Neuromagnetic beta-band oscillation for rhythmic processing induced by subjectively accented structure
Bibliographic record
Abstract
Musical rhythm facilitates synchronized body movements and schema-based, predictive timing perception. Our previous magnetoencephalography (MEG) study demonstrated that beta-band (~20 Hz) activity in bilateral auditory cortices shows synchronized modulation that predicts the time point of the next beat (Fujioka et al. 2009, 2012). Furthermore, after finger tapping to a different musical meter such as a march or waltz (every 2nd or 3rd beat), the broadband evoked response from auditory cortex differentiates the metric conditions (Fujioka et al., 2010). Here we examined how beta-band activity indexed subjective metrical perception during listening to unaccented beats (1) after listening to acoustically accented beats, and (2) after finger tapping with either the left or right index finger. The auditory cortices showed beat-synchronized modulation in line with the previous studies in both march and waltz conditions. However, distinction between down-beat and up-beat positions was stronger in march than in waltz condition, with symmetrical activities in the left and right auditory cortices. This contrast between the two metric conditions was stronger in the side contralateral to the tapping finger in the tapping condition. This suggests that contribution of auditory cortices to metric processing depends on both its timing structure and rhythmic movement in the contralateral hemisphere.
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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) | 0.003 | 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".