Emotional Facial Expression Detection in the Peripheral Visual Field
Bibliographic record
Abstract
BACKGROUND: In everyday life, signals of danger, such as aversive facial expressions, usually appear in the peripheral visual field. Although facial expression processing in central vision has been extensively studied, this processing in peripheral vision has been poorly studied. METHODOLOGY/PRINCIPAL FINDINGS: Using behavioral measures, we explored the human ability to detect fear and disgust vs. neutral expressions and compared it to the ability to discriminate between genders at eccentricities up to 40°. Responses were faster for the detection of emotion compared to gender. Emotion was detected from fearful faces up to 40° of eccentricity. CONCLUSIONS: Our results demonstrate the human ability to detect facial expressions presented in the far periphery up to 40° of eccentricity. The increasing advantage of emotion compared to gender processing with increasing eccentricity might reflect a major implication of the magnocellular visual pathway in facial expression processing. This advantage may suggest that emotion detection, relative to gender identification, is less impacted by visual acuity and within-face crowding in the periphery. These results are consistent with specific and automatic processing of danger-related information, which may drive attention to those messages and allow for a fast behavioral reaction.
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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 | 0.000 | 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) | 0.002 | 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 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".