Optimal performance in time-constrained and dynamically changing environments depends on making reliable predictions about future outcomes. In sporting tasks, performers have been found to employ multiple information sources to maximise the accuracy of their predictions, but questions remain about how different information sources are weighted and integrated to guide anticipation. In this paper, we outline how predictive processing approaches, and active inference in particular, provide a unifying account of perception and action that explains many of the prominent findings in the sports anticipation literature. Active inference proposes that perception and action are underpinned by the organism’s need to remain within certain stable states. To this end, decision making approximates Bayesian inference and actions are used to minimise future prediction errors during brain–body–environment interactions. Using a series of Bayesian neurocomputational models based on a partially observable Markov process, we demonstrate that key findings from the literature can be recreated from the first principles of active inference. In doing so, we formulate a number of novel and empirically falsifiable hypotheses about human anticipation capabilities that could guide future investigations in the field.

在时间受限和动态变化的环境中获得最佳性能取决于对未来结果做出可靠的预测。在体育任务中，已经发现表演者使用多种信息源来最大限度地提高预测的准确性，但是对于如何对不同的信息源进行加权和整合以指导预测的问题仍然存在。在本文中，我们概述了预测处理方法，特别是主动推理，如何提供对感知和行动的统一解释，解释了体育预测文献中的许多突出发现。主动推理提出，感知和行动是由有机体保持在某些稳定状态的需要所支撑的。为此，决策近似于贝叶斯推理，并且在大脑-身体-环境交互过程中使用动作来最小化未来的预测误差。使用基于部分可观察马尔可夫过程的一系列贝叶斯神经计算模型，我们证明了文献中的关键发现可以从主动推理的第一原理中重新创建。在这样做的过程中，我们提出了一些关于人类预期能力的新颖且可凭经验证伪的假设，这些假设可以指导该领域的未来调查。