Increasing attentional competition and uncertainty: An ecological approach to the cross-race effect
Bibliographic record
Abstract
The other-race effect (ORE: better recognition of own-race faces) is typically studied by presenting faces sequentially to participants and testing recognition using an old/new task. In the real world, people encounter multiple faces simultaneously and in complex scenes and so other-race faces compete for attention. Preferential attention to own-race faces on a daily basis and increased uncertainty when recognizing others in the real world may magnify the ORE in comparison to the relatively small effects typically found in the lab. We compared own- versus other-race face recognition under two different study and test conditions (n = 20 Caucasian participants per group). Participants studied 32 faces (16 Chinese; 16 Caucasian) sequentially (2s per face) or in arrays (shown for 24s) comprising eight faces (four Chinese) and multiple household objects. Recognition was tested using an old/new sequential-presentation task or a "lineup" task in which the proportion of old versus new faces varied across trials. We hypothesized accuracy would be higher in the traditional task but that the ORE would be larger in the array task. The d’ values were higher for own-race faces than other-race faces (p<.001). The magnitude of the ORE varied as a function of learning style (p=.03) but not testing style (p=.72). Surprisingly, accuracy was higher when faces were presented sequentially than in arrays and this effect was larger for own-race faces. Increasing presentation times of the arrays (40s; n = 20) did not alter this pattern of results. Our results suggest that even own-race faces are hard to recognize when they compete for attention with other stimuli (Mean d’ = .67) and that perceptual expertise for own-race faces may be most evident under ideal (i.e., sequential) viewing conditions. Meeting abstract presented at VSS 2013
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".