Cultural variation in social judgments of smiles: The role of ideal affect.
Bibliographic record
Abstract
While significant research has demonstrated that people's beliefs about a group shape how they judge members of that group, few studies have examined whether people's beliefs and values regarding emotion (their "ideal affect") shape how they socially judge people's emotional facial expressions. We predicted that the more people valued and ideally wanted to feel excitement and other high arousal positive states (HAP), the more affiliative (extraverted, agreeable) they would judge excited (vs. calm) faces. Moreover, because European Americans typically value HAP more than Hong Kong Chinese do, we predicted that European Americans would rate excited (vs. calm) targets as more affiliative than would Hong Kong Chinese. We found consistent support for these hypotheses in four studies. In Studies 1a and 1b, these effects held regardless of target race (White, Asian) and target sex (male, female); emerged for human as well as computer-generated faces; and did not consistently emerge for nonaffiliative social judgments (i.e., dominance, competence). In Studies 2 and 3, we replicated these findings in more realistic contexts. In Study 2, culture and ideal affect predicted participants' extraversion judgments of excited Facebook profiles. In Study 3, culture and ideal affect predicted participants' extraversion and agreeableness judgments of an excited job applicant, which increased their likelihood of hiring that applicant. Together, these findings suggest that people's culture and ideal affect shape how affiliative they judge excited (vs. calm) smiles. We discuss the role these processes may play in perpetuating biases in multicultural settings. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".