Revising the diagnosis of congenital amusia with the Montreal Battery of Evaluation of Amusia
Bibliographic record
Abstract
This article presents a critical survey of the prevalent usage of the Montreal Battery of Evaluation of Amusia (MBEA; Peretz et al., 2003) to assess congenital amusia, a neuro-developmental disorder that has been claimed to be present in 4% of the population (Kalmus and Fry, 1980). It reviews and discusses the current usage of the MBEA in relation to cut-off scores, number of used subtests, manner of testing, and employed statistics, as these vary in the literature. Furthermore, data are presented from a large-scale experiment with 228 German undergraduate students who were assessed with the MBEA and a comprehensive questionnaire. This experiment tested the difference between scores that were obtained in a web-based study (at participants' homes) and those obtained under laboratory conditions with a computerized version of the MBEA. In addition to traditional statistical procedures, the data were evaluated using Signal Detection Theory (SDT; Green and Swets, 1966), taking into consideration the individual's ability to discriminate and their response bias. Results show that using SDT for scoring instead of proportion correct offers a bias-free and normally distributed measure of discrimination ability. It is also demonstrated that a diagnosis based on an average score leads to cases of misdiagnosis. The prevalence of congenital amusia is shown to depend highly on the statistical criterion that is applied as cut-off score and on the number of subtests that is considered for the diagnosis. In addition, three different subtypes of amusics were found in our sample. Lastly, significant differences between the web-based and the laboratory group were found, giving rise to questions about the validity of web-based experimentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".