Successful coordination emerges from aligned self-predictions in a dynamic motor interaction

Abstract

A central challenge in understanding joint action is explaining how individuals achieve successful coordination in dynamic, real-world settings. Although coordination is thought to depend on anticipating future events, including the actions of others, the precise contribution of such predictive processing remains unclear, since most existing evidence comes from simplified laboratory tasks that infer anticipation indirectly from reaction times. To address this gap, we studied 70 participants (forming 64 pairs) during solo and joint sessions of a turn-taking ball-hitting task while recording multimodal behavioral, physiological, demographic, and social measures. We found that successful coordination was most strongly associated with anticipation of one’s own action outcomes, outperforming physiological synchrony, motor behavior, demographic characteristics, and social closeness. Partners whose predictions of their own action outcomes were more closely aligned coordinated better, likely because each behaved in ways the other implicitly expected. A Bayesian generative model further showed that well-coordinated pairs relied more heavily on prior expectations than on incoming sensory evidence when generating predictions during joint action. These results suggest that efficient coordination in dynamic real-world tasks emerges primarily when individuals’ internal models for predicting their own actions are well aligned across partners. Our quantitative multimodal approach provides a framework for disentangling the contributions of predictive, physiological, motor, and social factors to coordination in naturalistic interactions. Combining eye-movement, body-kinematics, heart-rate, and individual-characteristic data with machine learning, this study shows that coordination during dynamic motor interaction reflects how similarly partners predict their own actions.