The neural feedback response to error as a teaching signal for the motor learning system

Scott T. Albert, Reza Shadmehr

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


When we experience an error during a movement, we update our motor commands to partially correct for this error on the next trial. How does experience of error produce the improvement in the subsequent motor commands? During the course of an erroneous reaching movement, proprioceptive and visual sensory pathways not only sense the error, but also engage feedback mechanisms, resulting in corrective motor responses that continue until the hand arrives at its goal. One possibility is that this feedback response is co-opted by the learning system and used as a template to improve performance on the next attempt. Here we used electromyography (EMG) to compare neural correlates of learning and feedback to test the hypothesis that the feedback response to error acts as a template for learning. We designed a task in which mixtures of error-clamp and force-field perturbation trials were used to deconstruct EMG time courses into error-feedback and learning components. We observed that the error-feedback response was composed of excitation of some muscles, and inhibition of others, producing a complex activation/deactivation pattern during the reach. Despite this complexity, across muscles the learning response was consistently a scaled version of the error-feedback response, but shifted 125 ms earlier in time. Across people, individuals who produced a greater feedback response to error, also learned more from error. This suggests that the feedback response to error serves as a teaching signal for the brain. Individuals who learn faster have a better teacher in their feedback control system.

Original languageEnglish (US)
Pages (from-to)4832-4845
Number of pages14
JournalJournal of Neuroscience
Issue number17
StatePublished - Apr 27 2016


  • Error-feedback response
  • Motor control
  • Motor learning

ASJC Scopus subject areas

  • Neuroscience(all)


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