Topic: saccading

Predicting sensory input responses to update an internal ``world model'' is a core principle in cognitive learning theories that try to explain mechanisms behind representation learning in the brain [1,2]. Recent machine learning methods have also adopted predictive world modeling for self-supervised representation learning [3,4,5,6]. In this work, we investigate the perceptual and visuospatial capabilities of a self-supervised predictive world model trained in a manner approximating the active nature of human visual perception. We sample a sequence of fixations from image saliency maps [7], e