Statistical learning of speech, not music, in congenital amusia
Bibliographic record
Abstract
The acquisition of both speech and music uses general principles: learners extract statistical regularities present in the environment. Yet, individuals who suffer from congenital amusia (commonly called tone-deafness) have experienced lifelong difficulties in acquiring basic musical skills, while their language abilities appear essentially intact. One possible account for this dissociation between music and speech is that amusics lack normal experience with music. If given appropriate exposure, amusics might be able to acquire basic musical abilities. To test this possibility, a group of 11 adults with congenital amusia, and their matched controls, were exposed to a continuous stream of syllables or tones for 21-minute. Their task was to try to identify three-syllable nonsense words or three-tone motifs having an identical statistical structure. The results of five experiments show that amusics can learn novel words as easily as controls, whereas they systematically fail on musical materials. Thus, inappropriate musical exposure cannot fully account for the musical disorder. Implications of the results for the domain specificity of statistical learning are discussed.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".