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Predicting explorative motor learning using decision-making and motor noise.

Xiuli Chen1, Kieran Mohr1, Joseph M Galea1

  • 1School of Psychology, University of Birmingham, Birmingham, United Kingdom.

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|April 25, 2017
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Summary
This summary is machine-generated.

This study reveals that human motor learning, like decision-making, can be modeled as an optimal solution to a partially observable process. Performance in decision-making tasks predicts motor learning, accounting for neural noise.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Robotics

Background:

  • Human motor learning involves selecting actions based on sensory input and world knowledge.
  • This motor learning problem may share similarities with higher-level decision-making tasks.
  • Investigating the link between decision-making and motor learning performance is crucial.

Purpose of the Study:

  • To determine if decision-making task performance predicts motor learning performance.
  • To model explorative motor learning and decision-making using a unified framework.
  • To understand the role of neural noise in motor learning.

Main Methods:

  • Compared participant performance on an explorative motor learning task and a decision-making task.
  • Designed tasks with similar underlying structures, differing in motor execution noise.
  • Collected independent measurements of individual motor noise levels.

Main Results:

  • Both explorative motor learning and decision-making were modeled as optimal solutions to a Partially Observable Markov Decision Process (POMDP).
  • The model successfully predicted motor learning performance using parameters from the decision-making task and motor noise measurements.
  • Neural information processing noise was identified as a bounding factor.

Conclusions:

  • Explorative motor learning can be formalized as a sequential decision-making process adjusted for motor noise.
  • Decision-making and motor learning share underlying computational principles.
  • This research opens avenues for exploring the neural basis of motor learning.