Advertisement

Groove on the Brain

  • Peter VuustEmail author
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11265)

Abstract

A unique feature of music is its potential to make us want to move our feet and bodies to the rhythm of the musical beat. Even though the ability to synchronize our movements to music feels as a completely natural music-related behavior to most humans (but see [1, 2] for rare cases of so-called beat-deafness in humans) this ability is rarely observed in animals [3], and usually depends on specific training regimes [4]. Our brains structure the musical beat into strong and weak beats even without any such information present in the auditory stimulus [5]. Furthermore, the tendency to move to a regular beat, with isochronous intervals, may persist even if the music that we listen to emphasizes musical events that lies between these beats as for syncopated rhythms [6] or in the case of polyrhythm [7, 8]. This indicates a cognitive discrepancy between what is heard – the rhythm - and the brain’s internal structuring of the beat – which in musicology is termed the meter.

In the present paper, I shall argue that this discrepancy: (1) is related to prediction as a fundamental principle of brain processing, (2) gives rise to prediction error between lower - possibly sensory - and higher levels – possibly motor networks - in the hierarchical organized brain, and that (3) perception, learning and our inclination to move to the beat depends on the right balance between predictability and surprise. This predictive coding understanding of the brain mechanisms involved in movement related musical behavior may help us understand brain processes related to aesthetic experiences in general and in designing strategies for clinical intervention for patients with movement disorders.

Keywords

Groove Predictive coding Neuroscience 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Palmer, C., Lidji, P., Peretz, I.: Losing the beat: deficits in temporal coordination. Phil. Trans. R. Soc. B. 369 (2014)Google Scholar
  2. 2.
    Phillips-Silver, J., Toiviainen, P., Gosselin, N., et al.: Born to dance but beat deaf: a new form of congenital amusia. Neuropsychologia 49, 961–969 (2011)CrossRefGoogle Scholar
  3. 3.
    Patel, A.D., Iversen, J.R., Bregman, M.R., et al.: Experimental evidence for synchronization to a musical beat in a nonhuman animal. Curr. Biol. 19, 827–830 (2009)CrossRefGoogle Scholar
  4. 4.
    Cook, P., Rouse, A., Wilson, M., et al.: A California sea lion (Zalophus californianus) can keep the beat: motor entrainment to rhythmic auditory stimuli in a non vocal mimic. J. Comp. Psychol. 127, 412–427 (2013)CrossRefGoogle Scholar
  5. 5.
    Brochard, R., Abecasis, D., Potter, D., et al.: The “ticktock” of our internal clock: direct brain evidence of subjective accents in isochronous sequences. Psychol. Sci. 14, 362–366 (2003)CrossRefGoogle Scholar
  6. 6.
    Fitch, W.T.: Perception and production of syncopated rhythms. Music Percept. 25, 43–58 (2007)CrossRefGoogle Scholar
  7. 7.
    Handel, S., Oshinsky, J.S.: The meter of syncopated auditory polyrhythms. Percept. Psychophys. 30, 1–9 (1981)CrossRefGoogle Scholar
  8. 8.
    Vuust, P., Roepstorff, A., Wallentin, M., et al.: It don’t mean a thing… Keeping the rhythm during polyrhythmic tension, activates language areas (BA47). Neuroimage 31, 832–841 (2006)CrossRefGoogle Scholar
  9. 9.
    Friston, K.: A theory of cortical responses. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 815–836 (2005)CrossRefGoogle Scholar
  10. 10.
    Bar, M.: Predictions: a universal principle in the operation of the human brain. Philos. T. R. Soc. B. 364, 1181–1182 (2009)CrossRefGoogle Scholar
  11. 11.
    Rao, R.P., Ballard, D.H.: Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79–87 (1999)CrossRefGoogle Scholar
  12. 12.
    llinas, R.R.: Prediction is the ultimate function of the brain. In: Llinas, R.R. (ed.) I of the Vortex, pp. 21–52. The MIT Press, Massachusetts (2001)Google Scholar
  13. 13.
    Clark, A.: Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 181–204 (2013)CrossRefGoogle Scholar
  14. 14.
    Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010)CrossRefGoogle Scholar
  15. 15.
    Meyer, L.: Emotion and Meaning in Music. University of Chicago Press, Chicago (1956)Google Scholar
  16. 16.
    Huron, D.: Sweet Anticipation. The MIT Book, Cambridge (2006)Google Scholar
  17. 17.
    Vuust, P., Kringelbach, M.L.: The pleasure of making meaning: evidence from the neuroscience of music. Interdisc. Sci. Rev. (ISR) 35, 166–182 (2010)Google Scholar
  18. 18.
    Rohrmeier, M.A., Koelsch, S.: Predictive information processing in music cognition. A critical review. Int. J. Psychophysiol. 83, 164–175 (2012)CrossRefGoogle Scholar
  19. 19.
    Keller, P.E., Knoblich, G., Repp, B.H.: Pianists duet better when they play with themselves: on the possible role of action simulation in synchronization. Conscious. Cogn. 16, 102–111 (2007)CrossRefGoogle Scholar
  20. 20.
    Gebauer, L., Witek, M., Hansen, N., et al.: Oxytocin improves synchronisation in leader-follower interaction. Sci. Rep. 6 (2016)Google Scholar
  21. 21.
    Konvalinka, I., Vuust, P., Roepstorff, A., et al.: Follow you, follow me: continuous mutual prediction and adaptation in joint tapping. Q. J. Exp. Psychol. (2010)Google Scholar
  22. 22.
    Vuust, P., Ostergaard, L., Pallesen, K.J., et al.: Predictive coding of music. Cortex 45, 80–92 (2009)CrossRefGoogle Scholar
  23. 23.
    Friston, K., Friston, D.A.: A free energy formulation of music generation and perception: Helmholtz Revisited. In: Bader, R. (ed.) Sound - Perception - Performance, vol. 1, pp. 43–69. Springer, Cham (2013).  https://doi.org/10.1007/978-3-319-00107-4_2CrossRefGoogle Scholar
  24. 24.
    Schaefer, R.S., Overy, K., Nelson, P.: Affect and non-uniform characteristics of predictive processing in musical behaviour. Behav. Brain Sci. 36, 2 (2013)CrossRefGoogle Scholar
  25. 25.
    Witek, M.A.G., Clarke, E.F., Wallentin, M., et al.: Syncopation, body-movement and pleasure in groove music. PLoS ONE 9, e94446 (2014)CrossRefGoogle Scholar
  26. 26.
    Witek, M.A., Popescu, T., Clarke, E.F., et al.: Syncopation affects free body-movement in musical groove. Exp. Brain Res. 235, 995–1005 (2017)CrossRefGoogle Scholar
  27. 27.
    Witek, M.A.: Filling in: syncopation, pleasure and distributed embodiment in groove. Music Analysis (2016)Google Scholar
  28. 28.
    Witek, M.A.G., Clarke, E.F., Kringelbach, M.L., Vuust, P.: Effects of polyphonic context and instrumentation on syncopation in music. Music Percept. 32, 201–217 (2014)Google Scholar
  29. 29.
    Wundt, W.: Grundzuge der physiologischen psychologie. Englemann, Leipzig (1874)Google Scholar
  30. 30.
    North, A.C., Hargreaves, D.J.: Subjective complexity, familiarity, and liking for popular music. Psychomusicology 14, 77–93 (1995)CrossRefGoogle Scholar
  31. 31.
    North, A.C., Hargreaves, D.J.: Experimental aesthetics and everyday music listening. In: Hargreaves, D.J., North, A.C. (eds.) The Social Psychology of Music. Oxford University Press, Oxford (1997)Google Scholar
  32. 32.
    Orr, M.G., Ohlsson, S.: Relationship Between complexity and liking as a function of expertise. Music Percept. 22, 583–611 (2005)CrossRefGoogle Scholar
  33. 33.
    Berlyne, D.E.: Aesthetics and Psychobiology. Appleton-Century-Crofts, East Norwalk (1971)Google Scholar
  34. 34.
    Witek, M.A., Clarke, E.F., Wallentin, M., et al.: Correction: syncopation, body-movement and pleasure in groove music. PloS one. 10 (2015)CrossRefGoogle Scholar
  35. 35.
    Vuust, P., Dietz, M., Witek, M., Kringelbach, M.: Now you hear it: a predictive coding model for understanding rhythmic incongruity. Ann. New York Acad. Sci. (2018, in press)Google Scholar
  36. 36.
    Garrido, M.I., Sahani, M., Dolan, R.J.: Outlier responses reflect sensitivity to statistical structure in the human brain. PLoS Comput. Biol. 9, e1002999 (2013)CrossRefGoogle Scholar
  37. 37.
    Friston, K.: Learning and inference in the brain. Neural Netw. 16, 1325–1352 (2003)CrossRefGoogle Scholar
  38. 38.
    Brown, H., Adams, R.A., Parees, I., et al.: Active inference, sensory attenuation and illusions. Cogn. Process. 14, 411–427 (2013)CrossRefGoogle Scholar
  39. 39.
    Sams, M., Paavilainen, P., Alho, K., et al.: Auditory frequency discrimination and event-related potentials. Electroencephalogr. Clin. Neurophysiol. 62, 437–448 (1985)CrossRefGoogle Scholar
  40. 40.
    Näätänen, R., Paaviliainen, P., Alho, K., et al.: The mismatch negativity to intensity changes in an auditory stimulus sequence. Electroencephalogr. Clin. Neurophysiol. 40, 125–131 (1987)Google Scholar
  41. 41.
    Dietz, M.J., Friston, K.J., Mattingley, J.B., et al.: Effective connectivity reveals right-hemisphere dominance in audiospatial perception: implications for models of spatial neglect. J. Neurosci. 34, 5003–5011 (2014)CrossRefGoogle Scholar
  42. 42.
    Paavilainen, P., Karlsson, M.-L., Reinikainen, K., et al.: Mismatch negativity to change in spatial location of an auditory stimulus. Electroencephalogr. Clin. Neurophysiol. 73, 129–141 (1989)CrossRefGoogle Scholar
  43. 43.
    Paavilainen, P., Simola, J., Jaramillo, M., et al.: Preattentive extraction of abstract feature conjunctions from auditory stimulation as reflected by the mismatch negativity (MMN). Psychophysiology 38, 359–365 (2001)CrossRefGoogle Scholar
  44. 44.
    Van Zuijen, T.L., Sussman, E., Winkler, I., et al.: Grouping of sequential sounds-an event-related potential study comparing musicians and nonmusicians. J. Cogn. Neurosci. 16, 331–338 (2004)CrossRefGoogle Scholar
  45. 45.
    Friedman, D., Cycowicz, Y.M., Gaeta, H.: The novelty P3: an event-related brain potential (ERP) sign of the brain’s evaluation of novelty. Neurosci. Biobehav. Rev. 25, 355–373 (2001)CrossRefGoogle Scholar
  46. 46.
    Knight, R.T., Scabini, D.: Anatomic bases of event-related potentials and their relationship to novelty detection in humans. J. Clin. Neurophysiol. 15, 3–13 (1998)CrossRefGoogle Scholar
  47. 47.
    Woods, D.L.: The physiological basis of selective attention: implications of event-related potential studies. In: Event-related Brain Potentials: Basic Issues and Applications, pp. 178–209 (1990)Google Scholar
  48. 48.
    Schröger, E.: A neural mechanism for involuntary attention shifts to changes in auditory stimulation. J. Cogn. Neurosci. 8, 527–539 (1996)CrossRefGoogle Scholar
  49. 49.
    Escera, C., Alho, K., Schröger, E., et al.: Involuntary attention and distractibility as evaluated with event-related brain potentials. Audiol. Neurootol. 5, 151–166 (2000)CrossRefGoogle Scholar
  50. 50.
    Feldman, H., Friston, K.J.: Attention, uncertainty, and free-energy. Front. Hum. Neurosci. 4, 215 (2010)CrossRefGoogle Scholar
  51. 51.
    Grahn, J.A., McAuley, J.D.: Neural bases of individual differences in beat perception. Neuroimage 47, 1894–1903 (2009)CrossRefGoogle Scholar
  52. 52.
    van Rijn, H., Gu, B.-M., Meck, W.H.: Dedicated clock/timing-circuit theories of time perception and timed performance. In: Merchant, H., de Lafuente, V. (eds.) Neurobiology of interval timing. AEMB, vol. 829, pp. 75–99. Springer, New York (2014).  https://doi.org/10.1007/978-1-4939-1782-2_5CrossRefGoogle Scholar
  53. 53.
    Chen, J.L., Penhune, V.B., Zatorre, R.J.: Moving on time: brain network for auditory-motor synchronization is modulated by rhythm complexity and musical training. J. Cogn. Neurosci. 20, 226–239 (2008)CrossRefGoogle Scholar
  54. 54.
    Kung, S.J., Chen, J.L., Zatorre, R.J., et al.: Interacting cortical and basal ganglia networks underlying finding and tapping to the musical beat. J. Cogn. Neurosci. 25, 401–420 (2013)CrossRefGoogle Scholar
  55. 55.
    Grahn, J.A., Brett, M.: Rhythm and beat perception in motor areas of the brain. J. Cogn. Neurosci. 19, 893–906 (2007)CrossRefGoogle Scholar
  56. 56.
    Todd, N.P., Lee, C.S.: The sensory-motor theory of rhythm and beat induction 20 years on: a new synthesis and future perspectives. Front. Hum. Neurosci. 9, 444 (2015)CrossRefGoogle Scholar
  57. 57.
    Vuust, P., Ostergaard, L., Roepstorff, A.: Polyrhythmic communicational devices appear as language in the brains of musicians. In: International Conference on Music Perception and Cognition, vol. ICMPC9, pp. 1159–1167. ESCOM, Bologna (2006)Google Scholar
  58. 58.
    Vuust, P., Wallentin, M., Mouridsen, K., et al.: Tapping polyrhythms in music activates language areas. Neurosci. Lett. (2011)Google Scholar
  59. 59.
    Phillips-Silver, J., Trainor, L.J.: Feeling the beat: movement influences infant rhythm perception. Science 308, 1430 (2005)CrossRefGoogle Scholar
  60. 60.
    Burger, B., London, J., Thompson, M.R., et al.: Synchronization to metrical levels in music depends on low-frequency spectral components and tempo. Psychol. Res. (2017)Google Scholar
  61. 61.
    Grahn, J.A., Rowe, J.B.: Feeling the beat: premotor and striatal interactions in musicians and nonmusicians during beat perception. J. Neurosci. 29, 7540–7548 (2009)CrossRefGoogle Scholar
  62. 62.
    Large, E.W., Herrera, J.A., Velasco, M.J.: Neural networks for beat perception in musical rhythm. Front. Syst. Neurosci. 9, 159 (2015)CrossRefGoogle Scholar
  63. 63.
    Guckenheimer, J., Labouriau, J.S.: Bifurcation of the Hodgkin and Huxley equations: a new twist. Bull. Math. Biol. 55, 937 (1993)CrossRefGoogle Scholar
  64. 64.
    Blood, A.J., Zatorre, R.J.: Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion. Proc. Natl. Acad. Sci. U.S.A. 98, 11818–11823 (2001)CrossRefGoogle Scholar
  65. 65.
    Salimpoor, V.N., Benovoy, M., Longo, G., et al.: The rewarding aspects of music listening are related to degree of emotional arousal. PLoS ONE 4, e7487 (2009)CrossRefGoogle Scholar
  66. 66.
    Gebauer, L., Kringelbach, M.L., Vuust, P.: Ever-changing cycles of musical pleasure: the role of dopamine and anticipation. Psychomusicologys 22, 152–167 (2012)CrossRefGoogle Scholar
  67. 67.
    Schultz, W.: Behavioral dopamine signals. Trends Neurosci. 30, 203–210 (2007)CrossRefGoogle Scholar
  68. 68.
    Schultz, W., Preuschoff, K., Camerer, C., et al.: Explicit neural signals reflecting reward uncertainty. Phil. Trans. R. Soc. B Biol. Sci. 363, 3801–3811 (2008)CrossRefGoogle Scholar
  69. 69.
    Hansen, N.C., Dietz, M.J., Vuust, P.: Commentary: predictions and the brain: how musical sounds become rewarding. Front. Hum. Neurosci. 11 (2017)Google Scholar
  70. 70.
    Schultz, W., Dayan, P., Montague, P.R.: A neural substrate of prediction and reward. Science 275, 1593–1599 (1997)CrossRefGoogle Scholar
  71. 71.
    Kringelbach, M.L., Berridge, K.C.: Towards a functional neuroanatomy of pleasure and happiness. Trends Cogn. Sci. 13, 479–487 (2009)CrossRefGoogle Scholar
  72. 72.
    Hohwy, J.: The self-evidencing brain. Noûs 50, 259–285 (2016)CrossRefGoogle Scholar
  73. 73.
    Pearce, M.T., Wiggins, G.: Expectation in melody: the influence of context and learning. Music Percept. 23, 29 (2006)CrossRefGoogle Scholar
  74. 74.
    Green, A.C., Baerentsen, K.B., Stodkilde-Jorgensen, H., et al.: Listen, learn, like! Dorsolateral prefrontal cortex involved in the mere exposure effect in music. Neurol. Res. Int. 2012, 846270 (2012)CrossRefGoogle Scholar
  75. 75.
    Zajonc, R.B.: Attitudinal effects of mere exposure. J. Pers. Soc. Psychol. 9, 1 (1968)CrossRefGoogle Scholar
  76. 76.
    Grahn, J.A., Brett, M.: Impairment of beat-based rhythm discrimination in Parkinson’s disease. Cortex. 45, 54–61 (2009)CrossRefGoogle Scholar
  77. 77.
    Thaut, M.H., McIntosh, G.C.: Music therapy in mobility training with the elderly: a review of current research. Care Manag. J.: Journal of Case Management; The Journal of Long Term Home Health Care 1, 71–74 (1999)CrossRefGoogle Scholar
  78. 78.
    Benoit, C.-E., Dalla Bella, S., Farrugia, N., et al.: Musically cued gait-training improves both perceptual and motor timing in Parkinson’s disease. Front. Hum. Neurosci. 8, 494 (2014)CrossRefGoogle Scholar
  79. 79.
    Altenmüller, E., Marco-Pallares, J., Münte, T.F., et al.: Neural reorganization underlies improvement in stroke-induced motor dysfunction by music-supported therapy. Ann. N. Y. Acad. Sci. 1169, 395–405 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Center for Music in the Brain, Department of Clinical MedicineThe Royal Academy of Music Aarhus/Aalborg, Aarhus UniversityAarhusDenmark

Personalised recommendations

Cite paper

Buy options