Intrinsic and task-evoked oscillatory dynamics underpinning auditory-motor coupling
Bibliographic record
Abstract
Understanding the temporal dynamics of oscillatory interactions between auditory and motor cortices is crucial for unravelling the neural basis of coordinated actions necessary for speech and music processing.To this end, this investigation employed electrophysiology to explore intrinsic and task-evoked oscillatory dynamics underpinning auditory-motor coupling.The orst study examined intrinsic phasebased functional connectivity between auditory and motor cortices using restingstate magnetoencephalography across a sample of healthy young adults (n=90).As predicted, we observed greater phase-locking values in auditory-motor compared to visuomotor pairings across all frequency bands.Consistent with prior literature, the strongest synchronization was observed between right primary auditory regions and the right ventral premotor cortex, most prominently in the theta, alpha, and beta bands.Directed connectivity estimates conormed the expected motor-to-auditory preference in the beta band and an auditory-to-motor preference in the alpha band.The second study used electroencephalography to investigate the dynamics of mu suppression, an index of anticipatory motor activity in a melody learning task.Using a novel data-driven approach, we orst localized mu suppression preceding movements during motor training and then applied these coordinates to reveal mu suppression during passive listening to the previously learned melody.Crucially, suppression was observed at the single-note level.Together, these ondings reveal distinct but complementary time-based oscillatory mechanisms for auditory-motor integration.Overall, this thesis sheds light on the human brain9s capacity for managing remarkably low latencies between sounds and movements.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".