Department of Psychology and Neuroscience.
Department of Psychology, Binghamton University.
J Exp Psychol Gen. 2018 Nov;147(11):1571-1596. doi: 10.1037/xge0000517.
This article examines relational category learning in light of 2 influential theories of concept acquisition: the structure-mapping theory of analogy and theories of feature-based category learning. According to current theories of analogy, comparing 2 instances of a relational concept enables alignment of their elements and reveals their shared relational structure. Therefore, learning relationally defined categories should be faster when comparing items of the same category than when comparing items of different categories. By contrast, feature-based theories predict a benefit of between-category comparisons, because such comparisons direct attention to the features that discriminate the categories. The present experiments tested these predictions using a 2-category classification-learning task in which 2 items are presented on every trial: either in the same category (match condition) or in different categories (contrast condition). Subjects in the contrast condition outperformed those in the match condition for feature-based categories (Experiment 1) and across 4 different types of relational categories (Experiments 1-4). Although theorists have posited that structure-mapping theory is directly applicable to relational category learning, the present findings pose a distinct challenge to this assertion. We propose that many relational categories are learnable based solely on which relations are present in the stimulus rather than requiring explicitly compositional representations based on role-filler binding. This process would be akin to feature processing and would not require structural alignment. This theoretical proposal, together with the empirical results, may lead to a better understanding of when people do and do not engage in the cognitively demanding process of structural alignment. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
本文考察了关系范畴学习,涉及到类比的结构映射理论和基于特征的范畴学习理论这两个有影响力的概念获取理论。根据当前类比理论,比较一个关系概念的两个实例可以使它们的元素对齐,并揭示它们共享的关系结构。因此,当比较同一范畴的项目时,学习关系定义的范畴应该比比较不同范畴的项目更快。相比之下,基于特征的理论预测了跨范畴比较的好处,因为这种比较将注意力集中在区分范畴的特征上。本实验使用 2 类分类学习任务来检验这些预测,在该任务中,每个试验都会呈现 2 个项目:要么在同一范畴内(匹配条件),要么在不同范畴内(对比条件)。在对比条件下,基于特征的类别(实验 1)和 4 种不同类型的关系类别(实验 1-4)的被试比在匹配条件下的表现要好。尽管理论家们假设结构映射理论可以直接应用于关系范畴学习,但这一发现对这一说法提出了明显的挑战。我们提出,许多关系范畴仅基于刺激中存在哪些关系就可以学习,而不需要基于角色填充绑定的显式组合表示。这个过程类似于特征处理,而不需要结构对齐。这个理论建议,以及实证结果,可能会更好地理解人们在什么时候会以及不会进行认知上要求很高的结构对齐过程。(心理学文摘数据库记录(c)2018 APA,保留所有权利)。