Exploiting prior knowledge in continuous decision-making under uncertainty: the case of tennis experts

Leading theories of sensorimotor control propose that humans navigate uncertainty by integrating prior and sensory information in a Bayesian manner. However, empirical evidence is largely limited to static and constrained lab tasks. How humans exploit prior knowledge during continuously unfolding decision-making in naturalistic behavior remains unclear. Here, we study a task that pushes the human sensorimotor system to its limits: returning fast tennis serves. This task is particularly informative to gain insights into the dynamics of unfolding decisions in action due to a peculiarity of the return movement: the split step—a preparatory movement with a small jump to increase initial speed in the desired direction. In the experiment, experienced tennis players returned serves in an immersive extended reality setup with unconstrained movements and task demands matching real tennis. We manipulated the distributions of the opponent’s preferred serve locations (80% to the right vs. 20% to the left of the service box and vice versa in a second session). Results show that, over the experiment, participants increasingly rely on acquired prior knowledge of the more probable serve direction and exploit it to improve performance. To this end, participants continuously adjust their weight shift over the split step with incoming sensory information, while a bias toward the expected direction can be observed already before the serve. Using tennis as an exemplary case, our findings provide evidence that humans use prior and sensory information to probabilistically optimize continuous decision-making in complex sensorimotor behavior.