Electrophysiological evidence that own-race faces are recognized more automatically
Bibliographic record
Abstract
One of the more robust findings in the face processing literature is a recognition disadvantage for other-race faces. Despite the existence of many perceptual and sociocognitive models concerning other-race effects, the contribution of attentional mechanisms remains poorly understood. It was recently proposed that the own-race advantage might arise because of own-race faces being recognized more automatically at a perceptual level, whereas other-race face recognition requires more input from higher level attention-gated processing resources (Duncan et al., 2022 VSS Talk). The present study aims to explore electrophysiological correlates of these results. Scalp electroencephalography of twenty White participants was measured while they performed 960 trials each of a difficult dual task, which involved sequentially categorizing an auditory target (T1) and recognizing either a White (own-race) or East Asian (other-race) face (T2). Stimulus onset asynchrony (SOA) between T1 and T2 onsets (150, 300, 600 or 1,200ms) was used to modulate task overlap and thus, attentional impact on T2 processing. Behavioral results showed increased automatization of own-race vs. other-race face recognition, replicating our previous results. N170 and N250 event-related potentials, respectively reflective of early face-specific perceptual processing and activation of stored face representations, showed enhanced amplitudes for other- vs. own-race faces. N200 and P300 components, reflective of attentional processes, showed enhanced amplitudes for own- vs. other-race faces. Increasing task overlap (i.e., by reducing SOA) led to a reduction in N170 and N250 amplitudes for both own- and other-race faces, but an amplification of N200 and P300 amplitudes. Interestingly, this effect only interacted with the race of faces for N250 amplitude, such that increasing overlap amplified the observed difference between other- and own-race faces. Taken together, these findings reinforce the notion that, compared to other-race faces, recognition of own-race faces is more automatic: It is carried more efficiently, and with fewer processing resources.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".