沿着积极分裂型 - 自闭症特征连续体进行异常预测学习:来自模两可的社会信息处理的证据
Zi-Han Zhang1, Ke-Xin Hu1, Yi-Chen Shi1
1School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
具有较高的阳性精神分裂特征的个体更多地依赖于先前的信念,表现出不太灵活的学习. 自闭症特征在这个社会预测任务中没有表现出一致的效果.
科学领域:
- 认知神经科学
- 计算精神病学
- 社会认知
背景情况:
- 社会信息处理缺陷在精神分裂症谱系障碍 (SSD) 和自闭症谱系障碍 (ASD) 中很常见.
- 预测编码理论建议不同的机制:过度依赖SSD中的先验与ASD中的先验形成/使用的困难.
- 在ASD-SSD频谱中对社会预测学习的研究有限.
研究的目的:
- 在ASD-SSD连续上研究社会预测学习的行为标记.
- 检查先前的期望如何影响对模两可的社会信息的看法.
- 在积极的精神分裂和自闭症特征中区分预测编码机制.
主要方法:
- 对121名健康参与者 (年龄在17-25岁) 进行了一项新的提示结果关联式学习任务.
- 参与者完成了自我报告措施:分裂性人格问卷 (SPQ),社区心理经验评估 (CAPE) 和自闭症谱系指数 (AQ).
- 使用计算模型 (等级高斯波,HGF) 和相关分析.
主要成果:
- 较高的阳性精神分裂特征与对先前信念的依赖度增加相关.
- 增加的阳性精神分裂特征与更新预测错误的灵活性降低有关.
- 对自闭症特征的预期反向关联并未始终被观察到.
结论:
- 这些发现支持精神分裂症的超前假设,表明积极精神分裂症的异常预测机制.
- 预测编码框架可能有助于区分SSD和ASD的社会认知困难.
- 这项研究有助于开发针对社会认知缺陷的有针对性的干预措施.
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