Cobweb, a human like category learning system, differs from other incremental categorization models in constructing hierarchically organized cognitive tree-like structures using the category utility measure. Prior studies have shown that Cobweb can capture psychological effects such as the basic level, typicality, and fan effects. However, a broader evaluation of Cobweb as a model of human categorization remains lacking. The current study addresses this gap. It establishes Cobweb's alignment with classical human category learning effects. It also explores Cobweb's flexibility to exhibit both exemplar and prototype like learning within a single model. These findings set the stage for future research on Cobweb as a comprehensive model of human category learning.

翻译：摘要：Cobweb作为一种类人类别学习系统，区别于其他增量式分类模型之处在于，它采用类别效用度量构建层级组织的认知树状结构。已有研究表明，Cobweb能够捕捉基本水平效应、典型性效应及扇效应等心理现象。然而，目前尚缺乏对Cobweb作为人类分类模型更全面的评估。本研究填补了这一空白，既验证了Cobweb与经典人类类别学习效应的一致性，又探索了该模型在同一框架下同时呈现样例式与原型式学习的灵活性。这些发现为后续将Cobweb发展为人类类别学习的综合模型奠定了基础。