Literature DB >> 21123665

Sensorimotor adaptation error signals are derived from realistic predictions of movement outcomes.

Aaron L Wong1, Mark Shelhamer.   

Abstract

Neural systems that control movement maintain accuracy by adaptively altering motor commands in response to errors. It is often assumed that the error signal that drives adaptation is equivalent to the sensory error observed at the conclusion of a movement; for saccades, this is typically the visual (retinal) error. However, we instead propose that the adaptation error signal is derived as the difference between the observed visual error and a realistic prediction of movement outcome. Using a modified saccade-adaptation task in human subjects, we precisely controlled the amount of error experienced at the conclusion of a movement by back-stepping the target so that the saccade is hypometric (positive retinal error), but less hypometric than if the target had not moved (smaller retinal error than expected). This separates prediction error from both visual errors and motor corrections. Despite positive visual errors and forward-directed motor corrections, we found an adaptive decrease in saccade amplitudes, a finding that is well-explained by the employment of a prediction-based error signal. Furthermore, adaptive changes in movement size were linearly correlated to the disparity between the predicted and observed movement outcomes, in agreement with the forward-model hypothesis of motor learning, which states that adaptation error signals incorporate predictions of motor outcomes computed using a copy of the motor command (efference copy).

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Year:  2010        PMID: 21123665      PMCID: PMC3074418          DOI: 10.1152/jn.00394.2010

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  31 in total

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9.  Cerebellar contributions to adaptive control of saccades in humans.

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  42 in total

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5.  Generalization patterns for reach adaptation and proprioceptive recalibration differ after visuomotor learning.

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6.  Multiple timescales in the adaptation of the rotational VOR.

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9.  Fine-scale plasticity of microscopic saccades.

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10.  A long-memory model of motor learning in the saccadic system: a regime-switching approach.

Authors:  Aaron L Wong; Mark Shelhamer
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