Cobweb: An Incremental and Hierarchical Model of Human-Like Category Learning
This work addresses the need for a broader evaluation of Cobweb as a model of human category learning, which is incremental in nature.
The study tackled the gap in evaluating Cobweb as a model of human categorization by establishing its alignment with classical human category learning effects and demonstrating its flexibility to exhibit both exemplar- and prototype-like learning within a single framework.
Cobweb, a human-like category learning system, differs from most cognitive science models in incrementally constructing hierarchically organized tree-like structures guided by the category utility measure. Prior studies have shown that Cobweb can capture psychological effects such as 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 framework. These findings set the stage for further research on Cobweb as a robust model of human category learning.