Visuovestibular perception of self-motion modeled as a dynamic optimization process

Abstract

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.

Evaluating perception in driving simulation experiments

“…However, earlier studies have shown that the absence of physical motion in a driving simulator modifies the driver's reactions [19]. Moreover, computational models of self-motion perception [57] and studies performed on a moving-base driving simulator indicate that driver's control strategies on curved roads make use not only of visual, but also of extra-visual information, such as vestibular (see Box 3) and proprioceptive cues [14,58]. So, on the basis of models proposed in earlier studies [58,59], it has been suggested that these cues are used by the driver to control steering and regulate speed.…”

Simulating believable forward accelerations on a stewart motion platform

“…It has been known for a long time that the different sensory modalities interact in the perception of self-motion, but how exactly the brain estimates body orientation and self-motion from these cues is still a matter of debate [3,4]. See [5,6] for recent models.…”

Modeling Motion Sickness

“…The theoretical approach currently employs two theories: "Sensory Confl ict theory" (SC) and "Subjective Vertical Confl ict theory" (SVC). SC theory is based on the work of Reason and Brand [50,51], expanded by Oman [47,49], who developed the fi nal form of the mathematical description of this theory. Its essence is that all situations that cause motion sickness are determined by the state of the so-called sensory rearrangement [51].…”

“…SC theory is based on the work of Reason and Brand [50,51], expanded by Oman [47,49], who developed the fi nal form of the mathematical description of this theory. Its essence is that all situations that cause motion sickness are determined by the state of the so-called sensory rearrangement [51]. In this state there is a sensory confl ict not only between signals from the organ of vision, vestibular organ and other motion sensitive receptors.…”

“…The SVC theory, used in the second theoretical approach, was described by Bose and Bles [8]. It is a redefi nition of the theory of sensory rearrangement [51]. The authors of this theory assumed that all situations that provoke the onset of motion sickness are characterized by the state in which the sensed vertical is contrary to the subjective vertical (SV) expected on the basis of previous experience [9].…”