Training Mandarin-speaking amusics to recognize pitch direction: Pathway to treat musical disorders in congenital amusia?
Bibliographic record
Abstract
Congenital amusia is a lifelong disorder of musical perception and production that has been hypothesized as due to impaired pitch direction recognition. This study investigated whether amusics could be trained to identify pitch direction in speech and music, and if so, whether enhanced pitch direction recognition would benefit musical processing in amusia. Eighteen Mandarin-speaking amusics and 18 matched controls were evaluated using the Montreal Battery of Evaluation of Amusia (Peretz et al., 2003) and tested on two psychophysical pitch threshold tasks for identification of pitch direction in the Mandarin syllable /ma/ and its piano tone analog. Subsequently, nine of the eighteen amusics undertook a 10-session training program on pitch direction identification in /ma/ and piano tone. Compared with those untrained, trained amusics demonstrated significantly improved thresholds for pitch direction identification in speech syllables [pretest: 2.92 (4.63) semitones; posttest: 0.16 (0.06)], and marginally significant improvement in pitch direction identification thresholds for piano tones [pretest: 5.20 (4.56); posttest: 0.89 (1.46)]. These findings suggest that pitch sensitivity of individuals with congenital amusia could be improved through auditory training, providing evidence for neural plasticity in the amusic brain, which may lead the way to other rehabilitative programs for treating this musical disorder.
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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 | 0.001 | 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".