Social cognition has become a major focus in psychosis research aimed at explaining heterogeneity in functional outcome and developing interventions oriented to functional recovery. However, there is still no consensus on the structure of social cognition in psychosis, and research in this area has been plagued by lack of replication. Our first goal was to replicate the factor structure of social cognition using nearly identical tasks in independent samples. Our second goal was to externally validate the factors as they relate to nonsocial cognition and various symptoms in the prediction of functioning using machine learning. Confirmatory factor analyses validated a three-factor model for social cognition in psychosis (low-level, high-level, attributional bias factor). A least absolute shrinkage and selection operator regression and cross-validation provided evidence for external validity of data-driven linear models including the social-cognitive factors, nonsocial cognition, and symptoms. We addressed the replicability problems that have impeded research in this area, and our results will guide future psychosis studies.

中文翻译：

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精神分裂症社会认知的因素结构：通过确认性因素分析和机器学习关注复制

社会认知已成为精神病研究的主要焦点，旨在解释功能结果的异质性并开发面向功能恢复的干预措施。然而，目前关于精神病的社会认知结构还没有达成共识，这方面的研究一直缺乏可复制性。我们的第一个目标是在独立样本中使用几乎相同的任务复制社会认知的因素结构。我们的第二个目标是从外部验证这些因素，因为它们与非社会认知和使用机器学习预测功能的各种症状有关。验证性因素分析验证了精神病社会认知的三因素模型（低水平、高水平、归因偏差因素）。最小绝对收缩和选择算子回归和交叉验证为数据驱动的线性模型（包括社会认知因素、非社会认知和症状）的外部有效性提供了证据。我们解决了阻碍该领域研究的可复制性问题，我们的结果将指导未来的精神病研究。