Strategies and classification learning
Strategies and classification learning
复制标题
策略和分类学习
DOI：
10.1037/0278-7393.7.4.241
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发表时间：
1981
期刊：
影响因子：
--
通讯作者：
Edward E. Smith
中科院分区：
文献类型：
--
作者：
D. Medin;Edward E. Smith
策略如何影响缺乏必要和充分属性的类别的学习？通常的答案是，不同的策略对应不同的模型。在这篇文章中，我们提供了一个替代观点的证据-策略的变化引起的指令只影响的信息量表示的属性，而不是在这些表示的过程中操作。实验要求受试者将图式面孔分为两类。三组受试者使用不同的指令：大致上，形成每个类别的原型，将每个类别作为规则加例外或标准中性指令进行学习。除了学习面孔（第一阶段），受试者还接受了学习和新面孔的迁移测试（第二阶段）和学习面孔的快速分类测试（第三阶段）。有性能差异，在所有三个阶段，由于指令，但这些结果很容易占的具体变化的陈述所提出的背景模型的Medin和Schaffer，也就是说，策略似乎只影响存储的信息量关于每个样本的属性。
How do strategies affect the learning of categories that lack necessary and sufficient attributes? The usual answer is that different strategies correspond to different models. In this article we provide evidence for an alternative view— Strategy variations induced by instructions affect only the amount of information represented about attributes, not the process operating on these representations. The experiment required subjects to classify schematic faces into two categories. Three groups of subjects worked with different sets of instructions: roughly, form a prototype of each category, learn each category as a rule-plus-exception, or standard neutral instructions. In addition to learning the faces (Phase 1), subjects were given transfer tests on learned and novel faces (Phase 2) and speeded categorization tests on learned faces (Phase 3). There were performance differences in all three phases due to instructions, but these results were readily accounted for by specific changes in the representations posited by the context model of Medin and Schaffer; that is, strategies seemed to affect only the amount of information stored about each exemplar's attributes.