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The modulating influence of category size on the classification of exception patterns

机译:类别大小对异常模式分类的调节影响

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Generalization gradients to exception patterns and the category prototype were investigated in two experiments. In Experiment 1, participants first learned categories of large size that contained a single exception pattern, followed by a transfer test containing new instances that had a manipulated similarity relationship to the exception or a nonexception training pattern as well as distortions of the prototype. The results demonstrated transfer gradients tracked the prototype category rather than the feedback category of the exception category. In Experiment 2, transfer performance was investigated for categories varying in size (5, 10, 20), partially crossed with the number of exception patterns (1, 2, 4). Here, the generalization gradients tracked the feedback category of the training instance when category size was small but tracked the prototype category when category size was large. The benefits of increased category size still emerged, even with proportionality of exception patterns held constant. These, and other outcomes, were consistent with a mixed model of classification, in which exemplar influences were dominant with small-sized categories and/or high error rates, and prototype influences were dominant with larger sized categories.
机译:在两个实验中研究了异常模式和类别原型的泛化梯度。在实验1中,参与者首先学习了包含单个异常模式的大型类别,然后进行了包含新实例的传输测试,该新实例与异常或非异常训练模式具有可操纵的相似性关系,并且原型变形。结果表明,传递梯度跟踪的是原型类别,而不是异常类别的反馈类别。在实验2中,研究了大小(5、10、20),与异常模式的数量(1、2、4)部分交叉的类别的传输性能。在这里,泛化梯度在类别大小较小时跟踪训练实例的反馈类别,而在类别大小较大时跟踪原型样本。即使例外模式的比例保持不变,类别大小增加的好处仍然显现。这些结果以及其他结果与分类的混合模型一致,在分类模型中,示例性影响在小类别和/或高错误率中占主导地位,原型影响在大类别中占主导地位。

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