Cross-cultural differences in self-reported and behavioural emotional self-awareness between Japan and the UK
Bibliographic record
Abstract
OBJECTIVE: How we express and describe emotion is shaped by sociocultural norms. These sociocultural norms may also affect emotional self-awareness, i.e., how we identify and make sense of our own emotions. Previous studies have found lower emotional self-awareness in East Asian compared to Western samples using self-report measures. However, studies using behavioural methods did not provide clear evidence of reduced emotional self-awareness in East Asian groups. This may be due to different measurement tools capturing different facets of emotional self-awareness. RESULTS: To investigate this issue further, we compared the emotional self-awareness of Japanese (n = 29) and United Kingdom (UK) (n = 43) adults using the self-report Toronto Alexithymia Scale (TAS-20), alongside two behavioural measures - the Emotional Consistency Task (EC-Task) and the Photo Emotion Differentiation Task (PED-Task). Japanese adults showed higher TAS-20 scores than UK participants, indicating greater self-reported difficulties with emotional self-awareness. Japanese participants also had lower EC-Task scores than UK adults, indicating a lower ability to differentiate between levels of emotional intensity. PED-Task performance did not show clear group differences. These findings suggest that cross-cultural differences in emotional self-awareness vary with the task used, because different tasks assess distinct aspects of this ability. Future research should attempt to capture these different aspects of emotional self-awareness.
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.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".