Exemplar similarity and the development of automaticity.
Exemplar similarity and the development of automaticity.
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DOI：
10.1037//0278-7393.23.2.324
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发表时间：
1997-03
期刊：
影响因子：
--
通讯作者：
T. Palmeri
中科院分区：
文献类型：
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作者：
T. Palmeri
样例相似性对自动性发展的影响进行了研究，在一项任务中，被试判断6到11个点之间的随机模式的数量。经过几天的训练后，所有数量水平的反应时间都是相同的，这表明自动性的发展。在实验1中，对新模式的反应时间是它们与旧模式相似性的函数。在实验2中，反应的模式具有高类别内相似性变得更快的自动化比反应的模式具有低类别内相似性。在实验3中，反应的模式与高类别间相似性变得自动化更慢，比反应的模式与低类别间相似性。一个新的理论，基于范例的随机游走（EBRW）模型，被用来解释的结果。结合G. D. Logan（1988）的自动性实例理论和R. M. Nosofsky（1986）的广义语境范畴化模型，该理论在竞争性随机游走决策过程中嵌入了一个动态的基于相似性的记忆提取机制。
Effects of exemplar similarity on the development of automaticity were investigated with a task in which participants judged the numerosity of random patterns of between 6 and 11 dots. After several days of training, response times were the same at all levels of numerosity, signaling the development of automaticity. In Experiment 1, response times to new patterns were a function of their similarity to old patterns. In Experiment 2, responses to patterns with high within-category similarity became automatized more quickly than responses to patterns with low within-category similarity. In Experiment 3, responses to patterns with high between-category similarity became automatized more slowly than responses to patterns with low between-category similarity. A new theory, the exemplar-based random walk (EBRW) model, was used to explain the results. Combining elements of G. D. Logan's (1988) instance theory of automaticity and R. M. Nosofsky's (1986) generalized context model of categorization, the theory embeds a dynamic similarity-based memory retrieval mechanism within a competitive random walk decision process.