Stability of Neural Oscillations Supports Auditory‐Motor Synchronization
Bibliographic record
Abstract
Previous findings suggest that auditory-motor synchronization is supported by increased coactivation of auditory and motor brain networks. Here, we compare synchronization accuracy and consistency with the temporal dynamics of neural signals during auditory-motor synchronization. Recurrence quantification analysis, a nonlinear technique for characterizing the temporal dynamics of complex systems, was applied to participants' neurophysiological activity recorded via electroencephalography (EEG) during an auditory-motor synchronization task. Changes in participants' neural predictability and stability were compared with their behavioral synchronization accuracy and consistency. EEG was recorded while participants produced a familiar melody at a comfortable rate as a measure of optimal temporal stability. Then, participants synchronized their taps with an auditory metronome presented at each participant's optimal rate and at rates 15% and 30% slower. EEG-based outcomes of determinism (predictability) and meanline (stability) were compared with behavioral synchronization measures. Participants showed decreased synchronization accuracy and higher EEG-based determinism at slower rates, consistent with lower flexibility. Furthermore, neural stability measures correlated with synchronization consistency across stimulus rates; as neural stability increased, so did behavioral synchronization consistency. Recurrence-based measures of neural stability may indicate a general mechanism supporting the maintenance of auditory-motor synchronization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".