Reward-dependent modulation of movement variability

Sarah E. Pekny, Jun Izawa, Reza Shadmehr

Research output: Contribution to journalArticle

Abstract

Movement variability is often considered an unwanted byproduct of a noisy nervous system. However, variability can signal a form of implicit exploration, indicating that the nervous system is intentionally varying the motor commands in search of actions that yield the greatest success. Here, we investigated the role of the human basal ganglia in controlling reward-dependent motor variability as measured by trial-to-trial changes in performance during a reaching task. We designed an experiment in which the only performance feedback was successorfailure and quantified how reach variability was modulated as a functionofthe probability of reward. In healthy controls, reach variability increasedasthe probability ofreward decreased. Controlof variability dependedonthe history ofpast rewards, with the largest trial-to-trial changes occurring immediately after an unrewarded trial. In contrast, in participants with Parkinson’s disease, a known example of basal ganglia dysfunction, reward was a poor modulator of variability; that is, the patients showed an impaired ability to increase variability in response to decreases in the probability of reward. This was despite the fact that, after rewarded trials, reach variability in the patients was comparable to healthy controls. In summary, we found that movement variability is partially a form of exploration driven by the recent history of rewards. When the function of the human basal ganglia is compromised, the reward-dependent control of movement variability is impaired, particularly affecting the ability to increase variability after unsuccessful outcomes.

Original languageEnglish (US)
Pages (from-to)4015-4024
Number of pages10
JournalJournal of Neuroscience
Volume35
Issue number9
DOIs
StatePublished - Jan 1 2015

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Keywords

  • Computational models
  • Motor control
  • Parkinson’s disease
  • Reaching

ASJC Scopus subject areas

  • Neuroscience(all)

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