The use of aftereffects in the study of relationships among emotion categories.
Bibliographic record
Abstract
The perception of visual aftereffects has been long recognized, and these aftereffects reveal a relationship between perceptual categories. Thus, emotional expression aftereffects can be used to map the categorical relationships among emotion percepts. One might expect a symmetric relationship among categories, but an evolutionary, functional perspective predicts an asymmetrical relationship. In a series of 7 experiments, the authors tested these predictions. Participants fixated on a facial expression, then briefly viewed a neutral expression, then reported the apparent facial expression of the 2nd image. Experiment 1 revealed that happy and sad are opposites of one another; each evokes the other as an aftereffect. The 2nd and 3rd experiments reveal that fixating on any negative emotions yields an aftereffect perceived as happy, whereas fixating on a happy face results in the perception of a sad aftereffect. This suggests an asymmetric relationship among categories. Experiments 4-7 explored the mechanism driving this effect. The evolutionary and functional explanations for the category asymmetry are discussed.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".