Early neural dehumanization of other race faces
Bibliographic record
Abstract
Humans typically recognize racial ingroup faces more efficiently, while paradoxically categorizing outgroup faces more efficiently. These other race effects have been studied using an array of behavioral and electrophysiological tools; however, classical event-related inquiries have yielded mixed results. To shed new light on the issue, we adapted single trial electroencephalography (EEG) decoding to perform representational similarity analysis. Scalp EEG was measured on thirteen White participants who each completed 5,600 trials of 1-back task. Images were briefly presented (200ms), and a button press was required whenever a stimulus was repeated. Stimuli consisted of 160 natural images categorized into dark and light humans (half females, half males) and nonhumans (half primate faces and half chess pieces). In turn, each category consisted of four individuals (e.g., pawn, rook, queen, knight) with five variations (e.g., five different pawns). First, EEG was decoded at each time point (-200ms to 800ms post stimulus) across 12,720 pairs of stimuli using a cross-validated pairwise support vector machine. This step was performed 100 times, randomly assigning trials to learning and testing subsets on each iteration. Then, within-pair average decoding accuracies were assigned to a representational dissimilarity matrix. Finally, representational similarity analysis was carried by comparing the representational dissimilarity matrix to various model matrices using Spearman partial correlation. Overall, neural responses conveyed basic category information about human faces (peak latency 137ms), primate faces (133ms), and chess pieces (133ms). However, a striking contrast emerged between light and dark human faces. Whereas early (160ms) and late (350ms) representations of light human faces were largely distinct from primate faces and chess pieces, early (but not late) representations of dark human faces showed significant overlap with primate faces. Such early neural dehumanization might provide a novel mechanistic account of other race effects.
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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 | 0.002 |
| 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) | 0.003 | 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".