Rules and exemplars in category learning.
Rules and exemplars in category learning.
复制标题
类别学习的规则和范例。
DOI：
10.1037//0096-3445.127.2.107
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
Kruschke,JK
中科院分区：
文献类型：
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作者：
Erickson,MA;Kruschke,JK
分类的心理学理论通常侧重于基于规则或基于范例的解释。我们提出了两个实验，显示了规则归纳和范例编码的证据，以及连接主义模型 ATRIUM，它指定了一种将基于规则和基于范例的表示相结合的机制。在两项实验中，参与者学会了对项目进行分类，其中大多数都遵循简单的规则，尽管也有一些经常出现的例外。实验 1 研究了人们如何在训练范围之外进行推断。实验 2 检查了实例频率对泛化的影响。该模型很好地描述了分类行为，其中示例表示用于规则和异常处理。正确建模这些结果的一个关键要素是通过使用规则和样本之间的注意力转移来捕获基于规则和基于样本的表示之间的交互。
Psychological theories of categorization generally focus on either rule-or exemplar-based explanations. We present 2 experiments that show evidence of both rule induction and exemplar encoding as well as a connectionist model, ATRIUM, that specifies a mechanism for combining rule-and exemplar-based representation. In 2 experiments participants learned to classify items, most of which followed a simple rule, although there were a few frequently occurring exceptions. Experiment 1 examined how people extrapolate beyond the range of training. Experiment 2 examined the effect of instance frequency on generalization. Categorization behavior was well described by the model, in which exemplar representation is used for both rule and exception processing. A key element in correctly modeling these results was capturing the interaction between the rule-and exemplar-based representations by using shifts of attention between rules and exemplars.