Research in the vestibular field has revealed the existence of a central process, called ‘velocity storage’, that is activated by both visual and vestibular rotation cues and is modified by gravity, but whose functional relevance during natural motion has often been questioned. In this review, we explore spatial orientation in the context of a Bayesian model of vestibular information processing. In this framework, deficiencies/ambiguities in the peripheral vestibular sensors are compensated for by central processing to more accurately estimate rotation velocity, orientation relative to gravity, and inertial motion. First, an inverse model of semicircular canal dynamics is used to reconstruct rotation velocity by integrating canal signals over time. However, its low-frequency bandwidth is limited to avoid accumulation of noise in the integrator. A second internal model uses this reconstructed rotation velocity to compute an internal estimate of tilt and inertial acceleration. The bandwidth of this second internal model is also restricted at low frequencies to avoid noise accumulation and drift of the tilt/translation estimator over time. As a result, low-frequency translation can be erroneously misinterpreted as tilt. The time constants of these two integrators (internal models) can be conceptualized as two Bayesian priors of zero rotation velocity and zero linear acceleration, respectively. The model replicates empirical observations like ‘velocity storage’ and ‘frequency segregation’ and explains spatial orientation (e.g., ‘somatogravic’) illusions. Importantly, the functional significance of this network, including velocity storage, is found during short-lasting, natural head movements, rather than at low frequencies with which it has been traditionally studied.

Summary A fundamental challenge for maintaining spatial orientation and interacting with the world is knowledge of our orientation relative to gravity, i.e. tilt. Sensing gravity is complicated because of Einstein’s equivalence principle, where gravitational and translational accelerations are physically indistinguishable. Theory has proposed that this ambiguity is solved by tracking head tilt through multisensory integration. Here we identify a group of Purkinje cells in the caudal cerebellar vermis with responses that reflect an estimate of head tilt. These tilt-selective cells are complementary to translation-selective Purkinje cells, such that their population activities sum to the net gravito-inertial acceleration encoded by the otolith organs, as predicted by theory. These findings reflect the remarkable ability of the cerebellum for neural computation and provide novel quantitative evidence for a neural representation of gravity, whose calculation relies on long-postulated theoretical concepts such as internal models and Bayesian priors.

Complex self-motion stimulations in the dark can be powerfully disorienting and can create illusory motion percepts. In the absence of visual cues, the brain has to use angular and linear acceleration information provided by the vestibular canals and the otoliths, respectively. However, these sensors are inaccurate and ambiguous. We propose that the brain processes these signals in a statistically optimal fashion, reproducing the rules of Bayesian inference. We also suggest that this processing is related to the statistics of natural head movements. This would create a perceptual bias in favour of low velocity and acceleration. We have constructed a Bayesian model of self-motion perception based on these assumptions. Using this model, we have simulated perceptual responses to centrifugation and off-vertical axis rotation and obtained close agreement with experimental findings. This demonstrates how Bayesian inference allows to make a quantitative link between sensor noise and ambiguities, statistics of head movement, and the perception of self-motion.

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