Processes Underlying the Cross-Race Effect: An Investigation of Holistic, Featural, and Relational Processing of Own-Race versus Other-Race Faces
Bibliographic record
Abstract
Adults are often better at recognising own-race than other-race faces. Unlike previous studies that reported an own-race advantage after administering a single test of either holistic processing or of featural and relational processing, we used a cross-over design and multiple tasks to assess differential processing of faces from a familiar race versus a less familiar race. Caucasian and Chinese adults performed four tasks, each with Caucasian and Chinese faces. Two tasks measured holistic processing: the composite face task and the part/whole task. Both tasks indicated holistic processing of own-race and other-race faces that did not differ in degree. Two tasks measured featural and relational processing: the Jane/Ling task, in which same/ different judgments were made about face pairs that differed in features of their spacing, and the scrambled/blurred task, in which test faces were scrambled (isolates memory for components) or blurred (isolates memory for relations). Both tasks provided evidence of an own-race advantage in both featural and relational processing. We conclude that even when adults process other-race faces holistically, other manifestations of an own-race advantage remain.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".