Arousal and valence have dissociable effects on responses to schematic emotional faces.
Bibliographic record
Abstract
Studies examining behavioural responses to emotional stimuli usually report one of two patterns of responses to negative stimuli. Some studies find faster responses to negative material. Other studies find slower responses to negative stimuli. While the attentional mechanisms proposed to explain these findings (attentional capture in the former case, delayed disengagement in the latter) are not at odds with one another, the behavioural findings do need to be reconciled. We posit that arousal, being the primary differentiator of threatening and nonthreatening stimuli, needs to be more carefully considered. To this end, two experiments were conducted evaluating the role of stimulus arousal and valence in the processing of schematic emotional faces. In Experiment 1, stimulus arousal was manipulated via the presence or absence of eyebrows in the schematic faces in a faces flanker task. Results showed faster responses to faces with eyebrows but no differences in the faces flanker asymmetry between faces with and without eyebrows. In Experiment 2, participants rated the faces on an evaluative space grid. Results showed the presence of the eyebrows had a greater impact on negative ratings for negative faces than for the other expressions. This suggests that stimulus valence and arousal were manipulated by the eyebrows and the reaction time differences could not be attributed purely to perceptual differences. Together these results suggests that both valence and arousal impact the processing of emotional schematic faces, and that these effects are dissociable. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".