Improving Theory of Mind in Schizophrenia by Targeting Cognition and Metacognition with Computerized Cognitive Remediation: A Multiple Case Study
Bibliographic record
Abstract
Schizophrenia is associated with deficits in theory of mind (ToM) (i.e., the ability to infer the mental states of others) and cognition. Associations have often been reported between cognition and ToM, and ToM mediates the relationship between impaired cognition and impaired functioning in schizophrenia. Given that cognitive deficits could act as a limiting factor for ToM, this study investigated whether a cognitive remediation therapy (CRT) that targets nonsocial cognition and metacognition could improve ToM in schizophrenia. Four men with schizophrenia received CRT. Assessments of ToM, cognition, and metacognition were conducted at baseline and posttreatment as well as three months and 1 year later. Two patients reached a significant improvement in ToM immediately after treatment whereas at three months after treatment all four cases reached a significant improvement, which was maintained through 1 year after treatment for all three cases that remained in the study. Improvements in ToM were accompanied by significant improvements in the most severely impaired cognitive functions at baseline or by improvements in metacognition. This study establishes that a CRT program that does not explicitly target social abilities can improve ToM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 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 | 0.000 |
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".