Reading the Mind in the Eyes Test Scores Demonstrate Poor Structural Properties in Nine Large Non-Clinical Samples
Bibliographic record
Abstract
The Reading the Mind in the Eyes Test (RMET) is widely used in clinical and non-clinical research. However, the structural properties of RMET scores have yet to be rigorously examined. We analyzed the structural properties of RMET scores in nine existing datasets comprising non-clinical samples ranging from 558 to 9,267 (median = 1,112) participants each. We used confirmatory factor analysis to assess two theoretically derived factor models, exploratory factor analysis to identify possible alternative factor models, and reliability estimates to assess internal consistency. Neither of the theoretically derived models was a good fit for any of the nine datasets, and we were unable to identify any better fitting multidimensional models. Internal consistency metrics were acceptable in six of the nine datasets, but these metrics are difficult to interpret given the uncertain factor structures. Our findings contribute to a growing body of evidence questioning the reliability and validity of RMET scores.
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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 | 0.001 | 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".