Don’t look at me like that: Integration of gaze direction and facial expression.
Bibliographic record
Abstract
Efficient decoding of facial expressions and gaze direction supports reactions to social environments. Although both cues are processed fast and accurately, when and how these cues are integrated is still debated. We investigated the temporal integration of gaze and emotion cues. Participants responded to letters that were randomly presented on four faces. Two of these faces initially showed direct gaze, two showed averted gaze. Upon target presentation, two faces changed gaze direction (from averted to direct and vice versa). Simultaneously, facial expressions changed from neutral to either an approach- or an avoidance-oriented emotion expression (Experiment 1a: angry/fearful; Experiment 1b: happy/disgusted). Although angry and fearful expressions diminished any effects of gaze direction (Experiment 1a), a direct gaze advantage was found for happy and an averted gaze advantage for disgusted faces (Experiment 1b). This pattern is consistent with hypotheses suggesting a processing benefit when emotion expression and gaze information are congruent in terms of approach- or avoidance-orientation. In Experiment 2, we tracked eye movements and, again, found evidence for an approach-avoidance-congruency advantage for happy and disgusted faces both in performance and gaze behavior. Gaze behavior analyses suggested an integration of gaze and emotion information that was already visible from 300 ms after target onset. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".