Value computations underpin flexible emotion expression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it