Modeling human echolocation using a Kalman filter

Abstract

Some blind individuals use echolocation, a skill that allows them to better navigate their environment using echoes from self-generated mouth clicks reflected off surrounding surfaces. Echolocation involves a complex interplay of sensory accumulation, information processing, dynamic prediction, motor planning and execution in real-time. Computational modeling offers a valuable approach to understanding the cognitive and neural mechanisms underlying echolocation performance, in particular the temporal dynamics of the process. We present a computational model of human echolocation behavior based on a Kalman filter, where we treat the echolocator as an active sensor that maintains an internal belief about the targets location and continuously refines it via echo feedback. The model, based on observations of echolocation in blind human experts, simulates the use of mouth clicks and returning echoes to localize and orient toward a target under varying conditions. In the experiment, the target is placed at a random azimuth in the frontal plane. An echolocator aims a series of mouth clicks in various directions and infers the target azimuth using acoustic information received from the click echoes. The system integrates three major components: (1) a simulation of echoacoustic interaural time differences (ITD) to estimate the relative head-target angle; (2) a Kalman filter that processes these ITDs to iteratively update probabilistic beliefs about target location and associated uncertainty; and (3) a motor control system that modulates head movements with the current belief state. The Kalman filter serves as a representation of the internal state of the observer, where its beliefs drive the direction of head rotation, and its uncertainty estimates drive head velocity adjustments. Model performance demonstrates that simple predictive computational approaches can reproduce key aspects of echo-guided sensorimotor learning, providing a framework that may be leveraged to develop biologically plausible models, advance understanding of best practices, and potentially improve intervention strategies.