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Credit assignment to state-independent task representations and its relationship with model-based decision making.

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Shahar, Nitzan 
Moran, Rani 
Hauser, Tobias U 
Kievit, Rogier A 
McNamee, Daniel 


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.



decision making, motor learning, reinforcement learning

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Proc Natl Acad Sci U S A

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Proceedings of the National Academy of Sciences
Wellcome Trust (095844/Z/11/Z)
Wellcome Trust (107392/Z/15/Z)
Medical Research Council (MC_UP_1401/1)
Medical Research Council (MC_UU_00005/9)
Wellcome Trust (110257/Z/15/Z)