Electromagnetic Correlates of Musical Expertise in Processing of Tone Patterns
Bibliographic record
Abstract
Using magnetoencephalography (MEG), we investigated the influence of long term musical training on the processing of partly imagined tone patterns (imagery condition) compared to the same perceived patterns (perceptual condition). The magnetic counterpart of the mismatch negativity (MMNm) was recorded and compared between musicians and non-musicians in order to assess the effect of musical training on the detection of deviants to tone patterns. The results indicated a clear MMNm in the perceptual condition as well as in a simple pitch oddball (control) condition in both groups. However, there was no significant mismatch response in either group in the imagery condition despite above chance behavioral performance in the task of detecting deviant tones. The latency and the laterality of the MMNm in the perceptual condition differed significantly between groups, with an earlier MMNm in musicians, especially in the left hemisphere. In contrast the MMNm amplitudes did not differ significantly between groups. The behavioral results revealed a clear effect of long-term musical training in both experimental conditions. The obtained results represent new evidence that the processing of tone patterns is faster and more strongly lateralized in musically trained subjects, which is consistent with other findings in different paradigms of enhanced auditory neural system functioning due to long-term musical training.
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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) | 0.001 | 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".