Proceedings of the Annual Meeting of the Cognitive Science Society

Exploration in sequential decision problems is a computationally challenging problem. Yet, animals exhibit effective exploration strategies, discovering shortcuts and efficient routes toward rewarding sites. Characterizing this efficiency in animal exploration is an important goal in many areas of research, from ecology to psychology and neuroscience to machine learning. In this study, we aim to understand the exploration behavior of animals freely navigating a complex maze with many decision points. We propose an algorithm based on a few simple principles of animal movement from foraging studies in ecology and formalized using reinforcement learning. Our approach not only captures the search efficiency and turning biases of real animals but also uncovers longer spatial and temporal dependencies in the decisions of animals during their exploration of the maze. Through this work, we aspire to unveil a novel approach in cognitive science of drawing interdisciplinary inspiration to advancing the field's understanding of complex decision-making.