An examination of own- and other-race face recognition in adults
Bibliographic record
Abstract
One of the most well known biases in face perception is the other-race effect – the tendency for observers to show more accurate recognition for own-race compared to other-race faces. Past studies have largely examined face perception using highly standardized photographs in artificial lab contexts, limiting the conclusions we can make about human face recognition. Thus, the purpose of this dissertation was to understand own- and other-race face perception using more naturalistic approaches. In the first manuscript, identity discrimination was impaired across several measures of between- and within-person variability. Both East Asian and White adults performed poorer when perceiving face pairs that were judged as highly similar compared to highly dissimilar. Identity perception was also impaired when the face pair belonged to a different race (e.g., other-race) compared to the same race as perceivers. In the second manuscript, electrophysiological recordings revealed neural identity discrimination responses were impaired for own-race similar compared to dissimilar looking faces. The pattern was the same for other-race face discrimination. Finally, in the third manuscript, change detection was lower when a social partner belonged to a minority race compared to the majority race (i.e., White) only in a less diverse city but not a highly diverse one (Study 3a). Change detection was also lower when social partners belonged to a religious minority and a racial minority (Study 3b). There was no evidence that change detection was better for own-race compared to other-race individuals across both Studies 3a and b. Taken together, the studies in this dissertation sought to understand how the ORE manifests in contexts that more closely approximate face recognition as it occurs in real life.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".