Diagnosing Prosopagnosia: The Utility of Visual Noise in the Cambridge Face Recognition Test
Why this work is in the frame
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Bibliographic record
Abstract
Adding visual noise to facial images has been used to increase reliance on configural processing. Whether this enhances the ability of tests to diagnose prosopagnosia is not known. We examined 15 subjects with developmental prosopagnosia, 13 subjects with acquired prosopagnosia, and 38 control subjects with the Cambridge Face Memory Test. We compared their performance on the second phase, without visual noise, and on the third phase, which adds visual noise. We analyzed the results with signal detection theory methods. The performance of controls worsened more than did that of prosopagnosic subjects when noise was added. The second phase showed better ability to discriminate between prosopagnosic and control subjects than did the third phase. For developmental prosopagnosia, a test using only the 48 trials of the first and second phases yielded sensitivity of 88% and specificity of 91% with a criterion of 33/48 correct, performance characteristics that are similar for a criterion of 43/72 for the whole test. We conclude that a shortened Cambridge Face Memory Test without the noisy images may be a quicker yet equally effective instrument for diagnosing prosopagnosia. The theoretical advantage of noisy images is outweighed by the poorer performance of control subjects with visual noise.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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 it