Is Social Categorization Alone Sufficient to Induce Opposing Face Aftereffects?
Bibliographic record
Abstract
Adults encode individual faces in reference to a distinct face prototype that represents the average of all faces ever encountered. The prototype is not a static abstracted norm but rather a malleable face average that is continuously updated by experience (Valentine, 1991); for example, after prolonged viewing of faces with compressed features, adults rate similarly distorted faces as more normal and more attractive (simple attractiveness aftereffects). Recent studies have shown that adults possess category-specific face prototypes (e.g., based on race, sex). After viewing faces from two categories (e.g., Caucasian/Chinese) that are distorted in opposite directions, adults' attractiveness ratings shift in opposite directions (opposing aftereffects). Recent research has suggested that physical differences between face categories are not sufficient to elicit opposing aftereffects and that distinct social categories are necessary (Bestelmeyer et al., 2008). For example, opposing aftereffects emerge when participants adapt to faces from two distinct sex categories (female and male) but not when participants adapt to faces from within the same sex category (female and hyper-female). The present set of experiments was designed to investigate whether social categorical distinctions in the absence of salient physical differences are sufficient to induce opposing aftereffects. In each experiment, physical appearance was held constant (all Caucasian female faces) while social categorical information differed (university affiliation in Experiments 1 and 2 and personality type in Experiment 3), such that half the faces purportedly belonged to participants' in-group while half the faces belonged to their out-group. Across all three experiments, there was no evidence for opposing aftereffects, despite the fact that participants showed better recognition memory for in-group faces than for out-group faces (Experiment 3). These results suggest that both physical differences and a social categorical distinction are necessary in order to elicit category-contingent opposing face aftereffects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".