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论类别学习中先验知识与刺激结构的相互作用

On the interaction of prior knowledge and stimulus structure in category learning.

作者信息

Livingston K R, Andrews J K

机构信息

Department of Psychology, Vassar College, Poughkeepsie, New York 12601, USA.

出版信息

Q J Exp Psychol A. 1995 Feb;48(1):208-36. doi: 10.1080/14640749508401385.

DOI:10.1080/14640749508401385
PMID:7754083
Abstract

Contemporary theories of categorization propose that concepts are coherent in virtue of being embedded in a network of theories about the world. Those theories function to pick out some of the many possible features of a set of objects as most salient for purposes of classification, a process that is complex and still poorly understood (Murphy & Medin, 1985). Part of what makes this account incomplete is a lack of information as to (1) what makes a feature salient on a given occasion and (2) how feature salience interacts with category structure to determine the course of learning. We report on the results of three studies of category learning using complex schematic drawings to show that (1) the contrast set defined by one's initial encounters with category exemplars can be a source of individual differences in feature salience assignments; (2) such effects are short-lived in the face of clear evidence about actual feature diagnosticity; and (3) more robust prior hypotheses interact with category structure to either enhance learning or impede it. The enhancement occurs when the hypothesis emphasizes category-relevant features, even if the hypothesis is in fact incorrect. A hypothesis that assigns high salience to irrelevant features impedes learning. Learning does occur as feedback concerning category structure leads to enhanced salience for relevant features. Salience of irrelevant features remains high, however, suggesting that such learning as occurs involves augmentation and not total revision of the (incorrect) prior hypothesis.

摘要

当代分类理论认为,概念因其嵌入关于世界的理论网络而具有连贯性。这些理论的作用是在一组对象的众多可能特征中,挑选出对分类目的而言最为显著的一些特征,这一过程复杂且仍未得到充分理解(墨菲和梅丁,1985)。该解释不完整的部分原因在于缺乏以下两方面信息:(1)在特定情况下使某个特征显著的因素是什么;(2)特征显著性如何与类别结构相互作用以决定学习进程。我们报告了三项使用复杂示意图进行类别学习的研究结果,结果表明:(1)个体最初接触类别范例时所定义的对比集可能是特征显著性分配中个体差异的一个来源;(2)面对关于实际特征诊断性的明确证据时,此类影响是短暂的;(3)更稳固的先验假设与类别结构相互作用,要么促进学习,要么阻碍学习。当假设强调与类别相关的特征时,就会出现促进作用,即使该假设实际上是错误的。将高显著性赋予无关特征的假设会阻碍学习。随着关于类别结构的反馈导致相关特征的显著性增强,学习确实会发生。然而,无关特征的显著性仍然很高,这表明所发生的此类学习涉及对(错误的)先验假设的扩充而非彻底修正。

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