Modeling echolocation as an active pursuit of information via infotaxis

Abstract

Echolocation is a closed-loop active sensing modality in which animals not only choose how they move to acquire information, but also actively modulate incoming sensory (echo) information by shaping the acoustic signals they emit to probe the environment. While many models describe how echolocating animals react to prior echoes by adjusting subsequent behavior, few explicitly model how they cognitively reason about information embedded in echoes when determining future actions. Here, we extend "infotaxis," an information-greedy algorithm originally developed for olfactory search, to sonar sensing by formulating an echolocating agent searching for a single target under sensory uncertainty characterized by probabilities of miss and false alarm. Through analytical and computational analyses, we show that the characteristic exploration-exploitation balance of infotaxis also emerges in echolocation, and that the efficiency and reliability of infotaxis search depend strongly on sensory information quality. Compared with a maximum a posteriori agent that always directs the beam to the most probable target location, the infotaxis agent consistently completes searches with fewer pings and greater robustness to sensory uncertainty. These results highlight information as a powerful concept for understanding active sensing and developing models for sonar-guided autonomy in both biological and engineered systems.