The Neural Mechanisms of Visual and Vestibular Interaction in Self-Motion Perception

TL;DR

This paper reviews how the brain combines visual and inner ear signals to perceive self-motion and how this integration can be disrupted in certain conditions.

Contribution

The paper provides a comprehensive review of visual–vestibular integration mechanisms and their computational models.

Findings

The brain uses a reliability-weighted averaging strategy to integrate visual and vestibular cues.

Bayesian inference and divisive normalization are key models for understanding multisensory integration.

Disruptions in this integration are linked to motion sickness and neurological disorders like autism and Parkinson’s disease.

Abstract

The brain maintains a stable sense of self-motion by flexibly combining visual information (optic flow) with vestibular signals from the inner ear’s motion sensors. This integration allows us to move, walk, and balance effectively in everyday life. When visual and vestibular cues disagree—such as during virtual reality, turbulent motion, or certain neurological conditions—people can experience motion sickness, disorientation, or perceptual disturbances, which are also observed in disorders like autism and Parkinson’s disease. Research indicates that the brain uses a near-optimal, reliability-weighted averaging strategy: it dynamically assesses the trustworthiness of each cue and adjusts their influence accordingly. Understanding these mechanisms not only clarifies how we navigate complex environments but also informs potential interventions for conditions involving impaired sensory integration. This review synthesizes behavioral, neurophysiological, computational, and imaging findings to highlight how dynamic visual–vestibular integration underpins accurate self-motion perception and may guide therapeutic approaches for sensory integration disorders.

Self-motion perception is a complex multisensory process that relies on the integration of various sensory signals, particularly visual and vestibular inputs, to construct stable and unified perceptions. It is essential for spatial navigation and effective interaction with the environment. This review systematically explores the mechanisms and computational principles underlying visual–vestibular integration in self-motion perception. We first outline the individual contributions of visual and vestibular cues and then introduce Bayesian inference as a normative framework for the quantitative modeling of multisensory integration. We also discuss multisensory recalibration as a critical mechanism in resolving conflicts between sensory inputs and maintaining perceptual stability. Using heading perception as a model system, we further describe the relevant visual and vestibular pathways involved in this process, as well as the brain regions involved. Finally, we discuss the neural mechanisms mediating visual–vestibular interactions through models of the Bayesian optimal integration and divisive normalization.

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