Musical Training Enhances Automatic Encoding of Melodic Contour and Interval Structure
Bibliographic record
Abstract
In music, melodic information is thought to be encoded in two forms, a contour code (up/down pattern of pitch changes) and an interval code (pitch distances between successive notes). A recent study recording the mismatch negativity (MMN) evoked by pitch contour and interval deviations in simple melodies demonstrated that people with no formal music education process both contour and interval information in the auditory cortex automatically. However, it is still unclear whether musical experience enhances both strategies of melodic encoding. We designed stimuli to examine contour and interval information separately. In the contour condition there were eight different standard melodies (presented on 80% of trials), each consisting of five notes all ascending in pitch, and the corresponding deviant melodies (20%) were altered to descending on their final note. The interval condition used one five-note standard melody transposed to eight keys from trial to trial, and on deviant trials the last note was raised by one whole tone without changing the pitch contour. There was also a control condition, in which a standard tone (990.7 Hz) and a deviant tone (1111.0 Hz) were presented. The magnetic counterpart of the MMN (MMNm) from musicians and nonmusicians was obtained as the difference between the dipole moment in response to the standard and deviant trials recorded by magnetoencephalography. Significantly larger MMNm was present in musicians in both contour and interval conditions than in nonmusicians, whereas MMNm in the control condition was similar for both groups. The interval MMNm was larger than the contour MMNm in musicians. No hemispheric difference was found in either group. The results suggest that musical training enhances the ability to automatically register abstract changes in the relative pitch structure of melodies.
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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) | 0.003 | 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".