Task complexity interacts with state-space uncertainty in the arbitration between model-based and model-free learning

The brain dynamically arbitrates between two model-based and model-free reinforcement learning (RL). Here, the authors show that participants tended to increase model-based control in response to increasing task complexity, but resorted to model-free when both uncertainty and task complexity were hi...

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Autores principales: Dongjae Kim, Geon Yeong Park, John P. O′Doherty, Sang Wan Lee
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Acceso en línea:https://doaj.org/article/a966c6e9a9d94f03aa0fe7722aa4c2e6
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Sumario:The brain dynamically arbitrates between two model-based and model-free reinforcement learning (RL). Here, the authors show that participants tended to increase model-based control in response to increasing task complexity, but resorted to model-free when both uncertainty and task complexity were high.