Decoding self-motion: insights into visual-vestibular causal inference.

Self-motion perception relies on the integration of visual optic flow and vestibular signals, yet these cues are often noisy, unreliable, or mutually inconsistent. A central challenge is therefore not only how to integrate them, but also whether they should be integrated at all. Bayesian causal inference provides a framework for this problem by proposing that the brain infers whether visual and vestibular cues arise from a common cause and adjusts their integration accordingly. Behavioral findings, including disparity-dependent perceptual biases, reliability-dependent cue reweighting, and experience-dependent recalibration, are compatible with this framework, but are not diagnostic of causal inference in isolation and may also arise from alternative integration or decision mechanisms. At the neural level, studies in humans and nonhuman primates have identified a distributed self-motion network involving MSTd, VIP, VPS/PIVC, and frontal and parietal association areas. However, current evidence mainly concerns multisensory convergence, cue weighting, and conflict sensitivity rather than a direct neural representation of causal structure. Here, we review visual-vestibular causal inference from three linked perspectives: the computational framework and its domain-specific assumptions, the strength and limitations of the behavioral model evidence, and the candidate neural substrates and circuit mechanisms. We conclude by highlighting major open questions and future directions for understanding causal inference during naturalistic self-motion.