This article describes a computational model for the sensory perception of self-motion, considered as a compromise between sensory information and physical coherence constraints. This compromise is realized by a dynamic optimization process minimizing a set of cost functions. Measure constraints are expressed as quadratic errors between motion estimates and corresponding sensory signals, using internal models of sensor transfer functions. Coherence constraints are expressed as quadratic errors between motion estimates, and their prediction is based on internal models of the physical laws governing the corresponding physical stimuli. This general scheme leads to a straightforward representation of fundamental sensory interactions (fusion of visual and canal rotational inputs, identification of the gravity component from the, otolithic input, otolithic contribution to the perception of rotations, and influence of vection on the subjective vertical). The model is tuned and assessed using a range of well-known psychophysical results, including off-vertical axis rotations and centrifuge experiments. The ability of the model to predict and help analyze new situations is illustrated by a study of the vestibular contributions to self-motion perception during automobile driving and during acceleration cueing in driving simulators. The extendable structure of the model allows for further developments and applications, by using other cost functions representing additional sensory interactions. [References: 41]
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