Prosopagnosia and the role of face-sensitive areas in race perception
Bibliographic record
Abstract
Race is rapidly and effortlessly extracted from faces. Previous fMRI studies have reported race-related modulations in the bilateral Fusiform Face Areas (FFAs) and Occipital Face Areas (OFAs) during the categorization of faces by race. However, our recent findings revealed a comparable Other-Race Categorization Advantage between a well-studied case of pure acquired prosopagnosia-patient PS-and healthy controls. Notably, PS demonstrated faster categorization by race of other-compared to same-race faces, similar to healthy participants, despite sustaining lesions in the right OFA (rOFA) and left FFA (lFFA). This observation suggests that race processing can occur effectively even with damage to core face-sensitive regions, challenging the functional significance of race-related activations in the rOFA and lFFA observed in healthy individuals with fMRI. To address this apparent contradiction, we tested PS and age-matched controls during the categorization by race of same- to other-race morphed faces. Our data showed that PS required more visual information to accurately categorize racially ambiguous faces, indicating that intact rOFA and/or lFFA are crucial for extracting fine-grained racial information. These results refine our understanding of the functional roles of these key cortical regions and offer novel insights into the neural mechanisms underlying the perception of face race and prosopagnosia.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".