Moving heads, stable worlds: estimation, compensation, and calibration during active self-motion

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Knowledge of self-movement relative to the environment is fundamental to normal cognitive function for all mobile organisms. This knowledge depends largely on predictions of head motion derived from active motor commands to move the head on the body and through space. These motor-based predictions underlie our ability not only to perceive our own self-motion but also our capacity for spatial constancy, i.e. our ability to compensate for changing sensory stimulation in order to perceive a stable external world.Despite the fundamental importance of active head control for estimating and compensating self-motion, we still have only a preliminary understanding of how active control signals interact with incoming visual and vestibular signals. Thus, active head control represents a highly important but understudied model system for motor control. The current proposal investigates this system using cutting-edge technology to address important open questions: How are sensory and motor signals compared and integrated? What are the consequences of sensory-motor conflict? How do motor signals impact perception? Psychophysical experiments will be conducted using a virtual reality setup consisting of a hexapod motion platform and stereo visual display. Active head movements will be tracked with motion tracking equipment and visual and vestibular stimuli will be rendered contingent on the head motion. Experiments will evaluate how vestibular, visual and efference copy signals are compared and integrated to estimated head motion, body motion, or environmental motion. Probabilistic modeling of sensorimotor calibration will be used to predict how the nervous system adapts to persistent conflicts. Results will inform highly relevant medical and technical applications, such as vertigo treatment and head-mounted virtual reality display systems.