The Headscarf Effect Revisited: Further Evidence for a Culture-Based Internal Face Processing Advantage
Bibliographic record
Abstract
Encoding the internal features of unfamiliar faces poses a perceptual challenge that occasionally results in face recognition errors. Extensive experience with faces framed by a headscarf may, however, enhance perceivers' ability to process internal facial information. To examine this claim empirically, participants in the United Arab Emirates and the United States of America completed a standard part-whole face recognition task. Accuracy on the task was examined using a 2 (perceiver culture: Emirati vs American) x 2 (face race: Arab vs white) x 2 (probe type: part vs whole) x 3 (probe feature: eyes vs nose vs mouth) mixed-measures analysis of variance. As predicted, Emiratis outperformed Americans on the administered task. Although their recognition advantage occurred regardless of probe type, it was most pronounced for Arab faces and for trials that captured the processing of nose or mouth information. The findings demonstrate that culture-based experiences hone perceivers' face processing skills.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".