Departments of Physical Medicine and Rehabilitation, Physiology, and Applied Mathematics, Northwestern University, Chicago, Illinois. Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois.International Neuroscience Doctoral Programme, Champalimaud Neuroscience Programme, Institutio Gulbenkian de Ciência, Oeiras, Portugal.
Ann N Y Acad Sci. 2011 Apr;1224(1):22-39. doi: 10.1111/j.1749-6632.2011.05965.x.
Experiments on humans and other animals have shown that uncertainty due to unreliable or incomplete information affects behavior. Recent studies have formalized uncertainty and asked which behaviors would minimize its effect. This formalization results in a wide range of Bayesian models that derive from assumptions about the world, and it often seems unclear how these models relate to one another. In this review, we use the concept of graphical models to analyze differences and commonalities across Bayesian approaches to the modeling of behavioral and neural data. We review behavioral and neural data associated with each type of Bayesian model and explain how these models can be related. We finish with an overview of different theories that propose possible ways in which the brain can represent uncertainty.
对人类和其他动物的实验表明，由于信息不可靠或不完整而导致的不确定性会影响行为。最近的研究已经使不确定性形式化，并提出了哪些行为将最小化其影响。这种形式化导致了广泛的贝叶斯模型，这些模型源自对世界的假设，而且这些模型之间的关系似乎常常不清楚。在这篇综述中，我们使用图形模型的概念来分析贝叶斯方法在行为和神经数据建模方面的差异和共性。我们回顾了与每种贝叶斯模型类型相关的行为和神经数据，并解释了这些模型如何相关。最后，我们概述了不同的理论，这些理论提出了大脑可能表示不确定性的可能方式。
