Modeling rhythm perception and temporal adaptation: top-down influences on a gradually decaying oscillator
Bibliographic record
Abstract
Adapting to a dynamically-changing auditory environment is a challenging task that our brains are able to achieve most of the time seemingly without much effort. Dynamic attending theory posits that this ability is governed by rhythmic entrainment, namely, synchronization of internal oscillators to the regularities in sound signals. Here, we investigated the properties of these oscillators, based on their behavior prior to, during and after entraining to rhythmic stimuli. We fitted a linearized oscillator model and several variants to empirical datasets, obtained from a duration discrimination paradigm that involved a wide range of stimulus rates. Two sessions of the experiment, to which the models were fitted separately, differed in their requirement for temporal adaptation: trial-to-trial changes in stimulus rate were maximal in one session, and minimal in the other. We first compared models that assumed either complete, gradual, or no decay towards the preferred rate after cessation of a stimulus rhythm, either in a silent gap between the stimulus sequence and to-be-judged comparison interval, between consecutive trials of the experiment, or both. Then, we obtained parameter estimates from the best-fitting model and compared them across session types. Results revealed that the internal oscillators gradually decay towards their preferred rate, on similar timescales within and between trials. Critically, the oscillators’ behavior is mainly determined by their preferred rate and can be modulated by task demands. The findings are in line with theoretical predictions and neuroscientific literature on oscillatory mechanisms underlying rhythm perception and temporal adaptation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".