Exploring Visual Strategies and their Electrophysiological Correlates in Same and Other-Race Face Processing
Bibliographic record
Abstract
In the realm of face perception, it has been suggested that faces belonging to one's own race are processed differently than those of other races, leading to superior recognition of same-race faces (Meissner & Brigham, 2001; Malpass & Kravitz, 1969). This phenomenon, known as the Other-Race Effect (ORE), has been extensively examined, notably through eye-tracking studies that have shown that White individuals allocate less attention to the eyes of Black faces compared to White faces (e.g. Kawakami et al., 2014). To better understand this bias, we first asked 15 White participants to complete a face memory task, following an old/new paradigm with both Black and White faces. Replicating the ORE (i.e. better accuracy (d’) in memorizing white (M= 1.59, SD = .70) than black faces (M= .75, SD = .33): t(14) = 7.02, p < .001, Cohen’s d = 1.8, 95% CI [0.59, 1.1]), participants then completed two other tasks (gender and smile/neutrality discrimination) while their EEG signals were recorded (for a total of 6000 trials/participant). In each trial, distinct parts of Black and White faces were revealed using the bubbles method (Gosselin & Schyns, 2001). Multiple linear regression analyses using a Pixel Test (Stat4Ci Toolbox; Chauvin et al., 2005) on EEG amplitudes at specific electrodes of interest (e.g., PO8, PO7) revealed strong associations with the eye region within the N170 time window, regardless of the task or the race of the faces. These findings suggest that same and other-race faces undergo similar processing during the early stages of face perception, with differences likely emerging later in the face identification stream.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".