Psychology Department, Binghamton University.
Cogn Sci. 2021 Apr;45(4):e12972. doi: 10.1111/cogs.12972.
A fundamental question in the study of human cognition is how people learn to predict the category membership of an example from its properties. Leading approaches account for a wide range of data in terms of comparison to stored examples, abstractions capturing statistical regularities, or logical rules. Across three experiments, participants learned a category structure in a low-dimension, continuous-valued space consisting of regularly alternating regions of class membership (A B A B). The dependent measure was generalization performance for novel items outside the range of the training space. Human learners often extended the alternation pattern--a finding of critical interest given that leading theories of categorization based on similarity or dimensional rules fail to predict this behavior. In addition, we provide novel theoretical interpretations of the observed phenomenon.
在人类认知研究中，一个基本问题是人们如何学习根据示例的属性来预测其类别归属。主流方法通过与存储示例的比较、捕获统计规律的抽象或逻辑规则来解释各种数据。在三个实验中，参与者在一个低维、连续值空间中学习类别结构，该空间由规则交替的类别成员区域（A B A B）组成。因基于相似性或维度规则的分类理论未能预测到这种行为，所以依赖的衡量标准是对训练空间之外的新物品的泛化性能。人类学习者经常扩展交替模式——这一发现具有重要意义。此外，我们还对所观察到的现象提供了新颖的理论解释。
