Using a Virtual Serious Game (Deusto-e-motion1.0) to Assess the Theory of Mind in Primary School Children: Observational Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Given the interactive media characteristics and intrinsically motivating appeal, virtual serious games are often praised for their potential for assessment and treatment. OBJECTIVE: This study aims to validate and develop normative data for a virtual serious game (Deusto-e-motion1.0) for the evaluation of emotional facial expression recognition and social skills, both of which are components of the theory of mind. METHODS: A total of 1236 children took part in the study. The children were classified by age (8-12 years old), gender (males=639, females=597), and educational level (between the third and sixth years of Primary Education). A total of 10 schools from the Basque Country and 20 trained evaluators participated in this study. RESULTS: Differences were found in Deusto-e-motion1.0 scores between groups of children depending on age and gender. Moreover, there was a moderately significant correlation between the emotional recognition scores of Deusto-e-motion1.0 and those of the Feel facial recognition test. CONCLUSIONS: Deusto-e-motion1.0 shows concurrent validity with instruments that assess emotional recognition. Results support the adequacy of Deusto-e-motion1.0 in assessing components of the theory of mind in children.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".