Self-Calibrating Saccade-Vergence Interactions

Binocular saccades in the three-dimensional world entail changes in the direction and depth of gaze. Newborns must autonomously learn to control these two types of eye movements concurrently. Here, we put forward a computational model of the joint development of conjugate saccades and vergence, resulting in the self-calibration of disjunctive saccades. This work builds on principles of information theory and efficient coding. We propose that conjugate saccades and vergence are learned together with the shared motivation of maximizing the mutual information between binocular visual inputs and their neural representations. We train and evaluate our model using an infant embodiment in a simulated playroom. Our results are compatible with experimental evidence and show that vergence predicts the binocular disparity at the target of a saccade.