A Computational Model for Distance Perception Based on Visual Attention Redeployment in Locomotor Infants

Abstract

Self-locomotion experience of infants has been argued to improve perception of distance, as visual attention is drawn to previously undetected or ignored depth specifying information. We present a computational model to evaluate how does self-locomotion experience influences the estimation of distance in infants. The model assigns an estimated distance label to salient objects in the scene, through a Binocular Neural Network (BNN) that computes binocular disparities. Emphasizing on key aspects of locomotion experience, two BNN are trained, one for non-locomotor infants and one for locomotor infants. The validation and test stages of the process show a significant improvement on the distance estimation task for the BNN trained with locomotor experience. This result is added to previous evidence which supports that locomotion in infants is an important step in cognitive development.