Neural representation of the musical beat is facilitated but not contingent on the repetition of rhythmic patterns
Bibliographic record
Abstract
Music often entails perception of periodic beats which serve as internal temporal references to coordinate movements to music. Crucially, beat perception arises even in syncopated musical rhythms, which only weakly cue the beat periodicity. However, syncopated rhythms are often looped in music, suggesting that repetition of rhythmic patterns may facilitate beat perception by providing a periodic structure at a supra-second timescale. Here, we tested this hypothesis by recording separately electroencephalographic (EEG) and behavioral responses (finger tapping) while participants listened to different syncopated rhythmic sequences. These sequences either consisted of a repeated pattern (repetition of 4.8 and 9.6-s-long patterns) or were generated without repetition. Despite the degradation of pattern repetition, neural activity showed a periodized representation of the rhythmic input across conditions, at periodicities corresponding to those expressed in behavioral responses. However, this neural activity was further enhanced in the condition with shorter repeated patterns. Thus, pattern repetition was not necessary but strengthened the neural representation of the beat, demonstrating that supra-second periodicities in the rhythmic input further enhance sub-second periodicities in neural activity. These findings highlight the multiscale temporal processing of musical rhythm, and, more generally, complex rhythmic inputs involved in interpersonal interaction and communication.
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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 | 0.001 |
| 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".