  The active efficient coding (AEC) framework parsimoniously explains the joint
development of visual processing and eye movements, e.g., the emergence of
binocular disparity selective neurons and fusional vergence, the disjunctive
eye movements that align left and right eye images. Vergence can be driven by
information in both the fovea and periphery, which play complementary roles.
The high resolution fovea can drive precise short range movements. The lower
resolution periphery supports coarser long range movements. The fovea and
periphery may also contain conflicting information, e.g. due to objects at
different depths. While past AEC models did integrate peripheral and foveal
information, they did not explicitly take into account these characteristics.
We propose here a two-level hierarchical approach that does. The bottom level
generates different vergence actions from foveal and peripheral regions. The
top level selects one. We demonstrate that the hierarchical approach performs
better than prior approaches in realistic environments, exhibiting better
alignment and less oscillation.
