The precision of signals encoding active self-movement

Everyday actions like moving the head, walking around and reaching out to grasp objects are typically self-controlled. This presents a problem when studying the signals encoding such actions because active self-movement is difficult to experimentally control. Available techniques demand repeatable trials, but each action is unique, making it difficult to measure fundamental properties like psychophysical thresholds. Here, we present a novel paradigm that can be used to recover both precision and bias of self-movement signals with minimal constraint on the participant. The paradigm takes care of a hidden source of external noise not previously accounted for in techniques that link display motion to self-movement in real time (e.g. virtual reality). We use head rotations as an example of self-movement, and show that the precision of the signals encoding head movement depends on whether they are being used to judge visual motion or auditory motion. We find perceived motion is slowed during head movement in both cases, indicating that the non-image signals encoding active head rotation (motor commands, proprioception and vestibular cues) are biased to lower speeds and/or displacements. In a second experiment, we trained participants to rotate their heads at different rates and found that the precision of the head rotation signal rises proportionally with head speed (Webers Law). We discuss the findings in terms of the different motion cues used by vision and hearing, and the implications they have for Bayesian models of motion perception. NEW AND NOTEWORTHYWe present a psychophysical technique for measuring the precision of signals encoding active self-movements. Using head movements, we show that: (1) precision declines when active head rotation is combined with auditory as opposed to visual motion; (2) precision rises with head speed (Webers Law); (3) perceived speed is lower during head movement. The findings may reflect the steps needed to convert different cues into common currencies, and challenge standard Bayesian models of motion perception.