Diagnosing Prosopagnosia: The Utility of Visual Noise in the Cambridge Face Recognition Test
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.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".