Adaptation and stress independently influence the emotional categorization of facial expressions
Bibliographic record
Abstract
Introduction: Visual after-effects, in which repeated exposure to one category of visual stimulus tunes perception such that an ambiguous stimulus looks more like the opposite category, have been observed for facial emotion. For example, ambiguous facial expressions are categorized as more positive with repeated exposure to unambiguously angry facial expressions. While previous research has documented effects of stress on visual processing of facial emotion, the influence of stress on emotion adaptation effects is not known. Thus, the goal of the present study was to examine effects of acute stress on shifts in biases in categorization of facial expressions as happy or angry elicited by visual adaptation. Methods: 226 healthy young adults were assigned to either a stress condition (n=111), in which a socially evaluated cold-pressor test was administered, or a matched control condition (n=115). In a bias-probe task presented before and after adaptation, faces morphed to create a continuum of 15 frames ranging from unambiguously angry to unambiguously happy were presented in random order. Participants were asked to make forced-choice judgments of whether each facial expression presented was happy or angry. To generate adaptation effects we employed a 2-back task, in which participants were presented with a series of unambiguously angry faces and asked to indicate whether each face had appeared two frames earlier. Manipulation checks were also conducted. Results: A repeated measures ANOVA revealed a shift towards categorizing a higher proportion of faces as happy post-adaptation. Although there was an overall lower tendency to categorize unambiguously angry faces as angry under stress, there was no effect of stress on adaptation effects. Conclusion: This study provides further evidence that interpretation of facial expressions can be manipulated using adaptation. The presence of acute stress may not have a significant influence on changes in patterns of categorization bias with adaptation. Meeting abstract presented at VSS 2017
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".