Theory of Mind in Children with Traumatic Brain Injury
Bibliographic record
Abstract
Theory of mind (ToM) involves thinking about mental states and intentions to understand what other people know and to predict how they will act. We studied ToM in children with traumatic brain injury (TBI) and age- and gender-matched children with orthopedic injuries (OI), using a new three-frame Jack and Jill cartoon task that measures intentional thinking separate from contingent task demands. In the key ToM trials, which required intentional thinking, Jack switched a black ball from one hat to another of a different color, but Jill did not witness the switch; in the otherwise identical non-ToM trials, the switch was witnessed. Overall accuracy was higher in children with OI than in those with TBI. Children with severe TBI showed a larger decline in accuracy on ToM trials, suggesting a specific deficit in ToM among children with severe TBI. Accuracy was significantly higher on trials following errors than on trials following correct responses, suggesting that all groups monitored performance and responded to errors with increased vigilance. TBI is associated with poorer intentional processing in school-age children and adolescents relative to peers with OI; furthermore, children with TBI are challenged specifically by intentional demands, especially when their injury is severe. (JINS, 2012, 19, 1-9).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".