Attention affects overall gain but not selective contrast at meter frequencies in the neural processing of rhythm
Bibliographic record
Abstract
Abstract When listening to music, humans spontaneously perceive and synchronize movement to periodic pulses of meter. A growing body of evidence suggests that this widespread ability is related to neural processes that selectively enhance meter periodicities. However, to what extent these neural processes are affected by the attentional state of the listener remains largely unknown. Here, we recorded EEG while participants listened to auditory rhythms and detected small changes in tempo or pitch of the stimulus, or performed a visual task. The overall neural response to the auditory input decreased when participants attended the visual modality, indicating generally lower sensitivity to acoustic information. However, the selective contrast at meter periodicities did not differ across the three tasks. Moreover, this selective contrast could be trivially accounted for by biologically-plausible models of subcortical auditory processing, but only when meter periodicities were already prominent in the acoustic input. However, when meter periodicities were not prominent in the auditory input, the EEG responses could not be explained by low-level processing. This was also confirmed by early auditory responses that originate predominantly in early auditory areas and were recorded in the same EEG. The contrast at meter periodicities in these early responses was consistently smaller than in the EEG responses originating mainly from higher-level processing stages. Together, these results demonstrate that selective contrast at meter periodicities involves higher-level neural processes that may be engaged automatically, irrespective of behavioral context. This robust shaping of the neural representation of rhythm might thus contribute to spontaneous and effortless synchronization to musical meter in humans across cultures.
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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 | 0.000 |
| 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) | 0.002 | 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".