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Dynamic models for musical rhythm perception and coordination

Rhythmicity permeates large parts of human experience. Humans generate various motor and brain rhythms spanning a range of frequencies. We also experience and synchronize to externally imposed rhythmicity, for example from music and song or from the 24-h light-dark cycles of the sun. In the context...

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Autores principales: Large, Edward W., Roman, Iran, Kim, Ji Chul, Cannon, Jonathan, Pazdera, Jesse K., Trainor, Laurel J., Rinzel, John, Bose, Amitabha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229831/
https://www.ncbi.nlm.nih.gov/pubmed/37265781
http://dx.doi.org/10.3389/fncom.2023.1151895
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author Large, Edward W.
Roman, Iran
Kim, Ji Chul
Cannon, Jonathan
Pazdera, Jesse K.
Trainor, Laurel J.
Rinzel, John
Bose, Amitabha
author_facet Large, Edward W.
Roman, Iran
Kim, Ji Chul
Cannon, Jonathan
Pazdera, Jesse K.
Trainor, Laurel J.
Rinzel, John
Bose, Amitabha
author_sort Large, Edward W.
collection PubMed
description Rhythmicity permeates large parts of human experience. Humans generate various motor and brain rhythms spanning a range of frequencies. We also experience and synchronize to externally imposed rhythmicity, for example from music and song or from the 24-h light-dark cycles of the sun. In the context of music, humans have the ability to perceive, generate, and anticipate rhythmic structures, for example, “the beat.” Experimental and behavioral studies offer clues about the biophysical and neural mechanisms that underlie our rhythmic abilities, and about different brain areas that are involved but many open questions remain. In this paper, we review several theoretical and computational approaches, each centered at different levels of description, that address specific aspects of musical rhythmic generation, perception, attention, perception-action coordination, and learning. We survey methods and results from applications of dynamical systems theory, neuro-mechanistic modeling, and Bayesian inference. Some frameworks rely on synchronization of intrinsic brain rhythms that span the relevant frequency range; some formulations involve real-time adaptation schemes for error-correction to align the phase and frequency of a dedicated circuit; others involve learning and dynamically adjusting expectations to make rhythm tracking predictions. Each of the approaches, while initially designed to answer specific questions, offers the possibility of being integrated into a larger framework that provides insights into our ability to perceive and generate rhythmic patterns.
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spelling pubmed-102298312023-06-01 Dynamic models for musical rhythm perception and coordination Large, Edward W. Roman, Iran Kim, Ji Chul Cannon, Jonathan Pazdera, Jesse K. Trainor, Laurel J. Rinzel, John Bose, Amitabha Front Comput Neurosci Neuroscience Rhythmicity permeates large parts of human experience. Humans generate various motor and brain rhythms spanning a range of frequencies. We also experience and synchronize to externally imposed rhythmicity, for example from music and song or from the 24-h light-dark cycles of the sun. In the context of music, humans have the ability to perceive, generate, and anticipate rhythmic structures, for example, “the beat.” Experimental and behavioral studies offer clues about the biophysical and neural mechanisms that underlie our rhythmic abilities, and about different brain areas that are involved but many open questions remain. In this paper, we review several theoretical and computational approaches, each centered at different levels of description, that address specific aspects of musical rhythmic generation, perception, attention, perception-action coordination, and learning. We survey methods and results from applications of dynamical systems theory, neuro-mechanistic modeling, and Bayesian inference. Some frameworks rely on synchronization of intrinsic brain rhythms that span the relevant frequency range; some formulations involve real-time adaptation schemes for error-correction to align the phase and frequency of a dedicated circuit; others involve learning and dynamically adjusting expectations to make rhythm tracking predictions. Each of the approaches, while initially designed to answer specific questions, offers the possibility of being integrated into a larger framework that provides insights into our ability to perceive and generate rhythmic patterns. Frontiers Media S.A. 2023-05-17 /pmc/articles/PMC10229831/ /pubmed/37265781 http://dx.doi.org/10.3389/fncom.2023.1151895 Text en Copyright © 2023 Large, Roman, Kim, Cannon, Pazdera, Trainor, Rinzel and Bose. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Large, Edward W.
Roman, Iran
Kim, Ji Chul
Cannon, Jonathan
Pazdera, Jesse K.
Trainor, Laurel J.
Rinzel, John
Bose, Amitabha
Dynamic models for musical rhythm perception and coordination
title Dynamic models for musical rhythm perception and coordination
title_full Dynamic models for musical rhythm perception and coordination
title_fullStr Dynamic models for musical rhythm perception and coordination
title_full_unstemmed Dynamic models for musical rhythm perception and coordination
title_short Dynamic models for musical rhythm perception and coordination
title_sort dynamic models for musical rhythm perception and coordination
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229831/
https://www.ncbi.nlm.nih.gov/pubmed/37265781
http://dx.doi.org/10.3389/fncom.2023.1151895
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