| 不同维度特征的共存对归类不确定性特征推理的影响 |
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| 引用本文： | 刘志雅. 不同维度特征的共存对归类不确定性特征推理的影响[J]. 心理学报, 2008, 40(1): 37-46. DOI: |
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| 作者姓名： | 刘志雅 |
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| 作者单位： | 华南理工大学心理系，广州 510640 |
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| 基金项目： | 教育部哲学社会科学研究重大课题 , 广东省哲学社会科学规划心理学重点研究项目 |
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| 摘 要： | 探讨在归类不确定的情境下，目标特征和预测特征的共存对特征推理的影响。共包括了三个实验，其中实验1、2考察了非靶类别中目标特征和预测特征的共存性对特征推理的影响，实验3考察了靶类别中目标特征和预测特征的共存性对特征推理的影响。三个实验五个分实验的结果一致支持了修正后的Bayesian规则，排除了“单类说”、“综合条件概率模型”的假设，并进一步修正了Bayesian规则为：
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| 关 键 词： | 归类 特征推理 特征共存 Bayesian规则 |
| 收稿时间： | 2006-11-13 |
| 修稿时间： | 2006-11-13 |
Influence of the Coexistence of Dimensions in Feature Predicting when the Categories are Uncertain |
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| LIU Zhi-Ya. Influence of the Coexistence of Dimensions in Feature Predicting when the Categories are Uncertain[J]. Acta Psychologica Sinica, 2008, 40(1): 37-46. DOI: |
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| Authors: | LIU Zhi-Ya |
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| Affiliation: | Department of Psychology, South China University of Technology, Guangzhou 510640, China |
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| Abstract: | In this paper, we study the influence of the coexistence of two dimensions (Target Feature and Prediction Feature) in feature predicting under uncertain categorizing circumstances. Experiments 1 and 2 explore whether the coexistence of the two dimensions within the non-target categories promotes the use of the non-target category information. On the other hand, Experiment 3 explores whether the coexistence of the two dimensions within the target categories promotes the use of non-target category information. Anderson (1991) provided a Bayesian analysis of feature predicting; if an object contains feature F and belongs to category k, one can predict a novel feature j by using the following formula: . This is one method for calculating how likely the object is to be in each category k and how likely that category is to contain the property. Thus, one should consider all the categories in order to make the prediction. In short, the analysis suggests that people use multiple categories to make predictions when categorizing is uncertain. Murphy &; Ross (1994) suggested that people make feature predictions on the basis of a single category when categorization is uncertain. They found that even if the participants gave a fairly low rating of confidence in categorizing, they did not use multiple category information to make predictions. Wang Moyun &; Mo Lei (2005) presented another viewpoint—feature predicting is based on overall conditional probability instead of the probability of categorizing. Molei &; Zhao Haiyan (2002) found that the association or separation of the two dimensions would influence feature predicting under uncertain categorizing circumstances. The results suggested that the proportion of the association of the object and the feature (Ak) be incorporated into the Bayesian formula: . However, we found that most previous experimental data were significantly higher than the value of the two-formula theory. We revised the formula as follows: . In addition, we manipulated one factor to test the new formula. The results in Experiments 1 and 2 show that when the two feature dimensions are not in conjunction with non-target categories, raising the proportion of the coexistence of the dimensions within the non-target categories will not enhance the feature prediction probability. When two feature dimensions are in conjunction in non-target categories, raising the proportion will enhance the feature prediction probability. The results are not consistent with Murphy &; Ross’s single-category viewpoint and Anderson’s Bayesian Rule. Accordingly, this study introduces the proportion of conjunction of the two feature dimensions as a multiplicative variable into the formula of the Bayesian rule. The result of Experiment 3 is consistent with that of the study and shows that raising the proportion of the coexistence of the two feature dimensions within the target category will not improve the probability of feature prediction. The experimental outcomes are consistent and a better fit with our new, revised Bayesian rule. The coexistence of the two dimensions within the non-target categories promotes the use of non-target category information; the coexistence of the two dimensions within the target categories promotes the use of non-target category information. |
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| Keywords: | classification feature prediction feature coexistence Bayesian rule. |
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