Electrical Brain Responses Reveal Sequential Constraints on Planning during Music Performance
Bibliographic record
Abstract
Elements in speech and music unfold sequentially over time. To produce sentences and melodies quickly and accurately, individuals must plan upcoming sequence events, as well as monitor outcomes via auditory feedback. We investigated the neural correlates of sequential planning and monitoring processes by manipulating auditory feedback during music performance. Pianists performed isochronous melodies from memory at an initially cued rate while their electroencephalogram was recorded. Pitch feedback was occasionally altered to match either an immediately upcoming Near-Future pitch (next sequence event) or a more distant Far-Future pitch (two events ahead of the current event). Near-Future, but not Far-Future altered feedback perturbed the timing of pianists' performances, suggesting greater interference of Near-Future sequential events with current planning processes. Near-Future feedback triggered a greater reduction in auditory sensory suppression (enhanced response) than Far-Future feedback, reflected in the P2 component elicited by the pitch event following the unexpected pitch change. Greater timing perturbations were associated with enhanced cortical sensory processing of the pitch event following the Near-Future altered feedback. Both types of feedback alterations elicited feedback-related negativity (FRN) and P3a potentials and amplified spectral power in the theta frequency range. These findings suggest similar constraints on producers' sequential planning to those reported in speech production.
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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".