They all look different to me: Within-person variability affects identity perception for other-race faces more than own-race faces
Bibliographic record
Abstract
People are worse at recognizing other-race faces than own-race faces. This other-race effect (ORE) has been attributed to worse discrimination of other-race faces, as reflected in the phrase "they all look the same to me". A neglected challenge in face recognition has been the ability to recognize a face's identity across superficial changes (e.g., expression, hairstyle). Indeed, even for own-race faces, photos of the same person can be perceived as belonging to different individuals, unless that person is familiar (Jenkins et al., 2011). We investigated how within-person variability affects our perception of identity for own and other-race faces. Caucasians (n=49) were given 40 photographs of two unfamiliar people (20 photographs/model) and asked to sort them into piles such that each pile had all of the pictures of one person. The photos were either of own-race (UK celebrities) or other-race (Chinese celebrities) faces. Participants had more difficulty discriminating other-race faces; more participants put two different people into the same pile for other-race (92%) than own-race (63%) faces. Notably, participants sorted the photographs into significantly more identities for other-race (M = 10.96; range = 4 to 31) than for own-race faces (M = 4.79; range = 2 to 16: Cohen's d =1.18). It is unlikely that the smaller number of piles for own-race faces reflects less variability among the Caucasian photographs. In an ongoing study, Chinese participants (to date, n = 6) sorted the Caucasian photographs into an average of 15 identities (range = 8 to 20). These findings suggest that studies in which the same image of a face is used for presentation and test may under-estimate the challenge of recognizing other-race faces. In the real world it may be the case that 'they all look different to me'. Meeting abstract presented at VSS 2014
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".