迫害妄想的心理治疗,特别是精神病的认知行为疗法,是有效的;然而,机械理论解释了为什么它们很少工作到认知神经科学的水平。预测编码,一种植根于认知和计算神经科学的通用大脑处理理论,对解释精神病症状的实验支持越来越多,包括妄想的形成和维持。这里,我们描述了最近的进展认知行为疗法的精神病为基础的心理治疗的迫害妄想,它针对信息处理的计算级别的特定心理过程。我们概述了在预测编码中采用的贝叶斯学习模型如何优于简单的联想学习模型,以理解认知行为干预在算法层面的影响。我们回顾了分层预测编码,作为根植于预测错误信号的信念更新的一种解释。我们研究了这个过程在精神病患者中是如何异常的,获得嘈杂的感官数据,这些数据通过过度强烈的妄想先验的发展而变得有意义。我们认为,有效的精神病认知行为疗法系统地针对感官数据的选择方式,有经验的,和解释,从而允许加强另类信仰。最后,讨论了基于这些论点的未来方向。
妄想是令人痛苦和致残的精神症状。精神病的认知行为疗法(CBTp)是治疗妄想的主要心理治疗方法。预测编码是当代认知神经科学框架,越来越多地用于解释妄想机制。在这篇文章中,我们试图将CBTp集成到预测编码框架中,概述了有效的CBTp技术如何影响预测编码模型的各个方面,以促进对妄想的尖端治疗和认知神经科学研究,并为治疗进展提供建议。
Psychological treatments for persecutory delusions, particularly cognitive behavioral therapy for psychosis, are efficacious; however, mechanistic theories explaining why they work rarely bridge to the level of cognitive neuroscience. Predictive coding, a general brain processing theory rooted in cognitive and computational neuroscience, has increasing experimental support for explaining symptoms of psychosis, including the formation and maintenance of delusions. Here, we describe recent advances in cognitive behavioral therapy for psychosis-based
psychotherapy for persecutory delusions, which targets specific psychological processes at the computational level of information processing. We outline how Bayesian learning models employed in predictive coding are superior to simple associative learning models for understanding the impact of cognitive behavioral interventions at the algorithmic level. We review hierarchical predictive coding as an account of belief updating rooted in prediction error signaling. We examine how this process is abnormal in psychotic disorders, garnering noisy sensory data that is made sense of through the development of overly strong delusional priors. We argue that effective cognitive behavioral therapy for psychosis systematically targets the way sensory data are selected, experienced, and interpreted, thus allowing for the strengthening of alternative beliefs. Finally, future directions based on these arguments are discussed.
Delusions are distressing and disabling psychiatric symptoms. Cognitive behavioral therapy for psychosis (CBTp) is the leading psychotherapeutic approach for treating delusions. Predictive coding is a contemporary cognitive neuroscience framework that is increasingly being used to explain mechanisms of delusions. In this article, we attempt to integrate CBTp within the predictive coding framework, outlining how effective CBTp techniques impact aspects of the predictive coding model to contribute to cutting-edge treatment and cognitive neuroscience research on delusions and inform recommendations for treatment advancement.