Cobweb: An Incremental and Hierarchical Model of Human-Like Category Learning

TL;DR

Cobweb is a hierarchical, incremental model of human-like category learning that constructs tree structures guided by category utility, capturing key psychological effects and demonstrating flexible exemplar- and prototype-like learning.

Contribution

This study provides a comprehensive evaluation of Cobweb, confirming its alignment with classical human categorization effects and demonstrating its flexibility in learning styles.

Findings

Cobweb captures basic-level, typicality, and fan effects.

It exhibits both exemplar- and prototype-like learning.

The model aligns well with classical human categorization phenomena.

Abstract

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.