Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder.
Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder.
复制标题
精神分裂症和边缘性人格障碍中社会学习和决策的异常计算机制。
DOI：
10.1371/journal.pcbi.1008162
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发表时间：
2020-09
影响因子：
4.3
通讯作者：
Mathys C
中科院分区：
文献类型：
--
作者：
Henco L;Diaconescu AO;Lahnakoski JM;Brandi ML;Hörmann S;Hennings J;Hasan A;Papazova I;Strube W;Bolis D;Schilbach L;Mathys C
精神障碍普遍以使人衰弱的社会障碍为特征。这些困难被认为是由异常的社会推理产生的。为了阐明潜在的计算机制，被诊断为重度抑郁症（N = 29），精神分裂症（N = 31）和边缘型人格障碍（N = 31）以及健康对照（N = 34）的患者进行了概率奖励学习任务，参与者可以从社会和非社会信息中学习。精神分裂症和边缘型人格障碍患者在任务中的表现比健康对照组和重度抑郁症患者更差。按领域细分，边缘型人格障碍患者在社交领域的表现优于非社交领域。相比之下，对照组和重性抑郁症患者表现出相反的模式，精神分裂症患者表现出域之间没有差异。实际上，边缘型人格障碍患者放弃了可能的整体表现优势，将学习集中在社会领域，而牺牲了非社会领域。我们使用计算建模来评估每个参与者从他们的行为中估计的学习和决策参数。这使人们能够进一步深入了解基本的学习和决策机制。边缘型人格障碍患者从社会和非社会信息中学习的速度较慢，对环境波动性的变化敏感度过高，无论是在非社会领域还是在社会领域，但后者更是如此。关于决策的建模显示，与对照组和重性抑郁症患者相比，边缘型人格障碍和精神分裂症患者在做出选择时表现出对社会信息的依赖性更强。抑郁症患者在这方面与对照组没有显著差异。总的来说，我们的研究结果与边缘型人格障碍和精神分裂症中的一般人际关系超敏反应的概念一致，这种超敏反应基于一种共享的计算机制，其特征是在做出决定时过度依赖他人的信念，以及在学习过程中特别是在边缘型人格障碍中夸大对他人的理解。患有精神疾病的人经常在社会交往中遇到困难，例如使用社会信号来建立他人的表征并使用这些来指导行为的能力受损。学习和决策的计算模型使得能够表征学习和决策机制中的个体模式，这些模式可以是疾病特异性的或疾病一般性的。我们采用这种方法来调查健康参与者和诊断为抑郁症，精神分裂症和边缘型人格障碍的患者的行为，而他们执行的概率奖励学习任务，其中包括社会成分。精神分裂症和边缘型人格障碍患者比对照组和抑郁症患者在任务中表现更差。此外，边缘型人格障碍患者的学习努力更多地集中在社会性信息上，而非社会性信息。计算模型还显示，边缘型人格障碍患者在学习其预测价值时，对新获得的社会和非社会信息的加权灵活性降低。相反，我们发现了对社会和非社会信息波动性的夸大学习。此外，我们发现边缘型人格障碍和精神分裂症患者在决策过程中过度依赖对社会信息的预测。因此，我们的模型提供了一个计算帐户的夸大需要，使意义和依赖于一个人的解释他人的行为，这是突出的两种疾病。
Psychiatric disorders are ubiquitously characterized by debilitating social impairments. These difficulties are thought to emerge from aberrant social inference. In order to elucidate the underlying computational mechanisms, patients diagnosed with major depressive disorder (N = 29), schizophrenia (N = 31), and borderline personality disorder (N = 31) as well as healthy controls (N = 34) performed a probabilistic reward learning task in which participants could learn from social and non-social information. Patients with schizophrenia and borderline personality disorder performed more poorly on the task than healthy controls and patients with major depressive disorder. Broken down by domain, borderline personality disorder patients performed better in the social compared to the non-social domain. In contrast, controls and major depressive disorder patients showed the opposite pattern and schizophrenia patients showed no difference between domains. In effect, borderline personality disorder patients gave up a possible overall performance advantage by concentrating their learning in the social at the expense of the non-social domain. We used computational modeling to assess learning and decision-making parameters estimated for each participant from their behavior. This enabled additional insights into the underlying learning and decision-making mechanisms. Patients with borderline personality disorder showed slower learning from social and non-social information and an exaggerated sensitivity to changes in environmental volatility, both in the non-social and the social domain, but more so in the latter. Regarding decision-making the modeling revealed that compared to controls and major depression patients, patients with borderline personality disorder and schizophrenia showed a stronger reliance on social relative to non-social information when making choices. Depressed patients did not differ significantly from controls in this respect. Overall, our results are consistent with the notion of a general interpersonal hypersensitivity in borderline personality disorder and schizophrenia based on a shared computational mechanism characterized by an over-reliance on beliefs about others in making decisions and by an exaggerated need to make sense of others during learning specifically in borderline personality disorder. People suffering from psychiatric disorders frequently experience difficulties in social interaction, such as an impaired ability to use social signals to build representations of others and use these to guide behavior. Compuational models of learning and decision-making enable the characterization of individual patterns in learning and decision-making mechanisms that may be disorder-specific or disorder-general. We employed this approach to investigate the behavior of healthy participants and patients diagnosed with depression, schizophrenia, and borderline personality disorder while they performed a probabilistic reward learning task which included a social component. Patients with schizophrenia and borderline personality disorder performed more poorly on the task than controls and depressed patients. In addition, patients with borderline personality disorder concentrated their learning efforts more on the social compared to the non-social information. Computational modeling additionally revealed that borderline personality disorder patients showed a reduced flexibility in the weighting of newly obtained social and non-social information when learning about their predictive value. Instead, we found exaggerated learning of the volatility of social and non-social information. Additionally, we found a pattern shared between patients with borderline personality disorder and schizophrenia who both showed an over-reliance on predictions about social information during decision-making. Our modeling therefore provides a computational account of the exaggerated need to make sense of and rely on one’s interpretation of others’ behavior, which is prominent in both disorders.
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影响因子：
25
作者：
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影响因子：
3.6
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