Contextual cues about reciprocity impact ratings of smile sincerity
Bibliographic record
Abstract
Research has shown that context influences how sincere a smile appears to observers. That said, most studies on this topic have focused exclusively on situational cues (e.g. smiling while at a party versus smiling during a job interview) and few have examined other elements of context. One important element concerns any knowledge an observer might have about the smiler as an individual (e.g. their habitual behaviours, traits or attitudes). In this manuscript, we present three experiments that explored the influence of such knowledge on ratings of smile sincerity. In Experiments 1 and 2, participants rated the sincerity of Duchenne and non-Duchenne smiles after having been exposed to cues about the smiler's tendency to reciprocate (this person always, never or occasionally returns favours). In Experiment 3 they performed the same task but with cues about the smiler's love of learning (this person always, never or occasionally enjoys learning new tasks). The results show that cues about the smiler's reciprocity tendency influenced participants' ratings of smile sincerity and did so in a stronger manner than cues about the smiler's love of learning. Overall, these results both strengthen and broaden the literature on the role of context on judgements of smile sincerity.
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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 | 0.001 | 0.015 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".