Category Learning in Context : Modelling an Assimilation Process in Self-regulated Category Learning

Abstract

Category learning, a fundamental cognitive ability, is significantly influenced by variability. In this research, we propose a model describing how people adjust information search in self-regulated category learning to the level of category variability. Participants in the self-regulated category learning task sampled from two categories until they felt confident in categorizing novel objects. Our model assumes an influence of the variability of the focal and counter category on sampling by considering a within-category and between-category processes. In both processes, variability is quantified using an information-theoretic measure. Within this model, we test if a between-category process can be better conceptualized as either a contrasting or an assimilation process. The comparison of both processes support a between-category assimilation process, where the sample size adjusts to the counter category's variability. This novel focus sheds light on between-category dynamics, providing valuable insights into the mechanisms of category learning.