The honesty behind tears: Situational, individual, and cultural influences on the perception of emotional tears as sincere
Bibliographic record
Abstract
Emotional tears have been considered honest and sincere signals, most likely because they are difficult to shed on demand. At the same time, people acknowledge that tears can be strategically used to manipulate others - so-called crocodile tears. Hence, the question arises under what circumstances tears are perceived as honest signals and when as crocodile tears. Here, we investigate this question across three experimental studies and diverse populations. In a preliminary study (N = 7,007), we obtain the first evidence that emotional tears can increase perceptions of honesty, which might vary according to the situational context or the gender of the target. In two main studies (N = 3,488) using a varied pool of standardized and non-standardized portraits of tearful and non-tearful targets presented in different potentially manipulative vs. non-manipulative contexts and varied in their warmth, we test perceptions of honesty across five countries (Norway, Poland, South Africa, Canada, and the UK). Overall, the main effects are weak and suggest that perceptions of honesty depend on target characteristics, situational factors, and observer characteristics. We observe some evidence that emotional tears increase perceptions of honesty more strongly for targets low in warmth (experimentally manipulated via facial features or via target gender), which also affects support intentions. Manipulative contexts slightly reduced perceptions of honesty, but these effects were moderated by target characteristics. Individuals scoring high on psychopathy showed lower ratings of honesty for targets with emotional tears. Together, these findings provide further evidence that whether emotional tears signal honesty likely depends on various individual, situational, and cultural factors. The small effect sizes call for improved manipulations and more ecologically valid designs in the future.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".