在社会评估过程中跳到归因.
Isabel H W Lau1,2,3, Jessica Norman3, Melanie Stothard4
1MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
Scientific reports
|July 4, 2024
概括
这项研究揭示了人们如何快速进行社会评估,发现"分类-精制"模型比旧方法更好地解释了这些过程. 这对理解焦虑和抑郁有意义.
科学领域:
- 认知科学 认知科学
- 计算精神病学是一种计算精神病学.
背景情况:
- 社会学习和自我/其他评估对于人际关系和福祉至关重要.
- 这些过程与精神疾病有关,特别是焦虑和抑郁.
研究的目的:
- 在自我和他人评估中研究社会学习的计算机制.
- 开发和测试用于连续社会评估的新型计算模型.
主要方法:
- 利用修订后的社会评估学习任务 (SELT-R),旨在吸引青少年参与和适应.
- 将"分类-精制"计算模型与使用130名英国成年人的数据建立的贝叶斯模型进行了比较.
- 最适合模型与焦虑症状得分的相关参数.
主要成果:
- 与贝叶斯模型相比",分类-精制"模型在解释社会评估过程方面表现优越.
- 这些模型提供了关于个人如何推断他人的属性和动机的见解.
- 更高的焦虑症状与决策噪音增加和政策确定性更少的灵活性相关.
结论:
- "分类-精制"方法为理解社会评估提供了更准确的计算框架.
- 研究结果表明,与决策噪音和焦虑中的政策确定性相关的特定认知机制.
- 这项研究为未来对内化障碍中的社会认知研究奠定了基础.
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