Cortical and behavioral tracking of rhythm in music: Effects of pitch predictability, enjoyment, and expertise
Bibliographic record
Abstract
The cortical tracking of stimulus features is a crucial neural requisite of how we process continuous music. We here tested whether cortical tracking of the beat, typically related to rhythm processing, is modulated by pitch predictability and other top-down factors. Participants listened to tonal (high pitch predictability) and atonal (low pitch predictability) music while undergoing electroencephalography. We analyzed their cortical tracking of the acoustic envelope. Cortical envelope tracking was stronger while listening to atonal music, potentially reflecting listeners' violated pitch expectations and increased attention allocation. Envelope tracking was also stronger with more expertise and enjoyment. Furthermore, we showed cortical tracking of pitch surprisal (using IDyOM), which suggests that listeners' expectations match those computed by the IDyOM model, with higher surprisal for atonal music. Behaviorally, we measured participants' ability to finger-tap to the beat of tonal and atonal sequences in two experiments. Finger-tapping performance was better in the tonal condition, indicating a positive effect of pitch predictability on behavioral rhythm processing. Cortical envelope tracking predicted tapping performance for tonal music, as did pitch-surprisal tracking for atonal music, indicating that high and low predictability might impose different processing regimes. Taken together, our results show various ways that top-down factors impact musical rhythm processing.
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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 | 0.003 |
| 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 | 0.000 |
| 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".