Neural Signatures of Prediction Errors in a Decision-Making Task Are Modulated by Action Execution Failures

Decisions must be implemented through actions, and actions are prone to error. As such, when an expected outcome is not obtained, an individual should be sensitive to not only whether the choice itself was suboptimal but also whether the action required to indicate that choice was executed successfully. The intelligent assignment of credit to action execution versus action selection has clear ecological utility for the learner. To explore this, we used a modified version of a classic reinforcement learning task in which feedback indicated whether negative prediction errors were, or were not, associated with execution errors. Using fMRI, we asked if prediction error computations in the human striatum, a key substrate in reinforcement learning and decision making, are modulated when a failure in action execution results in the negative outcome. Participants were more tolerant of non-rewarded outcomes when these resulted from execution errors versus when execution was successful, but reward was withheld. Consistent with this behavior, a model-driven analysis of neural activity revealed an attenuation of the signal associated with negative reward prediction errors in the striatum following execution failures. These results converge with other lines of evidence suggesting that prediction errors in the mesostriatal dopamine system integrate high-level information during the evaluation of instantaneous reward outcomes. •Humans devalue choices less following execution versus selection errors•Reward prediction errors in the striatum are attenuated following execution errors•Different error classes have distinct neural signatures Failure to obtain a desired outcome could arise from selecting the wrong action or errors in executing the correct action. McDougle et al. show that humans are sensitive to this distinction, down-weighting outcomes credited to motor error. Activity in the striatum, a subcortical region critical for reinforcement learning, reflects this behavior.