Cobweb: A Model of Human-Like Category Learning

Core Concepts

Cobweb is an incremental and hierarchical model that aligns with classical human category learning effects, showcasing flexibility in exhibiting both exemplar- and prototype-like learning within a single model.

Abstract

Cobweb is a human-like category learning system that constructs hierarchically organized cognitive structures using the category utility measure. It captures psychological effects such as basic-level, typicality, and fan effects. The model shows potential as a comprehensive model of human categorization by aligning with classical findings and demonstrating flexibility in learning approaches.

Stats

Cobweb forms concepts given sequentially presented instances represented as attribute-value pairs. Cobweb uses Category Utility to evaluate feature predictive power for concept formation. The average category utility measure allows Cobweb to compare partitions with varying numbers of concepts. Cobweb predicts unobserved feature values based on specific nodes along the categorization path. The information-theoretic variant of category utility captures the informativeness of categories in reducing feature value uncertainty.

Quotes

"Cobweb can capture psychological effects such as basic-level, typicality, and fan effects." "Cobweb exhibits aspects of both prototype-like and exemplar-like representations." "Cobweb's flexibility enables it to span the spectrum between prototype-like and exemplar-like representations."

Key Insights Distilled From

Cobweb

by Xin Lian,Sas... at arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03835.pdf

Deeper Inquiries

Can Cobweb account for human categorization data handled by other models in cognitive science

Cobweb has shown promising results in aligning with human categorization data handled by other models in cognitive science. Through its hierarchical and incremental learning approach, Cobweb can capture various psychological effects such as the basic-level, typicality, and fan effects. This ability to account for these effects demonstrates its capability to model human category learning effectively.

Does Cobweb exhibit more prototype- or exemplar-like behavior, or is it somewhere in between

Cobweb exhibits a flexible behavior that encompasses aspects of both prototype-like and exemplar-like representations. It does not strictly adhere to either paradigm but rather adapts based on the hierarchical structure it employs. This flexibility allows Cobweb to generate predictions that range from prototype- to exemplar-like, showcasing a balanced approach between the two types of categorization behaviors.

How does the hierarchical structure of Cobweb contribute to its flexibility as a categorization model

The hierarchical structure of Cobweb plays a crucial role in enhancing its flexibility as a categorization model. By organizing concepts into hierarchically organized cognitive tree-like structures, Cobweb can operate at different levels within the hierarchy. This enables it to make predictions based on specific concept nodes with varying degrees of integrated information, allowing for a more nuanced understanding of categories and their relationships. The hierarchical nature also contributes to Cobweb's ability to capture different categorization effects across basic-level and subordinate concepts, making it a versatile and comprehensive model of human category learning.