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Causal inference for spatial constancy across whole body motion.

Florian Perdreau1, James R H Cooke1, Mathieu Koppen1

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|November 22, 2018
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Summary

The brain weighs visual information against self-motion updates to maintain spatial awareness. It uses Bayesian causal inference to decide whether visual feedback or internal estimates are more reliable, ensuring spatial constancy during movement.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Perception

Background:

  • The brain updates object locations using self-motion cues.
  • Discrepancies between internal updates and visual feedback pose a challenge for spatial constancy.
  • Estimating causal probabilities is crucial for integrating or segregating sensory information.

Purpose of the Study:

  • To investigate how the brain resolves discrepancies between self-motion-based spatial updates and visual feedback.
  • To test whether Bayesian causal inference underlies the integration of egocentric and exocentric spatial information.
  • To understand the neural mechanisms of spatial constancy during whole-body motion.

Main Methods:

  • A spatial updating task using passive whole-body translation on a vestibular sled.
  • Participants remembered a visual target's world-fixed position after translation.
  • Reafferent visual feedback was provided by a second target, and participants reported the initial location.

Main Results:

  • Participant responses showed a bias towards the visual feedback for small discrepancies.
  • This bias diminished with larger discrepancies between updated and feedback target locations.
  • A Bayesian causal inference model accurately predicted these response patterns.

Conclusions:

  • The brain implicitly estimates the probability of a common cause for internal updates and visual feedback.
  • This probability is used to dynamically weigh self-motion and visual information for spatial constancy.
  • This mechanism supports robust spatial perception despite potential conflicts in sensory information.