Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Downing Site, Cambridge.
Psychopharmacology (Berl). 2009 Nov;206(4):515-30. doi: 10.1007/s00213-009-1561-0. Epub 2009 May 28.
Various experimental manipulations, usually involving drug administration, have been used to produce symptoms of psychosis in healthy volunteers. Different drugs produce both common and distinct symptoms. A challenge is to understand how apparently different manipulations can produce overlapping symptoms. We suggest that current Bayesian formulations of information processing in the brain provide a framework that maps onto neural circuitry and gives us a context within which we can relate the symptoms of psychosis to their underlying causes. This helps us to understand the similarities and differences across the common models of psychosis.
The Bayesian approach emphasises processing of information in terms of both prior expectancies and current inputs. A mismatch between these leads us to update inferences about the world and to generate new predictions for the future. According to this model, what we experience shapes what we learn, and what we learn modifies how we experience things.
This simple idea gives us a powerful and flexible way of understanding the symptoms of psychosis where perception, learning and inference are deranged. We examine the predictions of the cognitive model in light of what we understand about the neuropharmacology of psychotomimetic drugs and thereby attempt to account for the common and the distinctive effects of NMDA receptor antagonists, serotonergic hallucinogens, cannabinoids and dopamine agonists.
By acknowledging the importance of perception and perceptual aberration in mediating the positive symptoms of psychosis, the model also provides a useful setting in which to consider an under-researched model of psychosis-sensory deprivation.
各种实验操作，通常涉及药物给药，已被用于在健康志愿者中产生精神病症状。不同的药物会产生共同和独特的症状。一个挑战是理解看似不同的操作如何产生重叠的症状。我们认为，当前大脑信息处理的贝叶斯公式提供了一个框架，映射到神经回路，并为我们提供了一个背景，使我们能够将精神病症状与其潜在原因联系起来。这有助于我们理解精神病常见模型的相似性和差异性。
贝叶斯方法强调信息处理的先验期望和当前输入。这些之间的不匹配导致我们更新对世界的推断，并为未来生成新的预测。根据这个模型，我们的体验塑造了我们的学习，我们的学习改变了我们对事物的体验。
这个简单的想法为我们提供了一种强大而灵活的方法来理解感知、学习和推理紊乱的精神病症状。我们根据我们对致幻药物的神经药理学的了解，检查认知模型的预测，从而试图解释 NMDA 受体拮抗剂、血清素致幻剂、大麻素和多巴胺激动剂的共同和独特作用。
通过承认感知和感知偏差在介导精神病阳性症状方面的重要性，该模型还为研究较少的精神病-感觉剥夺模型提供了一个有用的环境。
