Gibbs-Dean Toni, Katthagen Teresa, Tsenkova Iveta, Ali Rubbia, Liang Xinyi, Spencer Thomas, Diederen Kelly
Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
Department of Psychiatry and Neuroscience CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany.
Neurosci Biobehav Rev. 2023 Apr;147:105087. doi: 10.1016/j.neubiorev.2023.105087. Epub 2023 Feb 13.
Alterations in belief updating are proposed to underpin symptoms of psychiatric illness, including psychosis, depression, and anxiety. Key parameters underlying belief updating can be captured using computational modelling techniques, aiding the identification of unique and shared deficits, and improving diagnosis and treatment. We systematically reviewed research that applied computational modelling to probabilistic tasks measuring belief updating in stable and volatile (changing) environments, across clinical and subclinical psychosis (n = 17), anxiety (n = 9), depression (n = 9) and transdiagnostic samples (n = 9). Depression disorders related to abnormal belief updating in response to the valence of rewards, evidenced in both stable and volatile environments. Whereas psychosis and anxiety disorders were associated with difficulties adapting to changing contingencies specifically, indicating an inflexibility and/or insensitivity to environmental volatility. Higher-order learning models revealed additional difficulties in the estimation of overall environmental volatility across psychosis disorders, showing increased updating to irrelevant information. These findings stress the importance of investigating belief updating in transdiagnostic samples, using homogeneous experimental and computational modelling approaches.
信念更新的改变被认为是精神疾病症状的基础，包括精神病、抑郁症和焦虑症。信念更新背后的关键参数可以通过计算建模技术来捕捉，这有助于识别独特和共同的缺陷，并改善诊断和治疗。我们系统地回顾了将计算建模应用于测量稳定和不稳定（变化）环境中信念更新的概率任务的研究，涉及临床和亚临床精神病（n = 17）、焦虑症（n = 9）、抑郁症（n = 9）和跨诊断样本（n = 9）。抑郁症与在稳定和不稳定环境中对奖励效价的异常信念更新有关。而精神病和焦虑症则与难以适应特定的变化情况有关，表明对环境波动性缺乏灵活性和/或敏感性。高阶学习模型揭示了精神病性障碍在估计整体环境波动性方面存在额外困难，表现为对无关信息的更新增加。这些发现强调了使用同质的实验和计算建模方法在跨诊断样本中研究信念更新的重要性。
