Nosofsky R M, Kruschke J K, McKinley S C
Department of Psychology, Indiana University, Bloomington 47405.
J Exp Psychol Learn Mem Cogn. 1992 Mar;18(2):211-33. doi: 10.1037//0278-7393.18.2.211.
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.
对自适应网络模型和范例相似性模型预测类别学习和迁移数据的能力进行了比较。还测试了一种基于范例的网络（克鲁施克，1990a，1990b，1992），该网络结合了两种建模方法的关键方面。基于范例的网络采用基于范例的类别表示，其中范例通过与标准自适应网络中假定的相同误差驱动的交互式学习规则与类别相关联。实验1部分复制并扩展了格鲁克和鲍尔（1988a）的概率分类学习范式，证明了误差驱动学习规则的重要性。实验2扩展了梅丁和谢弗（1978）的分类学习范式，该范式区分了范例模型和原型模型，证明了基于范例的类别表示的重要性。只有基于范例的网络解释了所有主要的定性现象；它在两个实验中对学习和迁移数据也都实现了良好的定量预测。
