Optimal estimator model for human spatial orientation

Abstract

A model is presented to predict human dynamic spatial orientation in response to multisensory stimuli. Motion stimuli are first processed by dynamic models of the visual, vestibular, tactile, and proprioceptive sensors. Central nervous system function is modeled as a steady state Kalman filter that optimally blends information from the various sensors to form an estimate of spatial orientation. Where necessary, nonlinear elements preprocess inputs to the linear central estimator in order to reflect more accurately some nonlinear human response characteristics. Computer implementation of the model has shown agreement with several important qualitative characteristics of human spatial orientation.