During dynamic and time-constrained sporting tasks performers rely on both online perceptual information and prior contextual knowledge to make effective anticipatory judgments. It has been suggested that performers may integrate these sources of information in an approximately Bayesian fashion, by weighting available information sources according to their expected precision. In the present work, we extended Bayesian brain approaches to anticipation by using formal computational models to estimate how performers weighted different information sources when anticipating the bounce direction of a rugby ball. Both recreational (novice) and professional (expert) rugby players (n = 58) were asked to predict the bounce height of an oncoming rugby ball in a temporal occlusion paradigm. A computational model, based on a partially observable Markov decision process, was fitted to observed responses to estimate participants’ weighting of online sensory cues and prior beliefs about ball bounce height. The results showed that experts were more sensitive to online sensory information, but that neither experts nor novices relied heavily on prior beliefs about ball trajectories in this task. Experts, but not novices, were observed to down-weight priors in their anticipatory decisions as later and more precise visual cues emerged, as predicted by Bayesian and active inference accounts of perception.

在动态和时间受限的体育任务中，表演者依靠在线感知信息和先前的上下文知识来做出有效的预期判断。有人建议，表演者可以通过根据预期精度对可用信息源进行加权，以近似贝叶斯方式整合这些信息源。在目前的工作中，我们通过使用正式的计算模型来估计表演者在预测橄榄球的弹跳方向时如何权衡不同的信息源，从而扩展了贝叶斯大脑的预测方法。休闲（新手）和职业（专家）橄榄球运动员（ n = 58）都被要求在时间遮挡范例中预测迎面而来的橄榄球的弹跳高度。基于部分可观察的马尔可夫决策过程的计算模型适合观察到的反应，以估计参与者对在线感官线索的权重和对球弹跳高度的先验信念。结果表明，专家对在线感官信息更加敏感，但在这项任务中，专家和新手都不会严重依赖对球轨迹的先前信念。正如贝叶斯和感知的主动推理解释所预测的那样，随着后来更精确的视觉线索的出现，专家（而不是新手）在他们的预期决策中降低了先验的权重。