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

Version 3 2024-06-19, 16:25
Version 2 2024-06-06, 03:25
Version 1 2023-02-08, 22:15
journal contribution
posted on 2024-06-19, 16:25 authored by DJ Harris, T Arthur, David BroadbentDavid Broadbent, MR Wilson, SJ Vine, OR Runswick
AbstractOptimal 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.

History

Journal

Sports Medicine

Volume

52

Pagination

2023-2038

Location

New Zealand

ISSN

0112-1642

eISSN

1179-2035

Language

en

Publication classification

C1 Refereed article in a scholarly journal

Issue

9

Publisher

Springer Science and Business Media LLC