Effect of speech-related smile suppression on emotional valence
Bibliographic record
Abstract
The physical act of smiling has direct positive effects on mood [Kleinke et al., Pers. Soc. Psychol. 74, 272–279 (1998)]. Relatedly, Rummer et al. [Emotion, 14(2), 246–250 (2014)] observed that participants rated comics as funnier if they had just produced /i/ (which requires adopting a smile-like position) than if they produced /o/. The present study tests whether suppression of smile posture by speech movements can cause individuals to view a subject less positively. To do so, we ask participants to maintain a smile while we present a series of visual stimuli labeled with target and control sounds. Bilabial sounds are targeted as Liu et al. found that bilabial stops (/p/ and /b/) suppress smile posture [ISSP12, 130–133 (2021)]. After articulating a sound that either suppresses or does not suppress their smile posture, participants rate each image set on a measure of emotion. Results will be presented and discussed bearing on the prediction that in the smile condition, participants will rate the image as less positive if they have just produced a sentence which includes a bilabial stop. [Work supported by NIH and NSERC.]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".