2024/03/06 by Xin Lian, Sashank Varma, Lian, Xin +3 · 2 citations
Neuroscience · #Artificial Intelligence (cs.AI) #Cognitive Science and Education Research #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2403.03835
openalex publication_date 2024/03/06 · openalex created_date 2024/03/08 · openalex updated_date 2026/07/28
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.