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Visual Limit-Push Training Alters Movement Variability

Eyad Hajissa, Amit Shah, James L. Patton

Year
2017
Citations
7
Access
Open access

Abstract

In both movement training and neurorehabilitation, there have been numerous examples of how average performance can be manipulated through practice using enhanced visual feedback. OBJECTIVE: Rather than just influencing the mean, our objective was to use a novel feedback technique called limit-push to influence the trial-to-trial variability of motion by distorting vision. METHOD: Limit-push was previously done using robotic forces; the present study employed only visual distortions that imitated the limit-push approach. RESULTS: Like the robotic force treatment, our results showed how subjects significantly shifted the distributions of their motions. This effect was even greater than that of the original limit-push experiment that used robotic forces. SIGNIFICANCE: Such visual distortion interventions do not require a robot for enhanced training. CONCLUSION: The visual limit-push technique appears to be able to selectively alter both the central tendency and variability in performance training applications.

Keywords

Limit (mathematics)Visual feedbackComputer scienceNeurorehabilitationTraining (meteorology)Computer visionArtificial intelligenceDistortion (music)Movement (music)Robot

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