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Commonality and variation in mental representations of music revealed by a cross-cultural comparison of rhythm priors in 15 countries

Commonality and variation in mental representations of music revealed by a cross-cultural comparison of rhythm priors in 15 countries

Nori Jacoby ^1,2, Rainer Polak ³, Jessica A Grahn ⁴, Daniel J Cameron ⁵, Kyung Myun Lee ^6,7, Ricardo Godoy ⁸, Eduardo A Undurraga ^9,10, Tomás Huanca ¹¹, Timon Thalwitzer ¹², Noumouké Doumbia ¹³, Daniel Goldberg ¹⁴, Elizabeth H Margulis ¹⁵, Patrick C M Wong ¹⁶, Luis Jure ¹⁷, Martín Rocamora ^18,19, Shinya Fujii ²⁰, Patrick E Savage ^20,21, Jun Ajimi ²², Rei Konno ²⁰, Sho Oishi ²⁰, Kelly Jakubowski ²³, Andre Holzapfel ²⁴, Esra Mungan ²⁵, Ece Kaya ^26,27, Preeti Rao ²⁸, Mattur A Rohit ²⁸, Suvarna Alladi ²⁹, Bronwyn Tarr ^30,31, Manuel Anglada-Tort ^32,33, Peter M C Harrison ^32,34, Malinda J McPherson ^35,36,37, Sophie Dolan ^35,38, Alex Durango ^35,37,39, Josh H McDermott ^40,41,42,43

Nori Jacoby ^1,2, Rainer Polak ³, Jessica A Grahn ⁴

¹Computational Auditory Perception Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany. nori.jacoby@ae.mpg.de.
²Presidential Scholars in Society and Neuroscience, Columbia University, New York, NY, USA. nori.jacoby@ae.mpg.de.
³RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Blindern, Oslo, Norway.
⁴Brain and Mind Institute and Department of Psychology, University of Western Ontario, London, Ontario, Canada.
⁵Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada.
⁶School of Digital Humanities and Social Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
⁷Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
⁸Heller School for Social Policy and Management, Brandeis University, Waltham, MA, USA.
⁹Escuela de Gobierno, Pontificia Universidad Católica de Chile, Santiago, Chile.
¹⁰CIFAR Azrieli Global Scholars programme, CIFAR, Toronto, Ontario, Canada.
¹¹Centro Boliviano de Investigación y Desarrollo Socio Integral, San Borja, Bolivia.
¹²Department of Musicology, University of Vienna, Vienna, Austria.
¹³Sciences de l'Education, Université Catholique d'Afrique de l'Ouest, Bamako, Mali.
¹⁴Department of Music, University of Connecticut, Storrs, CT, USA.
¹⁵Department of Music, Princeton University, Princeton, NJ, USA.
¹⁶Department of Linguistics & Modern Languages and Brain and Mind Institute, Chinese University of Hong Kong, Hong Kong SAR, China.
¹⁷School of Music, Universidad de la República, Montevideo, Uruguay.
¹⁸Signal Processing Department, School of Engineering, Universidad de la República, Montevideo, Uruguay.
¹⁹Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain.
²⁰Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan.
²¹School of Psychology, University of Auckland, Auckland, New Zealand.
²²Department of Traditional Japanese Music, Tokyo University of the Arts, Tokyo, Japan.
²³Department of Music, Durham University, Durham, UK.
²⁴Division of Media Technology and Interaction Design, KTH Royal Institute of Technology, Stockholm, Sweden.
²⁵Department of Psychology, Bogazici University, Istanbul, Turkey.
²⁶Max Planck Research Group 'Neural and Environmental Rhythms', Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany.
²⁷Cognitive Science Master Program, Bogazici University, Istanbul, Turkey.
²⁸Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
²⁹Nizam's Institute of Medical Sciences, Hyderabad, India.
³⁰Department of Cognitive and Evolutionary Anthropology, University of Oxford, Oxford, UK.
³¹Department of Experimental Psychology, University of Oxford, Oxford, UK.
³²Computational Auditory Perception Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany.
³³Department of Psychology, Goldsmiths, University of London, London, UK.
³⁴Faculty of Music, University of Cambridge, Cambridge, UK.
³⁵Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
³⁶Program in Speech and Hearing Biosciences and Technology, Harvard University, Cambridge, MA, USA.
³⁷McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
³⁸Department of Brain and Cognitive Sciences, Wellesley College, Wellesley, MA, USA.
³⁹Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
⁴⁰Faculty of Music, University of Cambridge, Cambridge, UK. jhm@mit.edu.
⁴¹Program in Speech and Hearing Biosciences and Technology, Harvard University, Cambridge, MA, USA. jhm@mit.edu.
⁴²McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA. jhm@mit.edu.
⁴³Center for Brains, Minds & Machines, Massachusetts Institute of Technology, Cambridge, MA, USA. jhm@mit.edu.

⁰Computational Auditory Perception Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany. nori.jacoby@ae.mpg.de.

Nature Human Behaviour +

|

March 4, 2024

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View abstract on PubMed

Summary

This summary is machine-generated.

Music cognition reveals universal rhythm categories across diverse cultures. Despite variations in musical practices, a common preference for simple integer-ratio rhythms suggests a shared cognitive foundation for music worldwide.

Area Of Science

Cognitive Science
Ethnomusicology
Cross-cultural Psychology

Background

Music is a universal human phenomenon, yet its specific forms vary globally.
Understanding the cognitive underpinnings of music cognition is crucial for identifying universal aspects of human thought.

Purpose Of The Study

To investigate universal features of music cognition, specifically mental representations of rhythm.
To explore how cultural diversity in musical practices interacts with universal cognitive biases.

Main Methods

A cross-cultural study involving 39 participant groups in 15 countries.
Participants reproduced 'seed' rhythms in a 'telephone' game-like paradigm to estimate cognitive priors.
Analysis focused on biases in rhythm reproduction, particularly peaks at integer-ratio rhythms.

Main Results

All tested groups exhibited a sparse prior for rhythm, with a preference for integer-ratio rhythms.
The salience of specific integer ratios varied significantly across groups, correlating with local musical traditions.
Evidence suggests discrete rhythm categories based on small-integer ratios are a universal feature.

Conclusions

Discrete rhythm categories at small-integer ratios represent a common, universal aspect of music cognition.
These universal cognitive biases likely stabilize musical systems during cultural transmission.
The interaction between universal cognitive priors and culture-specific traditions explains the observed diversity in global music.

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