Bayesian decision theory is a mathematical framework that models reasoning and decision‐making under uncertain conditions. The past few decades have witnessed an explosion of Bayesian modeling within cognitive science. Bayesian models are explanatorily successful for an array of psychological domains. This article gives an opinionated survey of foundational issues raised by Bayesian cognitive science, focusing primarily on Bayesian modeling of perception and motor control. Issues discussed include the normative basis of Bayesian decision theory; explanatory achievements of Bayesian cognitive science; intractability of Bayesian computation; realist versus instrumentalist interpretation of Bayesian models; and neural implementation of Bayesian inference.

中文翻译：

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心智的贝叶斯建模：从规范到神经元

贝叶斯决策理论是一种数学框架，用于对不确定条件下的推理和决策进行建模。过去几十年见证了认知科学中贝叶斯建模的爆炸式增长。贝叶斯模型在一系列心理学领域取得了可解释性的成功。本文对贝叶斯认知科学提出的基本问题进行了有见地的调查，主要侧重于感知和运动控制的贝叶斯建模。讨论的问题包括贝叶斯决策理论的规范基础；贝叶斯认知科学的解释性成果；贝叶斯计算的难点；贝叶斯模型的现实主义与工具主义解释；和贝叶斯推理的神经实现。