超越奖励学习缺陷:探索-利用不稳定性揭示了早期精神病中基于价值的决策中的计算异质性
Cathy S Chen1,2, Evan Knep2, Veldon-James Laurie3
1Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, United States.
早期精神病 (EP) 涉及由于增加探索而导致决策能力受损,而不仅仅是学习缺陷. 贝叶斯模型揭示了与不确定性不容忍和决策噪音相关的亚型,为干预提供了目标.
科学领域:
- 神经科学是一个神经科学.
- 计算精神病学是一种计算精神病学.
- 认知心理学 认知心理学
背景情况:
- 精神病谱的疾病呈现出目标导向行为障碍和显著的神经生理学异质性.
- 了解这种异质性的神经计算基础对于开发有针对性的干预措施至关重要.
研究的目的:
- 研究早期精神病 (EP) 决策异质性的神经计算基础.
- 根据决策动态,在EP中识别不同的计算子类型.
主要方法:
- 75名参与者与EP和68名控制完成了动态决策任务.
- 贝叶斯模型被用来分析选择切换,奖励学习,过渡动态,不确定性不容忍和决策噪音.
主要成果:
- 欧洲议会的参与者表现出更多的选择转换,由从剥削转向勘探所驱动,独立于奖励学习缺陷.
- 贝叶斯模型确定了高不确定性不容忍度和决策噪音作为不足最佳决策的关键因素.
- 确定了三种不同的计算亚型:一个高决策噪音亚型 (学习缺陷,负面症状),一个规范亚型 (情绪症状) 和一个新的不确定性不容忍亚型 (更高的住院率).
结论:
- 精神病谱系疾病的异质性可以通过在不确定性不容忍和决策噪音中明显的微观认知障碍来解释.
- 这些计算子类型可以单独测量,并有可能指导有针对性的治疗干预.
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