关键词: 类别特征推理, 类别标签, 极化效应, 前提概率

Abstract: Categorization and inference are two important functions of category knowledge, besides categorization, category-based feature inference is an important application of category knowledge. Categorization is to infer an item's category label when its feature value is known, while inference is to infer an item's one or few unknown feature value when its category label is known. Category label is a symbol on behalf of category membership, while category feature is a symbol representing a feature of an item. This study aimed to contrast the polarization effect between category label and feature label in category-based feature inference. The experiments used learning-test two phase paradigm and a 2(type of category label: category and feature)×2(matching type of label: matching and non-matching)× 2(basal probability: high and low) experimental design. The results of the experiment showed that: 1)The polarization effect of category label was significantly higher than the one of feature label. 2)Category label was different from category feature essentially. 3)The basal probability affected the category-based feature inference. In summary, this study showed that category label is essentially different from category feature. And this study had a new finding that the basal probability did affect the category-based feature inference.

Key words: category-based feature inference, category label, polarization effect, basal probability