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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".