Interactions of Linguistic and Domain Overhypotheses in Category Learning

TL;DR

This paper investigates how linguistic labels and conceptual domain biases interact as overhypotheses in human category learning, revealing their combined influence on learning efficiency and accuracy.

Contribution

It models and expands on previous work by analyzing the interaction between linguistic and domain biases in category learning, highlighting their joint effects.

Findings

Both biases significantly influence learning outcomes.

Linguistic labels can facilitate or hinder category acquisition.

Domain biases shape the features deemed most relevant for categories.

Abstract

For humans learning to categorize and distinguish parts of the world, the set of assumptions (overhypotheses) they hold about potential category structures is directly related to their learning process. In this work we examine the effects of two overhypotheses for category learning: 1) the bias introduced by the presence of linguistic labels for objects; 2) the conceptual 'domain' biases inherent in the learner about which features are most indicative of category structure. These two biases work in tandem to impose priors on the learning process; and we model and detail their interaction and effects. This paper entails an adaptation and expansion of prior experimental work that addressed label bias effects but did not fully explore conceptual domain biases. Our results highlight the importance of both the domain and label biases in facilitating or hindering category learning.