标题:
类别归纳推理的贝叶斯模型The Bayesian Model of Category-Based
Induction
作者:
邓志超, 梁佩鹏, 钟宁
关键字:
类别归纳推理, 贝叶斯模型, 可计算模型Category-Based Induction, Bayesian Model, Computable Model
期刊名称:
《Advances in Psychology》, Vol.4 No.3, 2014-05-15
摘要:
贝叶斯模型为解释类别归纳推理的实验现象提供了一个统一的可计算框架。在该框架下，用不同的类别结构和随机过程表示不同的先验知识，并基于贝叶斯公式预测不同场景下的归纳力度。与其它模型相比，贝叶斯模型有较强的预测力度和更广的应用范围。文章总结了该模型的发展历史及现状，并首次系统阐述了其建模过程。未来研究可结合功能磁共振实验和计算语言学等方法，进一步拓展该模型的推理能力，提高其实际可用性。
The Bayesian model (BM) of category-based induction provides a unified computable framework for explaining the experimental phenomena (including the premise-conclusion similarity effect, the premise diversity effect, the premise monotonic effect and the premise-conclusion asymmetric effect, etc.) in category-based induction. Within this framework, the inductive reasoning in different contexts (such as induction about the generic biological properties or the causally transmitted properties) requires the constraint of different kinds of prior knowledge. Different kinds of prior knowledge can be represented by different kinds of category structures (i.e., the relationship between categories) and the corresponding stochastic process (i.e., the distribution of features/ properties in the category structure). Thus, BM can get the prior probability distributions for the Bayesian inference engine, and finally, the strength of an inductive argument can be calculated. As compared to the similarity coverage model (SCM) and feature-based inductive model (FBIM), BM can reflect the interaction of categories and properties, and has a clear mathematical basis, and also shows a better ability of prediction. This paper firstly reviews the research history and state of the art of the BM, and summarizes the process of computational cognitive modeling using BM. Secondly, BM is compared with the other models, and then the advantages and disadvantages of the BM are commented in details. Finally, some potential research directions are proposed: 1) further improving the ability of BM to deal with the common sense knowledge (e.g., the predatory behavior of animal), which may help to expand its application scope; 2) further increasing the power of BM to handle multiple objects and features/properties (if we learn that the animal A has the property X, what’s the possibility of the animal B having the property Y?); 3) that in combination with other methodologies (e.g., functional magnetic resonance imaging (fMRI) and computational linguistics, such as corpora), BM may improve its practical availability and reasoning abilities.