Increased Belief Instability in Psychotic Disorders Predicts Treatment Response to Metacognitive Training.
Increased Belief Instability in Psychotic Disorders Predicts Treatment Response to Metacognitive Training.
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DOI：
10.1093/schbul/sbac029
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发表时间：
2022-06-21
影响因子：
6.6
通讯作者：
Andreou, C.
中科院分区：
文献类型：
--
作者：
Hauke, D. J.;Roth, V;Karvelis, P.;Adams, R. A.;Moritz, S.;Borgwardt, S.;Diaconescu, A. O.;Andreou, C.
关键词：
在一个复杂的世界里，收集信息和调整我们对世界的看法是至关重要的。文献表明，精神病患者倾向于根据有限的证据得出早期结论，这被称为“直接得出结论的偏见”，但很少有研究调查了这种偏见和相关的信念更新偏见背后的计算机制。在这里，我们采用一种计算方法来理解草率结论、精神障碍和妄想之间的关系。我们用层次高斯滤波器对261名精神病患者和56名健康对照者在信息采样任务-鱼任务-中进行概率推理建模。随后，我们通过测试计算参数（通过将模型拟合到每个个体的行为中获得）是否可以预测使用机器学习进行元认知训练的治疗反应，来检验这种计算方法的临床实用性。我们观察到在精神病患者和健康对照者、有和没有跳跃结论偏差的参与者之间概率推理的差异，但在低电流妄想和高电流妄想患者之间没有差异。计算分析表明，精神障碍患者的信念不稳定性增加。直接下结论会增加信念的不稳定性和更大的先验不确定性。最后，信念不稳定性在个体水平上预测元认知训练的治疗反应。我们的研究结果表明，信念不稳定性的增加是精神障碍中概率推理的关键计算机制。我们提供了一个概念证明，这种计算方法可能有助于确定适合精神障碍个体患者的治疗方法。
In a complex world, gathering information and adjusting our beliefs about the world is of paramount importance. The literature suggests that patients with psychotic disorders display a tendency to draw early conclusions based on limited evidence, referred to as the jumping-to-conclusions bias, but few studies have examined the computational mechanisms underlying this and related belief-updating biases. Here, we employ a computational approach to understand the relationship between jumping-to-conclusions, psychotic disorders, and delusions. We modeled probabilistic reasoning of 261 patients with psychotic disorders and 56 healthy controls during an information sampling task—the fish task—with the Hierarchical Gaussian Filter. Subsequently, we examined the clinical utility of this computational approach by testing whether computational parameters, obtained from fitting the model to each individual’s behavior, could predict treatment response to Metacognitive Training using machine learning. We observed differences in probabilistic reasoning between patients with psychotic disorders and healthy controls, participants with and without jumping-to-conclusions bias, but not between patients with low and high current delusions. The computational analysis suggested that belief instability was increased in patients with psychotic disorders. Jumping-to-conclusions was associated with both increased belief instability and greater prior uncertainty. Lastly, belief instability predicted treatment response to Metacognitive Training at the individual level. Our results point towards increased belief instability as a key computational mechanism underlying probabilistic reasoning in psychotic disorders. We provide a proof-of-concept that this computational approach may be useful to help identify suitable treatments for individual patients with psychotic disorders.
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影响因子：
16.6
作者：
Jardri R;Duverne S;Litvinova AS;Denève S
通讯作者：
Denève S
影响因子：
6.8
作者：
Hauke DJ;Schmidt A;Studerus E;Andreou C;Riecher-Rössler A;Radua J;Kambeitz J;Ruef A;Dwyer DB;Kambeitz-Ilankovic L;Lichtenstein T;Sanfelici R;Penzel N;Haas SS;Antonucci LA;Lalousis PA;Chisholm K;Schultze-Lutter F;Ruhrmann S;Hietala J;Brambilla P;Koutsouleris N;Meisenzahl E;Pantelis C;Rosen M;Salokangas RKR;Upthegrove R;Wood SJ;Borgwardt S;PRONIA Group
通讯作者：
PRONIA Group
影响因子：
14.5
作者：
Jardri, Renaud;Deneve, Sophie
通讯作者：
Deneve, Sophie
影响因子：
3.1
作者：
Freeman, D;Garety, PA
通讯作者：
Garety, PA
影响因子：
10.5
作者：
GARETY, P
通讯作者：
GARETY, P