Cultural shaping of emotion differentiation: Socially engaging and disengaging emotions.
Bibliographic record
Abstract
There is a growing consensus that emotion differentiation-the ability to discern specific emotions-is healthy. To assess this ability, studies so far have exclusively relied on the dimension of emotion pleasantness by lumping together various types of emotions that fall within the same valence category. However, this approach neglects the possibility that individuals may represent certain types of emotions in a more differentiated fashion, if these emotions are functionally adaptive and therefore are more frequently experienced in their cultural environments. Here, we propose social orientation as another dimension to analyze emotion differentiation and test a hypothesis that the ability to differentiate socially engaging (vs. disengaging) emotions is reinforced more and is associated with better health in interdependent (vs. independent) cultural contexts. In a longitudinal daily diary study conducted in the United States and Korea between 2019 and 2020, we assessed the extent to which participants differentiated engaging or disengaging emotions based on 2 weeks of daily affective reports. For both positive and negative emotions, Koreans differentiated engaging emotions more than European Americans did. Conversely, European Americans differentiated disengaging emotions more than Koreans did. Moreover, for both cultural groups, the extent to which they differentiated emotions that are valued more in their respective culture-engaging for Koreans and disengaging for European Americans-predicted better health 2 months later, indirectly via reducing their tendency to ruminate over time. These results suggest that culture shapes how we represent emotions, and doing so in a culturally preferred way has a potential to bring health benefits. (PsycInfo Database Record (c) 2026 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 | 0.000 | 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".