Stability of Neural Oscillations Supports Auditory-Motor Synchronization
Bibliographic record
Abstract
Abstract Previous findings suggest that musical training leads to increased coactivation of auditory and motor brain networks, as well as enhanced auditory-motor synchronization. Less is known about the temporal dynamics of auditory-motor network interactions and how these temporal dynamics are shaped by musical training. The current study applied Recurrence Quantification Analysis, a nonlinear technique for characterizing the temporal dynamics of complex systems, to participants’ neurophysiological activity recorded via electroencephalography (EEG) during an auditory-motor synchronization task. We investigated changes in neural predictability and stability with musical training, and how these changes were related to synchronization accuracy and consistency. EEG was recorded while musicians and nonmusicians first tapped a familiar melody at a comfortable rate, called Spontaneous Production Rate (SPR). Then participants synchronized their taps with an auditory metronome presented at each participant’s SPR and at rates 15% and 30% slower than their SPR. EEG-based outcomes of determinism (predictability) and meanline (stability) were compared with behavioral synchronization measures. Musicians synchronized more consistently overall than nonmusicians. Both groups of participants showed decreased synchronization accuracy at slower rates, and higher EEG-based determinism (predictability) at slower rates. Furthermore, neural meanline (stability) measures correlated with synchronization consistency across all participants and stimulus rates; as neural stability increased, so did synchronization consistency. Neural stability may be a general mechanism supporting the maintenance of synchronization across rates, which may improve with musical training.
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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 | 0.000 | 0.004 |
| 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".