类别归纳推理中的非对称性：模型与挑战

摘要

非对称性是类别归纳推理中一种重要的心理现象，即某些情况下交换前提类别与结论类别会导致显著不同的归纳力度。关于类别归纳推理非对称性的理论模型可分为两大类：基于相似性的模型和基于知识的模型。其中，基于相似性的模型包括相似覆盖模型、基于特征的模型和特征迁移模型；基于知识的模型包括假设评价模型、关联理论和贝叶斯模型。本文对这些模型进行了综述，并分别分析了其优缺点，进而对类别归纳推理非对称性的未来研究进行了展望。 Asymmetry is a typical mental phenomenon in category-based induction, i.e., exchange of premise category and conclusion category may lead to significantly different inductive strength. There are two kinds of cognitive models concerning asymmetry: similarity-based model and knowledge-based model. Similarity-based model includes similarity-coverage model (SCM), feature-based induction model (FBIM) and feature transfer model (FTM). Knowledge-based model contains hypothesis assessment model (HAM), the relevance theory and Bayesian model. The current paper reviews these models and presents their merits and demerits, and the future research directions of asymmetry in category-based induction are further discussed.