# Exploring Deep Neural Networks in Simulating Human Vision through Five Optical Illusions
## 4 Discussion
These observed differences may stem from unique strategies used by neural networks in processing the color, shape, and spatial positioning, and inherent limitations in attempting to mimic the complexity of the human visual system. This underscores the need for deeper adjustments and optimization of DNN architectures and processing mechanisms in future research to more precisely emulate the complex processes of the human brain in processing visual information. Based on human behavioral data, DNNs can emulate certain functions of the human ventral pathway, especially in processing more direct visual tasks like color assimilation and Hermann grid illusions. However, in more complex visual scenes, such as the Poggendorff illusion, DNNs show clear limitations in emulating the advanced visual processing capabilities of the ventral pathway. The ventral pathway is a key part of the human visual system for object recognition and scene understanding [45], playing a crucial role in processing complex visual information. The results (Figures 9, 10C and 12B) suggest that while DNNs show potential in emulating the ventral pathway in processing some visual illusions, their capabilities in higher-level visual cognitive functions still need improvement. Recalling our study, as for datasets, we utilize deep neural network (DNN) models that have been pre-trained on extensive and varied image datasets, such as ImageNet. These models demonstrate exceptional proficiency in handling a broad spectrum of visual features, showcasing their versatility across diverse visual processing tasks. However, this approach also unveils the intrinsic challenge of employing general-purpose models to emulate specific visual phenomena, such as illusions. According to the high representational similarity of DNNs (deep neural networks) in RDMs (representational dissimilarity matrices), further evidence supports the findings of [PERSON] (2019) that the Muller-Lyer illusion is manifested in DNNs. However, it also highlights a potential issue, namely, that DNNs themselves are incapable of understanding illusion tasks. This problem is evident from the feature heatmap of the Zollner illusion (Figure 15). The use of feature heatmaps underscores a critical problem identified in previous research--the illusion performance of DNNs is related to whether they are trained and the nature of the training set itself. This revelation underscores a critical avenue for future research: the development of models and training datasets that more accurately reflect the unique attributes and principles underlying various visual illusions [46]. To deepen our understanding and simulation of human visual perception, future research would benefit from the utilization of datasets meticulously curated to encompass imagery with explicit perspectives, shapes, and color contrasts. Such precise enhancements are likely to significantly bolster the models' capabilities in recognizing and accurately simulating visual illusions. Furthermore, the exploration and potential creation of new DNN architectures, designed to more effectively grasp the complexities of these phenomena, represent another promising direction. These architectural advancements could facilitate a more detailed emulation of the sophisticated mechanisms of human visual perception, indicating the potential for DNNs to more precisely mimic human visual processing mechanisms in the future by refining training datasets and model frameworks. Our study enriches the understanding of DNNs' capacity to manage complex vision tasks, particularly in mimicking biological vision systems. Despite some advancements, these findings also highlight DNNs' limitations in comprehending and processing complex visual information, signaling the necessity for ongoing research and optimization.