Subjective emotional experience mediates cross-cultural differences in emotion perception
Bibliographic record
Abstract
Abstract Understanding the cross-cultural differences in emotion perception has captured the interest of researchers for decades. While various mechanisms have emerged to explain such differences, the general inclination to differentiate emotions, irrespective of whether they relate to others or oneself, has remained unclear. To investigate this overarching propensity, we selected the Toronto Alexithymia Scale (TAS-20) as a suitable instrument for measuring the extent to which individuals typically recognize and describe their own emotional experience. Here we examined the mediating role of self-emotional experience in the context of cross-cultural differences in emotion perception. To make the test more representative of real-life situations, we employed an emotion perception task that simulates naturalistic and complex emotional scenarios. The findings revealed that Caucasian Australian students scored higher than Asian Australian students in the emotion perception task and were more inclined to report subjective emotional experience. Notably, the differences in emotion perception between the two cultural groups were fully accounted for by scores on the TAS-20. This suggests that these distinctions were linked to the general tendency to differentiate between subjective emotions, rather than stimulus-related factors, such as the ethnicity of actors or actresses. Furthermore, we examined whether the propensity of distinguishing between emotion concepts could provide insight into the cross-cultural differences in this general emotion perception tendency. However, no significant correlations were found between conceptual differentiation and either TAS-20 or emotion perception. These null results underscore the importance of contextual settings in emotion studies.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".