The predictive mind: An introduction to Bayesian Brain Theory

被引:3
作者
Bottemanne, H. [1 ,2 ,3 ]
Longuet, Y. [4 ]
Gauld, C. [5 ,6 ]
机构
[1] Sorbonne Univ, Paris Brain Inst, Inst Cerveau ICM, CNRS,Inserm, Paris, France
[2] Sorbonne Univ, Dept Philosophy, SND Res Unit, UMR 8011,CNRS, Paris, France
[3] Sorbonne Univ, Pitie Salpetriere Hosp, Assistance Publ Hop Paris AP HP, Dept Psychiat,DMU Neurosci, Paris, France
[4] Claude Bernard Lyon 1 Univ, Dept Psychiat, F-69000 Lyon, France
[5] Univ Grenoble, Dept Psychiat, F-38000 Grenoble, France
[6] Sorbonne Univ, IHPST UMR 8590, F-1 Paris, France
来源
ENCEPHALE-REVUE DE PSYCHIATRIE CLINIQUE BIOLOGIQUE ET THERAPEUTIQUE | 2022年 / 48卷 / 04期
关键词
Predictive processing; Predictive coding; Bayesian brain; Belief; Bayesianism; Computational neuroscience; Computational psychiatry; Belief updating; Interoception; INFORMATION; BEHAVIOR; ERROR;
D O I
10.1016/j.encep.2021.09.011
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The question of how the mind works is at the heart of cognitive science. It aims to understand and explain the complex processes underlying perception, decision-making and learning, three fundamental areas of cognition. Bayesian Brain Theory, a computational approach derived from the principles of Predictive Processing (PP), offers a mechanistic and mathematical formulation of these cognitive processes. This theory assumes that the brain encodes beliefs (probabilistic states) to generate predictions about sensory input, then uses prediction errors to update its beliefs. In this paper, we present an introduction to the fundamentals of Bayesian Brain Theory. We show how this innovative theory hybridizes concepts inherited from the philosophy of mind and experimental data from neuroscience, and how it translates complex cognitive processes such as perception, action, emotion, or belief, or even the psychiatric symptomatology. (c) 2021 L'Encephale, Paris.
引用
收藏
页码:436 / 444
页数:9
相关论文
共 70 条
[51]   The mirror-neuron system: a Baynesian perspective [J].
Kilner, James M. ;
Friston, Karl J. ;
Frith, Chris D. .
NEUROREPORT, 2007, 18 (06) :619-623
[52]  
Kim J., 2011, Philosophy of Mind
[53]   The Bayesian brain: the role of uncertainty in neural coding and computation [J].
Knill, DC ;
Pouget, A .
TRENDS IN NEUROSCIENCES, 2004, 27 (12) :712-719
[54]   ON INFORMATION AND SUFFICIENCY [J].
KULLBACK, S ;
LEIBLER, RA .
ANNALS OF MATHEMATICAL STATISTICS, 1951, 22 (01) :79-86
[55]   Behavioural and neural characterization of optimistic reinforcement learning [J].
Lefebvre, Germain ;
Lebreton, Mael ;
Meyniel, Florent ;
Bourgeois-Gironde, Sacha ;
Palminteri, Stefano .
NATURE HUMAN BEHAVIOUR, 2017, 1 (04)
[56]  
LIEBOWITZ SJ, 1995, J LAW ECON ORGAN, V11, P205
[57]   Mood Instability and Reward Dysregulation-A NeurocomputationalModel of Bipolar Disorder [J].
Mason, Liam ;
Eldar, Eran ;
Rutledge, Robb B. .
JAMA PSYCHIATRY, 2017, 74 (12) :1275-1276
[58]   Computational psychiatry [J].
Montague, P. Read ;
Dolan, Raymond J. ;
Friston, Kari J. ;
Dayan, Peter .
TRENDS IN COGNITIVE SCIENCES, 2012, 16 (01) :72-80
[59]   Bayesian Approaches to Autism: Towards Volatility, Action, and Behavior [J].
Palmer, Colin J. ;
Lawson, Rebecca P. ;
Hohwy, Jakob .
PSYCHOLOGICAL BULLETIN, 2017, 143 (05) :521-542
[60]  
RALL WILFRID, 1962, BIOPHYS JOUR, V2, P145