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是一种类似人类的类别学习系统，与其他增量式分类模型不同，它通过类别效用度量构建层级组织的认知树结构。先前研究表明，Cobweb能够捕捉基本层级、典型性效应和扇效应等心理现象。然而，将Cobweb作为人类分类模型的更广泛评估仍然缺失。本研究填补了这一空白，确立了Cobweb与经典人类类别学习效应的一致性，并探讨了其在单一模型中同时表现出实例学习和原型学习灵活性的能力。这些发现为未来将Cobweb作为人类类别学习的综合模型研究奠定了基础。