Abstract
Recent literature established the highly adaptive nature of echolocation: both toothed whales and bats modify their signals and sonar beam movements dynamically based on echoes from previous emissions in a task-depending manner. However, no theoretical framework systematically interprets these search processes across varying taxa and experimental conditions. To address this need, we cast the “infotaxis” model originally proposed for simulating moth odor tracking into the context of echolocation-based target search. Infotaxis posits that the animal chooses their next action to maximize the expected information gain. By doing so, the searching animal balances the exploration of new information about its environment with exploitation of existing information. The echolocator chooses the next sonar beam location and signal content based on its internal representations of the search space and sensory uncertainty. We model the echolocator’s internal representation of the search space, the echo signatures of the target and clutter objects, and information acquired from echoes as probabilistic distributions and employ a Bayesian update of the target distribution after each echolocation reception. Simulations show the repeated inspection and switching among objects commonly observed in behavioral experiments of echolocation search. [Work supported by ONR MURI program.]