人工类别学习的研究对于理解以概念/规则形成为中心的学习活动有重要意义,到目前为止,有代表性的人工类别学习研究范式主要有基于规则的任务、信息整合任务、原型变形任务和天气预报。我们对这些任务在人类学习问题研究中的价值和启示做了讨论,也提出类别归纳加工在理解类别学习中可能具有核心地位,这些分析将有益于开展更具整合性的类别学习研究。 The study of artificial category learning is of great significance for understanding the learning activities centered on concept / rule formation. So far, the representative study paradigms of artificial categories mainly include rule-based tasks, information integration tasks, prototyping tasks and weather forecast. We discuss the value and implications of these tasks in the study of human learning and also suggest that categorical induction may play a central role in understanding class learning and that these analyzes will benefit the more integrated category study.