Experience-driven Predictability Does Not Influence Neural Entrainment to the Beat
Bibliographic record
Abstract
Humans spontaneously synchronize movements to a perceived underlying pulse, or beat, in music. Beat perception may be indexed by the synchronization of neural oscillations to the beat, marked by increases in EEG amplitude at the beat frequency [Nozaradan, S., Peretz, I., Missal, M., & Mouraux, A. Tagging the neuronal entrainment to beat and meter. Journal of Neuroscience, 31, 10234-10240, 2011]. Indeed, neural synchronization to the beat appears stronger for strong-beat than non-beat rhythms [Tal, I., Large, E. W., Rabinovitch, E., Wei, Y., Schroeder, C. E., Poeppel, D., et al. Neural entrainment to the beat: The "missing-pulse" phenomenon. Journal of Neuroscience, 37, 6331-6341, 2017] and may underlie the generation of an internal representation of beat. However, because we are exposed disproportionately to strong-beat rhythms (e.g., most Western music) in the environment, comparisons of neural responses to strong-beat and non-beat rhythms may be confounded by relative differences in familiarity. Here, we dissociated beat-related and familiarity-related neural responses by comparing EEG amplitudes during the perception of strong-beat and non-beat rhythms that were either novel or made familiar through training. First, we recorded EEG from participants while they listened to a set of strong-beat, weak-beat, and non-beat rhythms. Then, they were trained on half of the rhythms over four behavioral sessions by listening to and tapping along with them, such that half of the rhythms were familiar by the end of training. Finally, EEG responses to the full rhythm set (half now familiar, half still unfamiliar) were recorded posttraining. Results show no effect of training on EEG amplitude at beat or stimulus-related frequencies and little evidence of familiarity-driven changes in EEG amplitude for weak- and non-beat rhythms. This suggests that oscillatory entrainment to the beat is not driven by familiarity and therefore likely reflects beat processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".