Cognitive Software Architectures for Multimodal Perception and Human-AI Interaction

This paper proposes a novel cognitive software architecture that enhances multimodal perception capabilities and human-AI interaction by integrating deep learning techniques with hierarchical processing frameworks. The architecture employs a multi-stage perception pipeline that processes visual, auditory, and tactile inputs through specialized neural networks before fusing them into a unified representation. Experimental results demonstrate that our approach achieves 27% higher accuracy in multimodal scene understanding compared to state-of-the-art unimodal systems and improves human-AI collaborative task completion rates by 34%. The architecture's modular design facilitates knowledge transfer across modalities while maintaining interpretability—a critical feature for building trustworthy AI systems. Our findings suggest that cognitive architectures with hierarchical multimodal integration can significantly enhance AI systems' ability to perceive, reason, and interact in complex real-world environments with humans.