Trial-to-trial adaptation in control of arm reaching and standing posture

Abstract

Classical theories of motor learning hypothesize that adaptation is driven by sensorimotor error; this is supported by studies of arm and eye movements that have shown that trial-to-trial adaptation increases with error. Studies of postural control have shown that anticipatory postural adjustments increase with the magnitude of a perturbation. However, differences in adaptation have been observed between the two modalities, possibly due to either the inherent instability or sensory uncertainty in standing posture. Therefore, we hypothesized that trial-to-trial adaptation in posture should be driven by error, similar to what is observed in arm reaching, but the nature of the relationship between error and adaptation may differ. Here we investigated trial-to-trial adaptation of arm reaching and postural control concurrently; subjects made reaching movements in a novel dynamic environment of varying strengths, while standing and holding the handle of a force-generating robotic arm. We found that error and adaptation increased with perturbation strength in both arm and posture. Furthermore, in both modalities, adaptation showed a significant correlation with error magnitude. Our results indicate that adaptation scales proportionally with error in the arm and near proportionally in posture. In posture only, adaptation was not sensitive to small error sizes, which were similar in size to errors experienced in unperturbed baseline movements due to inherent variability. This finding may be explained as an effect of uncertainty about the source of small errors. Our findings suggest that in rehabilitation, postural error size should be considered relative to the magnitude of inherent movement variability.

Document Details

Document Type
Pub Defense Publication
Publication Date
Dec 01, 2016
Source ID
10.1152/jn.00537.2016

Entities

People

  • Alaa A. Ahmed
  • Alison Pienciak-siewert
  • Dylan P. Horan

Organizations

  • National Science Foundation
  • University of Colorado

Tags

Fields of Study

  • Psychology

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Exercise and Sports Science.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy
  • Autonomy
  • Autonomy - Autonomous System Control