Risk optimization during ongoing movement: insights from movement and gaze behavior in throwing

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 a virtual reality throwing task across three experiments with 20 participants each. Their task was to throw balls at a target circle, partially overlapped by a penalty circle. 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 ("less risk seeking") but also closer to the statistically optimal (expected gain-maximizing) location compared with 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. Based on these results, we propose the hypothesis that risk evaluation is not completed in a pre-movement planning phase but is further optimized during movement execution.<b>NEW & NOTEWORTHY</b> 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.