Prototype and exemplar accounts of category learning and attentional allocation: a reassessment.
Prototype and exemplar accounts of category learning and attentional allocation: a reassessment.
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类别学习和注意力分配的原型和范例说明：重新评估。
DOI：
10.1037/0278-7393.29.6.1160
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Cohen,AndrewL
中科院分区：
文献类型：
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作者：
Zaki,SafaR;Nosofsky,RobertM;Stanton,RogerD;Cohen,AndrewL
在最近的一篇文章中，JP Minda和JD Smith（2002）认为，对于来自经典DL Medin和MM Schaffer（1978）5/4分类范式的个体受试者数据，范例模型提供的定量拟合比替代原型模型更差。此外，他们认为，范例模型通过对观察者如何分配注意力做出站不住脚的假设来实现其拟合。在这篇文章中，我们证明，当模型等同于他们的响应规则的灵活性，样本模型提供了一个更好的说明分类数据比原型或混合模型。此外，我们指出了JP Minda和JD Smith（2002）进行的注意力分配分析中的缺陷。当这些缺点得到纠正，我们没有发现任何证据，挑战注意力分配假设的范例模型。
In a recent article, JP Minda and JD Smith (2002) argued that an exemplar model provided worse quantitative fits than an alternative prototype model to individual subject data from the classic DL Medin and MM Schaffer (1978) 5/4 categorization paradigm. In addition, they argued that the exemplar model achieved its fits by making untenable assumptions regarding how observers distribute their attention. In this article, we demonstrate that when the models are equated in terms of their response-rule flexibility, the exemplar model provides a substantially better account of the categorization data than does a prototype or mixed model. In addition, we point to shortcomings in the attention-allocation analyses conducted by JP Minda and JD Smith (2002). When these shortcomings are corrected, we find no evidence that challenges the attention-allocation assumptions of the exemplar model.