The subjective value of a smile alters social behaviour
Bibliographic record
Abstract
Face-to-face social behaviour is difficult to explain, leading some researchers to call it the "dark matter" of psychology/neuroscience [1]. We apply an idea from neuroeconomics to this problem, suggesting that how people subjectively value facial expressions should predict usage differences during unconstrained interaction. Specifically, we ask whether the subjective value of smiles is malleable as a consequence of immediate social experience and how this relates to smiling during face-to-face interactions. We measured the value of a smile in monetary terms and found that increases in people's social neediness caused devaluation of polite smiles but no changes in how they valued genuine smiles. This result predicts that participants induced to feel high levels of social need should be less responsive to their social partners' polite smiles in a subsequent unconstrained social interaction. As expected, high social-need participants returned fewer polite smiles when interacting with a partner, leading to poor interaction outcomes. Genuine smile reciprocity remained unchanged. Findings show that social states influence real-world interactions by changing social-cue valuation, highlighting a potential mechanism for understanding the moment-to-moment control of social behaviour and how behaviour changes based on people's subjective evaluations of the social environment.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".