M49. BEHAVIOURAL SOCIAL COGNITION IN SCHIZOPHRENIA SPECTRUM DISORDERS IN COMPARISON TO AUTISM SPECTRUM DISORDER: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Schizophrenia spectrum disorders (SSDs) and autism spectrum disorder (ASD) both feature social cognitive deficits, which are highly debilitating. These include lower-level processes (e.g. emotion recognition), thought to be subserved by a frontoparietal mirroring network, and higher-level mentalizing processes (e.g. theory of mind), involving cortical midline and lateral temporal brain regions. Across both disorders, impairments in social cognition persist over time, drive disability, and predict functional outcome. Overlapping symptoms in SSDs and ASD have long been recognized, particularly in the realm of social deficits. However, despite some studies including both individuals with SSDs and ASD showing similar levels of social cognitive impairment, including lower-level and higher-level deficits, results are mixed. Thus, our objective was to determine based on the extant literature how deficits in social cognition diverge or overlap between individuals with SSDs and ASD by conducting a systematic review and meta-analysis of studies directly comparing these groups on behavioural social cognitive measures. Methods Literature searches were conducted in MEDLINE, Embase, PsycINFO, and Web of Science to identify articles that utilized behavioural measures to assess social cognition in both SSD and ASD samples. Of 3682 articles identified, 28 met all inclusion criteria. Across the accepted articles, lower-level (e.g. facial and/or context-embedded emotion recognition) and higher-level (e.g. intention understanding, perspective taking) social cognitive measures were identified, and random-effects meta-analyses were conducted for each category. A separate meta-analysis was also conducted for the Reading the Mind in the Eyes test given that it was the most commonly used social cognitive metric. Effect sizes were estimated using Hedges’ g. Homogeneity of effects and publication bias were also assessed for each meta-analysis. Results A significant difference in lower-level social cognitive performance was found between individuals with SSDs and ASD, with the SSD group performing better than the ASD group (Hedges’ g = 0.30, 95% CI [0.05, 0.56], p = .018). In contrast, there was no significant difference in higher-level social cognitive performance between SSD and ASD groups (Hedges’ g = -0.14, 95% CI [-0.52, 0.24], p = .46). Similarly, the Reading the Mind in the Eyes test meta-analysis revealed no significant difference in effect sizes between disorders (Hedges’ g = 0.24, 95% CI [-0.07, 0.55], p = .14). Effect size distributions were significantly heterogeneous in all three cases (all p < .001). Discussion Based on meta-analyses of the extant literature, both shared and differential social cognitive deficits may be present between individuals with SSDs and ASD. Though no differences were detected between SSD and ASD groups on higher-level social cognitive tasks or the Reading the Mind in the Eyes test, lower-level social cognitive deficits were found to be more severe in individuals with ASD than SSDs. Notably, the majority of studies included in the meta-analyses had small sample sizes, and heterogeneity of effect sizes was apparent. Thus, studies including larger sample sizes and validated measures of social cognition in conjunction with other methodologies are needed to substantiate these results, and better understand the shared and unique behavioural underpinnings and associated neural circuit abnormalities underlying social cognitive deficits in SSDs and ASD.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".