Understanding Artificial Intelligence: A Multidisciplinary Analysis of AI's Distinct Cognitive Architecture

As Artificial Intelligence (AI) systems become increasingly sophisticated and pervasive, understanding their true nature and limitations requires moving beyond single-discipline perspectives. This paper argues that effectively developing and deploying AI systems demands an integrated framework combining insights from computer science, cognitive psychology, linguistics, and cyberpsychology. We examine three complementary theoretical frameworks that together reveal AI's fundamental nature: Psychomatics demonstrates how AI systems create sophisticated syntax-semantic mappings but lack true experiential understanding; System 0 thinking positions AI as a distinct cognitive layer that preprocesses information while remaining dependent on human interpretation; and Cyberpsychology illuminates how humans form mental models and relationships with AI systems. These insights converge on a central argument: effective AI development requires embracing rather than obscuring the fundamental differences between artificial and human intelligence. Instead of pursuing AI systems that attempt to replicate human cognition, we should focus on developing systems that transparently complement human capabilities while acknowledging their inherent limitations in meaning-making and understanding.