# Exploring Deep Neural Networks in Simulating Human Vision through Five Optical Illusions
## 4 Discussion
However, the evolution of color processing strategies in deeper layers, and the ResNet_v2 series' differentiation changes post the initial layer, signify a departure from early similarities. Interestingly, [PERSON] et al. (2017) ([PERSON] et al. (2020) and our findings highlight the initial high color sensitivity in DNNs, aligning with the biological visual system's basic color processing mechanisms. Yet, as [PERSON] et al. (2020) articulate, the quest to model human vision through CNNs must navigate the intricate balance between linear approximations and the divergent outcomes of nonlinear, flexible architectures. This juxtaposition not only provides a deeper understanding of DNNs' color processing capabilities but also highlights the challenges in creating models that fully encapsulate the complexity of human vision ([PERSON] et al., 2020). In the Hermann grid illusion, models showed non-monotonic changes at deeper network layers (Figure 9), while in the earlier layers, there was a monotonic increase in dissimilarity. This suggests that this type of visual illusion, under the same mapping in the ventral pathway, particularly in later stages like V4/IT, leads to the perception of illusions. This finding aligns with [PERSON]'s (2015) research ([PERSON], 2015), suggesting the higher-level structures of DNNs play a key role in mimicking advanced human visual processing mechanisms. However, this contrasts with the explanation of the Hermann grid illusion being caused by the responses of simple S1-type cells in the primary visual cortex to the grid ([PERSON], 2015). In the Muller-Lyer illusion, across different network depths of DNNs (Figure 10C), there were minimal changes between the initial and deeper layers, with a slight increase in representational similarity, except for a few models. The currently prevailing explanation is that the Muller-Lyer illusion results from the interaction between the ventral and dorsal streams in response to length variation ([PERSON], 2015). Similarly, the Poggendorff illusion, which involves IPS and LOC, is not solely a result of the ventral stream ([PERSON], 2015). From Figure 12B, the Poggendorff illusion also exhibits minor representational similarity differences between initial and deeper layers. This may indicate that the ventral pathway, in processing Muller-Lyer and Poggendorff illusions, is not limited to basic processing of visual information but involves more complex spatial relationships and shape perceptions at deeper levels. Although there are minor differences in representational similarity between initial and deeper layers, these differences may reflect the capabilities and limitations of DNNs in mimicking the human visual system's ventral pathway. The ventral pathway plays a key role in object recognition and categorization, as well as in processing illusions; it may reveal the depth and complexity of neural networks in understanding and interpreting visual information. Furthermore, this suggests that deep learning models require further optimization and adjustment to more accurately mimic the complex mechanisms of the human brain in processing illusions. In the Zollner illusion, despite feature heatmaps showing that models could not differentiate angular differences of lines (Figure 15), focusing only on overall representational similarity, analysis through RDM and changes in network depth yielded results similar to the Muller-Lyer and Poggendorff illusions. This further suggests that while DNNs may not fully replicate the detail processing in biological visual systems, they can replicate some basic illusion phenomena in human vision to some extent. This indicates that DNNs have made some progress in emulating the functions of the ventral pathway (responsible for object recognition and shape processing). However, this similarity might be limited to the manifestation of illusions, and there remain significant differences in the underlying mechanisms of illusions between human visual systems and DNNs.