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Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment

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  • Assaf Breska
  • Leon Y Deouell

Abstract

Predicting the timing of upcoming events enables efficient resource allocation and action preparation. Rhythmic streams, such as music, speech, and biological motion, constitute a pervasive source for temporal predictions. Widely accepted entrainment theories postulate that rhythm-based predictions are mediated by synchronizing low-frequency neural oscillations to the rhythm, as indicated by increased phase concentration (PC) of low-frequency neural activity for rhythmic compared to random streams. However, we show here that PC enhancement in scalp recordings is not specific to rhythms but is observed to the same extent in less periodic streams if they enable memory-based prediction. This is inconsistent with the predictions of a computational entrainment model of stronger PC for rhythmic streams. Anticipatory change in alpha activity and facilitation of electroencephalogram (EEG) manifestations of response selection are also comparable between rhythm- and memory-based predictions. However, rhythmic sequences uniquely result in obligatory depression of preparation-related premotor brain activity when an on-beat event is omitted, even when it is strategically beneficial to maintain preparation, leading to larger behavioral costs for violation of prediction. Thus, while our findings undermine the validity of PC as a sign of rhythmic entrainment, they constitute the first electrophysiological dissociation, to our knowledge, between mechanisms of rhythmic predictions and of memory-based predictions: the former obligatorily lead to resonance-like preparation patterns (that are in line with entrainment), while the latter allow flexible resource allocation in time regardless of periodicity in the input. Taken together, they delineate the neural mechanisms of three distinct modes of preparation: continuous vigilance, interval-timing-based prediction and rhythm-based prediction.Author summary: Making predictions is a major adaptive brain function. Predicting the timing of upcoming events enables the brain to prepare for them. However, it is unclear how this is achieved. It is believed that in rhythmic environmental context, such as in music and speech, temporal predictions are achieved by synchronizing the naturally occurring brain rhythms to the external rhythms, a process referred to as oscillatory entrainment. We tested whether rhythm-based predictions have unique expressions in behavior and brain activity—as measured by electroencephalogram (EEG)—by comparing rhythmic streams to less rhythmic streams that enable prediction based on memorizing repeating intervals. We show that phase concentration (PC) of neural oscillations—a neural pattern commonly associated with entrainment—occurs to a similar extent in both predictive rhythmic and less rhythmic streams. Using a computational model, we show that these results are not consistent with the predictions of an entrainment mechanism, indicating that PC is not a unique indicator of entrainment. However, we also show that when an expected event is omitted, memory-based predictions can be flexibly reoriented, while rhythm-based predictions lead to immediate obligatory resource withdrawal and larger behavioral costs. This is consistent with resonance, a different prediction of entrainment model. As a whole, the results identify the overlapping and distinct mechanisms of rhythm- and memory-based temporal predictions.

Suggested Citation

  • Assaf Breska & Leon Y Deouell, 2017. "Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment," PLOS Biology, Public Library of Science, vol. 15(2), pages 1-30, February.
  • Handle: RePEc:plo:pbio00:2001665
    DOI: 10.1371/journal.pbio.2001665
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    2. Benjamin Morillon & Charles E. Schroeder & Valentin Wyart, 2014. "Motor contributions to the temporal precision of auditory attention," Nature Communications, Nature, vol. 5(1), pages 1-9, December.
    3. Berens, Philipp, 2009. "CircStat: A MATLAB Toolbox for Circular Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i10).
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    1. Miriam Heynckes & Peter De Weerd & Giancarlo Valente & Elia Formisano & Federico De Martino, 2020. "Behavioral effects of rhythm, carrier frequency and temporal cueing on the perception of sound sequences," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-17, June.

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