Attentional Biases and Recognition Accuracy: What Happens When Multiple Own- and Other-Race Faces are Encountered Simultaneously?
Bibliographic record
Abstract
Adults recognize own-race faces more accurately than other-race faces. We investigated three characteristics of laboratory investigations hypothesized to minimize the magnitude of the own-race recognition advantage (ORA): lack of competition for attention and instructions that emphasize individuating faces during the study phase, and a lack of uncertainty during the test phase. Across two experiments, participants studied faces individually, in arrays comprising multiple faces and household objects, or in naturalistic scenes (presented on an eye-tracker); they were instructed to remember everything, memorize faces, or form impressions ofpeople. They then completed one of two recognition tasks--an old/new recognition task or a lineup recognition task. Task instructions influenced time spent looking at faces but not the allocation of attention to own- versus other-race faces. The magnitude of the ORA was independent of both task instructions and test protocol, with some modulation by how faces were presented in the study phase. We discuss these results in light of current theories of the ORA.
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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 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".