Attentional Mechanisms Shape the Recognition of Own- and Other-Race Faces
Bibliographic record
Abstract
The other-race effect refers to a recognition disadvantage for other-race compared to own-race faces. Although perceptual and social factors are well-studied, attentional mechanisms are often overlooked. Recent findings using behavioral measures (Duncan et al., VSS 2022) reveal that own-race faces, unlike other-race faces, are recognized automatically. To pinpoint this effect at the electrophysiological level, the exact same dual-task paradigm was used while recording EEG. Twenty-nine White participants completed a dual-task. In each block, they memorized a pair of White (own-race) or East-Asian (other-race) faces. They then performed a tone (Target 1; T1) categorization task followed by a delayed face (Target 2; T2) recognition task whereby they were asked whether T2 more closely matched the left or right face of the memory set. T1 and T2 presentations were separated by a stimulus onset asynchrony (SOA: 150, 300, 600, 1,200ms) to control potential conflict for central attention resources. T2 difficulty was varied by presenting either full signal faces (i.e., 0% Identity 1 - 100% Identity 2), or morphed faces (i.e., 60% Identity 1 - 40% Identity 2).Behavioral results confirmed previous findings (Duncan et al., VSS 2022), showing greater automatization for own-race versus other-race face recognition. For own-race faces, task difficulty effects diminished at shorter SOAs, suggesting cognitive slack absorption and perceptual handling of difficulty. Conversely, for other-race faces, difficulty effects remained consistent across SOAs, indicating post-perceptual processing. All electrophysiological components showed attentional modulation, with the P300 component revealing a marginal interaction between attentional modulation and race. Specifically, the P300 component, reflecting the allocation of limited-capacity attentional resources, showed higher amplitudes for own-race faces under shorter SOA conditions. These findings suggest that the P300 could be a potential locus for the behavioral effect, i.e., the automatization of own-race face recognition.
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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".