The detection of fearful and angry expressions in visual search
Bibliographic record
Abstract
Background: The “anger-superiority hypothesis” states an angry facial expression is detected faster than a non-threatening facial expression when either is embedded in a crowd of neutral faces. Presumably, this occurs because an angry face signals impending direct-threat to the observer. However, it is not known whether angry facial expressions are detected more efficiently than other negative expressions that signal danger. Objective: In this study we compared search efficiency for angry and fearful expressions. Methods: Subjects completed a visual search task in which the goal was to detect either an angry or fearful facial expression amid a crowd of neutral expression distractor faces. Crowd size (small vs. large) and view (frontal vs. side) were manipulated. As an angry face signals direct-threat, but a fearful face only signals indirect threat, we expected better search efficiency for angry expressions. Furthermore, we hypothesized angry faces would be more efficiently detected in frontal-view, given that this signals a direct threat from the image to the observer, but that there would be no effect of view on fearful faces. Results: Subjects were faster and more efficient in detecting fearful than angry faces. Target interacted with crowd size: performance declined with increasing crowd size for angry targets, crowd size did not affect performance for fearful targets. We confirmed that angry targets were detected more efficiently in frontal-view, but view had no effect for fearful targets. Conclusions: Contrary to the anger-superiority hypothesis, we found even better performance for fearful faces. This may indicate paradoxically that an indirect threat, possibly shared by the subject and observer, is more salient than a direct-threat. Detection of direct threat is greatest in a view where the threat can be perceived as directed at the observer, but a similar view effect is not seen for the indirect-threat represented by fearful faces. Meeting abstract presented at VSS 2015
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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 | 0.000 | 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) | 0.000 | 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".