Letter

Musical evolution in the lab exhibits rhythmic universals

  • Nature Human Behaviour 1, Article number: 0007 (2016)
  • doi:10.1038/s41562-016-0007
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Abstract

Music exhibits some cross-cultural similarities, despite its variety across the world. Evidence from a broad range of human cultures suggests the existence of musical universals 1 , here defined as strong regularities emerging across cultures above chance. In particular, humans demonstrate a general proclivity for rhythm 2 , although little is known about why music is particularly rhythmic and why the same structural regularities are present in rhythms around the world. We empirically investigate the mechanisms underlying musical universals for rhythm, showing how music can evolve culturally from randomness. Human participants were asked to imitate sets of randomly generated drumming sequences and their imitation attempts became the training set for the next participants in independent transmission chains. By perceiving and imitating drumming sequences from each other, participants turned initially random sequences into rhythmically structured patterns. Drumming patterns developed into rhythms that are more structured, easier to learn, distinctive for each experimental cultural tradition and characterized by all six statistical universals found among world music 1 ; the patterns appear to be adapted to human learning, memory and cognition. We conclude that musical rhythm partially arises from the influence of human cognitive and biological biases on the process of cultural evolution 3 .

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References

  1. 1.

    , , & Statistical universals reveal the structures and functions of human music. Proc. Natl Acad. Sci. USA 112, 8987–8992 (2015).

  2. 2.

    in Language and Music as Cognitive Systems (eds Rebuschat, P. et al. ) Ch. 11, 73–95 (Oxford Univ. Press, 2009).

  3. 3.

    Cross-cultural convergence of musical features. Proc. Natl Acad. Sci. USA 112, 8809–8810 (2015).

  4. 4.

    et al. Primate drum kit: a system for studying acoustic pattern production by non-human primates using acceleration and strain sensors. Sensors 13, 9790–9820 (2013).

  5. 5.

    , , & Synchrony and motor mimicking in chimpanzee observational learning. Sci. Rep. 4, 5283 (2014).

  6. 6.

    , , & Chimpanzee drumming: a spontaneous performance with characteristics of human musical drumming. Sci. Rep. 5, 11320 (2015).

  7. 7.

    The Evolutionary Origins and Archaeology of Music PhD thesis, Cambridge Univ. (2003).

  8. 8.

    et al. Universal recognition of three basic emotions in music. Curr. Biol. 19, 573–576 (2009).

  9. 9.

    , & Tracking an imposed beat within a metrical grid. Music Percept. 26, 1–18 (2008).

  10. 10.

    , , , & Newborn infants detect the beat in music. Proc. Natl Acad. Sci. USA 106, 2468–2471 (2009).

  11. 11.

    & Universals in the world’s musics. Psychol. Music 41, 229–248 (2013).

  12. 12.

    , & The evolution of musical diversity: the key role of vertical transmission. PloS ONE 11, e0151570 (2016).

  13. 13.

    et al. Experimental evidence for the co-evolution of hominin tool-making teaching and language. Nat. Commun. 6, 6029 (2015).

  14. 14.

    , & Innateness and culture in the evolution of language. Proc. Natl Acad. Sci. USA 104, 5241–5245 (2007).

  15. 15.

    & in Simulating the Evolution of Language (eds Cangelosi, A. & Parisi, D. ) 121–147 (Springer, 2002).

  16. 16.

    & The multiple roles of cultural transmission experiments in understanding human cultural evolution. Phil. Trans. R. Soc. B 363, 3489–3501 (2008).

  17. 17.

    , & Emergence of combinatorial structure and economy through iterated learning with continuous acoustic signals. J. Phonetics 43, 57–68 (2014).

  18. 18.

    & Culture: copying, compression, and conventionality. Cogn. Sci. 39, 171–183 (2015).

  19. 19.

    , & Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human language. Proc. Natl Acad. Sci. USA 105, 10681–10686 (2008).

  20. 20.

    , , & Compression and communication in the cultural evolution of linguistic structure. Cognition 141, 87–102 (2015).

  21. 21.

    , & Systems from sequences: an iterated learning account of the emergence of systematic structure in a non-linguistic task. In Proc. 35th Annual Conf. Cognitive Science Soc. (eds Knauff, M. et al. ) 340–345 (Cognitive Science Society, 2013).

  22. 22.

    Hearing in Time: Psychological Aspects of Musical Meter (Oxford Univ. Press, 2012).

  23. 23.

    Visualizing and interpreting rhythmic patterns using phase space plots. Music Percept. (in the press).

  24. 24.

    , & Look at the beat, feel the meter: top-down effects of meter induction on auditory and visual modalities. Front. Hum. Neurosci. 10, 108 (2016).

  25. 25.

    The distribution of the flora in the alpine zone. New Phytol. 11, 37–50 (1912).

  26. 26.

    in The Cambridge Companion to Percussion (ed. Hartenberger, R. ) Ch. 21, 267–280 (Cambridge Univ. Press, 2016).

  27. 27.

    , & The evolution of dance. Curr. Biol. 26, R5–R9 (2016).

  28. 28.

    & The evolutionary biology of dance without frills. Curr. Biol. 26, R878–R879 (2016).

  29. 29.

    & The formation of rhythmic categories and metric priming. Perception 32, 341–365 (2002).

  30. 30.

    , & Systematic distortions in musicians: reproduction of cyclic three-interval rhythms. Music Percept. 30, 291–305 (2013).

  31. 31.

    & Eliminating unpredictable variation through iterated learning. Cognition 116, 444–449 (2010).

  32. 32.

    , & Iterated learning: intergenerational knowledge transmission reveals inductive biases. Psychon. Bull. Rev. 14, 288–294 (2007).

  33. 33.

    The origins of duality of patterning in artificial whistled languages. Lang. Cogn. 4, 357–380 (2012).

  34. 34.

    , , , & Can iterated learning explain the emergence of graphical symbols? Interact. Stud. 11, 33–50 (2010).

  35. 35.

    , & Structure emerges faster during cultural transmission in children than in adults. Cognition 136, 247–254 (2015).

  36. 36.

    , & Word meanings evolve to selectively preserve distinctions on salient dimensions. Cogn. Sci. 39, 212–226 (2015).

  37. 37.

    A perceptual model of pulse salience and metrical accent in musical rhythms. Music Percept. 11, 409–464 (1994).

  38. 38.

    & Dynamical systems theory for music dynamics. Chaos 5, 501–508 (1995).

  39. 39.

    & Studying cumulative cultural evolution in the laboratory. Phil. Trans. R. Soc. B 363, 3529–3539 (2008).

  40. 40.

    Observing entrainment in music performance: video-based observational analysis of Indian musicians’ tanpura playing and beat marking. Music. Sci. 11, 27–59 (2007).

  41. 41.

    & Applying an exemplar model to the artificial-grammar task: inferring grammaticality from similarity. Q. J. Exp. Psychol. 62, 550–575 (2009).

  42. 42.

    , & Iterated learning and the evolution of language. Curr. Opin. Neurobiol. 28, 108–114 (2014).

  43. 43.

    Binary codes capable of correcting deletions, insertions, and reversals. Sov. Physi. Doklady 10, 707–710 (1966).

  44. 44.

    , & Opportunity to assimilate and pressure to discriminate can generate cultural divergence in the laboratory. Evol. Hum. Behav. 33, 759–770 (2012).

  45. 45.

    & Measuring teaching through hormones and time series analysis: towards a comparative framework. Behav. Brain Sci. 38, 40–41 (2015).

  46. 46.

    & Seeking temporal predictability in speech: comparing statistical approaches on 18 world languages. Front. Hum. Neurosci. 10, 586 (2016).

  47. 47.

    Who belongs in the family? Psychometrika 18, 267–276 (1953).

  48. 48.

    & The perception of musical rhythms. Perception 11, 115–128 (1982).

  49. 49.

    & The rhythmic interpretation of monophonic music. Music Percept. 1, 424–441 (1984).

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Acknowledgements

A.R. was supported by Fonds Wetenschappelijk Onderzoek Vlaanderen grant no. V439315N, and European Research Council (ERC) grant (283435 ABACUS, to B. de Boer). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank P. Filippi, B. Thompson, B. de Boer, H. Little, S. van der Ham, N. Chr. Hansen, J. Iversen, D. Bowling, T. Grossi, A.C. Miralles, P. Norton, V. Spinosa, Y.-H. Su, P. Tinits and K. Smith, as well as all members of the Centre for Language Evolution (Edinburgh), AI-Lab (VUB Brussels), Biolinguistics (Barcelona) and attendants of Evolang XI, IBAC XXV, Statistical Learning 2015 and the DZG Graduate Meeting 2016 for their comments and advice.

Author information

Affiliations

  1. Centre for Language Evolution, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh EH8 9AD, UK

    • Andrea Ravignani
    • , Tania Delgado
    •  & Simon Kirby
  2. Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels 1050, Belgium

    • Andrea Ravignani
  3. Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0515, USA.

    • Tania Delgado

Authors

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Contributions

A.R. and S.K. conceived the study. A.R., T.D. and S.K. designed research. T.D. performed the research. A.R. and S.K. wrote the Python scripts for the data analysis and experimental testing. A.R., T.D. and S.K. analysed the data and wrote the paper.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Andrea Ravignani.

Supplementary information

PDF files

  1. 1.

    Supplementary information

    Supplementary Figure 1, Supplementary Tables 1-3, Supplementary Notes, Supplementary Methods

CSV files

  1. 1.

    Raw data

    The patterns heard and produced by each participant are organized and sorted by number of experimental chain, number of drumming pattern, and participant number (equivalent to generation number) within an experimental transmission chain.