Emotional modulation of gaze-cuing proceeds in absence of lower face information
Bibliographic record
Abstract
Human attention is spontaneously oriented in the direction of eye gaze. Such gaze following behavior is more pronounced if the person shifts their gaze and reacts emotionally with a facial expression, which is thought to be adaptive for facilitating orienting towards environmentally important events. It remains unknown, however, how the emotional expression on the face is processed along with eye-gaze information to produce the gaze following enhancement. Here we investigated this question by presenting participants with faces that averted their gaze and then either reacted emotionally or remained neutral. They responded to peripheral targets appearing in gaze-congruent or gaze-incongruent locations. Critically, half of the faces were unoccluded, while the other half had their lower part occluded by a surgical mask. Experiment 1 (N=74) presented fear, happy, and neutral expressions. Experiment 2 (N=77) presented disgust, surprise, and neutral expressions (pre-registered: https://osf.io/8uzgf). Thus, facial expressions varied both in eye size (i.e., smaller for disgust, larger for surprise) and in whether their most emotionally diagnostic facial features were visible (e.g. wide eyes for fear remaining unoccluded, wrinkled nose for disgust occluded). In both experiments, participants were overall significantly faster to locate gaze-congruent compared to gaze-incongruent targets, demonstrating classic gaze following behavior. This effect was larger when the faces displayed emotional expressions (fearful and happy vs. neutral in Experiment 1; disgusted and surprised vs. neutral in Experiment 2), but critically did not significantly vary with face occlusion condition. This shows that the information from the eye-region alone appears to contain enough emotional information within the face to drive the emotional enhancement of gaze following, highlighting the powerful nature of the eyes in emotional perception.
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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 | 0.002 |
| 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".