PDF(Pubmed)
Abstract：
Paranoid delusions or unfounded beliefs that others intend to deliberately cause harm are a frequent and burdensome symptom in early psychosis, but their emergence and consolidation still remains opaque. Recent theories suggest that overly precise prediction errors lead to an unstable model of the world providing a breeding ground for delusions. Here, we employ a Bayesian approach to test for such an unstable model of the world and investigate the computational mechanisms underlying emerging paranoia. We modelled behaviour of 18 first-episode psychosis patients (FEP), 19 individuals at clinical high risk for psychosis (CHR-P), and 19 healthy controls (HC) during an advice-taking task designed to probe learning about others\' changing intentions. We formulated competing hypotheses comparing the standard Hierarchical Gaussian Filter (HGF), a Bayesian belief updating scheme, with a mean-reverting HGF to model an altered perception of volatility. There was a significant group-by-volatility interaction on advice-taking suggesting that CHR-P and FEP displayed reduced adaptability to environmental volatility. Model comparison favored the standard HGF in HC, but the mean-reverting HGF in CHR-P and FEP in line with perceiving increased volatility, although model attributions in CHR-P were heterogeneous. We observed correlations between perceiving increased volatility and positive symptoms generally as well as with frequency of paranoid delusions specifically. Our results suggest that FEP are characterised by a different computational mechanism - perceiving the environment as increasingly volatile - in line with Bayesian accounts of psychosis. This approach may prove useful to investigate heterogeneity in CHR-P and identify vulnerability for transition to psychosis.
摘要：
偏执妄想或毫无根据的信念，认为其他人打算故意造成伤害是早期精神病的常见和繁重的症状，但是它们的出现和巩固仍然不透明。最近的理论表明，过于精确的预测误差导致世界模型不稳定，为妄想提供了温床。这里,我们采用贝叶斯方法来测试这种不稳定的世界模型，并研究新兴偏执狂的计算机制。我们模拟了18例首发精神病患者（FEP）的行为，19名临床精神病高危人群（CHR-P），和19个健康对照（HC）在一项旨在探索学习他人改变意图的建议任务中。我们制定了竞争假设，比较了标准的分层高斯滤波器(HGF)，贝叶斯信念更新方案，使用均值回归的HGF来模拟对波动性的改变的感知。在咨询方面存在显着的按波动率的相互作用，表明CHR-P和FEP对环境波动的适应性降低。模型比较有利于HC中的标准HGF，但CHR-P和FEP中的平均恢复HGF与感知增加的波动性一致，尽管CHR-P中的模型归因是异质的。我们观察到通常感觉到波动性增加与阳性症状之间的相关性，以及与偏执妄想的频率之间的相关性。我们的结果表明，FEP的特征在于不同的计算机制-感知环境越来越不稳定-符合贝叶斯对精神病的解释。这种方法可能有助于研究CHR-P的异质性并确定向精神病过渡的脆弱性。
