Opposing aftereffects between a White male face set and a diverse face set
Bibliographic record
Abstract
Opposing aftereffects have been observed for faces categorized by gender, race, and age. In order to form opposing aftereffects, it appears that the two face sets must be both physically distinct and differ in terms of social meaning. The current study tests whether (1) a face set that is diverse with respect to sex and race can produce a coherent aftereffect and (2) whether this diversity itself is socially meaningful enough to support opposing aftereffects. Participants adapted to a homogenous face set consisting of only White male Republican congressmen and a diverse face set consisting of White, Asian, Black, and Latino male and female Democratic congress members. Opposing aftereffects were observed: participants adapted simultaneously and in opposite directions to the face sets. These results are the first evidence of adaptation to a face set that varies based on race and sex, and the first evidence of diversity being perceived as a socially meaningful category marker.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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 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".