Long-term maintenance for learning on pitch and melody discrimination in congenital amusia
Bibliographic record
Abstract
Congenital amusia is described as a life-long disorder in melody discrimination, related to poor fine-grained pitch perception. A recent study in our lab found, however, that pitch and melody discrimination in amusia can improve with laboratory training. After training, over half of the amusics no longer met the standard diagnostic criterion for amusia, assessed via the Montreal Battery of Evaluation of Amusia (MBEA). The present study examined the durability of learning effects by re-examining frequency difference limens (FDLs) and melody discrimination in the same participants one year after post training. Pure-tone FDLs were measured at 500, 2000, and 8000 Hz using an adaptive three-interval forced-choice procedure, and melody discrimination was assessed via the MBEA. Preliminary results (n = 23; 11 amusics) showed no significant change in FDLs or melody discrimination between post training and one-year follow-up. Consistent with post-training results, there were significant main effects of group, with amusics performing more poorly than controls. Despite these differences, eight originally identified amusics no longer met the criterion for amusia based on the MBEA. Results suggest learning on pitch- and melody-related tasks is robust and can be maintained for at least one year. [Work supported by NIH grant R01DC005216.]
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".