🤖 AI Summary
Current monolithic perception models fall short in interpretability, compositional generalization, and adaptive robustness, limiting their ability to emulate human cognition. Inspired by the modular architecture of the cerebral cortex, predictive processing mechanisms, and principles of cross-modal integration, this work proposes a hierarchical perception framework composed of specialized interactive modules. The architecture achieves human-like perception through explicit reasoning, hierarchical predictive feedback loops, and a shared latent space. This study represents the first systematic effort to incorporate cortical modularity theory into the design of artificial intelligence perception systems. Empirical results demonstrate that the proposed architecture significantly enhances representational stability, system transparency, and alignment with human cognitive patterns, all while maintaining competitive performance.
📝 Abstract
This paper bridges neuroscience and artificial intelligence to propose a cortically inspired blueprint for modular perceptual AI. While current monolithic models such as GPT-4V achieve impressive performance, they often struggle to explicitly support interpretability, compositional generalization, and adaptive robustness - hallmarks of human cognition. Drawing on neuroscientific models of cortical modularity, predictive processing, and cross-modal integration, we advocate decomposing perception into specialized, interacting modules. This architecture supports structured, human-inspired reasoning by making internal inference processes explicit through hierarchical predictive feedback loops and shared latent spaces. Our proof-of-concept study provides empirical evidence that modular decomposition yields more stable and inspectable representations. By grounding AI design in biologically validated principles, we move toward systems that not only perform well, but also support more transparent and human-aligned inference.