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 et al., 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.
