Cognitive Informatics for Next-Generation AI: Models, Challenges, and Opportunities

Abstract

Cognitive Informatics has become a critical interdisciplinary field, which links human cognitive functions with next-generation artificial intelligences, which facilitates the creation of intelligent solutions of the next generation. This survey provides a comprehensive overview of cognitive informatics and how it is essential in defining the current AI through incorporating the concepts of neuroscience, cognitive science, and computational intelligence. The paper examines on the underlying theories of human thinking, cognitive structures, and information processing systems that stimulate the intelligent design of systems. It also discusses different cognitive models such as symbolic, sub-symbolic and hybrid models of thought and their relevance in improving the reasoning, learning and decision-making processes in AI systems. Other major application domains covered by the survey include natural language processing, computer vision, healthcare, human-computer interaction, and autonomous systems where cognitive informatics has been found to contribute greatly in enhancing contextual knowledge and system adaptability. Moreover, such critical issues like the complexity of modeling, data constraints, ethical issues, explainability, and scalability are discussed. New directions, such as neuro-symbolic AI, multimodal learning, cognitive digital twins, and brain-inspired computing, are mentioned to define the directions of the future research and opportunities. The survey highlights the need to create explainable, trustworthy, and human-centric AI systems by integrating cognition. This survey presents essential information about the modern situation and the future of cognitive informatics as the way toward the next-generation AI technologies.