策略与分类学习

D. Medin, Edward E. Smith
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引用次数: 178

摘要

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。在本文中,我们为另一种观点提供了证据——由指令引起的策略变化只影响表征属性的信息量,而不影响在这些表征上操作的过程。该实验要求受试者将图式脸分为两类。三组受试者使用不同的指令:大致形成每个类别的原型,作为规则加例外学习每个类别,或标准中性指令。除了学习面孔(第一阶段)外,还对被试进行了习得面孔和新面孔的迁移测试(第二阶段)和习得面孔的快速分类测试(第三阶段)。由于指令的不同,这三个阶段的表现都存在差异,但这些结果很容易被Medin和Schaffer的情境模型假设的表征的具体变化所解释;也就是说,策略似乎只影响存储在每个范例属性中的信息量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strategies and classification learning
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.
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