Behavior-Relevant Periodized Neural Representation of Acoustic But Not Tactile Rhythm in Humans
Bibliographic record
Abstract
Music makes people move. This human propensity to coordinate movement with musical rhythm requires multiscale temporal integration, allowing fast sensory events composing rhythmic input to be mapped onto slower, behavior-relevant, internal templates such as periodic beats. Relatedly, beat perception has been shown to involve an enhanced representation of the beat periodicities in neural activity. However, the extent to which this ability to move to the beat and the related "periodized" neural representation are shared across the senses beyond audition remains unknown. Here, we addressed this question by recording separately the electroencephalographic (EEG) responses and finger tapping to a rhythm conveyed either through acoustic or tactile inputs in healthy volunteers of either sex. The EEG responses to the acoustic rhythm, spanning a low-frequency range (below 15 Hz), showed enhanced representation of the perceived periodic beat, compatible with behavior. In contrast, the EEG responses to the tactile rhythm, spanning a broader frequency range (up to 25 Hz), did not show significant beat-related periodization and yielded less stable tapping. Together, these findings suggest a preferential role of low-frequency neural activity in supporting neural representation of the beat. Most importantly, we show that this neural representation, as well as the ability to move to the beat, is not systematically shared across the senses. More generally, these results, highlighting multimodal differences in beat processing, reveal a process of multiscale temporal integration that allows the auditory system to go beyond mere tracking of onset timing and to support higher-level internal representation and motor entrainment to rhythm.
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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) | 0.002 | 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".