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  • 标题:Credit assignment to state-independent task representations and its relationship with model-based decision making
  • 本地全文:下载
  • 作者:Nitzan Shahar ; Rani Moran ; Tobias U. Hauser
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2019
  • 卷号:116
  • 期号:32
  • 页码:15871-15876
  • DOI:10.1073/pnas.1821647116
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要:Model-free learning enables an agent to make better decisions based on prior experience while representing only minimal knowledge about an environment’s structure. It is generally assumed that model-free state representations are based on outcome-relevant features of the environment. Here, we challenge this assumption by providing evidence that a putative model-free system assigns credit to task representations that are irrelevant to an outcome. We examined data from 769 individuals performing a well-described 2-step reward decision task where stimulus identity but not spatial-motor aspects of the task predicted reward. We show that participants assigned value to spatial-motor representations despite it being outcome irrelevant. Strikingly, spatial-motor value associations affected behavior across all outcome-relevant features and stages of the task, consistent with credit assignment to low-level state-independent task representations. Individual difference analyses suggested that the impact of spatial-motor value formation was attenuated for individuals who showed greater deployment of goal-directed (model-based) strategies. Our findings highlight a need for a reconsideration of how model-free representations are formed and regulated according to the structure of the environment.
  • 关键词:reinforcement learning ; decision making ; motor learning
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