DOI: 10.5061/dryad.5vb8h
title: Data from: Neural mechanisms of rhythm-based temporal prediction: delta phase-locking reflects temporal predictability but not rhythmic entrainment

description: 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.
description: S101_EEGEEG data fileEntrainRhythm1B_S101.bdfS102_EEGEEG data filesEntrainRhythm1B_S102.bdfS103_EEGEEG data fileEntrainRhythm1B_S103.bdfS104_EEGEntrainRhythm1B_S104.bdfS104_EEG_1EEG data fileEntrainRhythm1B_S104_1.bdfS105_EEGEEG data fileEntrainRhythm1B_S105.bdfS106_EEGEEG data fileEntrainRhythm1B_S106.bdfS107_EEGEEG data fileEntrainRhythm1B_S107.bdfS108_EEGEEG data fileEntrainRhythm1B_S108.bdfS109_EEGEEG data fileEntrainRhythm1B_S109.bdfS110_EEGEEG data fileEntrainRhythm1B_S110.bdfS111_EEGEEG data fileEntrainRhythm1B_S111.bdfS112_EEGEEG data fileEntrainRhythm1B_S112.bdfS112_EEG2EEG data fileEntrainRhythm1B_S112_2.bdfS114_EEGEEG data fileEntrainRhythm1B_S114.bdfS116_EEGEEG data fileEntrainRhythm1B_S116.bdfS117_EEGEEG data fileEntrainRhythm1B_S117.bdfS118_EEGEEG data fileEntrainRhythm1B_S118.bdfS119_EEGEEG data fileEntrainRhythm1B_S119.bdfS120_EEGEEG data fileEntrainRhythm1B_S120.bdfS121_EEGEEG data fileEntrainRhythm1B_S121.bdfsubject dataDemographical data of subjectssubjects data.xlsxBehavioral dataBehavioral data filesSimulatedPhase_entrainmentModelentrainment model simulationsData cover informationInformation regarding column headers of the behavioral data, stimuli associated with numerical markers in the EEG data, and results of entrainment model simulations