Bayesian processing of vestibular information

Abstract

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.

Spinning versus Wobbling: How the Brain Solves a Geometry Problem

“…This estimate can be used to extract the inertial and gravitational components of the otolith afferent signals, in agreement with previous studies (Angelaki et al, , 2002(Angelaki et al, , 2004Merfeld et al, 1999). Our analysis suggests that conflicting motion signals are resolved by a process of optimal estimation, similar as shown in numerous previous studies (Ernst and Banks, 2002;Weiss et al, 2002;MacNeilage et al, 2007;Angelaki et al, 2009;Fetsch et al, 2009) as well as in modeling work on vestibular information processing (Laurens et al, 2007. By demonstrating that the brain effectively uses geometrically consistent three-dimensional representations, this study supports the notion that the internal model hypothesis as formulated in the general context of motor control (Ito, 1989;Kawato, 1999;Davidson and Wolpert, 2005) is also an important concept for understanding how the brain solves spatial orientation problems.…”

“…The experimental results were compared with simulations performed with an optimal Bayesian model as described by Laurens and Droulez (2007Droulez ( , 2008. This model computes an optimal estimate of head and body motion in space by assigning a probability to "every" possible motion in space-in fact, the model only considers plausible motions in space, which are selected by a sampling process called particle filtering (Maskell and Gordon, 2002).…”

“…bias, which, however, plays a minor role in modeling the present paradigms. Finally, it is assumed that the visual-optokinetic pathways carry head velocity information relative to the environment with a certain signal-to-noise ratio that allows reproducing the phenomena of optokinetic nystagmus and afternystagmus (Laurens and Droulez, 2007). We used the same model parameters as in the study by Laurens and Droulez (2008) …”

Measurement of instantaneous perceived self-motion using continuous pointing

“…6c). The decrease in perceived velocity during constant-velocity travel could result from either a leaky integration of inertial information, high-pass Wltering of velocity information, or Bayesian assumptions about stationarity in the absence of an otolith signal (Laurens and Droulez 2007). These models would likely make diVerent predictions about the responses to more complex trajectories and could be diVerentiated through further applications of the continuous-pointing method.…”

Modeling Verticality Estimation During Locomotion

“…This structure makes it difficult to interpret this model in accordance with physical and mechanical principles. More recently, J. Laurens and J. Droulez constructed a Bayesian processing model of self-motion perception in [121]. It was proposed that the brain processes these signals in a statically optimal fashion, reproducing the rules of Bayesian inference.…”