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Updated: Sep 14, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
Bayesian estimation of sensory priors in human spatial orientation
Jing Tian1, Min Jung Kim1, Ha-Jun Yoon2
1Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States.
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 are 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 centered 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.NEW & NOTEWORTHY By combining measurements of subjective visual vertical (SVV) with perceived head and eye orientations, we applied the Bayesian observer model to derive the sensory priors for eye and head orientation. Our findings reveal that the Bayesian spatial model can effectively identify the sensory priors of eye and head orientation that contribute to spatial orientation. This approach demonstrates the potential of sensory priors as individualized functional markers for the neural mechanisms underlying spatial orientation.
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