The use of contextual priors and kinematic information during anticipation in sport: toward a Bayesian integration framework

Gredin, N. Viktor; Bishop, Daniel; Williams, A. Mark; Broadbent, David P.

doi:10.1080/1750984x.2020.1855667

Cited by 31 publications

(51 citation statements)

References 111 publications

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“…The simulations also suggest that prior context and incoming sensory cues may differentially affect the speed with which agents are able to respond to environmental changes (e.g., the contrasting effect of context reversal in Figure 2 Panels C and D). 1 A comprehensive review of anticipation findings is beyond the current scope, but we refer the reader to the original MIDASS paper 6 or previous reviews of sporting anticipation 5,8,9 for further examples.…”

Section: Principle 1 -'Both Contextual and Current Sensory Information Can Influence Anticipation Performance Directly But This Effect Ismentioning

confidence: 99%

“…As active inference is based on Bayesian probabilistic integration, the relative influence of contextual information and sensory cues at different timepoints is a direct result of the expected precision of the respective information sources. When multiple information sources -e.g., prior beliefs about context and online sensory information -are integrated to form a posterior belief (joint estimate), each source is inherently weighted according to its surety or precision 8,14,31 . Hence, precise sensory information will overwhelm an imprecise prior belief about context to dictate the posterior, and subsequently action policy selection (see Figure 3 Panel A).…”

Section: Principle 1 -'Both Contextual and Current Sensory Information Can Influence Anticipation Performance Directly But This Effect Ismentioning

confidence: 99%

“…Consequently, effective performance depends on anticipating future outcomes 4 . Research into anticipatory behaviour has identified that during tem-porally constrained tasks, skilled individuals make better use of multiple sources of probabilistic information to anticipate future outcomes [5][6][7][8][9] . However, a number of fundamental questions remain about when different information sources become available, the relative weighting afforded to each source, and how they are integrated in an efficient and effective manner 6,8,10 .…”

mentioning

confidence: 99%

“…Research into anticipatory behaviour has identified that during tem-porally constrained tasks, skilled individuals make better use of multiple sources of probabilistic information to anticipate future outcomes [5][6][7][8][9] . However, a number of fundamental questions remain about when different information sources become available, the relative weighting afforded to each source, and how they are integrated in an efficient and effective manner 6,8,10 . Answers to these questions may require looking beyond the traditional anticipation literature to more fundamental research into how the brain uses generative models to make predictions [11][12][13] .…”

mentioning

confidence: 99%

“…For example, in a handball penalty kick task, Helm et al 32 found that trained novice participants integrated different information sources (opponents' kinematic information and action tendencies) according to their relative reliability, such that more informative action cues dominated over other ambiguous information. While this recent work provides compelling evidence that performers may employ Bayesian reliability-based strategies (for review see ref 8 ), it has neglected to account for how the anticipating agent can themselves influence likely outcomes and the dynamic interaction between the anticipator and the opponent 6 . This is partly due to the frequent use of video stimuli in experiments, which preclude any interaction between the an-ticipating agent and the environment.…”

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confidence: 99%

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An active inference account of skilled anticipation in sport

Harris¹,

Arthur²,

Broadbent³

et al. 2021

Preprint

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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 maximize 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 active inference, a unifying account of perception and action, explains many of the prominent findings in the sports anticipation literature. Active inference proposes that perception and action are underpinned by the need to minimize prediction errors and optimise a predictive model of the world. To this end, decision making approximates Bayesian inference and actions are used to minimize future prediction errors. 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, empirically falsifiable hypotheses about human anticipation capabilities which could guide future investigations in the field.

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Section: Principle 1 -'Both Contextual and Current Sensory Information Can Influence Anticipation Performance Directly But This Effect Ismentioning

confidence: 99%

Section: Principle 1 -'Both Contextual and Current Sensory Information Can Influence Anticipation Performance Directly But This Effect Ismentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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An active inference account of skilled anticipation in sport

Harris¹,

Arthur²,

Broadbent³

et al. 2021

Preprint

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A Bayesian computational model to investigate expert anticipation of a seemingly unpredictable ball bounce

Harris

North

Runswick

2022

Psychological Research

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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.

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

et al. 2022

Self Cite

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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.

show abstract

The use of contextual priors and kinematic information during anticipation in sport: toward a Bayesian integration framework

Cited by 31 publications

References 111 publications

An active inference account of skilled anticipation in sport

An active inference account of skilled anticipation in sport

A Bayesian computational model to investigate expert anticipation of a seemingly unpredictable ball bounce

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

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