# Exploring Deep Neural Networks in Simulating Human Vision through Five Optical Illusions
## 4 Discussion
This is likely due to the VGG series' architecture focusing on consecutive convolution operations, making it proficient in processing single-dimensional shape features. However, this structure is limited when facing complex image structures, as it focuses only on single-point features or rectangular edges and is unable to fully capture subtle color changes. The Muller-Lyer illusion, involving distortion in length perception where angles between lines affect length judgment, saw VGG16 and VGG19 performing well, effectively revealing their understanding of length and spatial relationships by focusing on the combination of arrows and lines. The more structurally simplistic AlexNet did not show significant visual illusion responses. In contrast, the higher-ranking brain score ResNet series showed high representational similarity between different colors in the color assimilation experiment (see Figure 4). This might be due to the pre-activation modules in ResNet architecture, allowing effective feature transmission at deeper levels but possibly reducing sensitivity to color details. In the Hermann grid experiment, all ResNet models [16; 29; 30] showed only weak responses to gray grid flickering dots, reflecting limitations in processing complex visual information. However, in the Poggendorff illusion, ResNet152, with its deep network and complex feature extraction capabilities, successfully focused on line features in the feature heatmap, demonstrating its advantages in processing certain visual illusions. The DenseNet series [31] showed increasing representational dissimilarity in color assimilation with increasing architectural size, possibly due to DenseNet's dense connection strategy effectively capturing subtle color changes. However, despite some level of visual illusion response in Hermann grid and Muller-Lyer illusions, DenseNet's performance in the Poggendorff illusion was not significant, suggesting limitations in processing more complex spatial relationships. The Inception series [15] showed varied performance in color assimilation and Hermann grid illusions. For instance, Inception v3 performed better than v4 in the Hermann grid illusion, possibly due to its modular design being more adapted to this specific task. However, in the Muller-Lyer illusion, neither showed significant visual illusion responses, possibly due to limitations of the Inception models in processing length and spatial positioning. In the Poggendorff illusion, v3 showed more focus on line features than v4, possibly indicating its stronger capability in processing visual illusions. To understand the internal mechanisms of DNNs, we conducted a detailed analysis of 12 models at 6 different depth levels. In the color assimilation experiment, these models showed a consistent color processing pattern at the primary layer (see Figure 4C, depth 1), indicating similar color recognition strategies in early processing stages across different models. Notably, this early layer similarity might reflect a shared foundational mechanism in color processing, analogous to findings by [PERSON] et al. (2020) ([PERSON] et al. (2020)), who observed that CNNs trained for low-level visual tasks demonstrate human-like responses to brightness and color illusions. However, they also highlighted a divergence in illusion perception between CNNs and human vision, emphasizing the limitations of linear models and the complex, nonlinear behavior of more sophisticated network architectures. Figure 4C illustrates the highest frequency ranking changes of color depth under varying depths in DNNs, with most models undergoing significant changes in color processing strategies beyond the initial layer. This is in contrast to [PERSON] et al. 's observation that simple CNNs exhibit features akin to the human visual system, such as center-surround receptive fields. Our analysis further shows that only DenseNet121 and DenseNet169 maintained the same 132 ranking as the initial layer for many colors, underscoring a potential consistency with biological visual systems at early layers.