条件句推理与基于Bayes法则的概率模型<br>Conditional Reasoning and Probabilistic Model Based on Bayes Rule

Abstract

条件句的研究是推理心理学的难点和重点。现成的理论模型有Mental rule，Mental model以及最近兴起的基于Bayes法则的概率模型。本文结合相关实验结果，对这些模型的解释力进行了评价，特别指出的是，Bayes模型能成功地预测许多有关实验结果。但是在涉及反事实条件句推理的理论基础等方面，Bayesian模型存在严重的不足。本文最后建议解决这个不足需要基于Bayes法则的图模型，也许它比基于Bayes法则的概率模型更好地解释反事实条件句推理的实验结果。Conditional reasoning is the difficult and central points in psychology of reasoning. The current models include the mental logic, mental model, Logic Programming and recent probabilistic model based on Bayes rule. This paper discusses the strengths and shortcomings of these models. It must be pointed out that the probabilistic model has many advantages in explaining relevant experimental results over the mental logic and mental model theories. However, this model can not successfully deal with the reasoning about counterfactuals. This paper ends with the comments that the graph model which derives from the Bayes rule may be a better theory than the probabilistic model related to the counterfactuals reasoning.