An Encoding Advantage for Own-Race versus Other-Race Faces
Bibliographic record
Abstract
Studies have shown that individuals are better able to recognise the faces of people from their own race than the faces of people from other races. Although the so-called own-race effect has been generally regarded as an advantage in recognition memory, differences in the processing of the own-race versus other-race faces might also be found at the earlier stages of perceptual encoding. In this study, the perceptual basis of the own-race effect was investigated by generating a continuum of images by morphing an East Asian parent face with a Caucasian parent face. In a same/different discrimination task, East Asian and Caucasian participants judged whether the morph faces were physically identical to, or different from, their parent faces. The results revealed a significant race-of-participant by race-of-face interaction such that East Asian participants were better able to discriminate East Asian faces, whereas Caucasian participants were better able to discriminate Caucasian faces. These results indicate that an own-race advantage occurs at the encoding stage of face processing.
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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 | 0.000 | 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".