Exploring Deep Neural Networks in Simulating Human Vision through Five Optical Illusions

Abstract

Recent research has delved into the biological parallels between deep neural networks (DNNs) in vision and human perception through the study of visual illusions. However, the bulk of this research is currently constrained to the investigation of visual illusions within a single model focusing on a singular type of illusion. There exists a need for a more comprehensive explanation of visual illusions in DNNs, as well as an expansion in the variety of illusions studied. This study is pioneering in its application of representational dissimilarity matrices and feature activation visualization techniques for a detailed examination of how five classic visual illusions are processed by DNNs. Our findings uncover the potential of DNNs to mimic human visual illusions, particularly highlighting notable differences in how these networks process illusions pertaining to color, contrast, length, angle, and spatial positioning. Although there are instances of consistency between DNNs and human perception in certain illusions, the performance distribution and focal points of interest within the models diverge from those of human observers. This study significantly advances our comprehension of DNNs’ capabilities in handling complex visual tasks and their potential to emulate the human biological visual system. It also underscores the existing gaps in our understanding and processing of intricate visual information. While DNNs have shown progress in simulating human vision, their grasp of the nuance and intricacy of complex visual data still requires substantial improvement.