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An Active Inference Account of Skilled Anticipation in Sport: Using Computational Models to Formalise Theory and Generate New Hypotheses

Research output: Contribution to journalArticlepeer-review

David J Harris, Tom Arthur, David P Broadbent, Mark R Wilson, Samuel J Vine, Oliver Runswick

Original languageEnglish
Pages (from-to)2023-2038
Number of pages16
JournalSports Medicine
Volume52
Issue number9
Early online date3 May 2022
DOIs
E-pub ahead of print3 May 2022
PublishedSep 2022

Bibliographical note

Publisher Copyright: © 2022, The Author(s).

King's Authors

Abstract

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.

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