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      Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems.

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          Abstract

          Human behavior is sometimes determined by habit and other times by goal-directed planning. Modern reinforcement-learning theories formalize this distinction as a competition between a computationally cheap but inaccurate model-free system that gives rise to habits and a computationally expensive but accurate model-based system that implements planning. It is unclear, however, how people choose to allocate control between these systems. Here, we propose that arbitration occurs by comparing each system's task-specific costs and benefits. To investigate this proposal, we conducted two experiments showing that people increase model-based control when it achieves greater accuracy than model-free control, and especially when the rewards of accurate performance are amplified. In contrast, they are insensitive to reward amplification when model-based and model-free control yield equivalent accuracy. This suggests that humans adaptively balance habitual and planned action through on-line cost-benefit analysis.

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          Author and article information

          Journal
          Psychol Sci
          Psychological science
          SAGE Publications
          1467-9280
          0956-7976
          Sep 2017
          : 28
          : 9
          Affiliations
          [1 ] 1 Department of Psychology, Harvard University.
          [2 ] 2 Center for Brain Science, Harvard University.
          Article
          10.1177/0956797617708288
          28731839
          61cb0010-5b98-46c9-83d3-3af0a854095d
          History

          cognitive control,decision making,open data,open materials,reinforcement learning

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