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.

中文翻译：

---

关于简单交替类别结构的推广

人类认知研究中的一个基本问题是人们如何学习从示例的属性中预测其类别成员。领先的方法在与存储的示例进行比较、捕获统计规律的抽象或逻辑规则方面考虑了广泛的数据。在三个实验中，参与者在由定期交替的类成员区域 (ABAB) 组成的低维、连续值空间中学习了类别结构。依赖度量是训练空间范围之外的新项目的泛化性能。人类学习者经常扩展交替模式——鉴于基于相似性或维度规则的领先分类理论无法预测这种行为，这一发现非常有趣。此外，