Bayesian models: the structure of the world, uncertainty, behavior, and the brain
Bayesian models: the structure of the world, uncertainty, behavior, and the brain
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DOI：
10.1111/j.1749-6632.2011.05965.x
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发表时间：
2011-01-01
期刊：
影响因子：
--
通讯作者：
Kording, Konrad
中科院分区：
文献类型：
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作者：
Vilares, Iris;Kording, Konrad
对人类和其他动物的实验表明，由于不可靠或不完整的信息而导致的不确定性会影响行为。最近的研究已经正式确定了不确定性，并询问哪些行为可以最大限度地减少其影响。这种形式化产生了一系列源自对世界的假设的贝叶斯模型，而且这些模型之间的相互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行为和神经数据建模的贝叶斯方法的差异和共性。我们回顾与每种类型的贝叶斯模型相关的行为和神经数据，并解释这些模型如何关联。最后我们概述了不同的理论，这些理论提出了大脑表示不确定性的可能方式。
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.