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States versus Rewards: Dissociable neural prediction error signals underlying model-based and model-free reinforcement learning

Reinforcement learning (RL) uses sequential experience with situations (“states”) and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outco...

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Autori principali: Gläscher, Jan, Daw, Nathaniel, Dayan, Peter, O’Doherty, John P.
Natura: Artigo
Lingua:Inglês
Pubblicazione: 2010
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Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC2895323/
https://ncbi.nlm.nih.gov/pubmed/20510862
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.neuron.2010.04.016
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