An explicit religious label impacts visual adaptation to Christian and Muslim faces
Bibliographic record
Abstract
Opposing aftereffects can be induced across two sets of face categories. The current literature suggests that in order to create opposing aftereffects, the two categories must (1) be perceptually distinct and (2) represent distinct meaningfully social categories. The current study was designed to test whether religion is one of the types of social categories that can support the formation of opposing aftereffects. Experiment 1 reports the creation and validation of a Christian and Muslim face set, demonstrating that the religious membership of the face images is visually identifiable. In experiment 2 we attempted to create opposing aftereffects by having adult participants fixate on Christian and Muslim faces that were expanded and contracted. Participants either heard religious membership explicit or control audio recordings. Opposing aftereffects were observed only when Christian and Muslim faces were explicitly labeled. In experiment 3, eight-year-olds were adapted to a similar paradigm, with explicit religious information provided. Opposing aftereffects were not observed. Results of these experiments suggest that for adults, religion might be the kind of meaningful social category required for the formation of opposing aftereffects, but only if religious category membership is made explicit. Eight-year-old children's understanding of religious categories may still be developing.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".