Religious labels and food preferences, but not country of origin, support opposing face aftereffects
Bibliographic record
Abstract
Face templates can be experimentally manipulated, and category-contingent aftereffects suggest discrete templates across social groups. We tested whether 1) explicit religious labels, 2) food preferences, and 3) country of origin would support religion-contingent aftereffects across Christians and Muslims face sets. While viewing face images, ninety-three participants heard audio that stated either 1) a character's religious identity, 2) preferred food, or 3) country of origin. Participants viewed contracted Christian faces and expanded Muslim faces during the training phase. To measure adaptation, before and after the training phases, participants selected the face out of a pair of expanded and contracted Christian or Muslim faces that they found more attractive. Contingent aftereffects were found in the religious explicit ( t (30) = 2.49, p = 0.02, Cohen's d = 0.58) and food conditions ( t (30) = −3.77, p < 0.01, Cohen's d = −0.82), but not the country condition ( t (30) = 1.64, p = 0.11, Cohen's d = 0.31). This suggests that religious labels and food preferences create socially meaningful groups, but country of origin does not. This is evidence of an impact of social categorization on visual processing. • Replicated Foglia et al. (2021) who found that religious labels support category-contingent aftereffects. • Social information delivered via the auditory domain affects the categorization of faces. • Food preferences support the formation of aftereffects as reliably as religious labels do. • Country of origin does not support the formation of aftereffects. • Religious labels and food preferences effectively cue social category membership.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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 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".