Lower- and Higher-Level Social Cognitive Factors Across Individuals With Schizophrenia Spectrum Disorders and Healthy Controls: Relationship With Neurocognition and Functional Outcome
Bibliographic record
Abstract
BACKGROUND: Schizophrenia spectrum disorders (SSDs) often feature social cognitive deficits. However, little work has focused on the factor structure of social cognition, and results have been inconsistent in schizophrenia. This study aimed to elucidate the factor structure of social cognition across people with SSDs and healthy controls. It was hypothesized that a 2-factor model, including lower-level "simulation" and higher-level "mentalizing" factors, would demonstrate the best fit across participants. METHODS: Participants with SSDs (N = 164) and healthy controls (N = 102) completed social cognitive tasks ranging from emotion recognition to complex mental state inference, as well as clinical and functional outcome, and neurocognitive measures. Structural equation modeling was used to test social cognitive models, models of social cognition and neurocognition, measurement invariance between cases and controls, and relationships with outcome measures. RESULTS: A 2-factor (simulation and mentalizing) model fit the social cognitive data best across participants and showed adequate measurement invariance in both SSD and control groups. Patients showed lower simulation and mentalizing scores than controls, but only mentalizing was significantly associated with negative symptoms and functional outcome. Social cognition also mediated the relationship between neurocognition and both negative symptoms and functional outcome. CONCLUSIONS: These results uniquely indicate that distinct lower- and higher-level aspects of social cognition exist across SSDs and healthy controls. Further, mentalizing may be particularly linked to negative symptoms and functional outcome. This informs future studies of the neural circuitry underlying social cognition and the development of targeted treatment options for improving functional outcome.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".