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Multi-sensory weights depend on contextual noise in reference frame transformations.

Jessica Katherine Burns1, Gunnar Blohm

  • 1Centre for Neuroscience Studies, Queen's University Kingston, ON, Canada.

Frontiers in Human Neuroscience
|December 18, 2010
PubMed
Summary
This summary is machine-generated.

The brain adjusts how it combines vision and proprioception based on head position. This sensory integration accounts for noise in head roll signals, optimizing reaching movements.

Keywords:
Bayesian integrationcontexthead rollmulti-sensoryproprioceptionreachingreference framesvision

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Motor Control

Background:

  • Multi-sensory integration combines visual and proprioceptive information for accurate hand localization during reaching.
  • Extraretinal signals, like head roll, are crucial for aligning sensory reference frames before integration.
  • Noisy head roll signals can impact the reliability of reference frame transformations, influencing sensory weighting.

Purpose of the Study:

  • To investigate how extraretinal head roll signals affect multi-sensory integration during reaching.
  • To determine if noisy reference frame transformations influence the weighting of visual and proprioceptive information.
  • To test the predictions of a novel probabilistic multi-sensory integration model incorporating explicit reference frame transformations.

Main Methods:

  • Developed a novel probabilistic (Bayesian) multi-sensory integration model with noisy reference frame transformations.
  • Conducted a reaching experiment introducing conflicts between visual and actual hand position.
  • Measured reach errors under varying head roll orientations to assess multi-sensory integration.

Main Results:

  • Eccentric head roll orientations increased movement variability, aligning with model predictions.
  • The weighting of vision and proprioception was found to depend on head roll.
  • Results suggest the brain dynamically adjusts sensory reliability based on context.

Conclusions:

  • The brain possesses online knowledge of the statistics of its internal sensory representations.
  • Sensory reliability is utilized context-dependently to modulate multi-sensory integration weights for reaching.
  • Head roll influences the weighting of visual and proprioceptive signals due to signal-dependent noise.