The use of contextual priors and kinematic information during anticipation in sport: toward a Bayesian integration framework

Abstract

Expert performance across a range of domains is underpinned by superior perceptual-cognitive skills. Over the last five decades, researchers have provided evidence that experts can identify and interpret opponent kinematics more effectively than their less experienced counterparts. More recently, researchers have demonstrated that experts also use non-kinematic information, in this paper termed contextual priors, to inform their predictive judgments. While the body of literature in this area continues to grow exponentially, researchers have yet to develop an overarching theoretical framework that can predict and explain anticipatory behaviour and provide empirically testable hypotheses to guide future work. In this paper, we propose that researchers interested in anticipation in sport could adopt a Bayesian model for probabilistic inference as an overarching framework. We argue that athletes employ Bayesian reliability-based strategies in order to integrate contextual priors with evolving kinematic information during anticipation. We offer an insight into Bayesian theory and demonstrate how contemporary literature in sport psychology fits within this framework. We hope that the paper encourages researchers to engage with the Bayesian literature in order to provide greater insight into expert athletes’ assimilation of various sources of information when anticipating the actions of others in complex and dynamic environments.