Bayesian estimation of sensory priors in human spatial orientation

Abstract

Perception of spatial orientation relies on integration of sensory information encoding body position with external cues, such as visual inputs from the environment. Inherent variability in sensory inputs plays a critical role in how sensory information is processed. Unlike physical stimuli, neural encoding and transformation of these signals is subject to noise introduced at multiple levels within the sensory pathway. The Bayesian observer model provides a robust computational framework for understanding how the brain accommodates uncertainty to generate earth-vertical perception. Within the Bayesian framework, Gaussian prior distributions serve to constrain noisy sensory signals, allowing the brain to generate reliable estimates even in the presence of ambiguous input. These sensory priors are typically assumed to be centred at zero based on the premise that the brain defaults to upright body orientation when interpreting sensory signals. In this study, we compute the sensory priors associated with the eye and head orientation as two key sensory contributors to spatial orientation. These priors were estimated as free parameters within the Bayesian spatial model by combining measurements of subjective visual vertical (SVV) with perceived head and eye orientations. Our findings reveal that the Bayesian framework can effectively identify the sensory priors for eye and head orientation that contribute to spatial orientation. The results demonstrate the potential of using sensory priors as individualized functional markers for the neural mechanisms underlying spatial orientation.