Accent-induced Modulation of Neural and Movement Patterns during Spontaneous Synchronization to Auditory Rhythms
Bibliographic record
Abstract
Human rhythmic movements spontaneously synchronize with auditory rhythms at various frequency ratios. The emergence of more complex relationships-for instance, frequency ratios of 1:2 and 1:3-is enhanced by adding a congruent accentuation pattern (binary for 1:2 and ternary for 1:3), resulting in a 1:1 movement-accentuation relationship. However, this benefit of accentuation on movement synchronization appears to be stronger for the ternary pattern than for the binary pattern. Here, we investigated whether this difference in accent-induced movement synchronization may be related to a difference in the neural tracking of these accentuation profiles. Accented and control unaccented auditory sequences were presented to participants who concurrently produced finger taps at their preferred frequency, and spontaneous movement synchronization was measured. EEG was recorded during passive listening to each auditory sequence. The results revealed that enhanced movement synchronization with ternary accentuation was accompanied by enhanced neural tracking of this pattern. Larger EEG responses at the accentuation frequency were found for the ternary pattern compared with the binary pattern. Moreover, the amplitude of accent-induced EEG responses was positively correlated with the magnitude of accent-induced movement synchronization across participants. Altogether, these findings show that the dynamics of spontaneous auditory-motor synchronization is strongly driven by the multi-time-scale sensory processing of auditory rhythms, highlighting the importance of considering neural responses to rhythmic sequences for understanding and enhancing synchronization performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".