Individual neurophysiological signatures of spontaneous rhythm processing
Bibliographic record
Abstract
When sensory input conveys rhythmic regularity, we can form predictions about the timing of upcoming events. Although rhythm processing capacities differ considerably between individuals, these differences are often obscured by participant- and trial-level data averaging procedures in M/EEG research. Here, we systematically assessed neurophysiological variability displayed by individuals listening to isochronous (1.54 Hz) equitone sequences interspersed with unexpected (amplitude-attenuated) deviant tones. Our approach aimed at revealing time-varying adaptive neural mechanisms for sampling the acoustic environment at multiple timescales. Rhythm tracking analyses confirmed that individuals encode temporal regularities and form temporal expectations, as indicated in delta-band (1.54 Hz) power and its anticipatory phase alignment to expected tone onsets. Zooming into tone- and participant-level data, we further characterized intra- and inter-individual variabilities in phase-alignment across auditory sequences. Further, individual modeling of beta-band tone-locked responses showed that a subset of auditory sequences was sampled rhythmically by superimposing binary (strong-weak; S-w), ternary (S-w-w) and mixed accentuation patterns. In these sequences, neural responses to standard and deviant tones were modulated by a binary accentuation pattern, thus pointing towards a mechanism of dynamic attending. Altogether, the current results point toward complementary roles of delta- and beta-band activity in rhythm processing and further highlight diverse and adaptive mechanisms to track and sample the acoustic environment at multiple timescales, even in the absence of task-specific instructions.
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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 | 0.001 |
| 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".