Brain Entrain: Acoustic Features of Music that Drive You to Synch
Bibliographic record
Abstract
Tapping along with a metronome or the beat of music is a relatively easy task. Certain acoustic features of music have been found to support this behavioural synchronization. For example, lower frequency content has been found to be related to higher tapping velocity and lower tapping variability (Stupacher, Hove, & Janata, 2016). Neurons will also entrain their firing to the beat of music, but it is unknown whether those same acoustic features that support behavioural synchronization will also support neural entrainment. The current study seeks to investigate which acoustic features of music support the entrainment of neurons that are related to behavioural synchronization, such as those in premotor areas of the brain. In a previous study, participants listened to music while EEG was measured from the surface of the scalp. Independent components analysis was used to identify sources of activity in auditory and premotor areas of the brain. In a post-hoc analysis, certain acoustic features of the music were found to correlate with neural entrainment. Specifically, tempo and RMS were found to correlate with entrainment of premotor areas, whereas low energy rate (the proportion of the signal below the average energy) and spectral centroid were found to correlate with beta-band phase coherence of auditory and premotor areas. In a second [pilot] study, a stimulus set was created to specifically investigate these features and their ability to entrain neurons in premotor areas of the brain.
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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 | 0.002 |
| 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) | 0.004 | 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".