EEG assessment of the impacts of race and implicit bias on facial expression processing
Bibliographic record
Abstract
Apparent race of a face impacts processing efficiency, typically leading to an own-race advantage. For instance, own-race facial expressions are more accurately recognized, and their intensity better appraised, compared to other-race faces. Furthermore, these effects appear susceptible to implicit bias. Here, we aimed to better understand impacts of race and implicit racial bias on facial expression processing by looking at automatic and nonautomatic expression processing stages. To this end, scalp electroencephalography was recorded off a group of White participants while they completed a psychological refractory period dual-task paradigm in which they viewed neutral or fearful White (i.e. own-race) and Black (i.e. other-race) faces. Results showed that, irrespective of race, early perceptual expression processing indexed by the N170 event-related potential was independent of central attention resources and racial attitudes. On the other hand, later emotional content evaluation indexed by the late positive potential (LPP) was dependent on central resources. Furthermore, negative attitudes toward Black individuals amplified LPP emotional response to White (vs. Black) faces irrespective of central attention resources. Thus, it seems it is racial bias, more than race per se, that impacts facial expression processing, but this effect only manifests itself during later semantic processing of facial expression content.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".