Bayesian brain: Can we model emotion?

作     者：Bottemanne, H. 

作者机构：Hop La Pitie Salpetriere AP HP Dept Psychiat Paris France Sorbonne Univ Paris Brain Inst Control Interocept Attent Team Paris France 

出 版 物：《ENCEPHALE-REVUE DE PSYCHIATRIE CLINIQUE BIOLOGIQUE ET THERAPEUTIQUE》 (脑；临床生物精神病学和治疗精神病学)

年 卷 期：2021年第47卷第1期

页      面：58-63页

核心收录：

学科分类：1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 10[医学] 

主　　题：Bayesian brain Emotion Belief Predictive coding Active inference Perception Decision-making Computational neurosciences Belief updating 

摘      要：Computational modeling builds mathematical models of cognitive phenomena to simulate patterns of perception, decision-making, and belief updating. These models mathematically represent the information processing by combining an anterior probability distribution, a likelihood function and a set of parameters and hyperparameters. Their use popularized the conception of a nervous system functioning as a predictive machine, or bayesian brain. Applied to psychiatry, these models seek to explain how psychiatric dysfunction may emerge mechanistically. Despite the significance of emotions for cognitive phenomena and for psychiatric disorders, few computational models offer mathematical representations of emotion or incorporate emotional factors into their modeling parameters. We present here some computational hypotheses for the modeling of affective parameters, and we suggest that computational psychiatry would benefit from these modeling parameters. (C) 2020