Mikus Nace, Lamm Claus, Mathys Christoph
Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, Universität Wien, Vienna, Austria; Interacting Minds Centre, Aarhus University, Aarhus, Denmark.
Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, Universität Wien, Vienna, Austria.
Biol Psychiatry. 2025 Jan 15;97(2):188-197. doi: 10.1016/j.biopsych.2024.08.021. Epub 2024 Aug 30.
Psychotic experiences are thought to emerge from various interrelated patterns of disrupted belief updating, such as overestimating the reliability of sensory information and misjudging task volatility, yet these substrates have never been jointly addressed under one computational framework, and it is not clear to what degree they reflect trait-like computational patterns.
We introduce a novel hierarchical Bayesian model that describes how individuals simultaneously update their beliefs about the task volatility and noise in observation. We applied this model to data from a modified predictive inference task in a test-retest study with healthy volunteers (N = 45, 4 sessions) and examined the relationship between model parameters and schizotypal traits in a larger online sample (N = 437) and in a cohort of patients with schizophrenia (N = 100).
The interclass correlations were moderate to high for model parameters and excellent for averaged belief trajectories and precision-weighted learning rates estimated through hierarchical Bayesian inference. We found that uncertainty about the task volatility was related to schizotypal traits and to positive symptoms in patients, when learning to gain rewards. In contrast, negative symptoms in patients were associated with more rigid beliefs about observational noise, when learning to avoid losses.
These findings suggest that individuals with schizotypal traits across the psychosis continuum are less likely to learn or use higher-order statistical regularities of the environment and showcase the potential of clinically relevant computational phenotypes for differentiating symptom groups in a transdiagnostic manner.
精神病性体验被认为源于各种相互关联的信念更新中断模式，例如高估感官信息的可靠性和误判任务的波动性，但这些基础从未在一个计算框架下共同探讨过，而且它们在多大程度上反映了特质性计算模式尚不清楚。
我们引入了一种新颖的分层贝叶斯模型，该模型描述了个体如何同时更新他们对任务波动性和观察噪声的信念。我们将此模型应用于健康志愿者（N = 45，4个阶段）的重测研究中经过修改的预测推理任务的数据，并在一个更大的在线样本（N = 437）和一组精神分裂症患者（N = 100）中检验了模型参数与分裂型特质之间的关系。
模型参数的组内相关性为中度到高度，通过分层贝叶斯推理估计的平均信念轨迹和精确加权学习率的相关性则非常好。我们发现，在学习获取奖励时，对任务波动性的不确定性与分裂型特质以及患者的阳性症状有关。相比之下，在学习避免损失时，患者的阴性症状与对观察噪声的更僵化信念有关。
这些发现表明，精神病连续体上具有分裂型特质的个体不太可能学习或利用环境的高阶统计规律，并展示了临床相关计算表型以跨诊断方式区分症状组的潜力。
