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Updated: Aug 23, 2025

Assessment of Static Graviceptive Perception in the Roll-Plane using the Subjective Visual Vertical Paradigm
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Natural statistics of head roll: implications for Bayesian inference in spatial orientation.

Sophie C M J Willemsen1, Leonie Oostwoud Wijdenes1, Robert J van Beers1,2

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Journal of Neurophysiology
|November 2, 2022
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Summary

A Gaussian prior best explains spatial orientation perception, despite natural head movements having non-Gaussian statistics. This Bayesian model accurately predicts subjective visual vertical and body tilt, outperforming models with non-Gaussian priors.

Keywords:
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Area of Science:

  • Neuroscience
  • Perception
  • Computational modeling

Background:

  • Bayesian models are used to understand multisensory integration in spatial orientation.
  • A previously proposed Gaussian prior model successfully explained perceptual phenomena like subjective visual vertical and body tilt.
  • Natural head motion statistics are increasingly recognized as non-Gaussian, prompting an investigation into their impact on Bayesian models.

Purpose of the Study:

  • To investigate the performance of a Bayesian model for spatial orientation using a non-Gaussian prior.
  • To compare the explanatory power of Gaussian versus non-Gaussian priors for perceptual data.
  • To explore the computational and neurophysiological basis for the choice of prior distribution in vertical perception.

Main Methods:

  • Experimentally characterized natural head orientation statistics, quantifying them as a t-location-scale distribution.
  • Compared the performance of the original Bayesian model with a Gaussian prior against variants using a t-distributed prior.
  • Evaluated model performance on previously published data for subjective visual vertical and subjective body tilt tasks.

Main Results:

  • Natural head orientation statistics exhibit long tails, consistent with a t-location-scale distribution.
  • Bayesian model variants employing a t-distributed prior performed substantially worse than the original model with a Gaussian prior.
  • The Gaussian prior demonstrated a superior account of perceptual observations compared to non-Gaussian alternatives.

Conclusions:

  • A Gaussian prior centered on upright head orientation remains the most effective for explaining spatial orientation perception.
  • Despite natural head motion statistics being non-Gaussian, a Gaussian prior offers a better fit for perceptual data.
  • The precision-accuracy trade-off associated with the Gaussian prior likely underlies its effectiveness across the tilt range.