Reliable estimation of internal oscillator properties from a novel, fast-paced tapping paradigm
Bibliographic record
Abstract
Rhythmic structure in speech, music, and other auditory signals helps us track, anticipate, and understand the sounds in our environment. The dynamic attending framework proposes that biological systems possess internal rhythms, generated via oscillatory mechanisms, that synchronize with (entrain to) rhythms in the external world. Here, we focused on two properties of internal oscillators: preferred rate, the default rate of an oscillator in the absence of any input, and flexibility, the oscillator's ability to adapt to changes in external rhythmic context. We aimed to develop methods that can reliably estimate preferred rate and flexibility on an individual basis. The experiment was a synchronization-continuation finger tapping paradigm with a unique design: the stimulus rates were finely sampled over a wide range of rates and were presented only once. Individuals tapped their finger to 5-event isochronous stimulus sequences and continued the rhythm at the same pace. Preferred rate was estimated by assessing the best-performance conditions where the difference between the stimulus rate and continuation tapping rate (tempo-matching error) was minimum. The results revealed harmonically related, multiple preferred rates for each individual. We maximized the differences in stimulus rate between consecutive trials to challenge individuals' flexibility, which was then estimated by how much tempo-matching errors in synchronization tapping increase with this manipulation. Both measures showed test-retest reliability. The findings demonstrate the influence of properties of the auditory context on rhythmic entrainment, and have implications for development of methods that can improve attentional synchronization and hearing.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".