An Active Inference Account of Skilled Anticipation in Sport: Using Computational Models to Formalise Theory and Generate New Hypotheses.
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 h...
| Publicado en: | Sports Medicine Vol. 52; no. 9; pp. 2023 - 2039 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158610644&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158610644 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01121642 C5H jtl: Sports Medicine issn: 01121642 maglogo: N pubinfo: dt: Sep2022 vid: 52 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 158610644 156648201 158610644 158610644 10.1007/s40279-022-01689-w 158610644 ppf: 2023 ppct: 16 formats: tig: atl: An Active Inference Account of Skilled Anticipation in Sport: Using Computational Models to Formalise Theory and Generate New Hypotheses. aug: au: Harris, David J. Arthur, Tom Broadbent, David P. Wilson, Mark R. Vine, Samuel J. Runswick, Oliver R. affil: School of Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, St Luke's Campus, EX1 2LU, Exeter, UK sug: subj: Sports Theory Models, Theoretical Skill Acquisition Task Performance and Analysis Human Simulations Policy Making Descriptive Statistics Kinematics Cricket (Sports) Sensation Baseball Throwing ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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