Prior knowledge and functionally relevant features in concept learning.
Prior knowledge and functionally relevant features in concept learning.
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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
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
Wisniewski,EJ
中科院分区：
文献类型：
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作者：
Wisniewski,EJ
经验学习模型通常侧重于特征的统计方面(例如，线索和类别有效性)。一般来说，这些模型没有解决人们在该类别之外的先验知识与他们对该类别的经验之间的联系。研究了这些模型的各种扩展，它们结合了先验知识和经验学习。在4个实验中对这些模型的预测进行了比较。这些研究将特征的线索和类别有效性与人们对特征与新奇人工制品功能的相关性的先验知识进行了对比。这些发现表明，知识和经验的影响比一些模型预测的更紧密地结合在一起。此外，将知识结合到经验学习算法中的相对直接的方法似乎不够充分(例如，使用知识根据总体相关性来加权特征或单独加权特征)。建议对这些模型进行其他扩展，将重点放在中间特征、一致性和概念性角色的重要性上。
Empirical learning models have typically focused on statistical aspects of features (eg, cue and category validity). In general, these models do not address the contact between people's prior knowledge that lies outside the category and their experiences of the category. A variety of extensions to these models are examined, which combine prior knowledge with empirical learning. Predictions of these models were compared in 4 experiments. These studies contrasted the cue and category validity of features with people's prior knowledge about the relevance of features to the functions of novel artifacts. The findings suggest that the influences of knowledge and experience are more tightly integrated than some models would predict. Furthermore, relatively straightforward ways of incorporating knowledge into an empirical learning algorithm appear insufficient (eg, use of knowledge to weight features by general relevance or to individually weight features). Other extensions to these models are suggested that focus on the importance of intermediary features, coherence, and conceptual roles.(PsycINFO Database Record (c) 2016 APA, all rights reserved)