References
Zatorre, R. J., Chen, J. L. & Penhune, V. B. When the brain plays music: auditory–motor interactions in music perception and production. Nat. Rev. Neurosci. 8, 547–558 (2007). A seminal review of auditory–motor coupling in music.
Koelsch, S. Toward a neural basis of music perception–a review and updated model. Front. Psychol. 2, 110 (2011).
Maes, P. J., Leman, M., Palmer, C. & Wanderley, M. M. Action-based effects on music perception. Front. Psychol. 4, 1008 (2014).
Koelsch, S. Brain correlates of music-evoked emotions. Nat. Rev. Neurosci. 15, 170–180 (2014). In this review, the author shows how music engages phylogenetically old reward networks in the brain to evoke emotions, and not merely subjective feelings.
Vuust, P. & Witek, M. A. Rhythmic complexity and predictive coding: a novel approach to modeling rhythm and meter perception in music. Front. Psychol. 5, 1111 (2014).
Friston, K. The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010). This review posits that several global brain theories may be unified by the free-energy principle.
Koelsch, S., Vuust, P. & Friston, K. Predictive processes and the peculiar case of music. Trends Cogn. Sci. 23, 63–77 (2019). This review focuses specifically on predictive coding in music.
Meyer, L. Emotion and Meaning in Music (Univ. of Chicago Press, 1956).
Lerdahl, F. & Jackendoff, R. A Generative Theory of Music (MIT Press, 1999).
Huron, D. Sweet Anticipation (MIT Press, 2006). In this book, Huron draws on evolutionary theory and statistical learning to propose a general theory of musical expectation.
Hansen, N. C. & Pearce, M. T. Predictive uncertainty in auditory sequence processing. Front. Psychol. https://doi.org/10.3389/fpsyg.2013.01008 (2014).
Vuust, P., Brattico, E., Seppanen, M., Naatanen, R. & Tervaniemi, M. The sound of music: differentiating musicians using a fast, musical multi-feature mismatch negativity paradigm. Neuropsychologia 50, 1432–1443 (2012).
Altenmüller, E. O. How many music centers are in the brain? Ann. N. Y. Acad. Sci. 930, 273–280 (2001).
Monelle, R. Linguistics and Semiotics in Music (Harwood Academic Publishers, 1992).
Rohrmeier, M. A. & Koelsch, S. Predictive information processing in music cognition. A critical review. Int. J. Psychophysiol. 83, 164–175 (2012).
Vuust, P., Dietz, M. J., Witek, M. & Kringelbach, M. L. Now you hear it: a predictive coding model for understanding rhythmic incongruity. Ann. N. Y. Acad. Sci. https://doi.org/10.1111/nyas.13622 (2018).
Vuust, P., Ostergaard, L., Pallesen, K. J., Bailey, C. & Roepstorff, A. Predictive coding of music–brain responses to rhythmic incongruity. Cortex 45, 80–92 (2009).
Vuust, P. & Frith, C. Anticipation is the key to understanding music and the effects of music on emotion. Behav. Brain Res. 31, 599–600 (2008). This is the foundation for the PCM model used in this Review.
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).
Lumaca, M., Baggio, G., Brattico, E., Haumann, N. T. & Vuust, P. From random to regular: neural constraints on the emergence of isochronous rhythm during cultural transmission. Soc. Cogn. Affect. Neurosci. 13, 877–888 (2018).
Quiroga-Martinez, D. R. et al. Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non-musicians. Eur. J. Neurosci. https://doi.org/10.1111/ejn.14667 (2019).
Koelsch, S., Schröger, E. & Gunter, T. C. Music matters: preattentive musicality of the human brain. Psychophysiology 39, 38–48 (2002).
Koelsch, S., Schmidt, B.-h & Kansok, J. Effects of musical expertise on the early right anterior negativity: an event-related brain potential study. Psychophysiology 39, 657–663 (2002).
Lumaca, M., Dietz, M. J., Hansen, N. C., Quiroga-Martinez, D. R. & Vuust, P. Perceptual learning of tone patterns changes the effective connectivity between Heschl’s gyrus and planum temporale. Hum. Brain Mapp. 42, 941–952 (2020).
Lieder, F., Daunizeau, J., Garrido, M. I., Friston, K. J. & Stephan, K. E. Modelling trial-by-trial changes in the mismatch negativity. PLoS Comput. Biol. 9, e1002911 (2013).
Wacongne, C., Changeux, J. P. & Dehaene, S. A neuronal model of predictive coding accounting for the mismatch negativity. J. Neurosci. 32, 3665–3678 (2012).
Kiebel, S. J., Garrido, M. I. & Friston, K. J. Dynamic causal modelling of evoked responses: the role of intrinsic connections. Neuroimage 36, 332–345 (2007).
Feldman, H. & Friston, K. J. Attention, uncertainty, and free-energy. Front. Hum. Neurosci. 4, 215 (2010).
Cheung, V. K. M. et al. Uncertainty and surprise jointly predict musical pleasure and amygdala, hippocampus, and auditory cortex activity. Curr. Biol. 29, 4084–4092 e4084 (2019). This fMRI study ties uncertainty and surprise to musical pleasure.
McDermott, J. H. & Oxenham, A. J. Music perception, pitch, and the auditory system. Curr. Opin. Neurobiol. 18, 452–463 (2008).
Thoret, E., Caramiaux, B., Depalle, P. & McAdams, S. Learning metrics on spectrotemporal modulations reveals the perception of musical instrument timbre. Nat. Hum. Behav. 5, 369–377 (2020).
Siedenburg, K. & McAdams, S. Four distinctions for the auditory “wastebasket” of timbre. Front. Psychol. 8, 1747 (2017).
Bendor, D. & Wang, X. The neuronal representation of pitch in primate auditory cortex. Nature 436, 1161–1165 (2005).
Zatorre, R. J. Pitch perception of complex tones and human temporal-lobe function. J. Acoustical Soc. Am. 84, 566–572 (1988).
Warren, J. D., Uppenkamp, S., Patterson, R. D. & Griffiths, T. D. Separating pitch chroma and pitch height in the human brain. Proc. Natl Acad. Sci. USA 100, 10038–10042 (2003). Using fMRI data, this study shows that pitch chroma is represented anterior to the primary auditory cortex, and pitch height is represented posterior to the primary auditory cortex.
Rauschecker, J. P. & Scott, S. K. Maps and streams in the auditory cortex: nonhuman primates illuminate human speech processing. Nat. Neurosci. 12, 718–724 (2009).
Leaver, A. M., Van Lare, J., Zielinski, B., Halpern, A. R. & Rauschecker, J. P. Brain activation during anticipation of sound sequences. J. Neurosci. 29, 2477–2485 (2009).
Houde, J. F. & Chang, E. F. The cortical computations underlying feedback control in vocal production. Curr. Opin. Neurobiol. 33, 174–181 (2015).
Lee, Y. S., Janata, P., Frost, C., Hanke, M. & Granger, R. Investigation of melodic contour processing in the brain using multivariate pattern-based fMRI. Neuroimage 57, 293–300 (2011).
Janata, P. et al. The cortical topography of tonal structures underlying Western music. Science 298, 2167–2170 (2002).
Saffran, J. R., Aslin, R. N. & Newport, E. L. Statistical learning by 8-month-old infants. Science 274, 1926–1928 (1996).
Saffran, J. R., Johnson, E. K., Aslin, R. N. & Newport, E. L. Statistical learning of tone sequences by human infants and adults. Cognition 70, 27–52 (1999).
Krumhansl, C. L. Perceptual structures for tonal music. Music. Percept. 1, 28–62 (1983).
Margulis, E. H. A model of melodic expectation. Music. Percept. 22, 663–714 (2005).
Temperley, D. A probabilistic model of melody perception. Cogn. Sci. 32, 418–444 (2008).
Pearce, M. T. & Wiggins, G. A. Auditory expectation: the information dynamics of music perception and cognition. Top. Cogn. Sci. 4, 625–652 (2012).
Sears, D. R. W., Pearce, M. T., Caplin, W. E. & McAdams, S. Simulating melodic and harmonic expectations for tonal cadences using probabilistic models. J. N. Music. Res. 47, 29–52 (2018).
Näätänen, R., Gaillard, A. W. & Mäntysalo, S. Early selective-attention effect on evoked potential reinterpreted. Acta Psychol. 42, 313–329 (1978).
Näätänen, R., Paavilainen, P., Rinne, T. & Alho, K. The mismatch negativity (MMN) in basic research of central auditory processing: a review. Clin. Neurophysiol. 118, 2544–2590 (2007). This classic review covers three decades of MMN research to understand auditory perception.
Wallentin, M., Nielsen, A. H., Friis-Olivarius, M., Vuust, C. & Vuust, P. The Musical Ear Test, a new reliable test for measuring musical competence. Learn. Individ. Differ. 20, 188–196 (2010).
Tervaniemi, M. et al. Top-down modulation of auditory processing: effects of sound context, musical expertise and attentional focus. Eur. J. Neurosci. 30, 1636–1642 (2009).
Burunat, I. et al. The reliability of continuous brain responses during naturalistic listening to music. Neuroimage 124, 224–231 (2016).
Burunat, I. et al. Action in perception: prominent visuo-motor functional symmetry in musicians during music listening. PLoS ONE 10, e0138238 (2015).
Alluri, V. et al. Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm. Neuroimage 59, 3677–3689 (2012). A free-listening fMRI study showing brain networks involved in perception of distinct acoustical features of music.
Halpern, A. R. & Zatorre, R. J. When that tune runs through your head: a PET investigation of auditory imagery for familiar melodies. Cereb. Cortex 9, 697–704 (1999).
Herholz, S. C., Halpern, A. R. & Zatorre, R. J. Neuronal correlates of perception, imagery, and memory for familiar tunes. J. Cogn. Neurosci. 24, 1382–1397 (2012).
Pallesen, K. J. et al. Emotion processing of major, minor, and dissonant chords: a functional magnetic resonance imaging study. Ann. N. Y. Acad. Sci. 1060, 450–453 (2005).
McPherson, M. J. et al. Perceptual fusion of musical notes by native Amazonians suggests universal representations of musical intervals. Nat. Commun. 11, 2786 (2020).
Helmholtz H. L. F. On the Sensations of Tone as a Physiological Basis for the Theory of Music (Cambridge Univ. Press, 1954).
Vassilakis, P. N. & Kendall, R. A. in Human Vision and Electronic Imaging XV. 75270O (International Society for Optics and Photonics, 2010).
Plomp, R. & Levelt, W. J. M. Tonal consonance and critical bandwidth. J. Acoustical Soc. Am. 38, 548–560 (1965).
McDermott, J. H., Schultz, A. F., Undurraga, E. A. & Godoy, R. A. Indifference to dissonance in native Amazonians reveals cultural variation in music perception. Nature 535, 547–550 (2016). An ethnomusicology study showing that consonance preference may be absent in people with minimal exposure to Western music.
Mehr, S. A. et al. Universality and diversity in human song. Science https://doi.org/10.1126/science.aax0868 (2019).
Patel, A. D., Gibson, E., Ratner, J., Besson, M. & Holcomb, P. J. Processing syntactic relations in language and music: an event-related potential study. J. Cogn. Neurosci. 10, 717–733 (1998). This classic study compares responses to syntactic incongruities in both language and Western tonal music.
Janata, P. The neural architecture of music-evoked autobiographical memories. Cereb. Cortex 19, 2579–2594 (2009).
Maess, B., Koelsch, S., Gunter, T. C. & Friederici, A. D. Musical syntax is processed in Broca’s area: an MEG study. Nat. Neurosci. 4, 540–545 (2001).
Koelsch, S. et al. Differentiating ERAN and MMN: an ERP study. Neuroreport 12, 1385–1389 (2001). Using EEG, the authors show that ERAN and MMN reflect different cognitive mechanisms.
Loui, P., Grent-‘t-Jong, T., Torpey, D. & Woldorff, M. Effects of attention on the neural processing of harmonic syntax in Western music. Cogn. Brain Res. 25, 678–687 (2005).
Koelsch, S., Fritz, T., Schulze, K., Alsop, D. & Schlaug, G. Adults and children processing music: an fMRI study. Neuroimage 25, 1068–1076 (2005).
Tillmann, B., Janata, P. & Bharucha, J. J. Activation of the inferior frontal cortex in musical priming. Ann. N. Y. Acad. Sci. 999, 209–211 (2003).
Garza-Villarreal, E. A., Brattico, E., Leino, S., Ostergaard, L. & Vuust, P. Distinct neural responses to chord violations: a multiple source analysis study. Brain Res. 1389, 103–114 (2011).
Leino, S., Brattico, E., Tervaniemi, M. & Vuust, P. Representation of harmony rules in the human brain: further evidence from event-related potentials. Brain Res. 1142, 169–177 (2007).
Sammler, D. et al. Co-localizing linguistic and musical syntax with intracranial EEG. Neuroimage 64, 134–146 (2013).
Loui, P., Wessel, D. L. & Hudson Kam, C. L. Humans rapidly learn grammatical structure in a new musical scale. Music. Percept. 27, 377–388 (2010).
Loui, P., Wu, E. H., Wessel, D. L. & Knight, R. T. A generalized mechanism for perception of pitch patterns. J. Neurosci. 29, 454–459 (2009).
Cheung, V. K. M., Meyer, L., Friederici, A. D. & Koelsch, S. The right inferior frontal gyrus processes nested non-local dependencies in music. Sci. Rep. 8, 3822 (2018).
Haueisen, J. & Knosche, T. R. Involuntary motor activity in pianists evoked by music perception. J. Cogn. Neurosci. 13, 786–792 (2001).
Bangert, M. et al. Shared networks for auditory and motor processing in professional pianists: evidence from fMRI conjunction. Neuroimage 30, 917–926 (2006).
Baumann, S. et al. A network for audio-motor coordination in skilled pianists and non-musicians. Brain Res. 1161, 65–78 (2007).
Lahav, A., Saltzman, E. & Schlaug, G. Action representation of sound: audiomotor recognition network while listening to newly acquired actions. J. Neurosci. 27, 308–314 (2007).
Bianco, R. et al. Neural networks for harmonic structure in music perception and action. Neuroimage 142, 454–464 (2016).
Eerola, T., Vuoskoski, J. K., Peltola, H.-R., Putkinen, V. & Schäfer, K. An integrative review of the enjoyment of sadness associated with music. Phys. Life Rev. 25, 100–121 (2018).
Huron, D. M. D. The harmonic minor scale provides an optimum way of reducing average melodic interval size, consistent with sad affect cues. Empir. Musicol. Rev. 7, 15 (2012).
Huron, D. A comparison of average pitch height and interval size in major-and minor-key themes: evidence consistent with affect-related pitch prosody. 3, 59-63 (2008).
Juslin, P. N. & Laukka, P. Communication of emotions in vocal expression and music performance: different channels, same code? Psychol. Bull. 129, 770 (2003).
Fritz, T. et al. Universal recognition of three basic emotions in music. Curr. Biol. 19, 573–576 (2009).
London, J. Hearing in Time: Psychological Aspects of Musical Meter (Oxford Univ. Press, 2012).
Honing, H. Without it no music: beat induction as a fundamental musical trait. Ann. N. Y. Acad. Sci. 1252, 85–91 (2012).
Hickok, G., Farahbod, H. & Saberi, K. The rhythm of perception: entrainment to acoustic rhythms induces subsequent perceptual oscillation. Psychol. Sci. 26, 1006–1013 (2015).
Yabe, H., Tervaniemi, M., Reinikainen, K. & Näätänen, R. Temporal window of integration revealed by MMN to sound omission. Neuroreport 8, 1971–1974 (1997).
Andreou, L.-V., Griffiths, T. D. & Chait, M. Sensitivity to the temporal structure of rapid sound sequences — an MEG study. Neuroimage 110, 194–204 (2015).
Jongsma, M. L., Meeuwissen, E., Vos, P. G. & Maes, R. Rhythm perception: speeding up or slowing down affects different subcomponents of the ERP P3 complex. Biol. Psychol. 75, 219–228 (2007).
Graber, E. & Fujioka, T. Endogenous expectations for sequence continuation after auditory beat accelerations and decelerations revealed by P3a and induced beta-band responses. Neuroscience 413, 11–21 (2019).
Brochard, R., Abecasis, D., Potter, D., Ragot, R. & Drake, C. The “ticktock” of our internal clock: direct brain evidence of subjective accents in isochronous sequences. Psychol. Sci. 14, 362–366 (2003).
Lerdahl, F. & Jackendoff, R. An overview of hierarchical structure in music. Music. Percept. 1, 229–252 (1983).
Large, E. W. & Kolen, J. F. Resonance and the perception of musical meter. Connect. Sci. 6, 177–208 (1994).
Large, E. W. & Jones, M. R. The dynamics of attending: how people track time-varying events. Psychol. Rev. 106, 119–159 (1999).
Cutietta, R. A. & Booth, G. D. The influence of metre, mode, interval type and contour in repeated melodic free-recall. Psychol. Music 24, 222–236 (1996).
Smith, K. C. & Cuddy, L. L. Effects of metric and harmonic rhythm on the detection of pitch alterations in melodic sequences. J. Exp. Psychol. 15, 457–471 (1989).
Palmer, C. & Krumhansl, C. L. Mental representations for musical meter. J. Exp. Psychol. 16, 728–741 (1990).
Einarson, K. M. & Trainor, L. J. Hearing the beat: young children’s perceptual sensitivity to beat alignment varies according to metric structure. Music. Percept. 34, 56–70 (2016).
Large, E. W., Herrera, J. A. & Velasco, M. J. Neural networks for beat perception in musical rhythm. Front. Syst. Neurosci. 9, 159 (2015).
Nozaradan, S., Peretz, I., Missal, M. & Mouraux, A. Tagging the neuronal entrainment to beat and meter. J. Neurosci. 31, 10234–10240 (2011).
Nozaradan, S., Peretz, I. & Mouraux, A. Selective neuronal entrainment to the beat and meter embedded in a musical rhythm. J. Neurosci. 32, 17572–17581 (2012).
Nozaradan, S., Schonwiesner, M., Keller, P. E., Lenc, T. & Lehmann, A. Neural bases of rhythmic entrainment in humans: critical transformation between cortical and lower-level representations of auditory rhythm. Eur. J. Neurosci. 47, 321–332 (2018).
Lenc, T., Keller, P. E., Varlet, M. & Nozaradan, S. Neural and behavioral evidence for frequency-selective context effects in rhythm processing in humans. Cereb. Cortex Commun. https://doi.org/10.1093/texcom/tgaa037 (2020).
Jacoby, N. & McDermott, J. H. Integer ratio priors on musical rhythm revealed cross-culturally by iterated reproduction. Curr. Biol. 27, 359–370 (2017).
Hannon, E. E. & Trehub, S. E. Metrical categories in infancy and adulthood. Psychol. Sci. 16, 48–55 (2005).
Hannon, E. E. & Trehub, S. E. Tuning in to musical rhythms: infants learn more readily than adults. Proc. Natl Acad. Sci. USA 102, 12639–12643 (2005).
Vuust, P. et al. To musicians, the message is in the meter pre-attentive neuronal responses to incongruent rhythm are left-lateralized in musicians. Neuroimage 24, 560–564 (2005).
Grahn, J. A. & Brett, M. Rhythm and beat perception in motor areas of the brain. J. Cogn. Neurosci. 19, 893–906 (2007). This fMRI study investigates participants listening to rhythms of varied complexity.
Toiviainen, P., Burunat, I., Brattico, E., Vuust, P. & Alluri, V. The chronnectome of musical beat. Neuroimage 216, 116191 (2019).
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).
Levitin, D. J., Grahn, J. A. & London, J. The psychology of music: rhythm and movement. Annu. Rev. Psychol. 69, 51–75 (2018).
Winkler, I., Haden, G. P., Ladinig, O., Sziller, I. & Honing, H. Newborn infants detect the beat in music. Proc. Natl Acad. Sci. USA 106, 2468–2471 (2009).
Phillips-Silver, J. & Trainor, L. J. Feeling the beat: movement influences infant rhythm perception. Science 308, 1430–1430 (2005).
Cirelli, L. K., Trehub, S. E. & Trainor, L. J. Rhythm and melody as social signals for infants. Ann. N. Y. Acad. Sci. https://doi.org/10.1111/nyas.13580 (2018).
Cirelli, L. K., Einarson, K. M. & Trainor, L. J. Interpersonal synchrony increases prosocial behavior in infants. Dev. Sci. 17, 1003–1011 (2014).
Repp, B. H. Sensorimotor synchronization: a review of the tapping literature. Psychon. Bull. Rev. 12, 969–992 (2005).
Repp, B. H. & Su, Y. H. Sensorimotor synchronization: a review of recent research (2006-2012). Psychon. Bull. Rev. 20, 403–452 (2013). This review, and Repp (2005), succinctly covers the field of sensorimotor synchronization.
Zarco, W., Merchant, H., Prado, L. & Mendez, J. C. Subsecond timing in primates: comparison of interval production between human subjects and rhesus monkeys. J. Neurophysiol. 102, 3191–3202 (2009).
Honing, H., Bouwer, F. L., Prado, L. & Merchant, H. Rhesus monkeys (Macaca mulatta) sense isochrony in rhythm, but not the beat: additional support for the gradual audiomotor evolution hypothesis. Front. Neurosci. 12, 475 (2018).
Hattori, Y. & Tomonaga, M. Rhythmic swaying induced by sound in chimpanzees (Pan troglodytes). Proc. Natl Acad. Sci. USA 117, 936–942 (2020).
Danielsen, A. Presence and Pleasure. The Funk Grooves of James Brown and Parliament (Wesleyan Univ. Press, 2006).
Madison, G., Gouyon, F., Ullen, F. & Hornstrom, K. Modeling the tendency for music to induce movement in humans: first correlations with low-level audio descriptors across music genres. J. Exp. Psychol. Hum. Percept. Perform. 37, 1578–1594 (2011).
Stupacher, J., Hove, M. J., Novembre, G., Schutz-Bosbach, S. & Keller, P. E. Musical groove modulates motor cortex excitability: a TMS investigation. Brain Cogn. 82, 127–136 (2013).
Janata, P., Tomic, S. T. & Haberman, J. M. Sensorimotor coupling in music and the psychology of the groove. J. Exp. Psychol. 141, 54 (2012). Using a systematic approach, this multiple-studies article shows that the concept of groove can be widely understood as a pleasurable drive towards action.
Witek, M. A. et al. A critical cross-cultural study of sensorimotor and groove responses to syncopation among Ghanaian and American university students and staff. Music. Percept. 37, 278–297 (2020).
Friston, K., Mattout, J. & Kilner, J. Action understanding and active inference. Biol. Cybern. 104, 137–160 (2011).
Longuet-Higgins, H. C. & Lee, C. S. The rhythmic interpretation of monophonic music. Music. Percept. 1, 18 (1984).
Sioros, G., Miron, M., Davies, M., Gouyon, F. & Madison, G. Syncopation creates the sensation of groove in synthesized music examples. Front. Psychol. 5, 1036 (2014).
Witek, M. A., Clarke, E. F., Wallentin, M., Kringelbach, M. L. & Vuust, P. Syncopation, body-movement and pleasure in groove music. PLoS ONE 9, e94446 (2014).
Kowalewski, D. A., Kratzer, T. M. & Friedman, R. S. Social music: investigating the link between personal liking and perceived groove. Music. Percept. 37, 339–346 (2020).
Bowling, D. L., Ancochea, P. G., Hove, M. J. & Tec*mseh Fitch, W. Pupillometry of groove: evidence for noradrenergic arousal in the link between music and movement. Front. Neurosci. 13, 1039 (2019).
Matthews, T. E., Witek, M. A. G., Heggli, O. A., Penhune, V. B. & Vuust, P. The sensation of groove is affected by the interaction of rhythmic and harmonic complexity. PLoS ONE 14, e0204539 (2019).
Matthews, T. E., Witek, M. A., Lund, T., Vuust, P. & Penhune, V. B. The sensation of groove engages motor and reward networks. Neuroimage 214, 116768 (2020). This fMRI study shows that the sensation of groove engages both motor and reward networks in the brain.
Vaquero, L., Ramos-Escobar, N., François, C., Penhune, V. & Rodríguez-Fornells, A. White-matter structural connectivity predicts short-term melody and rhythm learning in non-musicians. Neuroimage 181, 252–262 (2018).
Zatorre, R. J., Halpern, A. R., Perry, D. W., Meyer, E. & Evans, A. C. Hearing in the mind’s ear: a PET investigation of musical imagery and perception. J. Cogn. Neurosci. 8, 29–46 (1996).
Benadon, F. Meter isn’t everything: the case of a timeline-oriented Cuban polyrhythm. N. Ideas Psychol. 56, 100735 (2020).
London, J., Polak, R. & Jacoby, N. Rhythm histograms and musical meter: a corpus study of Malian percussion music. Psychon. Bull. Rev. 24, 474–480 (2017).
Huron, D. Is music an evolutionary adaptation? Ann. N. Y. Acad. Sci. 930, 43–61 (2001).
Koelsch, S. Towards a neural basis of music-evoked emotions. Trends Cogn. Sci. 14, 131–137 (2010).
Eerola, T. & Vuoskoski, J. K. A comparison of the discrete and dimensional models of emotion in music. Psychol. Music. 39, 18–49 (2010).
Lonsdale, A. J. & North, A. C. Why do we listen to music? A uses and gratifications analysis. Br. J. Psychol. 102, 108–134 (2011).
Juslin, P. N. & Laukka, P. Expression, perception, and induction of musical emotions: a review and a questionnaire study of everyday listening. J. N. Music. Res. 33, 217–238 (2004).
Huron, D. Why is sad music pleasurable? A possible role for prolactin. Music. Sci. 15, 146–158 (2011).
Brattico, E. et al. It’s sad but I like it: the neural dissociation between musical emotions and liking in experts and laypersons. Front. Hum. Neurosci. 9, 676 (2015).
Sachs, M. E., Damasio, A. & Habibi, A. Unique personality profiles predict when and why sad music is enjoyed. Psychol. Music https://doi.org/10.1177/0305735620932660 (2020).
Sachs, M. E., Habibi, A., Damasio, A. & Kaplan, J. T. Dynamic intersubject neural synchronization reflects affective responses to sad music. Neuroimage 218, 116512 (2020).
Juslin, P. N. & Vastfjall, D. Emotional responses to music: the need to consider underlying mechanisms. Behav. Brain Sci. 31, 559–575 (2008). Using a novel theoretical framework, the authors propose that the mechanisms that evoke emotions from music are not unique to music.
Rickard, N. S. Intense emotional responses to music: a test of the physiological arousal hypothesis. Psychol. Music. 32, 371–388 (2004).
Cowen, A. S., Fang, X., Sauter, D. & Keltner, D. What music makes us feel: at least 13 dimensions organize subjective experiences associated with music across different cultures. Proc. Natl Acad. Sci. USA 117, 1924–1934 (2020).
Argstatter, H. Perception of basic emotions in music: culture-specific or multicultural? Psychol. Music. 44, 674–690 (2016).
Stevens, C. J. Music perception and cognition: a review of recent cross-cultural research. Top. Cogn. Sci. 4, 653–667 (2012).
Pearce, M. Cultural distance: a computational approach to exploring cultural influences on music cognition. in Oxford Handbook of Music and the Brain Vol. 31 (Oxford Univ. Press, 2018).
van der Weij, B., Pearce, M. T. & Honing, H. A probabilistic model of meter perception: simulating enculturation. Front. Psychol. 8, 824 (2017).
Kringelbach, M. L. & Berridge, K. C. Towards a functional neuroanatomy of pleasure and happiness. Trends Cogn. Sci. 13, 479–487 (2009).
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. USA 98, 11818–11823 (2001). This seminal positron emission tomography study shows that the experience of musical chills correlates with activity in the reward system.
Salimpoor, V. N. & Zatorre, R. J. Complex cognitive functions underlie aesthetic emotions: comment on “From everyday emotions to aesthetic emotions: towards a unified theory of musical emotions” by Patrik N. Juslin. Phys. Life Rev. 10, 279–280 (2013).
Salimpoor, V. N. et al. Interactions between the nucleus accumbens and auditory cortices predict music reward value. Science 340, 216–219 (2013).
Salimpoor, V. N., Benovoy, M., Larcher, K., Dagher, A. & Zatorre, R. J. Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nat. Neurosci. 14, 257–262 (2011).
Salimpoor, V. N., Benovoy, M., Longo, G., Cooperstock, J. R. & Zatorre, R. J. The rewarding aspects of music listening are related to degree of emotional arousal. PLoS ONE 4, e7487 (2009).
Mas-Herrero, E., Zatorre, R. J., Rodriguez-Fornells, A. & Marco-Pallares, J. Dissociation between musical and monetary reward responses in specific musical anhedonia. Curr. Biol. 24, 699–704 (2014).
Martinez-Molina, N., Mas-Herrero, E., Rodriguez-Fornells, A., Zatorre, R. J. & Marco-Pallares, J. Neural correlates of specific musical anhedonia. Proc. Natl Acad. Sci. USA 113, E7337–E7345 (2016).
Gebauer, L. K., M., L. & Vuust, P. Musical pleasure cycles: the role of anticipation and dopamine. Psychomusicology 22, 16 (2012).
Shany, O. et al. Surprise-related activation in the nucleus accumbens interacts with music-induced pleasantness. Soc. Cogn. Affect. Neurosci. 14, 459–470 (2019).
Gold, B. P., Pearce, M. T., Mas-Herrero, E., Dagher, A. & Zatorre, R. J. Predictability and uncertainty in the pleasure of music: a reward for learning? J. Neurosci. 39, 9397–9409 (2019).
Swaminathan, S. & Schellenberg, E. G. Current emotion research in music psychology. Emot. Rev. 7, 189–197 (2015).
Madison, G. & Schiölde, G. Repeated listening increases the liking for music regardless of its complexity: implications for the appreciation and aesthetics of music. Front. Neurosci. 11, 147 (2017).
Corrigall, K. A. & Schellenberg, E. G. Liking music: genres, contextual factors, and individual differences. in Art, Aesthetics, and the Brain (Oxford Univ. Press, 2015).
Zentner, A. Measuring the effect of file sharing on music purchases. J. Law Econ. 49, 63–90 (2006).
Rentfrow, P. J. & Gosling, S. D. The do re mi’s of everyday life: the structure and personality correlates of music preferences. J. Pers. Soc. Psychol. 84, 1236–1256 (2003).
Vuust, P. et al. Personality influences career choice: sensation seeking in professional musicians. Music. Educ. Res. 12, 219–230 (2010).
Rohrmeier, M. & Rebuschat, P. Implicit learning and acquisition of music. Top. Cogn. Sci. 4, 525–553 (2012).
Münthe, T. F., Altenmüller, E. & Jäncke, L. The musician’s brain as a model of neuroplasticity. Nat. Rev. Neurosci. 3, 1–6 (2002). This review highlights how professional musicians represent an ideal model for investigating neuroplasticity.
Habibi, A. et al. Childhood music training induces change in micro and macroscopic brain structure: results from a longitudinal study. Cereb. Cortex 28, 4336–4347 (2018).
Schlaug, G., Jancke, L., Huang, Y., Staiger, J. F. & Steinmetz, H. Increased corpus callosum size in musicians. Neuropsychologia 33, 1047–1055 (1995).
Baer, L. H. et al. Regional cerebellar volumes are related to early musical training and finger tapping performance. Neuroimage 109, 130–139 (2015).
Kleber, B. et al. Voxel-based morphometry in opera singers: increased gray-matter volume in right somatosensory and auditory cortices. Neuroimage 133, 477–483 (2016).
Gaser, C. & Schlaug, G. Brain structures differ between musicians and non-musicians. J. Neurosci. 23, 9240–9245 (2003). Using a morphometric technique, this study shows a grey matter volume difference in multiple brain regions between professional musicians and a matched control group of amateur musicians and non-musicians.
Sluming, V. et al. Voxel-based morphometry reveals increased gray matter density in Broca’s area in male symphony orchestra musicians. Neuroimage 17, 1613–1622 (2002).
Palomar-García, M.-Á., Zatorre, R. J., Ventura-Campos, N., Bueichekú, E. & Ávila, C. Modulation of functional connectivity in auditory–motor networks in musicians compared with nonmusicians. Cereb. Cortex 27, 2768–2778 (2017).
Schneider, P. et al. Morphology of Heschl’s gyrus reflects enhanced activation in the auditory cortex of musicians. Nat. Neurosci. 5, 688–694 (2002).
Bengtsson, S. L. et al. Extensive piano practicing has regionally specific effects on white matter development. Nat. Neurosci. 8, 1148–1150 (2005).
Zamorano, A. M., Cifre, I., Montoya, P., Riquelme, I. & Kleber, B. Insula-based networks in professional musicians: evidence for increased functional connectivity during resting state fMRI. Hum. Brain Mapp. 38, 4834–4849 (2017).
Kraus, N. & Chandrasekaran, B. Music training for the development of auditory skills. Nat. Rev. Neurosci. 11, 599–605 (2010).
Koelsch, S., Schröger, E. & Tervaniemi, M. Superior pre-attentive auditory processing in musicians. Neuroreport 10, 1309–1313 (1999).
Münte, T. F., Kohlmetz, C., Nager, W. & Altenmüller, E. Superior auditory spatial tuning in conductors. Nature 409, 580 (2001).
Seppänen, M., Brattico, E. & Tervaniemi, M. Practice strategies of musicians modulate neural processing and the learning of sound-patterns. Neurobiol. Learn. Mem. 87, 236–247 (2007).
Guillot, A. et al. Functional neuroanatomical networks associated with expertise in motor imagery. Neuroimage 41, 1471–1483 (2008).
Bianco, R., Novembre, G., Keller, P. E., Villringer, A. & Sammler, D. Musical genre-dependent behavioural and EEG signatures of action planning. a comparison between classical and jazz pianists. Neuroimage 169, 383–394 (2018).
Vuust, P., Brattico, E., Seppänen, M., Näätänen, R. & Tervaniemi, M. Practiced musical style shapes auditory skills. Ann. N. Y. Acad. Sci. 1252, 139–146 (2012).
Bangert, M. & Altenmüller, E. O. Mapping perception to action in piano practice: a longitudinal DC-EEG study. BMC Neurosci. 4, 26 (2003).
Li, Q. et al. Musical training induces functional and structural auditory-motor network plasticity in young adults. Hum. Brain Mapp. 39, 2098–2110 (2018).
Herholz, S. C., Coffey, E. B. J., Pantev, C. & Zatorre, R. J. Dissociation of neural networks for predisposition and for training-related plasticity in auditory-motor learning. Cereb. Cortex 26, 3125–3134 (2016).
Putkinen, V., Tervaniemi, M. & Huotilainen, M. Musical playschool activities are linked to faster auditory development during preschool-age: a longitudinal ERP study. Sci. Rep. 9, 11310–11310 (2019).
Putkinen, V., Tervaniemi, M., Saarikivi, K., Ojala, P. & Huotilainen, M. Enhanced development of auditory change detection in musically trained school-aged children: a longitudinal event-related potential study. Dev. Sci. 17, 282–297 (2014).
Jentschke, S. & Koelsch, S. Musical training modulates the development of syntax processing in children. Neuroimage 47, 735–744 (2009).
Chobert, J., François, C., Velay, J. L. & Besson, M. Twelve months of active musical training in 8-to 10-year-old children enhances the preattentive processing of syllabic duration and voice onset time. Cereb. Cortex 24, 956–967 (2014).
Moreno, S. et al. Musical training influences linguistic abilities in 8-year-old children: more evidence for brain plasticity. Cereb. Cortex 19, 712–723 (2009).
Putkinen, V., Huotilainen, M. & Tervaniemi, M. Neural encoding of pitch direction is enhanced in musically trained children and is related to reading skills. Front. Psychol. 10, 1475 (2019).
Wong, P. C., Skoe, E., Russo, N. M., Dees, T. & Kraus, N. Musical experience shapes human brainstem encoding of linguistic pitch patterns. Nat. Neurosci. 10, 420–422 (2007).
Virtala, P. & Partanen, E. Can very early music interventions promote at-risk infants’ development? Ann. N. Y. Acad. Sci. 1423, 92–101 (2018).
Flaugnacco, E. et al. Music training increases phonological awareness and reading skills in developmental dyslexia: a randomized control trial. PLoS ONE 10, e0138715 (2015).
Fiveash, A. et al. A stimulus-brain coupling analysis of regular and irregular rhythms in adults with dyslexia and controls. Brain Cogn. 140, 105531 (2020).
Schellenberg, E. G. Correlation = causation? music training, psychology, and neuroscience. Psychol. Aesthet. Creat. Arts 14, 475–480 (2019).
Sala, G. & Gobet, F. Cognitive and academic benefits of music training with children: a multilevel meta-analysis. Mem. Cogn. 48, 1429–1441 (2020).
Saffran, J. R. Musical learning and language development. Ann. N. Y. Acad. Sci. 999, 397–401 (2003).
Friston, K. The free-energy principle: a rough guide to the brain? Trends Cogn. Sci. 13, 293–301 (2009).
Pearce, M. T. Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation. Ann. N. Y. Acad. Sci. 1423, 378–395 (2018).
Novembre, G., Knoblich, G., Dunne, L. & Keller, P. E. Interpersonal synchrony enhanced through 20 Hz phase-coupled dual brain stimulation. Soc. Cogn. Affect. Neurosci. 12, 662–670 (2017).
Konvalinka, I. et al. Frontal alpha oscillations distinguish leaders from followers: multivariate decoding of mutually interacting brains. Neuroimage 94C, 79–88 (2014).
Novembre, G., Mitsopoulos, Z. & Keller, P. E. Empathic perspective taking promotes interpersonal coordination through music. Sci. Rep. 9, 12255 (2019).
Wolpert, D. M., Ghahramani, Z. & Jordan, M. I. An internal model for sensorimotor integration. Science 269, 1880–1882 (1995).
Patel, A. D. & Iversen, J. R. The evolutionary neuroscience of musical beat perception: the action simulation for auditory prediction (ASAP) hypothesis. Front. Syst. Neurosci. 8, 57 (2014).
Sebanz, N. & Knoblich, G. Prediction in joint action: what, when, and where. Top. Cogn. Sci. 1, 353–367 (2009).
Friston, K. J. & Frith, C. D. Active inference, communication and hermeneutics. Cortex 68, 129–143 (2015). This article proposes a link between active inference, communication and hermeneutics.
Konvalinka, I., Vuust, P., Roepstorff, A. & Frith, C. D. Follow you, follow me: continuous mutual prediction and adaptation in joint tapping. Q. J. Exp. Psychol. 63, 2220–2230 (2010).
Wing, A. M. & Kristofferson, A. B. Response delays and the timing of discrete motor responses. Percept. Psychophys. 14, 5–12 (1973).
Repp, B. H. & Keller, P. E. Sensorimotor synchronization with adaptively timed sequences. Hum. Mov. Sci. 27, 423–456 (2008).
Vorberg, D. & Schulze, H.-H. Linear phase-correction in synchronization: predictions, parameter estimation, and simulations. J. Math. Psychol. 46, 56–87 (2002).
Novembre, G., Sammler, D. & Keller, P. E. Neural alpha oscillations index the balance between self-other integration and segregation in real-time joint action. Neuropsychologia 89, 414–425 (2016). Using dual-EEG, the authors propose alpha oscillations as a candidate for regulating the balance between internal and external information in joint action.
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).
Fairhurst, M. T., Janata, P. & Keller, P. E. Leading the follower: an fMRI investigation of dynamic cooperativity and leader-follower strategies in synchronization with an adaptive virtual partner. Neuroimage 84, 688–697 (2014).
Heggli, O. A., Konvalinka, I., Kringelbach, M. L. & Vuust, P. Musical interaction is influenced by underlying predictive models and musical expertise. Sci. Rep. 9, 1–13 (2019).
Heggli, O. A., Cabral, J., Konvalinka, I., Vuust, P. & Kringelbach, M. L. A Kuramoto model of self-other integration across interpersonal synchronization strategies. PLoS Comput. Biol. 15, e1007422 (2019).
Heggli, O. A. et al. Transient brain networks underlying interpersonal strategies during synchronized action. Soc. Cogn. Affect. Neurosci. 16, 19–30 (2020). This EEG study shows that differences in interpersonal synchronization are reflected by activity in a temporoparietal network.
Patel, A. D. Music, Language, and the Brain (Oxford Univ. Press, 2006).
Molnar-Szakacs, I. & Overy, K. Music and mirror neurons: from motion to ‘e’motion. Soc. Cogn. Affect. Neurosci. 1, 235–241 (2006).
Beaty, R. E., Benedek, M., Silvia, P. J. & Schacter, D. L. Creative cognition and brain network dynamics. Trends Cogn. Sci. 20, 87–95 (2016).
Limb, C. J. & Braun, A. R. Neural substrates of spontaneous musical performance: an FMRI study of jazz improvisation. PLoS ONE 3, e1679 (2008).
Liu, S. et al. Neural correlates of lyrical improvisation: an FMRI study of freestyle rap. Sci. Rep. 2, 834 (2012).
Rosen, D. S. et al. Dual-process contributions to creativity in jazz improvisations: an SPM-EEG study. Neuroimage 213, 116632 (2020).
Boasen, J., Takesh*ta, Y., Kuriki, S. & Yokosawa, K. Spectral-spatial differentiation of brain activity during mental imagery of improvisational music performance using MEG. Front. Hum. Neurosci. 12, 156 (2018).
Berkowitz, A. L. & Ansari, D. Generation of novel motor sequences: the neural correlates of musical improvisation. Neuroimage 41, 535–543 (2008).
Loui, P. Rapid and flexible creativity in musical improvisation: review and a model. Ann. N. Y. Acad. Sci. 1423, 138–145 (2018).
Beaty, R. E. The neuroscience of musical improvisation. Neurosci. Biobehav. Rev. 51, 108–117 (2015).
Vuust, P. & Kringelbach, M. L. Music improvisation: a challenge for empirical research. in Routledge Companion to Music Cognition (Routledge, 2017).
Norgaard, M. Descriptions of improvisational thinking by artist-level jazz musicians. J. Res. Music. Educ. 59, 109–127 (2011).
Kringelbach, M. L. & Deco, G. Brain states and transitions: insights from computational neuroscience. Cell Rep. 32, 108128 (2020).
Deco, G. & Kringelbach, M. L. Hierarchy of information processing in the brain: a novel ‘intrinsic ignition’ framework. Neuron 94, 961–968 (2017).
Pinho, A. L., de Manzano, O., Fransson, P., Eriksson, H. & Ullen, F. Connecting to create: expertise in musical improvisation is associated with increased functional connectivity between premotor and prefrontal areas. J. Neurosci. 34, 6156–6163 (2014).
Pinho, A. L., Ullen, F., Castelo-Branco, M., Fransson, P. & de Manzano, O. Addressing a paradox: dual strategies for creative performance in introspective and extrospective networks. Cereb. Cortex 26, 3052–3063 (2016).
de Manzano, O. & Ullen, F. Activation and connectivity patterns of the presupplementary and dorsal premotor areas during free improvisation of melodies and rhythms. Neuroimage 63, 272–280 (2012).
Beaty, R. E. et al. Robust prediction of individual creative ability from brain functional connectivity. Proc. Natl Acad. Sci. USA 115, 1087–1092 (2018).
Daikoku, T. Entropy, uncertainty, and the depth of implicit knowledge on musical creativity: computational study of improvisation in melody and rhythm. Front. Comput. Neurosci. 12, 97 (2018).
Belden, A. et al. Improvising at rest: differentiating jazz and classical music training with resting state functional connectivity. Neuroimage 207, 116384 (2020).
Arkin, C., Przysinda, E., Pfeifer, C. W., Zeng, T. & Loui, P. Gray matter correlates of creativity in musical improvisation. Front. Hum. Neurosci. 13, 169 (2019).
Bashwiner, D. M., Wertz, C. J., Flores, R. A. & Jung, R. E. Musical creativity “revealed” in brain structure: interplay between motor, default mode, and limbic networks. Sci. Rep. 6, 20482 (2016).
Przysinda, E., Zeng, T., Maves, K., Arkin, C. & Loui, P. Jazz musicians reveal role of expectancy in human creativity. Brain Cogn. 119, 45–53 (2017).
Large, E. W., Kim, J. C., Flaig, N. K., Bharucha, J. J. & Krumhansl, C. L. A neurodynamic account of musical tonality. Music. Percept. 33, 319–331 (2016).
Large, E. W. & Palmer, C. Perceiving temporal regularity in music. Cogn. Sci. 26, 1–37 (2002). This article proposes an oscillator-based approach for the perception of temporal regularity in music.
Cannon, J. J. & Patel, A. D. How beat perception co-opts motor neurophysiology. Trends Cogn. Sci. 25, 137–150 (2020). The authors propose that cyclic time-keeping activity in the supplementary motor area, termed ‘proto-actions’, is organized by the dorsal striatum to support hierarchical metrical structures.
Keller, P. E., Novembre, G. & Loehr, J. Musical ensemble performance: representing self, other and joint action outcomes. in Shared Representations: Sensorimotor Foundations of Social Life Cambridge Social Neuroscience (eds Cross, E. S. & Obhi, S. S.) 280-310 (Cambridge Univ. Press, 2016).
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).
Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 181–204 (2013).
Kahl, R. Selected Writings of Hermann Helmholtz (Wesleyan Univ. Press, 1878).
Gregory, R. L. Perceptions as hypotheses. Philos. Trans. R. Soc. Lond. B Biol. Sci. 290, 181–197 (1980).
Gibson, J. J. The Ecological Approach to Visual Perception (Houghton Mifflin, 1979).
Fuster, J. The Prefrontal Cortex Anatomy, Physiology and Neuropsychology of the Frontal Lobe (Lippincott-Raven, 1997).
Neisser, U. Cognition and Reality: Principles and Implications of Cognitive Psychology (W H Freeman/Times Books/ Henry Holt & Co, 1976).
Arbib, M. A. & Hesse, M. B. The Construction of Reality (Cambridge Univ. Press, 1986).
Cisek, P. & Kalaska, J. F. Neural mechanisms for interacting with a world full of action choices. Annu. Rev. Neurosci. 33, 269–298 (2010).
Isomura, T., Parr, T. & Friston, K. Bayesian filtering with multiple internal models: toward a theory of social intelligence. Neural Comput. 31, 2390–2431 (2019).
Friston, K. & Frith, C. A duet for one. Conscious. Cogn. 36, 390–405 (2015).
Hunt, B. R., Ott, E. & Yorke, J. A. Differentiable generalized synchronization of chaos. Phys. Rev. E 55, 4029–4034 (1997).
Ghazanfar, A. A. & Takahashi, D. Y. The evolution of speech: vision, rhythm, cooperation. Trends Cogn. Sci. 18, 543–553 (2014).
Wilson, M. & Wilson, T. P. An oscillator model of the timing of turn-taking. Psychon. Bull. Rev. 12, 957–968 (2005).
