Abstract

Quilty-Dunn et al. argue that DCNNs optimized for image classification exemplify structural disanalogies to human vision. A different kind of artificial vision—found in reinforcement learning agents navigating artificial three-dimensional environments—can be expected to be more human-like. Recent work suggests that language-like representations substantially improves these agents’ performance, lending some indirect support to the LoTH.