Vocal Accuracy and Neural Plasticity Following Micromelody-Discrimination Training
Bibliographic record
Abstract
BACKGROUND: Recent behavioral studies report correlational evidence to suggest that non-musicians with good pitch discrimination sing more accurately than those with poorer auditory skills. However, other studies have reported a dissociation between perceptual and vocal production skills. In order to elucidate the relationship between auditory discrimination skills and vocal accuracy, we administered an auditory-discrimination training paradigm to a group of non-musicians to determine whether training-enhanced auditory discrimination would specifically result in improved vocal accuracy. METHODOLOGY/PRINCIPAL FINDINGS: We utilized micromelodies (i.e., melodies with seven different interval scales, each smaller than a semitone) as the main stimuli for auditory discrimination training and testing, and we used single-note and melodic singing tasks to assess vocal accuracy in two groups of non-musicians (experimental and control). To determine if any training-induced improvements in vocal accuracy would be accompanied by related modulations in cortical activity during singing, the experimental group of non-musicians also performed the singing tasks while undergoing functional magnetic resonance imaging (fMRI). Following training, the experimental group exhibited significant enhancements in micromelody discrimination compared to controls. However, we did not observe a correlated improvement in vocal accuracy during single-note or melodic singing, nor did we detect any training-induced changes in activity within brain regions associated with singing. CONCLUSIONS/SIGNIFICANCE: Given the observations from our auditory training regimen, we therefore conclude that perceptual discrimination training alone is not sufficient to improve vocal accuracy in non-musicians, supporting the suggested dissociation between auditory perception and vocal 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 | 0.000 |
| 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".