# Exploring Deep Neural Networks in Simulating Human Vision through Five Optical Illusions
## 1 Introduction  
Visual illusions, as fascinating phenomena, have long been a hot topic in psychological and neuroscience research. These illusions reveal the limitations and characteristics of the human visual perception system in processing visual information [1, 2]. Traditional studies of visual illusions emphasize the subjectivity and complicated internal mechanisms of the brain in processing visual stimuli [3, 4]. These studies provide critical insights into how the human brain interprets complex visual environments, aiding our understanding of perceptual biases that may arise under various environmental conditions. With the advent of deep neural networks (DNNs), inspired by the structural and functional aspects of the human brain, and the development of interdisciplinary approaches combining neuroscience and artificial intelligence, researchers have begun to leverage these computational models to simulate and probe into the brain's processing of visual information [5, 6]. This mutual inspiration and influence signify an integrative evolution of neuroscience and AI, offering novel insights and innovative possibilities to both fields (Figure 1A). Particularly, convolutional neural networks (CNNs), a subtype of DNNs, have demonstrated remarkable capabilities in a variety of visual tasks, mirroring the brain's hierarchical organization to manage complex visual patterns [7, 8]. This synergy has not only deepened our theoretical understanding of visual processing but also introduced fresh methodologies for visual illusion studies. For example, employing DNNs to simulate visual illusions hasshed light on their utility in decoding facets of human visual perception, with some models exhibiting responses strikingly similar to human behavior [9; 10; 11; 12]. Such comparisons enrich our comprehension of the neural substrates underlying visual illusions and hint at underlying computational principles that could be mimicked in visual systems [13; 14]. This burgeoning field intersection not only redefines our grasp of the human visual system but also propels AI research forward, demonstrating that emulating the brain's visual information processing approach can lead to advanced visual cognitive functions in AI [15; 16]. Concurrently, it equips neuroscientists with innovative tools to dissect complex visual phenomena [17; 18]. However, the quest to perfectly replicate the nuanced and diverse nature of human perception in DNNs reveals the models' current limitations and underscores the vast uncharted territories in both domains [19; 20]. Despite deep neural network (DNN) strides in approximating human visual processing, their application in exploring visual illusions remains surprisingly narrow, typically confined to demonstrating the presence of illusions in isolated models. For instance, [PERSON] et al. (2019) [21] utilized DNNs for binary classification to discern the presence of visual illusions, identifying the possibility of illusion in the Muller-Lyer illusion, yet failing to demonstrate the presence of illusions in four other geometric visual illusions. This approach underscores the limitations of relying on single models to test and explain phenomena, given the plethora of brain-like DNN architectures available. Similarly, [PERSON] (2021) [11] observed visual illusions in the context of ImageNet but was unable to explain why these illusions occur in DNNs. These examples highlight the need for a more comprehensive approach that encompasses multiple models and types of illusions to better understand the phenomenon and its implications for both artificial and human vision. Addressing this gap, our study pioneers the examination of 5 classic visual illusions--color assimilation, Hermann grid, Muller-Lyer, Zollner, and Poggendorff--through 12 DNNs,  
Figure 1: Interdisciplinary: Learning relationship between neuroscience based on human behavior and deep learning.