The time constant of the somatogravic illusion.

Without visual feedback, humans perceive tilt when experiencing a sustained linear acceleration. This tilt illusion is commonly referred to as the somatogravic illusion. Although the physiological basis of the illusion seems to be well understood, the dynamic behavior is still subject to discussion. In this study, the dynamic behavior of the illusion was measured experimentally for three motion profiles with different frequency content. Subjects were exposed to pure centripetal accelerations in the lateral direction and were asked to indicate their tilt percept by means of a joystick. Variable-radius centrifugation during constant angular rotation was used to generate these motion profiles. Two self-motion perception models were fitted to the experimental data and were used to obtain the time constant of the somatogravic illusion. Results showed that the time constant of the somatogravic illusion was on the order of two seconds, in contrast to the higher time constant found in fixed-radius centrifugation studies. Furthermore, the time constant was significantly affected by the frequency content of the motion profiles. Motion profiles with higher frequency content revealed shorter time constants which cannot be explained by self-motion perception models that assume a fixed time constant. Therefore, these models need to be improved with a mechanism that deals with this variable time constant. Apart from the fundamental importance, these results also have practical consequences for the simulation of sustained accelerations in motion simulators.

Integration of visual and inertial cues in the perception of angular self-motion.

The brain is able to determine angular self-motion from visual, vestibular, and kinesthetic information. There is compelling evidence that both humans and non-human primates integrate visual and inertial (i.e., vestibular and kinesthetic) information in a statistically optimal fashion when discriminating heading direction. In the present study, we investigated whether the brain also integrates information about angular self-motion in a similar manner. Eight participants performed a 2IFC task in which they discriminated yaw-rotations (2-s sinusoidal acceleration) on peak velocity. Just-noticeable differences (JNDs) were determined as a measure of precision in unimodal inertial-only and visual-only trials, as well as in bimodal visual-inertial trials. The visual stimulus was a moving stripe pattern, synchronized with the inertial motion. Peak velocity of comparison stimuli was varied relative to the standard stimulus. Individual analyses showed that data of three participants showed an increase in bimodal precision, consistent with the optimal integration model; while data from the other participants did not conform to maximum-likelihood integration schemes. We suggest that either the sensory cues were not perceived as congruent, that integration might be achieved with fixed weights, or that estimates of visual precision obtained from non-moving observers do not accurately reflect visual precision during self-motion.

Neural correlates of decision making on whole body yaw rotation: An fNIRS study.

Prominent accounts of decision making state that decisions are made on the basis of an accumulation of sensory evidence, orchestrated by networks of prefrontal and parietal neural populations. Here we assess whether these findings generalize to decisions on self-motion. Participants were presented with whole body yaw rotations of different durations in a 2-Interval-Forced-Choice paradigm, and tasked to discriminate motions on the basis of their amplitude. The cortical hemodynamic response was recorded using functional near-infrared spectroscopy (fNIRS) while participants were performing the task. The imaging data was used to predict the specific response on individual experimental trials, and to predict whether the comparison stimulus would be judged larger than the reference. Classifier performance on the former variable was negligible. However, considerable performance was achieved for the latter variable, specifically using parietal imaging data. The findings provide support for the notion that activity in the parietal cortex reflects modality independent decision variables that represent the strength of the neural evidence in favor of a decision. The results are encouraging for the use of fNIRS as a method to perform neuroimaging in moving individuals.