Selective Neuronal Entrainment to the Beat and Meter Embedded in a Musical Rhythm
Bibliographic record
Abstract
Fundamental to the experience of music, beat and meter perception refers to the perception of periodicities while listening to music occurring within the frequency range of musical tempo. Here, we explored the spontaneous building of beat and meter hypothesized to emerge from the selective entrainment of neuronal populations at beat and meter frequencies. The electroencephalogram (EEG) was recorded while human participants listened to rhythms consisting of short sounds alternating with silences to induce a spontaneous perception of beat and meter. We found that the rhythmic stimuli elicited multiple steady state-evoked potentials (SS-EPs) observed in the EEG spectrum at frequencies corresponding to the rhythmic pattern envelope. Most importantly, the amplitude of the SS-EPs obtained at beat and meter frequencies were selectively enhanced even though the acoustic energy was not necessarily predominant at these frequencies. Furthermore, accelerating the tempo of the rhythmic stimuli so as to move away from the range of frequencies at which beats are usually perceived impaired the selective enhancement of SS-EPs at these frequencies. The observation that beat- and meter-related SS-EPs are selectively enhanced at frequencies compatible with beat and meter perception indicates that these responses do not merely reflect the physical structure of the sound envelope but, instead, reflect the spontaneous emergence of an internal representation of beat, possibly through a mechanism of selective neuronal entrainment within a resonance frequency range. Taken together, these results suggest that musical rhythms constitute a unique context to gain insight on general mechanisms of entrainment, from the neuronal level to individual level.
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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 | 0.000 |
| 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".