Development and validation of the Korean version of the Reading the Mind in the Eyes Test
Bibliographic record
Abstract
The Reading the Mind in the Eyes Test (RMET) is one of the most widely used instruments for assessing the ability to recognize emotion. To examine the psychometric properties of the Korean version of the RMET and to explore the possible implications of poor performance on this task, 200 adults aged 19-32 years completed the RMET and the Korean version of the 20-item Toronto Alexithymia Scale (TAS-20K), the cognitive empathy domain of the Korean version of the Interpersonal Reactivity Index (IRI-C), and the Buss-Durkee Hostility Inventory-Aggression (BDHI-A). In the present study, confirmatory factor analyses confirmed that the hypothesized three-factor solution based on three different emotional valences of the items (positive, negative, or neutral) had a good fit to the data. The Korean version of the RMET also showed good test-retest reliability over a 4-week time interval. Convergent validity was also supported by significant correlations with subscales of the TAS-20K, and discriminant validity was identified by nonsignificant associations with IRI-C scores. In addition, no difference was found in RMET performance according to the sex of the photographed individuals or the sex or educational attainment of the participants. Individuals with poor RMET performance were more likely to experience alexithymia and aggression. The current findings will facilitate not only future research on emotion processing but also the assessment of conditions related to the decreased ability to decode emotional stimuli.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".