Smile variation leaks personality and increases the accuracy of interpersonal judgments
Bibliographic record
Abstract
Abstract People ubiquitously smile during brief interactions and first encounters, and when posing for photos used for virtual dating, social networking, and professional profiles. Yet not all smiles are the same: subtle individual differences emerge in how people display this nonverbal facial expression. We hypothesized that idiosyncrasies in people's smiles can reveal aspects of their personality and guide the personality judgments made by observers, thus enabling a smiling face to serve as a valuable tool in making more precise inferences about an individual's personality. Study 1 (N = 303) supported the hypothesis that smile variation reveals personality, and identified the facial-muscle activations responsible for this leakage. Study 2 (N = 987) found that observers use the subtle distinctions in smiles to guide their personality judgments, consequently forming slightly more accurate judgments of smiling faces than neutral ones. Smiles thus encode traces of personality traits, which perceivers utilize as valid cues of those traits.
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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 | 0.009 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".