Training Set Coherence and Set Size Effects on Concept Generalization and Recognition
Training Set Coherence and Set Size Effects on Concept Generalization and Recognition
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DOI：
10.1037/xlm0000824
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发表时间：
2020-08-01
影响因子：
2.6
通讯作者：
Zeithamova, Dagmar
中科院分区：
文献类型：
--
作者：
Bowman, Caitlin R.;Zeithamova, Dagmar
建立概念性知识，并将其推广到新的情况，是人类记忆的一个关键功能。范畴学习范式一直被用来理解知识泛化的机制。在本研究中，我们测试了促进新概念形成的条件。参与者接受了6个训练条件中的1个，每个类别的示例数量（集合大小）及其与类别平均值的相对相似性（集合一致性）不同。性能指标包括类别学习率，将类别概括为与原型相似的新项目的能力，以及对单个示例的识别记忆。在分类中，高的集合一致性导致更快的学习和更好的泛化，而集合大小的影响不大。不同条件的确认并无可靠差异。我们还测试了用于分类和识别决策的记忆表征的性质，使用定量原型和样本模型适合于行为反应。原型模型基于类别的集中趋势来描述抽象的类别表示，而范例模型则认为类别是由单个类别成员来表示的。原型策略的使用在分类过程中增加了集的一致性，这表明连贯的训练集有助于提取类别内的共性。我们的结论是，从一组连贯的例子中学习是形成广泛概括的抽象知识的有效手段。
Building conceptual knowledge that generalizes to novel situations is a key function of human memory. Category-learning paradigms have long been used to understand the mechanisms of knowledge generalization. In the present study, we tested the conditions that promote formation of new concepts. Participants underwent 1 of 6 training conditions that differed in the number of examples per category (set size) and their relative similarity to the category average (set coherence). Performance metrics included rates of category learning, ability to generalize categories to new items of varying similarity to prototypes, and recognition memory for individual examples. In categorization, high set coherence led to faster learning and better generalization, while set size had little effect. Recognition did not differ reliably among conditions. We also tested the nature of memory representations used for categorization and recognition decisions using quantitative prototype and exemplar models fit to behavioral responses. Prototype models posit abstract category representations based on the category's central tendency, whereas exemplar models posit that categories are represented by individual category members. Prototype strategy use during categorization increased with increasing set coherence, suggesting that coherent training sets facilitate extraction of commonalities within a category. We conclude that learning from a coherent set of examples is an efficient means of forming abstract knowledge that generalizes broadly.