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Learning stochastic reward distributions in a speeded pointing task.

Anna Seydell1, Brian C McCann, Julia Trommershäuser

  • 1Department of Psychology, University of Giessen, 35394 Giessen, Germany. anna.seydell@psychol.uni-giessen.de

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|April 25, 2008
PubMed
Summary

Humans can learn to adapt their hand movements to unpredictable environmental randomness, optimizing strategies for stochastic rewards and penalties through trial and error.

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Area of Science:

  • Cognitive Neuroscience
  • Motor Control
  • Decision Making

Background:

  • Humans effectively account for intrinsic motor noise in movement planning.
  • Difficulty exists in adapting to arbitrary environmental stochasticity in decision-making and sensorimotor tasks.

Purpose of the Study:

  • To investigate if humans can learn to optimize movement strategies in response to environmental randomness.
  • To determine if implicit learning of stochasticity improves performance in sensorimotor tasks.

Main Methods:

  • Subjects performed a hand-pointing task with stochastic penalty regions ('defenders').
  • Defenders randomly jumped from fixed probability distributions after each trial.
  • Performance was measured by points earned for hitting the target without penalty.

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Main Results:

  • Subjects approached optimal behavior after approximately 600 trials.
  • Learned strategies generalized to new penalty distributions, indicating learning of underlying statistics.
  • Subjects adapted movement planning to stochastic rewards and penalties.

Conclusions:

  • Humans can implicitly learn and adapt to environmental randomness over multiple trials.
  • This learning enables optimal planning of hand movements under stochastic conditions.
  • Knowledge of probability distributions, not just stimulus-contingent plans, is acquired.