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基于范畴的归纳中的内隐和外显加工:当我们不思考时,归纳效果是否最佳?

Implicit and explicit processes in category-based induction: is induction best when we don't think?

机构信息

Department of Psychology, New York University.

Department of Psychology, University of Illinois at Urbana- Champaign.

出版信息

J Exp Psychol Gen. 2014 Feb;143(1):227-46. doi: 10.1037/a0032064. Epub 2013 Mar 18.

Abstract

In category-based induction (CBI), people use category information to predict unknown properties of exemplars. When an item's classification is uncertain, normative principles and Bayesian models suggest that predictions should integrate information across all possible categories. However, researchers previously have found that people often base their predictions on only a single category. In the present studies, we investigated the possible distinction between implicit and explicit processes in CBI. Predictions of an object's motion took the form of either a catching task (implicit) or a verbal answer (explicit). When subjects made predictions implicitly (Experiment 1), they used categories as Bayesian models predict. Explicit predictions (Experiment 2) showed clearly nonnormative use of categories. This distinction between implicit and explicit processes was replicated with a within-subjects design (Experiment 3). When subjects learned categories implicitly (categories were never mentioned) in Experiment 4, their explicit predictions did not reflect integration of information across categories but again showed a nonnormative pattern of category use. These results provide support for a distinction between implicit and explicit processes in CBI and furthermore suggest that the same category knowledge may result in normative or nonnormative responding, depending on the response mode.

摘要

在基于类别归纳(CBI)中,人们使用类别信息来预测示例的未知属性。当项目的分类不确定时,规范原则和贝叶斯模型表明,预测应该整合所有可能类别的信息。然而,研究人员此前发现,人们通常仅基于单一类别进行预测。在本研究中,我们研究了 CBI 中可能存在的隐式和显式过程之间的区别。物体运动的预测形式要么是捕捉任务(隐式),要么是口头回答(显式)。当被试进行隐式预测时(实验 1),他们使用了贝叶斯模型预测的类别。显式预测(实验 2)清楚地显示了对类别的非规范使用。这种隐式和显式过程之间的区别在一项内被试设计实验(实验 3)中得到了复制。当被试在实验 4 中隐式学习类别(从未提及类别)时,他们的显式预测并没有反映出跨类别的信息整合,而是再次表现出非规范的类别使用模式。这些结果为 CBI 中的隐式和显式过程之间的区别提供了支持,并且进一步表明,相同的类别知识可能导致规范或非规范的反应,具体取决于反应模式。

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