Internalized Timing of Isochronous Sounds Is Represented in Neuromagnetic Beta Oscillations
Bibliographic record
Abstract
Moving in synchrony with an auditory rhythm requires predictive action based on neurodynamic representation of temporal information. Although it is known that a regular auditory rhythm can facilitate rhythmic movement, the neural mechanisms underlying this phenomenon remain poorly understood. In this experiment using human magnetoencephalography, 12 young healthy adults listened passively to an isochronous auditory rhythm without producing rhythmic movement. We hypothesized that the dynamics of neuromagnetic beta-band oscillations (~20 Hz)-which are known to reflect changes in an active status of sensorimotor functions-would show modulations in both power and phase-coherence related to the rate of the auditory rhythm across both auditory and motor systems. Despite the absence of an intention to move, modulation of beta amplitude as well as changes in cortico-cortical coherence followed the tempo of sound stimulation in auditory cortices and motor-related areas including the sensorimotor cortex, inferior-frontal gyrus, supplementary motor area, and the cerebellum. The time course of beta decrease after stimulus onset was consistent regardless of the rate or regularity of the stimulus, but the time course of the following beta rebound depended on the stimulus rate only in the regular stimulus conditions such that the beta amplitude reached its maximum just before the occurrence of the next sound. Our results suggest that the time course of beta modulation provides a mechanism for maintaining predictive timing, that beta oscillations reflect functional coordination between auditory and motor systems, and that coherence in beta oscillations dynamically configure the sensorimotor networks for auditory-motor coupling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".