Damian Beck

Background: This scoping review explored the application of predictive processing in sport and exercise science, examining how cognitive and computational models have been used to understand perceptual-motor skills, decision-making, and physical performance. The review provides a tutorial introduction to key concepts in the predictive processing literature and maps the extent of research in this area, identifying key domains of application and the level of empirical support. Methods: A systematic search of EMBASE, Medline, PsycINFO, and SPORTDiscus databases was performed, adhering to JBI methodology and the PRISMA Extension for Scoping Reviews. Results: Twenty relevant empirical studies were discovered across all domains of sport and exercise science. Despite promising evidence, the review also uncovers significant gaps, particularly in comparing predictive-processing-specific mechanisms to alternative theories and exploring the broader implications of predictive processing for skill acquisition and training interventions. Conclusions: Based on these findings, the review outlines recommendations for future research, calling for more targeted investigations that empirically test predictive processing predictions and an expansion of this approach to new areas of sport science.

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 for gaining insights into the dynamics of unfolding decisions in action, owing to a distinct feature of the return movement: the split step—a preparatory movement characterized by 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). As a continuous behavioral readout of the evolving decision-making process, we measured participants’ weight shift during the unfolding action. Results show that, over the experiment, participants increasingly rely on acquired prior knowledge of the more probable serve direction to improve performance. Participants exploit prior knowledge by shifting their weight toward the more probable serve direction—already before the serve—while continuously re-evaluating both options based on incoming sensory information and motor costs. 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.

This scoping review explored the application of predictive processing in sport and exercise science, examining how cognitive and computational models have been used to understand perceptual-motor skills, decision-making, and physical performance. The review provides a tutorial introduction to key concepts in the predictive processing literature and maps the extent of research in this area, identifying key domains of application and the level of empirical support. A systematic search of EMBASE, Medline, PsycINFO, and SPORTDiscus databases was performed, adhering to JBI methodology and the PRISMA Extension for Scoping Reviews. Eighteen relevant empirical studies were discovered across all domains of sport and exercise science. Despite promising evidence, the review also uncovers significant gaps, particularly in comparing predictive-processing-specific mechanisms to alternative theories and exploring the broader implications of predictive processing for skill acquisition and training interventions. Based on these findings, the review outlines recommendations for future research, calling for more targeted investigations that empirically test predictive processing predictions and an expansion of this approach to new areas of sport science.

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.

This study tests Bayesian integration in a complex sensorimotor task: returning tennis serves. We found reliability-based prior-likelihood integration on two timescales: 1) over a “match” (increasing reliability of prior information) and 2) over a single serve (increasing reliability of sensory information). More generally, this study exemplifies how leveraging extended reality technology provides a means to reduce the internal versus external validity trade-off by studying motor control under real-world task demands while ensuring rigorous experimental control.

We extend the study of sensorimotor behavior under risk from simple finger-pointing movements (Trommershäuser et al., Trends Cogn Sci 12: 291–297, 2008) to a complex throwing task in virtual reality. Our results suggest that, in complex sensorimotor behavior, risk evaluation of potential movements is not confined to a cognitive planning phase before movement but is optimized in action, with the motor system continuously biasing competing action options toward regions of higher expected rewards.

Extensive research suggests that humans integrate sensory information and prior expectations in a Bayesian manner to reduce uncertainty in perception and action. However, while Bayesian integration provides a powerful explanatory framework, the question remains as to what extent it explains human behaviour in naturalistic situations, including more complex movements and distributions. Here, we examine whether humans can learn bimodal priors in a complex sensorimotor task: returning tennis serves. Participants returned serves in an immersive virtual reality setup with realistic movements and spatiotemporal task demands matching those in real tennis. The location of the opponent’s serves followed a bimodal distribution. We manipulated visual uncertainty through three levels of ball speeds: slow, moderate, and fast. After extensive exposure to the opponent’s serves, participants’ movements were biased by the bimodal prior distribution. As predicted by Bayesian theory, the magnitude of the bias depends on visual uncertainty. Additionally, our data indicate that participants’ movements in this complex task were not only biased by prior expectations but also by biomechanical constraints and associated motor costs. Intriguingly, an explicit knowledge test after the experiment revealed that, despite incorporating prior knowledge of the opponent’s serve distribution into their behaviour, participants were not explicitly aware of the pattern. Our results show that humans can implicitly learn and utilise bimodal priors in complex sensorimotor behaviour.

Handling motor noise is fundamental to successful sensorimotor behavior, especially in high-risk situations. Research using finger-pointing tasks shows that humans account for motor noise and costs of potential outcomes in movement planning. However, does this mechanism generalize to more complex movement tasks? Here, we investigate sensorimotor behavior under risk in throwing across three experiments with 20 participants each. Their task was to throw balls at a target circle, partially overlapped by a penalty circle. This task challenged participants to find strategies that trade off potential penalties and rewards. In the experiments, penalty magnitude and the distance between the circles were manipulated. We measured the location of their final gaze fixation before movement—as an indicator of their planned aiming point—and the ball’s impact location. Without penalty, the final gaze fixation and the ball’s impact location were both centered on the target. In the penalty condition, the location of the participants’ final gaze fixations and the ball’s impact shifted away from the penalty circle, with larger shifts for higher penalties and smaller distances. Interestingly, the shifts in the ball’s impact locations were not only larger (“more conservative”) but also closer to the statistically optimal (expected gain-maximizing) location compared to the fixated aim points. Movement trajectory analyses show that, in penalty conditions, the shifts away from the penalty zone increased until the final phases of the movement. These results suggest that risk evaluation is not completed in a pre-movement planning phase but is further optimized during movement execution. NEW & NOTEWORTHY We extend the study of sensorimotor behavior under risk from simple finger-pointing movements (Trommershäuser et al., 2008) to a complex throwing task in virtual reality. Our results suggest that, in complex sensorimotor behavior, risk evaluation of potential movements is not confined to a cognitive planning phase before movement but is optimized in action, with the motor system continuously biasing competing action options toward regions of higher expected rewards.