A causal-model theory of conceptual representation and categorization
A causal-model theory of conceptual representation and categorization
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DOI：
10.1037/0278-7393.29.6.1141
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发表时间：
2003-11-01
影响因子：
2.6
通讯作者：
Rehder, B
中科院分区：
文献类型：
--
作者：
Rehder, B
本文提出了一种分类理论，该理论解释了与类别特征相关的因果知识的影响。根据因果模型理论，人们通过评价对象是否可能由这些机制产生来明确地表示连接类别特征和分类对象的概率因果机制。在三个实验中，参与者被告知与新类别的特征相关的因果知识。因果模型理论提供了一个很好的定量说明这种知识对个体特征和特征间相关性对分类的重要性的影响。通过实现精确的模型拟合和可解释的参数估计，因果模型理论有助于将基于理论的概念表示方法与众所周知的基于相似性的方法置于平等的地位。
This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches.