Perception of identity: Robust representation of familiar other-race faces despite natural variation in appearance
Bibliographic record
Abstract
The other-race effect (better recognition of own- compared to other-race faces) has been framed as a problem with discriminating among other-race identities. Another impairment in recognizing other-race faces is the ability to recognize the same identity across a set of images that incorporate natural variability in appearance (e.g., changes in expression, lighting conditions, head orientation), known as within-person variability. We recently reported that within-person variability affects identity perception more for unfamiliar other-race faces than unfamiliar own-race faces (Zhou, Laurence & Mondloch, 2014). In the current study we examined how within-person variability affects identity perception in familiar other-race faces (i.e., whether participants would mistake two images of the same other-race person as belonging to different people even when viewing photographs of familiar identities). Chinese participants (n=100) were given 40 images of two identities (20 images/model) and asked to sort them into piles according to identity such that each pile had all images of the same person. The two identities belonged to one of four categories: familiar own-race, familiar other-race, unfamiliar own-race, or unfamiliar other-race. There was a significant interaction between familiarity and race of faces, p = .007. When faces were unfamiliar, participants sorted photos into significantly more piles (i.e., perceived more identities) for other-race faces (M = 11.56) than for own-race faces (M = 7.28), p = .006. This own-race advantage was eliminated when the identities were familiar (Mean piles = 2.12 and 2.24 for own- and other-race faces respectively). We are currently replicating this finding by testing Caucasian participants (n=60) with Caucasian and African American faces that are either familiar (NBA players) or unfamiliar (College basketball players). Our study adds new evidence of a fundamental difference between familiar versus unfamiliar face recognition; the other-race effect is limited to unfamiliar faces. Meeting abstract presented at VSS 2015
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".