A novel optic flow pattern speeds split-belt locomotor adaptation

James M. Finley, Matthew A. Statton, Amy J. Bastian

Research output: Contribution to journalArticlepeer-review

Abstract

Visual input provides vital information for helping us modify our walking pattern. For example, artificial optic flow can drive changes in step length during locomotion and may also be useful for augmenting locomotor training for individuals with gait asymmetries. Here we asked whether optic flow could modify the acquisition of a symmetric walking pattern during split-belt treadmill adaptation. Participants walked on a split-belt treadmill while watching a virtual scene that produced artificial optic flow. For the Stance Congruent group, the scene moved at the slow belt speed at foot strike on the slow belt and then moved at the fast belt speed at foot strike on the fast belt. This approximates what participants would see if they moved over ground with the same walking pattern. For the Stance Incongruent group, the scene moved fast during slow stance and vice versa. In this case, flow speed does not match what the foot is experiencing, but predicts the belt speed for the next foot strike. Results showed that the Stance Incongruent group learned more quickly than the Stance Congruent group even though each group learned the same amount during adaptation. The increase in learning rate was primarily driven by changes in spatial control of each limb, rather than temporal control. Interestingly, when this alternating optic flow pattern was presented alone, no adaptation occurred. Our results demonstrate that an unnatural pattern of optic flow, one that predicts the belt speed on the next foot strike, can be used to enhance learning rate during split-belt locomotor adaptation.

Original languageEnglish (US)
Pages (from-to)969-976
Number of pages8
JournalJournal of neurophysiology
Volume111
Issue number5
DOIs
StatePublished - Mar 1 2014

Keywords

  • Adaptation
  • Locomotion
  • Motor learning
  • Optic flow

ASJC Scopus subject areas

  • Neuroscience(all)
  • Physiology

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