Judging the perceptual similarity of own- and other-race faces
Bibliographic record
Abstract
Most people are experts at recognizing faces, however, research has shown that we are less expert at recognizing faces from other races - the Other-Race Effect (ORE). An influential framework to account for the ORE is the face space model where faces are represented as points in a multi-dimensional similarity space with each dimension signifying a specific facial feature or attribute. According to this approach, own-race faces are perceived as more distinct and therefore, their representational points are more spatially separated in face space. In contrast, other-race faces are perceived as more similar and therefore, their points are more densely clustered in face space. To examine the face space representations of participants for own- and other-race faces, we employed PsiZ, a novel method for obtaining psychological embeddings–rich multi-dimensional representations of psychological similarity spaces that are inferred from behavioural similarity judgements. We predicted that the psychological embeddings will be more differentiated for own-race faces and more densely packed arrangements of other-race faces. For this study, we recruited 60 African, 60 Caucasian and 60 Chinese online participants who made similarity judgments to blocks of 20 African, 20 Caucsian and 20 Chinese faces. Our main results indicated that there was limited evidence to support the face space account of the ORE. Inspection of the psychological embeddings by race of the participant and face showed that own-race faces were not more differentiated in face space than other-race faces. However, upon examining faces by different racial groups, distinct patterns emerged. African participants exhibited an ORE for Caucasian faces, while Caucasian participants demonstrated an ORE for Chinese faces. Notably, Chinese participants did not display a discernible ORE, indicating variability in cross-race recognition effects among the studied groups. Participant similarity judgements were moderately correlated with simulated judgements based on VGG-16 perceptual features, with some differences by race.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".