Are Emotion and Mind Understanding Differently Linked to Young Children's Social Adjustment? Relationships Between Behavioral Consequences of Emotions, False Belief, and SCBE
Bibliographic record
Abstract
According to empirical findings, emotional knowledge and false belief understanding seem to be differently linked to social adjustment. However, whereas false belief is assessed through the capacity to identify its behavioral consequences, emotion tasks usually rely on the comprehension of facial expressions and of the situational causes of emotions. The authors examined if the documented relationship between social adjustment and emotion knowledge in children extends to the understanding of behavioral consequences of emotions. Eighty French-speaking preschoolers undertook false belief and consequence-of-emotion tasks. Their social adjustment was measured by the Social Competence and Behavior Evaluation. Children's language ability, their parent's level of education, and the familial socioeconomic score were taken into account. Results showed that children's social adjustment was significantly predicted by their knowledge of emotion, but not by their understanding of false belief. The findings confirm the special status of emotion among mental states for social adaptation and specify which dimensions of adaptation to peers and adults are predicted by the child's emotion understanding. They also suggest that the distinction between mind and emotion understanding may be conceptual rather than methodological.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".