Bridging Visual System and Embodied Perception

Abstract

This paper addresses an important gap at the intersection of cognitive science, perception studies, and artificial intelligence: how systems can bridge the divide between visual input and embodied subjective experience. While visual consciousness remains a hard problem, this article attempts to address one tiny piece of the difficulty. This study proposes a conceptual framework where visual-body mapping serves as the foundational mechanism for defining experienced reality. Rather than treating reality as absolute truth, we conceptualize it as dynamic interaction between internal cognitive processes and external environmental references, situated within a broader conceptual awareness structure. Through systematic layered inquiry (Q1, Q2, and Q3) combined with hypothetical thought experiments, we apply logical reasoning to explore perceptual boundaries and examine how conceptual frameworks shape our understanding of reality. The central contribution demonstrates that perceived “reality” emerges from the interplay of timing dynamics, structural positioning, and embodied perception. This visual-body mapping framework offers a forward-looking perspective for how AI systems might achieve human-like perception in the future - by recognizing the critical role of information processing speed and embodied perception in shaping real visual perception. From a scientific perspective, this paper outlines how possibly humans may experience visual reality, suggests two key implications: first, it provides a technological foundation for advancing human-centered AI innovation (robotics, intelligent machines, interface design, etc.). Second, it contributes a novel theoretical perspective on embodied cognition within cognitive science, suggesting new pathways for understanding how perception emerges through physical embodiment. While technological implementation may present challenges, it establishes valuable theoretical foundations and future experimental directions for future research in human-machine perception.