Department of Philosophy, University of California-Los Angeles (UCLA), Los Angeles, California, USA.
Wiley Interdiscip Rev Cogn Sci. 2021 Jan;12(1):e1540. doi: 10.1002/wcs.1540. Epub 2020 Aug 15.
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. This article is categorized under: Philosophy > Foundations of Cognitive Science.
贝叶斯决策理论是一个数学框架，用于在不确定条件下进行推理和决策。在过去的几十年里，贝叶斯建模在认知科学中得到了爆炸式的发展。贝叶斯模型在一系列心理领域具有解释上的成功。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，主要集中在感知和运动控制的贝叶斯建模上。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成就；贝叶斯计算的复杂性；贝叶斯模型的现实主义解释和工具主义解释；以及贝叶斯推理的神经实现。本文属于以下类别：哲学>认知科学基础。
