Failure to Apply Signal Detection Theory to the Montreal Battery of Evaluation of Amusia May Misdiagnose Amusia
Bibliographic record
Abstract
This article considers a signal detection theory (SDT) approach to evaluation of performance on the Montreal Battery of Evaluation of Amusia (MBEA). One hundred fifty-five individuals completed the original binary response version of the MBEA (n = 62) or a confidence rating version (MBEA-C; n = 93). Confidence ratings afforded construction of empirical receiver operator characteristic (ROC) curves and derivation of bias-free performance measures against which we compared the standard performance metric, proportion correct (PC), and an alternative signal detection metric, d ′. Across the board, PC was tainted by response bias and underestimated performance as indexed by Az, a nonparametric ROC-based performance measure. Signal detection analyses further revealed that some individuals performing worse than the standard PC-based cutoff for amusia diagnosis showed large response biases. Given that PC is contaminated by response bias, this suggests the possibility that categorizing individuals as having amusia or not, using a PC-based cutoff, may inadvertently misclassify some individuals with normal perceptual sensitivity as amusic simply because they have large response biases. In line with this possibility, a comparison of amusia classification using d ′- and PC-based cutoffs showed potential misclassification of 33% of the examined cases.
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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 | 0.016 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".