Learning Hierarchical Integration of Foveal and Peripheral Vision for Vergence Control by Active Efficient Coding
This work addresses the challenge of coordinating visual information from different retinal regions for precise eye movements, which is incremental as it builds on the active efficient coding framework.
The paper tackled the problem of integrating foveal and peripheral vision for vergence control in eye movements, proposing a hierarchical approach that outperformed prior methods with better alignment and reduced oscillation in realistic environments.
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.