Perception of emotional tears with body postures, visual scenes, and written scenarios
Bibliographic record
Abstract
Emotional tears tend to increase perceived sadness in facial expressions. However, it is unclear whether tears would still be seen as an indicator of sadness when a tearful face is observed in an emotional context (e.g., a touching moment during a wedding ceremony). We examine the influence of context on the sadness enhancement effect of tears in three studies. In Study 1, participants evaluated tearful or tearless expressions presented without body postures, with emotionally neutral postures, or with emotionally congruent postures (i.e., postures indicating the same emotion as the face). The results show that the presence of tears increases the perceived sadness of faces regardless of context. Similar results are found in Studies 2 and 3, which used visual scenes and written scenarios as contexts, respectively. Our findings demonstrate that tears on faces reliably indicate sadness, even in the presence of contextual information that suggests non‐sadness emotions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".