Beta‐band oscillations during passive listening to metronome sounds reflect improved timing representation after short‐term musical training in healthy older adults
Bibliographic record
Abstract
Sub-second time intervals in musical rhythms provide predictive cues about future events to performers and listeners through an internalized representation of timing. While the acuity of automatic, sub-second timing as well as cognitively controlled, supra-second timing declines with ageing, musical experts are less affected. This study investigated the influence of piano training on temporal processing abilities in older adults using behavioural and neuronal correlates. We hypothesized that neuroplastic changes in beta networks, caused by training in sensorimotor coordination with timing processing, can be assessed even in the absence of movement. Behavioural performance of internal timing stability was assessed with synchronization-continuation finger-tapping paradigms. Magnetoencephalography (MEG) was recorded from older adults before and after one month of one-on-one training. For neural measures of automatic timing processing, we focused on beta oscillations (13-30 Hz) during passive listening to metronome beats. Periodic beta-band modulations in older adults before training were similar to previous findings in young listeners at a beat interval of 800 ms. After training, behavioural performance for continuation tapping was improved and accompanied by an increased range of beat-induced beta modulation, compared to participants who did not receive training. Beta changes were observed in the caudate, auditory, sensorimotor and premotor cortices, parietal lobe, cerebellum and medial prefrontal cortex, suggesting that increased resources are involved in timing processing and goal-oriented monitoring as well as reward-based sensorimotor learning.
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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 | 0.000 |
| 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".