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Probabilistic mechanisms in sensorimotor control.

Konrad P Körding1, Daniel M Wolpert

  • 1Sobell Department of Motor Neuroscience, Institute of Neurology, University College London, London, UK.

Novartis Foundation Symposium
|May 3, 2006
PubMed
Summary

The brain uses Bayesian inference to combine prior knowledge and sensory feedback, reducing uncertainty for better sensorimotor control. This probabilistic approach optimizes movement and decision-making despite noisy sensors and muscles.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Motor Control

Background:

  • Human sensorimotor control is inherently limited by sensory, motor, and task uncertainties.
  • Noisy sensors and unpredictable task variations challenge the central nervous system (CNS).

Purpose of the Study:

  • To review computational strategies employed by the CNS to manage uncertainty in sensorimotor control.
  • To explore how the CNS integrates prior knowledge and sensory feedback for state estimation.
  • To examine decision optimization and motor output constraints within a probabilistic framework.

Main Methods:

  • Bayesian inference for combining prior knowledge and sensory feedback.
  • Analysis of state estimation for self-body movement.
  • Examination of error criteria in targeted movements.
  • Modeling of signal-dependent noise in motor output.

Main Results:

  • The CNS reduces uncertainty by employing Bayesian combinations of prior knowledge and sensory feedback.
  • These Bayesian mechanisms are applicable to estimating the body's state during movement.
  • Movement performance is optimized based on probabilistic models, considering signal-dependent noise.

Conclusions:

  • A probabilistic framework, utilizing Bayesian inference, effectively explains sensorimotor control.
  • This framework accounts for how the CNS handles sensory, motor, and task uncertainties.
  • Goal-directed movement emerges from optimizing action statistics within this probabilistic model.

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