Face recognition in three people, each with a different disorder: prosopagnosia, object agnosia, and pure alexia
Bibliographic record
Abstract
What aspects of face recognition, if any, are spared in a prosopagnosic (DC) whose object recognition and reading are intact? We compared DC's performance with that of a person with object agnosia and pure alexia (CK, see Moscovitch et al., 1997), and with that of a person with only pure alexia (WK), because reading, like object recognition, is presumed to be mediated by a part-based processing system which may also be involved in some aspects of face-recognition, such as recognition of inverted and fractured faces. We also included a control, DCB, matched for social and educational background to DC (his 2.5-years-older brother). DC could recognize only about 37% of all upright full-viewed faces, whereas CK, WK and DCB recognized about 80% of them. When these faces were inverted, DC and CK were impaired (9% and 14%) compared to DCB and WK (50% and 70%). For DC, this inversion weakness was true whether the internal or external facial features were inverted, but for CK, WK, and DCB, it was only true when the internal parts were inverted. When the faces were disguised and when parts were missing, DC only recognized about 27% of them, whereas CK, WK, and DCB had no difficulty. Only CK and DC were impaired on recognizing fractured faces. We conclude that (1) only the part-based object system contributes to some aspects of face recognition; (2) it does so by interacting with an intact face-system; and, (3) damage to the face system leads to global face recognition deficits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".