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类别内不连续性在知觉类别学习中与言语规则复杂性相互作用。

Within-category discontinuity interacts with verbal rule complexity in perceptual category learning.

作者信息

Maddox W Todd, Filoteo J Vincent, Lauritzen J Scott

机构信息

Department of Psychology, University of Texas at Austin, Austin, TX 78712, USA.

出版信息

J Exp Psychol Learn Mem Cogn. 2007 Jan;33(1):197-218. doi: 10.1037/0278-7393.33.1.197.

Abstract

A test of the predicted interaction between within-category discontinuity and verbal rule complexity on information-integration and rule-based category learning was conducted. Within-category discontinuity adversely affected information-integration category learning but not rule-based category learning. Model-based analyses suggested that some information-integration participants improved performance by recruiting more "units" in the discontinuous condition. Verbal rule complexity adversely affected rule-based category learning but not information-integration category learning. Model-based analyses suggested that the rule based effect was on both decision criterion learning and variability in decision criterion placement. These results suggest that within-category discontinuity and decision rule complexity differentially impact information-integration and rule-based category learning and provide information regarding the detailed processing characteristics of these two proposed category learning systems.

摘要

针对类别内不连续性与言语规则复杂性在信息整合和基于规则的类别学习上的预测交互作用进行了一项测试。类别内不连续性对信息整合类别学习产生了不利影响,但对基于规则的类别学习没有影响。基于模型的分析表明,一些信息整合参与者在不连续条件下通过招募更多“单元”来提高表现。言语规则复杂性对基于规则的类别学习产生了不利影响,但对信息整合类别学习没有影响。基于模型的分析表明,基于规则的效应体现在决策标准学习和决策标准放置的变异性上。这些结果表明,类别内不连续性和决策规则复杂性对信息整合和基于规则的类别学习有不同的影响,并提供了关于这两种提出的类别学习系统详细加工特征的信息。

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