Combining exemplar-based category representations and connectionist learning rules.
Combining exemplar-based category representations and connectionist learning rules.
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结合基于范例的类别表示和联结主义学习规则。
DOI：
10.1037//0278-7393.18.2.211
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发表时间：
1992
期刊：
影响因子：
--
通讯作者：
McKinley,SC
中科院分区：
文献类型：
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作者：
Nosofsky,RM;Kruschke,JK;McKinley,SC
比较了自适应网络模型和样本相似性模型在预测类别学习和传输数据方面的能力。还测试了结合两种建模方法的关键方面的基于样本的网络(Kruschke，1990a，1990b，1992)。基于样本的网络结合了基于样本的类别表示，其中样本通过与标准自适应网络中假定的相同的错误驱动的交互式学习规则与类别相关联。实验1部分复制和扩展了Gluck和Bower(1988a)的概率分类学习范式，证明了错误驱动学习规则的重要性。实验2扩展了Medin和Schaffer(1978)区分样本和原型模型的分类学习范式，证明了基于样本的类别表征的重要性。只有基于样本的网络解释了所有主要的定性现象；它还在两个实验中对学习和传输数据进行了良好的定量预测。
Adaptive network and exemplar-similarity models were compared on their ability to predict category learning and transfer data. An exemplar-based network (Kruschke, 1990a, 1990b, 1992) that combines key aspects of both modeling approaches was also tested. The exemplar-based network incorporates an exemplar-based category representation in which exemplars become associated to categories through the same error-driven, interactive learning rules that are assumed in standard adaptive networks. Experiment 1, which partially replicated and extended the probabilistic classification learning paradigm of Gluck and Bower (1988a), demonstrated the importance of an error-driven learning rule. Experiment 2, which extended the classification learning paradigm of Medin and Schaffer (1978) that discriminated between exemplar and prototype models, demonstrated the importance of an exemplar-based category representation. Only the exemplar-based network accounted for all the major qualitative phenomena; it also achieved good quantitative predictions of the learning and transfer data in both experiments.