Autonomous learning of cyclovergence control based on Active Efficient Coding

Abstract

A central aspect of the development of visual perception is the autonomous calibration of various kinds of eye movements including saccadic, pursuit, or vergence eye movements. An important but less well-studied class of eye movements are so-called torsional eye movements, where the eyes rotate around the line of sight. In humans, such torsional eye movements obey certain lawful relationships such as Listing's Law. However, it is still an open question how these eye movements develop and what learning processes may contribute to their development. Here we propose a model of the development of torsional eye movements based on the active efficient coding (AEC) framework. AEC models the joint development of sensory encoding and movements of the sense organs to maximize the overall coding efficiency of the perceptual system. Our results demonstrate that optimizing coding efficiency in this way leads to torsional eye movements consistent with Listing's Law describing torsional eye movements in humans. This suggests that humanoid robots aiming to maximize the coding efficiency of their visual systems could also benefit from physical or simulated torsional eye movements.