Reading the Mind in the Eyes of Children Test (RME-C-T): Development and Validation of a Complex Emotion Recognition Test
Bibliographic record
Abstract
Much research has been devoted to the development of emotion recognition tests that can be used to investigate how individuals identify and discriminate emotional expressions of other individuals. One of the most prominent emotion recognition tests, is the Reading the Mind in the Eyes Test (RME-T). The original RME-T has been widely used to investigate how individuals recognize complex emotional expressions from the eye region of adult faces. However, the RME-T can only be used to investigate inter-individual differences in complex emotion recognition during the processing of adult faces. To extend its usefulness, we developed a modified version of the RME-T, the Reading the Mind in the Eyes of Children Test (RME-C-T). The RME-C-T can be used to investigate how individuals recognize complex emotional expressions from the eye region of child faces. However, the validity of the RME-C-T has not been evaluated yet. We, thus, administered the RME-C-T together with the RME-T to a sample of healthy adult participants (n = 119). The Interpersonal Reactivity Index (IRI) and the Toronto Alexithymia Scale (TAS) were also administered. Participants’ RME-C-T performance correlated with participants’ RME-T performance, implying that the RME-C-T measures similar emotion recognition abilities as the RME-T. Participants’ RME-C-T performance also correlated with participants’ IRI and TAS scores, indicating that these emotion recognition abilities are affected by empathetic and alexithymic traits. Moreover, participants’ RME-C-T performance differed between participants with high and low TAS scores, suggesting that the RME-C-T is sensitive enough to detect impairments in these emotion recognition abilities. The RME-C-T, thus, turned out to be a valid measure of inter-individual differences in complex emotion recognition during the processing of child faces.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".