Towards a Unified Model Describing Multiple Tasks: Extending the Retrieving Effectively from Memory Model to Categorization

This study extends the Retrieving Effectively from Memory model, a prominent computational model of episodic memory, to the domain of categorization. Our modeling approach begins with the assumption that same-category items share common features representing defining characteristics of their category, and that they are encoded in the same category list context. We then assumed that category judgments occur based on the comparison of an item’s averaged similarity to the exemplars from each category. We use this model to explore how the learning modes of observation and classification might influence category learning and consider several strategies that may emerge during the classification mode. Model simulation results indicate that different strategies which people might adopt during classification can either confer an advantage or pose a disadvantage in category learning. These findings suggest potential avenues for future research, particularly in exploring diverse strategies employed during learning.This paper was submitted to the 2024 Annual Meeting of the Cognitive Science Society and appears in the conference proceedings.