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Reconciling reinforcement learning models with behavioral extinction and renewal: Implications for addiction, relapse, and problem gambling (2007)
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| Venue: | Psychological Review |
| Citations: | 41 - 4 self |
BibTeX
@ARTICLE{Redish07reconcilingreinforcement,
author = {A. David Redish and Steve Jensen and Adam Johnson and Zeb Kurth-nelson},
title = {Reconciling reinforcement learning models with behavioral extinction and renewal: Implications for addiction, relapse, and problem gambling},
journal = {Psychological Review},
year = {2007},
pages = {784--805}
}
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Abstract
Because learned associations are quickly renewed following extinction, the extinction process must include processes other than unlearning. However, reinforcement learning models, such as the temporal difference reinforcement learning (TDRL) model, treat extinction as an unlearning of associated value and are thus unable to capture renewal. TDRL models are based on the hypothesis that dopamine carries a reward prediction error signal; these models predict reward by driving that reward error to zero. The authors construct a TDRL model that can accommodate extinction and renewal through two simple processes: (a) a TDRL process that learns the value of situation–action pairs and (b) a situation recognition process that categorizes the observed cues into situations. This model has implications for dysfunctional states, including relapse after addiction and problem gambling.
Keyphrases
problem gambling behavioral extinction tdrl model dysfunctional state observed cue tdrl process situation recognition process reinforcement learning model simple process extinction process treat extinction reward error situation action pair temporal difference reinforcement learning reward prediction
