Due to noisy signals in the sensorimotor system, our perception is constantly subject to uncertainty. This is particularly evident in dynamic situations, such as returning a tennis serve. In fundamental research taking on a Bayesian approach to decision making, it has been shown that the weighted integration of multiple sources of information – e.g., prior knowledge and current sensory information – reduces uncertainty (Körding & Wolpert, 2006). Therefore, we investigate this mechanism in the context of complex movements. Specifically, we use a virtual tennis return situation (see video) to examine how the experienced serve locations of the opponent – given identical kinematic information in the serving motion – influence predictive gaze behavior and the perception of the ball's impact point. Based on a Bayesian explanatory framework, we hypothesize that the estimated impact point, or at least the predictive gaze behavior, will shift towards the developed expectation.
