Is it in the eyes? Dissociating the role of emotion and perceptual features of emotionally expressive faces in modulating orienting to eye gaze
Bibliographic record
Abstract
This study investigated the role of the eye region of emotional facial expressions in modulating gaze orienting effects. Eye widening is characteristic of fearful and surprised expressions and may significantly increase the salience of perceived gaze direction. This perceptual bias rather than the emotional valence of certain expressions may drive enhanced gaze orienting effects. In a series of three experiments involving low anxiety participants, different emotional expressions were tested using a gaze-cueing paradigm. Fearful and surprised expressions enhanced the gaze orienting effect compared with happy or angry expressions. Presenting only the eye regions as cueing stimuli eliminated this effect whereas inversion globally reduced it. Both inversion and the use of eyes only attenuated the emotional valence of stimuli without affecting the perceptual salience of the eyes. The findings thus suggest that low-level stimulus features alone are not sufficient to drive gaze orienting modulations by emotion. Rather, they interact with the emotional valence of the expression that appears critical. The study supports the view that rapid processing of fearful and surprised emotional expressions can potentiate orienting to another person's averted gaze in non-anxious people.
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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) | 0.001 | 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".