Internal models in active self-motion estimation: role of inertial sensory cues

Self-motion estimation is thought to depend on sensory information as well as on sensory predictions derived from motor output. In driving, the inertial motion cues (vestibular and somatosensory cues) can in principle be predicted based on the steering motor commands if an accurate internal model of the steering dynamics is available. Here, we used a closed-loop steering experiment to examine whether participants can build such an internal model of the steering dynamics. Participants steered a motion platform on which they were seated to align their body with a memorized visual target in complete darkness. We varied the gain between the steering wheel angle and the velocity of the motion platform across trials in three different ways: unpredictable (white noise), moderately predictable (random walk), or highly predictable (constant gain). We examined whether participants took the across-trial predictability of the gain into account to control their steering (internal model hypothesis), or whether they simply integrated the inertial feedback over time to estimate their travelled distance (path integration hypothesis). Results show that participants relied on the gain of the previous trial more when it followed a random walk across trials than when it varied unpredictably across trials. Furthermore, on interleaved trials with a large jump in the gain, participants made fast corrective responses, irrespective of gain predictability, showing they also relied on inertial feedback next to predictions. These findings suggest that the brain can construct an internal model of the steering dynamics to predict the inertial sensory consequences in driving and self-motion estimation.

This collection contains the Python code used to run the steering experiment, the raw data, the MATLAB scripts used to analyze the data, and the figure files used in the publication.