AI-Enhanced Cognitive Neural Modeling: Modern Approaches To Understanding Cognitive Processing Mechanisms

This study aims to examine the role of AI-enhanced neurocognitive modeling in understanding and analyzing cognitive processes and their disturbances at the individual level. Neurocognitive modeling constitutes a central scientific framework for explaining how the brain processes information, linking neural activity to cognitive performance, and diagnosing dysfunctions associated with key functions such as attention, memory, information processing, problem-solving, and decision-making—functions shaped by complex interactions among neural, psychological, and environmental factors. The integration of artificial intelligence techniques into neurocognitive models contributes to the development of precise computational simulations of cognitive processes, enabling the interpretation of neural activity and the prediction of its dynamic changes. This integration opens new avenues for the diagnosis of neurocognitive disorders and the design of evidence-based intervention strategies. The significance of this topic stems from the limitations of classical approaches, which often rely on a separation between cognitive and behavioral assessment tools and the recording of neural indicators, thereby restricting the transfer of findings from the structural to the functional level in an incomplete manner. In contrast, AI-supported neurocognitive modeling allows for the construction of accurate models of the mechanisms underlying human brain functioning by integrating neural structure, cognitive functions, and associated behavioral patterns within a unified explanatory framework. This approach provides a comprehensive understanding of attention, memory, problem-solving, and decision-making processes, bringing researchers closer to a deeper comprehension of the foundations of adaptive cognitive performance and the mechanisms underlying its disruption. Moreover, it enables psychologists and researchers to develop more precise and effective assessment and intervention tools. The central research question of this study concerns how cognitive neuropsychology can benefit from artificial intelligence in building accurate explanatory models of cognitive functions and in diagnosing their disorders with greater precision than traditional approaches, while identifying the neural determinants underlying these processes. The study adopts a descriptive-analytical