Automatic Encoding of Polyphonic Melodies in Musicians and Nonmusicians
Bibliographic record
Abstract
In music, multiple musical objects often overlap in time. Western polyphonic music contains multiple simultaneous melodic lines (referred to as "voices") of equal importance. Previous electrophysiological studies have shown that pitch changes in a single melody are automatically encoded in memory traces, as indexed by mismatch negativity (MMN) and its magnetic counterpart (MMNm), and that this encoding process is enhanced by musical experience. In the present study, we examined whether two simultaneous melodies in polyphonic music are represented as separate entities in the auditory memory trace. Musicians and untrained controls were tested in both magnetoencephalogram and behavioral sessions. Polyphonic stimuli were created by combining two melodies (A and B), each consisting of the same five notes but in a different order. Melody A was in the high voice and Melody B in the low voice in one condition, and this was reversed in the other condition. On 50% of trials, a deviant final (5th) note was played either in the high or in the low voice, and it either went outside the key of the melody or remained within the key. These four deviations occurred with equal probability of 12.5% each. Clear MMNm was obtained for most changes in both groups, despite the 50% deviance level, with a larger amplitude in musicians than in controls. The response pattern was consistent across groups, with larger MMNm for deviants in the high voice than in the low voice, and larger MMNm for in-key than out-of-key changes, despite better behavioral performance for out-of-key changes. The results suggest that melodic information in each voice in polyphonic music is encoded in the sensory memory trace, that the higher voice is more salient than the lower, and that tonality may be processed primarily at cognitive stages subsequent to MMN generation.
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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".