The eye of the beholder: Only White observers have an attentional bias toward Latino and South Asian faces than Own-race Faces
Bibliographic record
Abstract
The psychological grouping of individuals by gender, ethnicity or religion influences attentional allocation. Attentional bias—prioritizing attention toward certain stimuli—has been shown to favor specific ethnic groups. However, prior research has predominantly focused on binary comparisons (e.g., Black vs. White faces) with primarily White participants, limiting our understanding of attentional biases across multicultural participant samples and stimulus sets. This study examines attentional biases among a racially diverse sample (N = 104) in response to faces from five ethnic groups: White, Black, East Asian, South Asian, and Latino, four faces each. Methods: Participants completed a dot-probe task, in which they first viewed a fixation cross on the screen for 500 ms, followed by the presentation of two face images, one on the left and one on the right side of the screen, for another 500 ms. These images displayed models' heads to shoulders and were positioned 6.5 degrees apart horizontally. After the facial images disappeared, a dot appeared at the location where the center of one of the two images had been. Participants were instructed to locate the dot. Reaction times (RTs) and accuracy were recorded. Results showed no overall attentional bias toward faces from minority racial groups across the full sample. However, when analyzed by participant ethnicity, White participants displayed a significant attentional bias, showing faster RTs to Latino and South Asian faces and a trend toward faster responses to Black faces compared to own-race faces. East Asian participants exhibited balanced RTs across ethnicities, with a slight preference for own-race faces, while South Asian participants showed a clear bias for own-race faces, responding faster to South Asian faces than to some outgroup faces. Conclusion: These findings show that attentional allocation varies significantly by observer ethnicity, contributing to a more nuanced understanding of attentional biases across racial and ethnic groups.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".