Value computations underpin flexible emotion expression
Bibliographic record
Abstract
Emotion expressions constitute a vital channel for communication, coordination and connection with others, but despite such valuable functions, people sometimes engage in expressive suppression or substitution (expressing emotions they do not genuinely feel). Yet, how exactly do people decide when and what to express? To answer this question, we developed a computational model that casts emotion expressions as value-based communicative decisions. Our model reveals that while people (N = 254) indeed tended to suppress expressions of anger towards others in anticipation of potential social costs as past work theorizes, they also engaged in other nuanced forms of expressive regulation, especially when their reputation was at stake. Most strikingly, people selectively exaggerated/suppressed expressions of happiness when others made more/less equitable choices, seemingly to communicate stronger normative preferences for fairness than they privately held. Together, these findings yield insights into how people regulate their emotion expressions, providing a mechanistic and unified account of the different expressive behaviors people flexibly engage in to navigate their complex social interactions with others. People do not always express the emotions they feel truthfully. Computational modelling reveals that people flexibly regulate their emotion expressions by balancing their value as a communicative signal against the potential social costs they incur.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".