The “Reading the Mind in the Eyes” test: Systematic review of psychometric properties and a validation study in Italy
Bibliographic record
Abstract
INTRODUCTION: The "Reading the Mind in the Eyes" test (henceforth, Eyes test) is a simple but advanced Theory of Mind test, and it is widely used across different cultures. This study assessed the reliability and construct (convergent and discriminant) validity of the Eyes test in Italy. METHODS: A sample of 18- to 32-year-old undergraduate students of both sexes (N=200, males=46%) were invited to fill in the Italian version of the Eyes test, the Empathy Quotient (EQ), the Toronto Alexithymia Scale (TAS), and the Marlowe-Crowne Social Desirability Scale (SDS). RESULTS: Internal consistency (Cronbach's alpha) was .605. Confirmatory factor analysis provided evidence for a unidimensional model, with maximal weighted internal consistency reliability=.719. Test-retest reliability for the Eyes test, as measured by intraclass correlation coefficient, was .833 (95% confidence interval=.745 to .902). Females scored significantly higher than males on both the Eyes test and the EQ, replicating earlier work. Those participants who scored lower than 30 on the EQ (n=10) also scored lower on the Eyes test than those who did not (p<.05). Eyes test scores were not related to social desirability. CONCLUSIONS: This study confirms the validity of the Eyes test. Both internal consistency and test-retest stability were good for the Italian version of the Eyes test.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".