Vergence Dynamics Predict Fixation Disparity

Abstract

The neural origin of the steady-state vergence eye movement error, called binocular fixation disparity, is not well understood. Further, there has been no study that quantitatively relates the dynamics of the vergence system to its steady-state behavior, a critical test for the understanding of any oculomotor system. We investigate whether fixation disparity can be related to the dynamics of opponent convergence and divergence neural pathways. Using binocular eye movement recordings, we first show that opponent vergence pathways exhibit asymmetric angle-dependent gains. We then present a neural model that combines physiological properties of disparity-tuned cells and vergence premotor cells with the asymmetric gain properties of the opponent pathways. Quantitative comparison of the model predictions with our experimental data suggests that fixation disparity can arise when asymmetric opponent vergence pathways are driven by a distributed disparity code.