Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1. S. Nozaradan, I. Peretz, M. Missal, and A. Mouraux, “ Tagging the neuronal entrainment to beat and meter,” J. Neurosci. 31, 1023410240 (2011).
2. P. Lakatos, G. Karmos, A. D. Mehta, I. Ulbert, and C. E. Schroeder, “ Entrainment of neuronal oscillations as a mechanism of attentional selection,” Science 320, 110113 (2008).
3. T. Fujioka, L. J. Trainor, E. Large, and B. Ross, “ Internalized timing of isochronous sounds is represented in neuromagnetic beta oscillations,” J. Neurosci. 32(5), 17911802 (2012).
4. E. M. Zion Golumbic, N. Ding, S. Bickel, P. Lakatos, C. A. Schevon, G. M. McKhann, R. R. Goodman, R. Emerson, A. D. Mehta, J. Z. Simon, D. Poeppel, and C. E. Schroeder, “ Mechanisms underlying selective neuronal tracking of attended speech at a ‘Cocktail Party’ ,” Neuron 77(5), 980991 (2013).
5. G. Stefanics, B. Hangya, I. Hernádi, I. Winkler, P. Lakatos, and I. Ulbert, “ Phase entrainment of human delta oscillations can mediate the effects of expectation on reaction speed,” J. Neurosci. 30(41), 1357813585 (2010).
6. O. Ghitza and S. Greenberg, “ On the possible role of brain rhythms in speech perception: Intelligibility of time compressed speech with periodic and aperiodic insertions of silence,” Phonetica 66, 113126 (2009).
7. J. M. Mayville, S. L. Bressler, A. Fuchs, and J. A. S. Kelso, “ Spatiotemporal reorganization of electric activity in the human brain associated with a timing transition in rhythmic auditory-motor coordination,” Exp. Brain. Res. 127, 371381 (1999).
8. E. Large and M. Jones, “ The dynamics of attending: How people track time-varying events,” Psychol. Rev. 106(1), 119159 (1999).
9. O. Ghitza, “ On the role of theta-driven syllabic parsing in decoding speech: Intelligibility of speech with a manipulated modulation spectrum,” Front. Psychol. 3(238) (2011).
10. P. Lakatos, A. S. Shah, K. H. Knuth, I. Ulbert, G. Karmos, and C. E. Schroeder, “ An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex,” J. Neurophysiol 94, 19041911 (2005).
11. J. M. London, Hearing in Time: Psychological Aspects of Musical Meter ( Oxford University Press, Oxford, 2004).
12. A.-L. Giraud and D. Poeppel, “ Cortical oscillations and speech processing: Emerging computational principles,” Nat. Neurosci. 15, 511517 (2012).
13. H. Luo and D. Poeppel, “ Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex,” Neuron 54, 10011010 (2007).
14. C. Palmer, “ Music performance,” Annu. Rev. Psychol. 48, 115138 (1997).
15. H. Chapin, E. Large, K. Jantzen, J. Kelso, and F. Steinberg, “ Dynamic emotional and neural responses to music depend on performance expression and listener experience,” PLoS ONE 5(12), e13812 (2010).
16. S. Rankin, E. Large, and P. Fink, “ Fractal tempo fluctuation and pulse prediction,” Music Percept. 26(5), 401413 (2009).
17. B. Repp, “ Detecting deviations from metronomic timing in music: Effects of perceptual structure on the mental timekeeper,” Percept Psychophys. 61, 529548 (1999).
18. R. Voss and J. Clarke, “ 1/f noise in music and speech,” Nature 258(5533), 317318 (1975).
19.“International Piano-e-Competition,” (Last viewed July 20, 2010)
20. E. Large and S. Rankin, in MIDI Toolbox: MATLAB Tools for Music Research, edited by T. Eerola and P. Toiviainen ( University of Jyväskylä, Kopijyvä, Jyväskylä, Finland, 2007).
21. D. L. Turcotte, Fractals and Chaos in Geology and Geophysics ( Cambridge University Press, Cambridge, 1997).
22. B. Repp, in Common Mechanisms in Perception and Action: Attention and Performance XIX, edited by W. Prinz and B. Hommel ( Oxford University Press, Oxford, 2002), pp. 245265.
23. D. Stephen, N. Stepp, J. Dixon, and M. Turvey, “ Strong anticipation: Sensitivity to long-range correlations in synchronization behavior,” Physica A 387(21), 52715278 (2008).
24. M. van der Steen and P. E. Keller, “ The ADaptation and Anticipation Model (ADAM) of sensorimotor synchronization,” Front. Hum. Neurosci. 7(253) (2013).

Data & Media loading...


Article metrics loading...



1/ serial correlations and statistical self-similarity (fractal structure) have been measured in various dimensions of musical compositions. Musical performances also display 1/ properties in expressive tempo fluctuations, and listeners predict tempo changes when synchronizing. Here the authors show that the 1/ structure is sufficient for listeners to predict the onset times of upcoming musical events. These results reveal what information listeners use to anticipate events in complex, non-isochronous acoustic rhythms, and this will entail innovative models of temporal synchronization. This finding could improve therapies for Parkinson's and related disorders and inform deeper understanding of how endogenous neural rhythms anticipate events in complex, temporally structured communication signals.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd