Inference of perceptual priors from path dynamics of passive self-motion

Abstract

The monitoring of one's own spatial orientation depends on the ability to estimate successive self-motion cues accurately. This process has become to be known as path integration. A feature of sequential cue estimation, in general, is that the history of previously experienced stimuli, or priors, biases perception. Here, we investigate how during angular path integration, the prior imparted by the displacement path dynamics affects the translation of vestibular sensations into perceptual estimates. Subjects received successive whole-body yaw rotations and were instructed to report their position within a virtual scene after each rotation. The overall movement trajectory either followed a parabolic path or was devoid of explicit dynamics. In the latter case, estimates were biased toward the average stimulus prior and were well captured by an optimal Bayesian estimator model fit to the data. However, the use of parabolic paths reduced perceptual uncertainty, and a decrease of the average size of bias and thus the weight of the average stimulus prior were observed over time. The produced estimates were, in fact, better accounted for by a model where a prediction of rotation magnitude is inferred from the underlying path dynamics on each trial. Therefore, when passively displaced, we seem to be able to build, over time, from sequential vestibular measurements an internal model of the vehicle's movement dynamics. Our findings suggest that in ecological conditions, vestibular afference can be internally predicted, even when self-motion is not actively generated by the observer, thereby augmenting both the accuracy and precision of displacement perception.