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

Samuel D. McDougle, Peter A. Butcher, Darius E. Parvin, Fasial Mushtaq, Yael Niv, Richard B. Ivry, Jordan A. Taylor

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1606-1613.e5
JournalCurrent Biology
Issue number10
StatePublished - May 20 2019

All Science Journal Classification (ASJC) codes

  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences


  • decision making
  • error
  • reaching
  • reinforcement learning
  • striatum


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