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 motor-control research, it has been shown that uncertainty is reduced by the reliability-weighted integration of current sensory information and prior knowledge according to Bayesian principles (Körding & Wolpert, 2006). However, the question remains whether this mechanism explains behavior in more complex situations, as they are common in sports (Beck, Hossner & Zahno, 2023). To investigate this mechanism in complex movements, we developed an immersive virtual tennis task (see video). Specifically, we 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 framework, we hypothesize that the predictive gaze behavior will shift towards the developed expectation.
