Imagined Temporal Groupings Tune Oscillatory Neural Activity for Processing Rhythmic Sounds

in Timing & Time Perception
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Temporal patterns within complex sound signals, such as music, are not merely processed after they are heard. We also focus attention to upcoming points in time to aid perception, contingent upon regularities we perceive in the sounds’ inherent rhythms. Such organized predictions are endogenously maintained as meter — the patterning of sounds into hierarchical timing levels that manifest as strong and weak events. Models of neural oscillations provide potential means for how meter could arise in the brain, but little evidence of dynamic neural activity has been offered. To this end, we conducted a study instructing participants to imagine two-based or three-based metric patterns over identical, equally-spaced sounds while we recorded the electroencephalogram (EEG). In the three-based metric pattern, multivariate analysis of the EEG showed contrasting patterns of neural oscillations between strong and weak events in the delta (2–4 Hz) and alpha (9–14 Hz), frequency bands, while theta (4–9 Hz) and beta (16–24 Hz) bands contrasted two hierarchically weaker events. In two-based metric patterns, neural activity did not drastically differ between strong and weak events. We suggest the findings reflect patterns of neural activation and suppression responsible for shaping perception through time.

Imagined Temporal Groupings Tune Oscillatory Neural Activity for Processing Rhythmic Sounds

in Timing & Time Perception



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    Stimulus sequences used in the task. Each black bar represents frontward- or backward-presented click pairs. Participants counted along with the pattern according to metric type during the entire sequence. Deviant click pairs arise only once per trial in a random position of the deviant region. Priming sequences are not shown.

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    Salience plots of latent variables (LVs). Salience values indicate how strongly frequencies, time points, and channels of spectral power explain pre-defined conditions of metric beats. (a) LV1 contrasts T1 against T2 and T3 and corresponds to topographies and time series in Fig. 3. (b) LV2 contrasts T3 against T1 and T2 and corresponds to topographies and time series plots in Fig. 4.

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    Topographies and time series of normalized spectral alpha and delta power corresponding to LV1. Topographic maps are shown with shading corresponding to each channel’s salience to LV1 in the alpha band; channels that reliably contribute to LV1 determined by bootstrap tests are indicated by emboldened dots. Two representative electrodes’ normalized spectral power is plotted as time series waveforms. Vertical dashed lines in time series correspond to time points at which topographies are plotted, and asterisks denote time points reliably contributing to LV1. The 0 ms mark on time series plot abscissa indicates onset of a stimulus and the 600 ms indicates the onset of the subsequent stimulus. (a) Topographies and time series for alpha power. (b) Topographies and time series for delta power.

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    Topographies and time series of spectral power for components corresponding to LV2. (a) Topographies and time series for beta power. (b) Topographies and time series for theta power. See caption of Fig. 3 for details of topographic plots and time series plots.

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    (a) Salience plot for data uncorrected at the baseline. (b) Topographies of channels reliably contributing to LV3 in the high gamma band and two representative electrodes’ power time series.

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