Comparative Analysis of Social Cognitive and Neurocognitive Performance Across Autism and Schizophrenia Spectrum Disorders
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Social cognitive and neurocognitive performance is impacted in autism and schizophrenia spectrum disorders (SSDs). Here, we compared social cognitive and neurocognitive performance across a large transdiagnostic sample of participants with autism, SSDs, and typically developing controls (TDCs). STUDY DESIGN: Participants (total N = 584; autism N = 100, SSDs N = 275, TDCs N = 209; aged 16-55 years; 61% male assigned at birth) completed lower-level (eg, emotion processing) and higher-level (eg, theory of mind) social cognitive tasks, the MATRICS Consensus Cognitive Battery, and a measure of social functioning. Nonparametric groupwise comparisons were undertaken, adjusting for age and sex, and within-group correlations were used to examine associations between social cognition, neurocognition, and social functioning. STUDY RESULTS: Autistic and SSD groups performed worse than TDCs on lower- and higher-level social cognitive tasks, with few autism-SSD differences found. Autism and SSDs had lower neurocognitive scores than TDCs; SSDs demonstrated lower processing speed, working memory, verbal learning, and visual learning versus autism. Positive associations between social cognitive tasks and neurocognition were observed across groups, and self-reported measures of empathy were consistently correlated with social functioning. CONCLUSIONS: This study represents the largest transdiagnostic comparison of both social cognition and neurocognition in an autism/SSD sample reported to date. Autistic participants and those with SSDs showed similar performance on lower- and higher-level social cognitive tasks relative to controls, while neurocognition was less impacted in autism versus SSDs. These findings underscore the importance of transdiagnostic research into the mechanisms underlying social cognitive deficits and highlight the potential for developing transdiagnostic interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".