Provoked overt recognition in acquired prosopagnosia using multiple different images of famous faces.
Bibliographic record
Abstract
Provoked overt recognition refers to the fact that patients with acquired prosopagnosia can sometimes recognise faces when presented with arrays of individuals from the same category (e.g. actors or politicians). Here we ask whether a prosopagnosic patient might experience recognition when presented with multiple different images of the same famous face simultaneously. Over two testing sessions, patient Herschel, a 66-year-old British man with acquired prosopagnosia viewed face images individually or in arrays. On several occasions he failed to recognise single photos of an individual but successfully identified that person when the same photos were presented together. For example, Herschel failed to recognise any individual images of King Charles III, four days after he had acceded to the throne (i.e. at peak media exposure) but nevertheless recognised him in an array of these same pictures. Like prior reports of provoked recognition based on category membership, overt recognition here was transient and inconsistent over individual faces. These findings are discussed in terms of models of covert recognition, alongside more recent research on the importance of within-person variability for face perception.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".