Inference and classification learning of abstract coherent categories
Inference and classification learning of abstract coherent categories
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DOI：
10.1037/0278-7393.31.1.86
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发表时间：
2005-01-01
影响因子：
2.6
通讯作者：
Ross, BH
中科院分区：
文献类型：
--
作者：
Erickson, JE;Chin-Parker, S;Ross, BH
类别学习研究主要集中在人们如何学习使用简单的可观察特征对项目进行分类。然而，分类只是我学习类别的方法。此外，许多概念具有潜在的一致性，这解释了范例之间的特征相似性，例如抽象的一致性概念，其实例在其可观察特征上差异很大。在3个实验中，作者考察了抽象连贯范畴是如何通过分类和推理这两种常用的范畴学习方式获得的。由于推理比分类更关注类别内信息，他们假设推理学习会导致更好地理解抽象连贯类别的潜在连贯性。所有三个实验都支持这一预测。
Category learning research has primarily focused on how people learn to classify items using simple observable features. However, classification is only I way to learn categories. In addition, many concepts have an underlying coherence that explains the featural similarity among exemplars, such as abstract coherent concepts whose instances differ greatly on their observable features. In 3 experiments, the authors investigated how abstract coherent categories are acquired through 2 common means of category learning, classification and inference. Because inference promotes more focus on within-category information than does classification, they hypothesized that inference learning would lead to a better understanding of the underlying coherence of abstract coherent categories. All 3 experiments support this prediction.