# Exploring Deep Neural Networks in Simulating Human Vision through Five Optical Illusions
## 4 Discussion  
In this study, we utilized representational similarity analysis (RSA) and class activation mapping (CAM) visualization methods to delve into how brain-like deep neural networks (DNNs) respond to and process various visual illusions. We observed that DNNs display both similarities and differences with the human visual system in processing colors, contrasts, lengths, angles, and spatial relationships. For example, the high representational similarity of VGG19 RDMs constructed based on human perceptual data and stimulus data shows that DNNs produce length variation patterns similar to those seen in humans (Figures 11A,B). Also, the clear difference in the color ranking distribution of all DNNs in Figure 4B highlights the differences and gaps between DNNs and human perception. Specifically, by analyzing perception data from color assimilation, Hermann grid, Muller-Lyer, Zollner, and Poggendorff illusions (as shown in Figure 4 and Table 1), we evaluated whether DNNs exhibit responses similar to humans. RSA revealed the sensitivity of DNNs to specific features or patterns that might cause visual illusions. Post hoc interpretability methods like Grad-CAM or Grad-CAM++ helped us identify and visualize specific regions of interest that DNNs focus on while processing these illusions. Our experiments suggest that DNNs can certainly exhibit responses similar to human visual illusions. Particularly in Muller-Lyer and Poggendorff illusions, the high diagonal similarity in RDM and unified areas of attention revealed unique mechanisms in the model's decision-making process regarding visual illusions (see Figures 8B, 9, 10C and 11). However, in the Zollner illusion, despite the high representational similarity, CAM visualizations showed that models fail to comprehend angular differences and only focus on the whole (see Figure 15), highlighting the limitations of DNNs in processing visual information. In the color assimilation experiment, some models were able to simulate human-like perception rankings (as shown in Figure 4), demonstrating DNNs' capability in simulating basic visual processing. However,  
Figure 15: Class activation heatmap of DNNs for stimulus images of arrows at a 15-degree angle. in the analysis of the Hermann grid illusion, we found that the models' response patterns were non-monotonic, and the distribution of visual illusion responses significantly differed from human perception (see Figure 7), highlighting the limitations of DNNs in simulating complex visual phenomena. Previous studies on topics like the brain score [22; 23] and the brain BH score [24] have shown the potential for deep neural networks in simulating the human brain. As models that emulate the visual ventral stream, the architectural differences of DNNs significantly affect their effectiveness in visual tasks. These differences are reflected not only in the perception of visual illusions but also in how networks process complex visual information. The BH score emphasizes that networks with simpler, more uniform structures, such as AlexNet [7] and VGG series [8], have relatively higher brain-like indices. However, in the brain score, which combines brain-like scores from V1 to IT areas, these models rank lower compared to some like residual networks or deeper networks. Considering that deep neural networks (DNNs) pre-trained on large image datasets have been used to mimic human brain learning patterns, this study selects 12 pre-trained models (Figure 3), taking both standards into account. Further analysis suggests these differences may stem from varying model architectures. For example, the VGG series, with a higher BH score, excelled in illusions involving length and spatial positioning but struggled with more complex image structures (like grid images or color assimilation features), focusing only on single-point features or rectangle edges (see Figures 6, 8B and A1-A4).