Tauber Sean, Navarro Daniel J, Perfors Amy, Steyvers Mark
School of Psychology, University of New South Wales.
School of Psychology, University of Adelaide.
Psychol Rev. 2017 Jul;124(4):410-441. doi: 10.1037/rev0000052. Epub 2017 Mar 30.
Recent debates in the psychological literature have raised questions about the assumptions that underpin Bayesian models of cognition and what inferences they license about human cognition. In this paper we revisit this topic, arguing that there are 2 qualitatively different ways in which a Bayesian model could be constructed. The most common approach uses a Bayesian model as a normative standard upon which to license a claim about optimality. In the alternative approach, a descriptive Bayesian model need not correspond to any claim that the underlying cognition is optimal or rational, and is used solely as a tool for instantiating a substantive psychological theory. We present 3 case studies in which these 2 perspectives lead to different computational models and license different conclusions about human cognition. We demonstrate how the descriptive Bayesian approach can be used to answer different sorts of questions than the optimal approach, especially when combined with principled tools for model evaluation and model selection. More generally we argue for the importance of making a clear distinction between the 2 perspectives. Considerable confusion results when descriptive models and optimal models are conflated, and if Bayesians are to avoid contributing to this confusion it is important to avoid making normative claims when none are intended. (PsycINFO Database Record
心理学文献中的近期辩论对支撑认知贝叶斯模型的假设以及它们对人类认知所许可的推断提出了质疑。在本文中，我们重新审视这一主题，认为构建贝叶斯模型有两种质的不同方式。最常见的方法是将贝叶斯模型用作许可关于最优性主张的规范标准。在另一种方法中，描述性贝叶斯模型不必对应于关于潜在认知是最优或理性的任何主张，而仅用作实例化实质性心理学理论的工具。我们给出了三个案例研究，其中这两种观点导致了不同的计算模型，并对人类认知许可了不同的结论。我们展示了描述性贝叶斯方法如何能够用于回答与最优方法不同类型的问题，特别是当与用于模型评估和模型选择的有原则的工具相结合时。更一般地说，我们主张明确区分这两种观点的重要性。当描述性模型和最优模型被混为一谈时会产生相当大的混乱，如果贝叶斯主义者要避免助长这种混乱，重要的是在无意提出规范主张时避免这样做。（《心理学文摘数据库记录》 ）
