Religious-Contingent Aftereffects for Christian and Muslim Faces
Bibliographic record
Abstract
Introduction. Face recognition has been attributed to norm-based coding: individual faces are perceived based on their resemblance to a face prototype (Valentine, 1991). These face prototypes are malleable, updating based on changes in faces viewed. Adults have multiple face prototypes based on social categories such as race and gender (Jaquet, Rhodes, & Hayward 2008; Little, DeBruine, & Jones, 2005). Here we test whether faces depicting members of different religions (Christian and Muslim) are perceived using distinct face-templates using an opposing aftereffect paradigm. Methods.120 undergraduates participated, 60 of which were the control condition. During pre-adaptation, participants viewed 48 face pairs of the same model, one compressed by 10% and one expanded by 10% and selected which face they found more attractive. An audio clip introduced the face model with a name associated with one of the two religions. During adaptation, participants viewed Muslim and Christian faces expanded or contracted by 60%, with faces from each religious category altered in the opposite direction. An audio clip labelled the religious identity. During post-adaptation, participants again viewed 48 face pairs, selecting the most attractive. The control participants completed the same procedure with religiously neutral audio clips. Results. There was a significant difference in change scores when viewing Christian versus Muslim faces (t=2.30, p=.041). In addition, significantly more contracted Christian faces were selected as more attractive than expanded Christians faces in post adaptation (t=2.982, p=.018), consistent with adaptation. Though the scores for contracted Muslim faces selected did not significantly differ (t=−1.059, p=.320), the change was in the expected direction, consistent with adaptation. Preliminary results for the experimental condition reveal evidence of opposing aftereffects approaching. There were no significant differences in change scores in the control condition. Evidence of opposing aftereffects implies separate face templates for different categories of religion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".