归类不确定情景下特征推理的综合条件概率模型  

An Overall Conditional Probability Model of Feature Reasoning in Uncertain Categorization

作　　者：王墨耘[1] 莫雷[1] 

机构地区：[1]华南师范大学心理学系,广州510631

出　　处：《心理学报》2005年第4期482-490,共9页Acta Psychologica Sinica

基　　金：教育部人文社会科学研究项目(01JAXLX001);全国教育科学十五规划国家一般项目。

摘　　要：用大学生被试,通过三个实验探讨在集中呈现类别成员样本信息的归类不确定情景下的特征推理。实验结果表明,单纯的归类确定性程度和靶类别靶特征的代表性并不直接影响被试的特征推理,而是预测特征相对于目标特征的综合条件概率直接影响被试的特征推理;特征推理不是基于类别中介的间接推理,而是基于特征关联综合条件概率的直接推理。实验结果支持作者提出的预测特征综合条件概率模型。Three experiments with participants of college students were done to investigate feature reasoning in uncertain categorization in the concentrative presentation of samples of category members. The results showed that it is the overall conditional probability of the prediction feature relative to the target feature rather than category uncertainty and representativeness of target features that affects participants＇feature reasoning, and that feature reasoning is direct reasoning based on the overall conditional probability of feature relevancy rather than indirect reasoning based on categories. The results supported the authors＇overall conditional probability model of prediction features.

关 键 词：归类 特征推理 贝叶斯分析 单类说 预测特征综合条件概率模型 

分 类 号：B842[哲学宗教—基础心理学]