The other-race effect of face processing: Upper and lower parts play different roles
Bibliographic record
Abstract
We investigated whether individuals would show differential sensitivity to configural and featural changes in different regions of own- and other-race faces. Using the Face Dimensions Test, we systematically varied the size of key facial features (eyes and mouth) of own-race Chinese faces and other-race Caucasian faces, and the configuration (spacing) between eyes and between eyes and mouth of the two types of faces. The feature size and spacing between features were manipulated on three levels: small, medium, and large. On each trial, when a pair of faces (both Caucasian or both Chinese) was presented side by side, participants were asked to discriminate between them. Results revealed that the ORE is more pronounced when featural and spacing change was in the upper region than in the lower region of the face. Participants performed significantly better for own-race face pairs than other-race face pairs when size and spacing change were in upper region of the faces (i.e., eye size and the distance between two eyes), but not when size and spacing change were in lower region of the faces (i.e., mouth size and the distance between nose and mouth). These findings reveal that information from different regions of the face contributes differentially to the robust difference in recognition between own- and other-race faces. Meeting abstract presented at VSS 2014
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".