Opposing Aftereffects across a Caucasian Male face set and a face set that was diverse by gender and ethnicity
Bibliographic record
Abstract
Introduction. Opposing aftereffects have been seen across groups that differ by sex and race. For example, viewing Caucasian expanded faces and Chinese contracted faces lead to opposite face aftereffects when participants subsequently viewed each set. Here we tested whether viewing Caucasian male faces and a diverse set of faces (male, female; Caucasian, Asian, Latino, African American) during adaptation would result in opposing aftereffects. Methods. 62 participants underwent an opposing aftereffects paradigm viewing a set of Caucasian male faces and a diverse face set. 31 participants viewed a contracted set of diverse faces and an expanded set of Caucasian male faces, and 31 viewed the opposite. In pre-adaptation trials, participants viewed 64 face pairs, one contracted and one expanded by 10%, selecting which they found more attractive. During adaptation, participants viewed faces altered by 60%. Post-adaptation trials were identical to pre-adaptation trials. Results. Using a mean change score (contracted faces selected before vs after adaptation) as the dependent variable, there was a significant interaction between the two conditions (F(1,25)=7.360, p=<.05). Aftereffects were then assessed for each condition separately. For the contracted Caucasian male and expanded diverse condition, significant opposing aftereffects were observed, with a greater change in preference for the contracted diverse faces than the expanded Caucasian male faces (t(12)=-4.770, p= <0.001), consistent with adaptation. Though the scores for contracted Caucasian male and expanded diverse faces did not differ significantly (t(13)=.901, p= .384), the change was in the expected direction. These results are the first to suggest that it is possible to induce opposing aftereffects across a diverse and a homogeneous set of faces. Past research has found opposing aftereffects using face sets that are homogeneous within and distinct across face sets. These results suggest the potential of face templates that are sensitive to variance.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".