Moving in time: Bayesian causal inference explains movement coordination to auditory beats
Identifieur interne : 001019 ( Main/Merge ); précédent : 001018; suivant : 001020Moving in time: Bayesian causal inference explains movement coordination to auditory beats
Auteurs : Mark T. Elliott [Royaume-Uni] ; Alan M. Wing [Royaume-Uni] ; Andrew E. Welchman [Royaume-Uni]Source :
- Proceedings of the Royal Society B: Biological Sciences [ 0962-8452 ] ; 2014.
Abstract
Many everyday skilled actions depend on moving in time with signals that are embedded in complex auditory streams (e.g. musical performance, dancing or simply holding a conversation). Such behaviour is apparently effortless; however, it is not known how humans combine auditory signals to support movement production and coordination. Here, we test how participants synchronize their movements when there are potentially conflicting auditory targets to guide their actions. Participants tapped their fingers in time with two simultaneously presented metronomes of equal tempo, but differing in phase and temporal regularity. Synchronization therefore depended on integrating the two timing cues into a single-event estimate or treating the cues as independent and thereby selecting one signal over the other. We show that a Bayesian inference process explains the situations in which participants choose to integrate or separate signals, and predicts motor timing errors. Simulations of this causal inference process demonstrate that this model provides a better description of the data than other plausible models. Our findings suggest that humans exploit a Bayesian inference process to control movement timing in situations where the origin of auditory signals needs to be resolved.
Url:
DOI: 10.1098/rspb.2014.0751
PubMed: 24850915
PubMed Central: 4046422
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Wing</name><affiliation wicri:level="1"><nlm:aff id="af1"><addr-line>School of Psychology</addr-line>,<institution>University of Birmingham</institution>,<addr-line>Edgbaston B15 2TT</addr-line>,<country>UK</country></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Welchman, Andrew E" sort="Welchman, Andrew E" uniqKey="Welchman A" first="Andrew E." last="Welchman">Andrew E. 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Elliott</name><affiliation wicri:level="1"><nlm:aff id="af1"><addr-line>School of Psychology</addr-line>,<institution>University of Birmingham</institution>,<addr-line>Edgbaston B15 2TT</addr-line>,<country>UK</country></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Wing, Alan M" sort="Wing, Alan M" uniqKey="Wing A" first="Alan M." last="Wing">Alan M. Wing</name><affiliation wicri:level="1"><nlm:aff id="af1"><addr-line>School of Psychology</addr-line>,<institution>University of Birmingham</institution>,<addr-line>Edgbaston B15 2TT</addr-line>,<country>UK</country></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Welchman, Andrew E" sort="Welchman, Andrew E" uniqKey="Welchman A" first="Andrew E." last="Welchman">Andrew E. Welchman</name><affiliation wicri:level="1"><nlm:aff id="af2"><addr-line>Department of Psychology</addr-line>,<institution>University of Cambridge</institution>,<addr-line>Cambridge CB2 3EB</addr-line>,<country>UK</country></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author></analytic><series><title level="j">Proceedings of the Royal Society B: Biological Sciences</title><idno type="ISSN">0962-8452</idno><idno type="eISSN">1471-2954</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Many everyday skilled actions depend on moving in time with signals that are embedded in complex auditory streams (e.g. musical performance, dancing or simply holding a conversation). Such behaviour is apparently effortless; however, it is not known how humans combine auditory signals to support movement production and coordination. Here, we test how participants synchronize their movements when there are potentially conflicting auditory targets to guide their actions. Participants tapped their fingers in time with two simultaneously presented metronomes of equal tempo, but differing in phase and temporal regularity. Synchronization therefore depended on integrating the two timing cues into a single-event estimate or treating the cues as independent and thereby selecting one signal over the other. We show that a Bayesian inference process explains the situations in which participants choose to integrate or separate signals, and predicts motor timing errors. Simulations of this causal inference process demonstrate that this model provides a better description of the data than other plausible models. Our findings suggest that humans exploit a Bayesian inference process to control movement timing in situations where the origin of auditory signals needs to be resolved.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Grahn, Ja" uniqKey="Grahn J">JA Grahn</name></author><author><name sortKey="Rowe, Jb" uniqKey="Rowe J">JB Rowe</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zatorre, Rj" uniqKey="Zatorre R">RJ Zatorre</name></author><author><name sortKey="Chen, Jl" uniqKey="Chen J">JL Chen</name></author><author><name sortKey="Penhune, Vb" uniqKey="Penhune V">VB Penhune</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Grahn, Ja" uniqKey="Grahn J">JA Grahn</name></author><author><name sortKey="Brett, M" uniqKey="Brett M">M Brett</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mesgarani, N" uniqKey="Mesgarani N">N 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