The flip side of the other‐race coin: They all look <i>different</i> to me
Bibliographic record
Abstract
Poorer recognition of other-race faces than own-races faces has been attributed to a problem of discrimination (i.e., telling faces apart). The conclusion that 'they all look the same to me' is based on studies measuring the perception/memory of highly controlled stimuli, typically involving only one or two images of each identity. We hypothesized that such studies underestimate the challenge involved in recognizing other-race faces because in the real world, an individual's appearance varies in a number of ways (e.g., lighting, expression, hairstyle), reducing the utility of relying on pictorial cues to identity. In two experiments, Caucasian and East Asian participants completed a perceptual sorting task in which they were asked to sort 40 photographs of two unfamiliar identities into piles such that each pile contained all photographs of a single identity. Participants perceived more identities when sorting other-race faces than own-race faces, both when sorting celebrity (Experiment 1) and non-celebrity (Experiment 2) faces, suggesting that in the real world, 'they all look different to me'. We discuss these results in the light of models in which each identity is represented as a region in a multidimensional face space; we argue that this region is smaller for other-race than own-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 | 0.004 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".