Bayesian estimation of sensory priors in human spatial orientation

Area of Science:

• Neuroscience
• Computational Neuroscience
• Sensory Processing

Background:

• Spatial orientation relies on integrating body position with environmental cues.
• Neural processing of sensory information is susceptible to noise, impacting perception.
• The Bayesian observer model offers a framework for understanding how the brain handles uncertainty.

Purpose of the Study:

• To compute sensory priors for eye and head orientation within a Bayesian framework.
• To investigate the role of these priors in spatial orientation perception.
• To explore the potential of sensory priors as individualized neural markers.

Main Methods:

• Estimated sensory priors as free parameters in a Bayesian spatial model.
• Combined subjective visual vertical (SVV) measurements with perceived head and eye orientations.
• Applied Bayesian inference to analyze sensory signal processing.

Main Results:

• The Bayesian framework successfully identified sensory priors for eye and head orientation.
• These priors are crucial contributors to accurate spatial orientation.
• Demonstrated that sensory priors can be individualized.

Conclusions:

• Sensory priors are key to reliable spatial orientation despite sensory noise.
• Individualized sensory priors can serve as functional markers for neural mechanisms.
• This research advances our understanding of brain's uncertainty management in perception.