Bayesian integration of visual and vestibular signals for heading

Bayesian integration of visual and vestibular signals for heading

Abstract

Self-motion through an environment involves a composite of signals such as visual and vestibular cues. Building upon previous results showing that visual and vestibular signals combine in a statistically optimal fashion, we investigated the relative weights of visual and vestibular cues during self motion. This experiment is comprised of three experimental conditions; vestibular alone, visual alone (with four different standard heading values) and visual-vestibular combined. In the combined cue condition, inter-sensory conflicts were introduced (Δ=±6°or ±10°). Participants performed a 2-interval forced choice task in all conditions and were asked to judge “in which of the two intervals did you move more to the right”. The cue-conflict condition revealed the relative weights associated with each modality. We found that even when there was a relatively large conflict between the visual and vestibular cues, participants exhibited a statistically optimal reduction in variance. On the other hand, we found that the pattern of results in the unimodal conditions did not predict the weights in the combined cue condition. Specifically, visual-vestibular cue combination was not predicted solely by the reliability of each cue, but rather more weight was given to the vestibular cue.

Publication
Journal of Vision

And my badly painted version of the Figure 4:

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John S Butler
Lecturer in Mathematics and Statistics

My research interests are the application of computational, statistical and numerical methods for basic and translational research in Neuroscience, Neurology and Optometry.