Beta oscillations predict the envelope sharpness in a rhythmic beat sequence

Abstract

Periodic sensory inputs entrain oscillatory brain activity, reflecting a neural mechanism that might be fundamental to temporal prediction and perception. Most environmental rhythms and patterns in human behavior, such as walking, dancing, and speech do not, however, display strict isochrony but are instead quasi-periodic. Research has shown that neural tracking of speech is driven by modulations of the amplitude envelope, especially via sharp acoustic edges, which serve as prominent temporal landmarks. In the same vein, research on rhythm processing in music supports the notion that perceptual timing precision varies systematically with the sharpness of acoustic onset edges, conceptualized in the beat bin hypothesis. Increased envelope sharpness induces increased precision in localizing a sound in time. Despite this tight relationship between envelope shape and temporal processing, it is currently unknown how the brain uses predictive information about envelope features to optimize temporal perception. With the current EEG study, we show that the predicted sharpness of the amplitude envelope is encoded by pre-target neural activity in the beta band (15-25 Hz), and has an impact on the temporal perception of target sounds. We used probabilistic sound cues in a timing judgment task to inform participants about the sharpness of the amplitude envelope of an upcoming target sound embedded in a beat sequence. The predictive information about the envelope shape modulated task performance and pre-target beta power. Interestingly, these conditional beta-power modulations correlated positively with behavioral performance in the timing judgment task and with perceptual temporal precision in a click-alignment task. This study provides new insight into the neural processes underlying prediction of the sharpness of the amplitude envelope during beat perception, which modulate the temporal perception of sounds. This finding could reflect a process that is involved in temporal prediction, exerting top-down control on neural entrainment via the prediction of acoustic edges in the auditory stream.

Fig. 1

Hypothesis and Experimental Design. (A) Exemplary illustration of the beat bin hypothesis. Schematic illustrations of sensory input (Left) and induced perceptual beat precision (Right). The sharpness of the amplitude envelope shape of the sound modulates the temporal precision of the perceived beat. The acoustic shape of the sharp sound (red, short attack and short duration) induces a high-precision beat percept. The acoustic shape of the smooth sound (blue, gradual attack and long duration) induces a low-precision beat percept, which allows for enhanced flexibility with respect to perceived beat timing. The beat bin reflects the temporal width of the beat percept. Schematic probability density distributions for both sound types (Right) resulting from a synchronization task. The probability density distribution represents the variations across instances while participants localize a rhythmic event in time. A smooth envelope sound induces low perceptual timing precision, reflected in a wide and flat probability density distribution, and a sharp envelope sound induces high perceptual timing precision, reflected in a narrow (small standard deviation) probability density distribution. (B) Experimental design: two-alternative forced-choice timing judgment task incorporating a sound cue paradigm. Shown are all factor combinations: envelope sharpness, cue validity, and target timing (2 × 2 × 2). Participants were asked to judge whether the target sound was delayed or on time relative to an isochronous sequence of three entraining sounds. Target sounds were on time or delayed with 50/50% probability. The sharp sound cue indicated a sharp target sound, possibly inducing the prediction of high perceptual timing precision (red). The smooth sound cue indicated a smooth target sound, possibly inducing the prediction of low perceptual timing precision (blue). Stimulus plots are a visualization of the waveforms of the sounds used in the study: the sharp sound (red), the smooth sound (blue), and the entraining sound (black). The likelihood of a valid cue was 68%. In the case of an invalid cue (32%), the smooth target sound was presented for the sharp sound cue and vice versa. The slightly earlier onsets of the entraining and smooth sounds schematically illustrate the P-center alignment in the study.

Fig. 2

Behavioral Results. Bar plots represent the mean and black error bars represent the adjusted standard error (SE) across participants for a within-subject design, according to the Cousineau-Morey correction. Violin plots show kernel density estimates and data points. Asterisks refer to significant differences between conditions (*< 0.05, **< 0.01, ***< 0.001). (A) P-center location and variability (std) for all stimuli, sharp (red), entraining (gray), and smooth (blue) envelope sound from the click-alignment task. (B) Behavioral results for the timing judgment task. Left: Valid cue conditions are depicted in dark colors, and Invalid cue conditions are depicted in light colors. Sensitivity (d-prime) was relatively increased for the valid-sharp cue condition (dark red) compared to the invalid-smooth cue condition (light red) (i.e., when participants unexpectedly received a sharp target sound). Sensitivity was not affected by cue validity for the smooth target trials (valid-smooth cue condition [dark blue] versus invalid-sharp cue condition [light blue]). Upper Right: Individual delay threshold results from the training block preceding the main experiment. These thresholds were used for the main experimental blocks. Lower Right: Reaction times for the valid versus invalid cue conditions (pooled over the factor levels envelope sharpness and target timing).

Fig. 3

Beta power effects for the sharp versus smooth target prediction based on the cue. Shaded areas represent the adjusted standard error (SE) for a within-subject design, according to the Cousineau-Morey correction. Black lines indicate significant differences in the temporal dimension. The spatial extent of the significant clusters is depicted by bold stars in the topographical plots. The small gray arrow symbols for the target depict multiple possible onsets for the target according to individual P-Centers. (A) Pre-target beta-power time series (percentage change) for sharp (red) versus smooth (blue) cue condition. Pre-target beta power is significantly enhanced for the sharp cue condition. For visualization purposes, beta power was averaged over significant sensors of selected electrode placement sites: Frontal Pole (Fpz, Fp1, Fp2), Frontal (Fz, F1, F2, F4–7), Centrolateral (C4–6, T7), Centroparietal (CP4, CP6, TP7, TP8), Parietal (Pz, P1, P4–8). (B) Spectral modulation (Fourier transform) of beta-power time series for the sharp (red) versus smooth (blue) cue condition. The spectral modulation of the pre-target beta-power time series is significantly enhanced for the sharp cue condition within the delta (1–3 Hz; topographical plot shown on the right) and theta (4–7 Hz) frequency ranges (data not shown). The spectral modulation of beta power was averaged over significant sensors of selected electrode placement sites: Centrolateral (C4–6, T7), Parietal (P4, P6, P8).

Fig. 4

Although the cues were overall valid in 68% of the cases, cue reliability showed fluctuations on a short time scale due to the random nature of the experiment. (A) Cue reliability varied dynamically based on the history of recent valid and invalid cue trials. Cue reliability for each cue type (sharp or smooth) was calculated via transitional probabilities (see Methods), mirroring the probability of a valid cue based on the proportion of valid and invalid cue-target transitions for this cue type (visualized via audio waveforms). The weighting factor indicates the exponential decay function, that was used to downweight trials that were longer back in time, i.e., recent trials get a higher weight. (B) Pre-target beta-power time series for high (75th percentile or upper quartile) versus low (25th percentile or lower quartile) cue reliability for sharp cue trials, i.e., a high versus low transitional probability to receive a sharp target sound following a sharp cue. Differences are shown for the sharp cue condition only, to control for perceptual confounds caused by physical differences between the sharp and the smooth sound cue. Beta power was modulated in the same direction as for the cue-based contrast, showing increased beta power for a high relative to a low sharp cue reliability. For visualization purposes, beta power was averaged over significant sensors of selected electrode placement sites: Centroparietal (CPz, CP1–3, TP5), Parietal (Pz, P1 -4, P6).

Fig. 5

Neural-Behavioral Correlation. Top: topographical distribution of the correlation effects. Middle: correlation time courses of the significant midline sensors. Bottom: correlation at the individual subject level (each dot represents a participant for the significant midline sensors). (A) Correlation between the behavioral d-prime measure for the valid sharp cue condition and the individual beta-power contrast (t-value) between the sharp versus smooth cue conditions. There was a positive correlation between the individual beta-power effect and the d-prime result for the timing judgment task. (B) Negative correlation between the behavioral P-center variability measure (std) for the smooth sound and the individual beta-power contrast (t-values). Together, these results support the hypothesis that pre-target oscillatory beta activity encodes the predicted envelope sharpness and temporal precision of the upcoming target sound.