The relationship between acoustic and musical pitch processing in adolescents
Bibliographic record
Abstract
Amusia is defined as a difficulty processing the tonal pitch structure of music such that an individual cannot tell the difference between notes that are in-key and out-of-key. A fine-grained pitch discrimination deficit is often observed in people with amusia. It is possible that an intervention, early in development, could mitigate amusia; however, one challenge identifying amusia early in development is that identifying in- and out-of-key notes is a metacognitive task. Given the common co-occurrence of difficulties with pitch discrimination, it would be easier to identify amusia in developing children by using a pitch change detection task. The goal of this study was to explore the behavioural and neurophysiological profiles of adolescents with poor pitch processing (Poor PP) abilities compared with those with normal pitch processing (Normal PP) abilities. Neurophysiologically, the Poor PPs exhibited a similar event-related potential (ERP) profile to adult amusics during both acoustic and musical pitch discrimination tasks. That is, early ERPs (ERAN, MMN) were similar in Poor PPs compared with Normal PPs, whereas late positivities (P300, P600) were absent in Poor PPs, but present in Normal PPs. At the same time, behavioural data revealed a double dissociation between the abilities to detect a pitch deviant in acoustic and musical context, suggesting that about a third of the children would be missed by selecting a fine-grained acoustic pitch discrimination task to identify the presence of amusia in early childhood.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".