A Computational Analysis of the Behavior of an ACT-R–BasedCuriosity Model

Human curiosity is an intrinsic motivation that sustains learning and exploratory behavior, and clarifying the correspondence between its computational principles and their realization processes remains an important challenge. In recent years, computational models based on Bayesian inference have been developed as a framework for explaining what human cognition computes and why such computations are appropriate. However, how these computational assumptions are concretely realized as information-processing processes has not been sufficiently articulated. In this study, we propose a framework for connecting algorithmic-level curiosity models with computational-level explanations based on Marr’s three levels of analysis. Specifically, we analyze simulation results from an intellectual curiosity model implemented in the cognitive architecture ACT-R using a maze exploration task. For multiple models differing in their levels of cognitive processing, relationships among several simulation metrics are described using Bayesian networks. The results reveal that, in models involving knowledge utilization, intrinsic rewards corresponding to curiosity influence performance through sustained exploration and knowledge generation, forming a coherent dependency structure. This study provides a method for organizing the behavior of existing algorithmic-level models in a computationally interpretable manner and offers a practical approach for bridging the computational and algorithmic levels of explanation.