Social Categories Alone Are Insufficient to Elicit an In-Group Advantage in Perceptions of Within-Person Variability
Bibliographic record
Abstract
Within-person variability affects identity perception of other-race faces more than own-race faces; when participants sort images into piles representing different identities, they sort photographs of two other-race identities into more piles than two own-race identities. These results have been interpreted in terms of perceptual expertise, such that lack of experience with other-race faces leads to reduced ability to extract identity-relevant information across images. However, an alternative explanation is that sociocognitive factors (e.g., cognitive disregard for out-group faces) lead to differences in the number of perceived identities. Here, we examined whether social factors alone elicit an in-group advantage in perceptions of within-person variability. Caucasian participants sorted 40 photographs of two unfamiliar Caucasian identities (20 photographs/model) into piles based on the number of identities they believed were present. Half of the participants were told that the images were of students attending their university (in-group), whereas half were told that the images were of students attending a rival university (out-group). Participants sorted the photographs into a comparable number of identities for in- and out-group faces. This lack of an in-group advantage suggests that sociocognitive factors alone cannot account for differences in the number of perceived identities across faces from two categories.
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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 | 0.000 | 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 | 0.000 |
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".