Structured Statistical Models of Inductive Reasoning
Structured Statistical Models of Inductive Reasoning
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DOI：
10.1037/a0014282
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发表时间：
2009-01-01
影响因子：
5.4
通讯作者：
Tenenbaum, Joshua B.
中科院分区：
文献类型：
--
作者：
Kemp, Charles;Tenenbaum, Joshua B.
日常的归纳推理往往是在丰富的背景知识的指导下进行的。归纳的形式模型应该旨在整合这些知识，并解释不同类型的知识如何导致在不同归纳背景下发现的独特推理模式。本文提出了一个贝叶斯框架，试图满足这两个目标，并描述了该框架的4个应用：分类模型，空间模型，阈值模型和因果模型。每个模型都对新属性的扩展进行概率推断，但是4个模型的先验是在不同类型的结构上定义的，这些结构捕获了域中类别之间的不同关系。因此，该框架显示了统计推断如何在结构化背景知识上运作，作者认为，结构和统计之间的这种相互作用对于解释人类推理的能力和灵活性至关重要。
Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describe 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between Structure and statistics is critical for explaining the power and flexibility of human reasoning.