Convergence of Artificial Intelligence and Cognitive Neuroscience Innovations and Future Directions

The convergence of Artificial Intelligence (AI) and Cognitive Neuroscience has emerged as one of the most significant interdisciplinary developments in contemporary science and technology. While AI seeks to develop computational systems capable of performing tasks traditionally associated with human intelligence, Cognitive Neuroscience investigates the neural mechanisms underlying cognition, perception, learning, memory, language, and decision-making. This article examines the theoretical foundations, historical evolution, and practical applications of the relationship between AI and Cognitive Neuroscience. A review of 100 studies was undertaken to identify major cognitive domains, technological applications, methodological approaches, and emerging research directions. Descriptive analysis showed that Memory constituted the largest cognitive domain (20.0%), followed by Attention (16.0%), Emotion (15.0%), Executive Function (14.0%), Perception (13.0%), Language (12.0%), and Decision-Making (10.0%). Particular attention is given to artificial neural networks, deep learning, brain-inspired computing, neuromorphic systems, spiking neural networks, cognitive architectures, neuroimaging analysis, neurological disease prediction, and brain–computer interfaces. The review further examines Explainable Artificial Intelligence (XAI), neuro-symbolic AI, artificial general intelligence, personalized neurotechnology, digital brain simulations, and human–AI symbiosis. Ethical concerns involving neuroprivacy, algorithmic bias, autonomy, cybersecurity, and governance are also evaluated. The findings indicate a broad and increasingly interdisciplinary research landscape, with memory and attention receiving comparatively greater scholarly attention. However, the study is limited by reliance on secondary literature, methodological heterogeneity, rapid technological development, and limited empirical validation of emerging technologies. The study concludes that sustained interdisciplinary collaboration is essential for developing explainable, biologically grounded, and socially responsible intelligent systems.