contingency and Correlation in Reversal Learning. In 2nd Multi-Disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2015) (pp. 33).

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-DAB7-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-DAC9-4
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Pletras, B, Author
Stalnaker, TA, Author
Yu, T-L, Author
Schoenbaum, G, Author
Dayan, P1, Author              
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1External Organizations, ou_persistent22              

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 Abstract: Reversal learning is one of the most venerable paradigms for studying the acquisition, extinction, and reacquisition of knowledge in humans and other animals. It has been of particular value in asking questions about the roles played by prefrontal structures such as the orbitofrontal cortex (OFC). Indeed, evidence from rats and monkeys suggests that these areas are involved in various forms of context-sensitive inference about the contingencies linking cues and actions over time to the value and identity of predicted outcomes. In order to explore these roles in depth, we fit data from a substantial behavioural neuroscience study in rodents who experienced blocks of free- and forced-choice instrumental learning trials with identity or value reversals at each block transition. We constructed two classes of models, fit their parameters using a random effects treatment, tested their generative competence, and selected between them based on a complexity-sensitive integrated Bayesian Information Criteria score. One class of ’return’-based models was based on elaborations of a standard Q-learning algorithm, including parameters such as different learning rates or combination rules for forced- and fixed-choice trials, behavioural lapses, and eligibility traces. The other novel class of ’income’-based models exploited the weak notion of contingency over time advocated by Walton et al (2010) in their analysis of the choices of monkeys with OFC lesions. We show that income-based and return-based models are both able to predict the behaviour well, and examine their performance and implications for reinforcement learning. The outcome of this study sets the stage for the next phase of the research that will attempt to correlate the values of the parameters to neural recordings taken in the rats while performing the task.

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 Dates: 2015-06
 Publication Status: Published online
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Title: 2nd Multi-Disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2015)
Place of Event: Edmonton, AB, Canada
Start-/End Date: 2015-06-07 - 2015-06-10

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Title: 2nd Multi-Disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2015)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: M51 Start / End Page: 33 Identifier: -