Learning Hierarchical Integration of Foveal and Peripheral Vision for Vergence Control by Active Efficient Coding

TL;DR

This paper introduces a hierarchical active efficient coding model that integrates foveal and peripheral vision for vergence control, improving eye movement accuracy and stability in complex visual environments.

Contribution

It presents a novel two-level hierarchical model explicitly combining foveal and peripheral information for vergence, outperforming previous models.

Findings

Hierarchical model achieves better alignment of eye movements.

Model exhibits reduced oscillation compared to prior approaches.

Improved performance in realistic visual environments.

Abstract

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.