An other-race effect for configural and featural processing of faces: upper and lower face regions play different roles
Bibliographic record
Abstract
We examined whether Asian individuals would show differential sensitivity to configural vs. featural changes to own- and other-race faces and whether such sensitivity would depend on whether the changes occurred in the upper vs. lower regions of the faces. We systematically varied the size of key facial features (eyes and mouth) of own-race Asian faces and other-race Caucasian faces, and the configuration (spacing) between the eyes and between the nose and mouth of the two types of faces. Results revealed that the other-race effect (ORE) is more pronounced when featural and configural spacing changes are in the upper region than in the lower region of the face. These findings reveal that information from the upper vs. lower region of the face contributes differentially to the ORE in face processing, and that processing of face race is influenced more by information location (i.e., upper vs. lower) than by information type (i.e., configural vs. featural).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".