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Coordination Dynamics: A Foundation for Understanding Social Behavior
Humans’ interactions with each other or with socially competent machines exhibit lawful coordination patterns at multiple levels of description. According to Coordination Dynamics, such laws specify the flow of coordination states produced by functional synergies of elements (e.g., cells, body parts...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457017/ https://www.ncbi.nlm.nih.gov/pubmed/32922277 http://dx.doi.org/10.3389/fnhum.2020.00317 |
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author | Tognoli, Emmanuelle Zhang, Mengsen Fuchs, Armin Beetle, Christopher Kelso, J. A. Scott |
author_facet | Tognoli, Emmanuelle Zhang, Mengsen Fuchs, Armin Beetle, Christopher Kelso, J. A. Scott |
author_sort | Tognoli, Emmanuelle |
collection | PubMed |
description | Humans’ interactions with each other or with socially competent machines exhibit lawful coordination patterns at multiple levels of description. According to Coordination Dynamics, such laws specify the flow of coordination states produced by functional synergies of elements (e.g., cells, body parts, brain areas, people…) that are temporarily organized as single, coherent units. These coordinative structures or synergies may be mathematically characterized as informationally coupled self-organizing dynamical systems (Coordination Dynamics). In this paper, we start from a simple foundation, an elemental model system for social interactions, whose behavior has been captured in the Haken-Kelso-Bunz (HKB) model. We follow a tried and tested scientific method that tightly interweaves experimental neurobehavioral studies and mathematical models. We use this method to further develop a body of empirical research that advances the theory toward more generalized forms. In concordance with this interdisciplinary spirit, the present paper is written both as an overview of relevant advances and as an introduction to its mathematical underpinnings. We demonstrate HKB’s evolution in the context of social coordination along several directions, with its applicability growing to increasingly complex scenarios. In particular, we show that accommodating for symmetry breaking in intrinsic dynamics and coupling, multiscale generalization and adaptation are principal evolutions. We conclude that a general framework for social coordination dynamics is on the horizon, in which models support experiments with hypothesis generation and mechanistic insights. |
format | Online Article Text |
id | pubmed-7457017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74570172020-09-11 Coordination Dynamics: A Foundation for Understanding Social Behavior Tognoli, Emmanuelle Zhang, Mengsen Fuchs, Armin Beetle, Christopher Kelso, J. A. Scott Front Hum Neurosci Neuroscience Humans’ interactions with each other or with socially competent machines exhibit lawful coordination patterns at multiple levels of description. According to Coordination Dynamics, such laws specify the flow of coordination states produced by functional synergies of elements (e.g., cells, body parts, brain areas, people…) that are temporarily organized as single, coherent units. These coordinative structures or synergies may be mathematically characterized as informationally coupled self-organizing dynamical systems (Coordination Dynamics). In this paper, we start from a simple foundation, an elemental model system for social interactions, whose behavior has been captured in the Haken-Kelso-Bunz (HKB) model. We follow a tried and tested scientific method that tightly interweaves experimental neurobehavioral studies and mathematical models. We use this method to further develop a body of empirical research that advances the theory toward more generalized forms. In concordance with this interdisciplinary spirit, the present paper is written both as an overview of relevant advances and as an introduction to its mathematical underpinnings. We demonstrate HKB’s evolution in the context of social coordination along several directions, with its applicability growing to increasingly complex scenarios. In particular, we show that accommodating for symmetry breaking in intrinsic dynamics and coupling, multiscale generalization and adaptation are principal evolutions. We conclude that a general framework for social coordination dynamics is on the horizon, in which models support experiments with hypothesis generation and mechanistic insights. Frontiers Media S.A. 2020-08-14 /pmc/articles/PMC7457017/ /pubmed/32922277 http://dx.doi.org/10.3389/fnhum.2020.00317 Text en Copyright © 2020 Tognoli, Zhang, Fuchs, Beetle and Kelso. http://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 Tognoli, Emmanuelle Zhang, Mengsen Fuchs, Armin Beetle, Christopher Kelso, J. A. Scott Coordination Dynamics: A Foundation for Understanding Social Behavior |
title | Coordination Dynamics: A Foundation for Understanding Social Behavior |
title_full | Coordination Dynamics: A Foundation for Understanding Social Behavior |
title_fullStr | Coordination Dynamics: A Foundation for Understanding Social Behavior |
title_full_unstemmed | Coordination Dynamics: A Foundation for Understanding Social Behavior |
title_short | Coordination Dynamics: A Foundation for Understanding Social Behavior |
title_sort | coordination dynamics: a foundation for understanding social behavior |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457017/ https://www.ncbi.nlm.nih.gov/pubmed/32922277 http://dx.doi.org/10.3389/fnhum.2020.00317 |
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