Human Cognition and Artificial Intelligence: Comparative Analysis of Learning, Memory, and Information Processing

The emergence of artificial intelligence (AI) marks a transformative shift in human information processing, paralleling earlier revolutions in writing, printing, and digital computation. This review situates AI within the historical trajectory of informational technologies and grounds its analysis in contemporary neurobiology of learning and memory. Drawing on synaptic plasticity, engram theory, and systems consolidation, it highlights how biological mechanisms such as long-term potentiation (LTP), spike-timing dependent plasticity (STDP), and metaplasticity inspire algorithmic learning while differing in energy efficiency, adaptability, and meaning generation. Comparing the brain and AI in terms of memory, computation, and context sensitivity demonstrates that neural processes integrate affect, embodiment, and evolutionarily constrained priors, dimensions currently absent in artificial systems. The review further explores the epistemological implications of AI as a new information revolution, emphasizing that it reconfigures not only the storage and processing of knowledge but also its constitution and legitimacy within cognitive and social systems. By examining the convergence of neuroscience and machine learning, the paper raises critical questions about the nature of understanding, intelligence, and representation in both human and artificial agents. This synthesis underscores the potential and limitations of AI while offering a framework to guide the development of systems that are both inspired by and complementary to biological cognition.