An empirical evaluation of the reliability and validity of the "reading the mind in the eyes" test
Bibliographic record
Abstract
The Reading the Mind in the Eyes test (RMET) is a widely used measure of theory of mind (ToM) ability that was originally designed to detect ToM deficits in autistic adults and validated based on the performance of autistic individuals. Despite its popularity, there are questions regarding the test’s factor structure, whether it taps mental state reasoning components of ToM or simply emotion recognition ability, and its validity for use in non-autistic populations. In the current study, a US representative sample of 1,181 adults completed the RMET, the Toronto Alexithymia Scale, and the Autism Spectrum Quotient. Exploratory factor analysis (EFA) on the full sample and separate EFA on individuals with high and low levels of autistic traits provided evidence for a three-factor model and two overlapping, but distinct, three-factor models for individuals with high versus low levels of autistic traits. However, the RMET had poor psychometric properties for all three groups. Hierarchical regression analysis and structural equation modelling suggested that levels of alexithymia traits and autistic traits each predict performance on the RMET. I conclude that the lack of strong psychometric properties for the RMET, evidence of variation in performance across samples, and the absence of theoretical explanations for how the test captures ToM ability undermine the validity of the RMET. I argue that until these issues are satisfactorily addressed, researchers should not use the RMET as a measure of social cognition.
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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 | 0.021 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".