Identifying Other-Race Faces: It’s Less in the Eyes.
Bibliographic record
Abstract
Most studies in face recognition have focused on how individuals identify faces within their own ethnic group, highlighting the crucial role of the eye region in face identification (Vinette et al., 2004; Butler et al., 2010; Royer et al., 2018). Nevertheless, the general population encounters difficulties in identifying individuals from a different ethnicity, a phenomenon known as the “other-race effect” (ORE; Meissner & Brigham, 2001). Despite decades of investigation, the perceptual mechanisms associated with ORE remain inadequately understood. It is plausible that different perceptual strategies are employed in identifying other-race faces. In this study, 21 White participants initially learned to identify 8 Black and 8 White faces. Subsequently, they were tasked to recognize these same faces presented through small Gaussian apertures (“Bubbles”; Gosselin & Schyns, 2002). We also measured the extent of the ORE using an old/new recognition task. Collectively, participants exhibited an ORE, evidenced by a higher d' with own-race faces (μd=0.26, σd=0.27, t(20)=4.32, p<.001, d=0.94). Regarding the Bubbles results, Pixel Tests (p<.05; Stat4Ci Toolbox; Chauvin et al., 2005) revealed a significant reliance on the eyes and the mouth for faces of both race. Crucially, comparison between classification images of own- and other-race faces unveiled significant differences: greater eye reliance (z-score difference of 12.52) for own-race faces and increased nose and mouth reliance (z-score differences of -5.58 and -5.94, respectively) for other-race faces. We hypothesize that, at least as a group effect, the ORE may arise from diminished eye reliance and an excessive dependence on facial features associated with ethnic information (Levin, 1996). Additional participants, including a more diverse sample (e.g., African participants), are currently undergoing testing to explore individual and cultural differences in perceptual strategies employed to recognize own- and other-race 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".