Racial ambiguity impairs holistic face processing: Evidence from racially distinctive and racially ambiguous faces
Bibliographic record
Abstract
Humans possess a remarkable ability to efficiently process faces, a skill largely influenced by their experiences with individual faces. However, recent research has challenged the role of experience in holistic face processing. Our study examined the role of face-race experience in holistic face processing among Asian and White adults using Asian, White, and racially ambiguous faces. The findings showed that both Asians and Whites exhibited reliable holistic processing for their own-race faces but failed to exhibit this ability with racially ambiguous faces. Importantly, the failure was not solely due to the specific morphing procedure or response bias. These findings imply that face-race experience plays a crucial role in holistic processing. Notably, Asians maintained holistic processing for both own-race and other-race faces, whereas Whites only showed this for own-race faces, indicating differential impacts of face-race experience on holistic processing and highlight the need for further research across diverse cultural contexts.HighlightsAsian adults showed equivalent holistic processing with Asian and Caucasian facesCaucasian adults showed holistic processing with Caucasian faces but not with Asian facesBoth Asian and Caucasian adults did not show holistic processing with racially ambiguous faces
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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 | 0.001 |
| 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 | 0.001 |
| 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".