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.

Cobweb 是一种类似于人类的类别学习系统，通过使用类别效用度量构建具有层级组织的认知树状结构。该研究证明了 Cobweb 能够捕捉到基层、典型性和粉丝效应等心理效应，从而填补了之前对 Cobweb 作为人类类别学习模型的综合评估的缺失。同时，它还探索了 Cobweb 在一个模型中能够同时表现出示例和原型化学习的灵活性。这些发现为进一步研究 Cobweb 作为人类类别学习的综合模型奠定了基础。