Musical interaction is influenced by underlying predictive models and musical expertise

Abstract Musical interaction is a unique model for understanding humans’ ability to align goals, intentions, and actions, which also allows for the manipulation of participants’ internal predictive models of upcoming events. Here we used polyrhythms to construct two joint finger tapping tasks that e...

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Autores principales:	Ole A. Heggli, Ivana Konvalinka, Morten L. Kringelbach, Peter Vuust
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/76c9f3d92a114f05bf134198b0a84fa8
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spelling	oai:doaj.org-article:76c9f3d92a114f05bf134198b0a84fa82021-12-02T15:10:00ZMusical interaction is influenced by underlying predictive models and musical expertise10.1038/s41598-019-47471-32045-2322https://doaj.org/article/76c9f3d92a114f05bf134198b0a84fa82019-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-47471-3https://doaj.org/toc/2045-2322Abstract Musical interaction is a unique model for understanding humans’ ability to align goals, intentions, and actions, which also allows for the manipulation of participants’ internal predictive models of upcoming events. Here we used polyrhythms to construct two joint finger tapping tasks that even when rhythmically dissimilar resulted in equal inter-tap intervals (ITIs). Thus, behaviourally a dyad of two musicians tap isochronously at the same rate, yet with their own distinct rhythmical context model (RCM). We recruited 22 highly skilled musicians (in 11 dyads) and contrasted the effect of having a shared versus non-shared RCM on dyads’ synchronization behaviour. As expected, tapping synchronization was significantly worse at the start of trials with non-shared models compared to trials with a shared model. However, the musicians were able to quickly recover when holding dissimilar predictive models. We characterised the directionality in the tapping behaviour of the dyads and found patterns mostly of mutual adaptation. Yet, in a subset of dyads primarily consisting of drummers, we found significantly different synchronization patterns, suggesting that instrument expertise can significantly affect synchronization strategies. Overall, this demonstrates that holding different predictive models impacts synchronization in musicians performing joint finger tapping.Ole A. HeggliIvana KonvalinkaMorten L. KringelbachPeter VuustNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-13 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ole A. Heggli Ivana Konvalinka Morten L. Kringelbach Peter Vuust Musical interaction is influenced by underlying predictive models and musical expertise
description	Abstract Musical interaction is a unique model for understanding humans’ ability to align goals, intentions, and actions, which also allows for the manipulation of participants’ internal predictive models of upcoming events. Here we used polyrhythms to construct two joint finger tapping tasks that even when rhythmically dissimilar resulted in equal inter-tap intervals (ITIs). Thus, behaviourally a dyad of two musicians tap isochronously at the same rate, yet with their own distinct rhythmical context model (RCM). We recruited 22 highly skilled musicians (in 11 dyads) and contrasted the effect of having a shared versus non-shared RCM on dyads’ synchronization behaviour. As expected, tapping synchronization was significantly worse at the start of trials with non-shared models compared to trials with a shared model. However, the musicians were able to quickly recover when holding dissimilar predictive models. We characterised the directionality in the tapping behaviour of the dyads and found patterns mostly of mutual adaptation. Yet, in a subset of dyads primarily consisting of drummers, we found significantly different synchronization patterns, suggesting that instrument expertise can significantly affect synchronization strategies. Overall, this demonstrates that holding different predictive models impacts synchronization in musicians performing joint finger tapping.
format	article
author	Ole A. Heggli Ivana Konvalinka Morten L. Kringelbach Peter Vuust
author_facet	Ole A. Heggli Ivana Konvalinka Morten L. Kringelbach Peter Vuust
author_sort	Ole A. Heggli
title	Musical interaction is influenced by underlying predictive models and musical expertise
title_short	Musical interaction is influenced by underlying predictive models and musical expertise
title_full	Musical interaction is influenced by underlying predictive models and musical expertise
title_fullStr	Musical interaction is influenced by underlying predictive models and musical expertise
title_full_unstemmed	Musical interaction is influenced by underlying predictive models and musical expertise
title_sort	musical interaction is influenced by underlying predictive models and musical expertise
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/76c9f3d92a114f05bf134198b0a84fa8
work_keys_str_mv	AT oleaheggli musicalinteractionisinfluencedbyunderlyingpredictivemodelsandmusicalexpertise AT ivanakonvalinka musicalinteractionisinfluencedbyunderlyingpredictivemodelsandmusicalexpertise AT mortenlkringelbach musicalinteractionisinfluencedbyunderlyingpredictivemodelsandmusicalexpertise AT petervuust musicalinteractionisinfluencedbyunderlyingpredictivemodelsandmusicalexpertise
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Musical interaction is influenced by underlying predictive models and musical expertise

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