Concept learning and feature interpretation
Concept learning and feature interpretation
复制标题
概念学习和特征解释
作者：
Thomas L. Spalding;B. Ross
分类模型通常假设人们直接根据所呈现的、可观察到的特征对新实例进行分类。然而，最近的研究表明，一个类别的连贯性可能部分取决于更抽象的特征，这些特征可以将可观察到的特征联系在一起，否则这些特征可能看起来几乎没有相似性。因此，类别学习也可能涉及确定适当的抽象特征，这些特征是类别的基础，并将可观察到的特征联系在一起。我们表明，在四个实验中，一个类别成员的可观察到的功能往往被解释为一致的抽象功能，建议由其他高度可用的类别成员的可观察到的功能。我们的讨论集中在这些发现对未来研究的影响。
Models of categorization often assume that people classify new instances directly on the basis of the presented, observable features. Recent research, however, has suggested that the coherence of a category may depend in part on more abstract features that can link together observable features that might otherwise seem to have little similarity. Thus, category learning may also involve the determination of the appropriate abstract features that underlie a category and link together the observable features. We show in four experiments that observable features of a category member are often interpreted as congruent with abstract features that are suggested by observable features of other highly available category members. Our discussion focuses on the implications of these findings for future research.
概念学习中的先验知识和功能相关特征。
DOI：
10.1037//0278-7393.21.2.449
发表时间：
1995
期刊：
Journal of experimental psychology. Learning, memory, and cognition
影响因子：
--
作者：
Wisniewski,EJ
通讯作者：
Wisniewski,EJ