Computational models of subjective feelings in psychiatry
Computational models of subjective feelings in psychiatry
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DOI：
10.1016/j.neubiorev.2022.105008
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发表时间：
2023-01-06
影响因子：
8.2
通讯作者：
Rutledge, Robb B.
中科院分区：
文献类型：
--
作者：
Kao, Chang-Hao;Feng, Gloria W.;Rutledge, Robb B.
计算精神病学的研究主要是行为模型。行为任务中的主观体验还没有得到很好的理解，即使它应该与理解精神疾病的症状有关。在这里，我们弥合这一差距，并审查主观感受的计算模型的最新进展。例如，幸福感反映的不是人们做得有多好，而是他们是否比预期做得更好。尽管抑郁症状会降低任务期间的幸福感，但这种对最近奖励预测错误的依赖在重度抑郁症中是完整的。不确定性预示着在动荡的环境中主观的压力感受。社会预测错误影响的自我价值感更低的自尊的个人，尽管减少愿意改变信仰，由于社会反馈。在行为任务中测量情感状态为理解与行为无关的精神症状提供了一种工具。当纵向收集智能手机任务时，主观感受提供了一种潜在的手段来弥合基于实验室的行为任务与现实生活中的行为、情感和精神症状之间的差距。
Research in computational psychiatry is dominated by models of behavior. Subjective experience during behavioral tasks is not well understood, even though it should be relevant to understanding the symptoms of psychiatric disorders. Here, we bridge this gap and review recent progress in computational models for subjective feelings. For example, happiness reflects not how well people are doing, but whether they are doing better than expected. This dependence on recent reward prediction errors is intact in major depression, although depressive symptoms lower happiness during tasks. Uncertainty predicts subjective feelings of stress in volatile environments. Social prediction errors influence feelings of self-worth more in individuals with low self-esteem despite a reduced willingness to change beliefs due to social feedback. Measuring affective state during behavioral tasks provides a tool for understanding psychiatric symptoms that can be dissociable from behavior. When smartphone tasks are collected longitudinally, subjective feelings provide a potential means to bridge the gap between labbased behavioral tasks and real-life behavior, emotion, and psychiatric symptoms.