Fine-grained face race processing in prosopagnosia
Bibliographic record
Abstract
Race is extracted from faces almost instantly and this visual information strongly influences face processing. Healthy observers are more accurate in recognizing same- (SR) relative to other-race (OR) faces (i.e., the Same-Race Recognition Advantage – SRRA), but slower in categorizing by race those faces (i.e., the Other-Race Categorization Advantage – ORCA). Several fMRI studies showed sensitivity to race in the Fusiform Face Area (FFA) and Occipital Face Area (OFA), with some reporting brain-behavior correlations between the activation of the left FFA and the magnitude of both the SRRA and ORCA. However, we recently demonstrated with patient PS, a pure case of acquired prosopagnosia with lesions encompassing the left FFA and the right OFA, that an intact face-cortical network is not necessary to observe the other-race effects. To further clarify the functional role of these face-selective regions in the other-race effects, we asked patient PS, healthy young adults, and age-matched controls to perform a face categorization by race and a face recognition task with the use of more ambiguous stimuli. Specifically, we used continua of morphed SR (i.e., Western Caucasian) and OR (i.e., East Asian) faces created by averaging two face identities. As compared to the controls, PS showed impaired face race discrimination abilities and disrupted SRRA for the most difficult morph level. Our results suggest that while an intact left FFA and/or right OFA are not critical for observing other-race effects, they are required to perform fine-grained race and identity discrimination. These findings refine the knowledge of the functional role of these cortical regions and provide novel insights into the mechanisms related to the neural processing of race in the brain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".