Emotions facilitate the communication of ambiguous group memberships.
Bibliographic record
Abstract
It is well known that emotions intersect with obvious social categories (e.g., race), influencing both how targets are categorized and the emotions that are read from their faces. Here, we examined the influence of emotional expression on the perception of less obvious group memberships for which, in the absence of obvious and stable physical markers, emotion may serve as a major avenue for group categorization and identification. Specifically, we examined whether emotions are embedded in the mental representations of sexual orientation and political affiliation, and whether people may use emotional expressions to communicate these group memberships to others. Using reverse correlation methods, we found that mental representations of gay and liberal faces were characterized by more positive facial expressions than mental representations of straight and conservative faces (Study 1). Furthermore, participants were evaluated as expressing more positive emotions when enacting self-defined "gay" and "liberal" versus "straight" and "conservative" facial expressions in the lab (Study 2). In addition, neutral faces morphed with happiness were perceived as more gay than when morphed with anger, and when compared to unmorphed controls (Study 3). Finally, we found that affect facilitated perceptions of sexual orientation and political affiliation in naturalistic settings (Study 4). Together, these studies suggest that emotion is a defining characteristic of person construal that people tend to use both when signaling their group memberships and when receiving those signals to categorize others.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".