Religious labels and food preferences, but not country of origin, support opposing aftereffects on the basis of religion
Bibliographic record
Abstract
Faces are visually represented as mental templates, and people have discrete templates for different social categories such as sex and race. These templates develop as an average all of the faces one has encountered (Leopold et al., 2001; Valentine et al., 2004) and are malleable: they can be updated when an individual views new faces (Hurlbert, 2001). Face templates can be experimentally manipulated to test whether opposing aftereffects can be created, suggesting that the faces of social groups are represented by discrete templates (Little et al., 2005, 2008). The current study used an opposing aftereffects paradigm to determine which cues lead individuals to view Christians and Muslims faces as different social categories. We tested whether 1) explicit religious labels, 2) food preferences, and 3) country of origin would support opposing aftereffects. Ninety-three participants were assigned to 1 of 3 conditions: while viewing face images, they heard audio that either 1) stated the character’s religious identity explicitly, 2) named a preferred food, or 3) named their country of origin. The countries and foods that were used in the audio descriptions were validated, and only those which were strongly associated with Christianity or Islam were chosen. Participants in all 3 conditions viewed 60% contracted Christian faces and 60% expanded Muslim faces during the training phase. Opposing aftereffects were found in the religious explicit (t(54) = 2.27, p = 0.03, Cohen’s d = 0.58) and food audio conditions (t(57) = 3.23, p < 0.01, Cohen’s d = 0.82), but not in the country audio condition (t(57) = 1.21, p = 0.23, Cohen’s d = 0.31). This suggests that explicit religious labels and food preferences create a socially meaningful distinction between religious groups, but country of origin does not. Among other inferences, this is evidence of an impact of social categorization on visual processing.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".