Mu suppression reveals auditory-motor predictions after short musical training
Bibliographic record
Abstract
Abstract Auditory-motor coupling is a bidirectional neural mechanism that supports speech and music, with evidence of motor system activation during passive listening to both spoken language and learned melodies. Such activation is anticipatory, occurs in non-musicians, and can be elicited at the single-note level. These findings support the idea that motor activity guides auditory perception by relaying predictive timing information. However, the neural processes underlying this activity are not fully understood. EEG studies in musicians have linked it to mu-band suppression, but the temporal scale and the generalizability to the broader population remain unclear. We recruited 25 non-musicians who learned to play a simple melody on a piano-like keyboard. Before and after training, participants passively listened to the trained melody and control melodies. Offline, EEG data from the motor training were used to create a time-frequency mask with which to identify mu suppression occurring during passive listening. Significant mu suppression emerged before each note only during post-training exposure to the practiced melody. Results suggest that mu suppression occurs at the single-note level following short motor training and is not dependent on prior musical experience. Our findings support the notion that motor activity aids perception by anticipating the unfolding of learned auditory-motor sequences.
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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".