Cognitive Dual-Process Theories Applied to Artificial Intelligence

In his 2011 book Thinking, Fast and Slow, Daniel Kahneman popularized the dual- process theory of cognition through the distinction between System 1—fast, automatic, and associative—and System 2—slow, deliberate, and rule-based. His research, which integrates economic theory with cognitive psychology, revealed the pervasiveness of cognitive biases and showed how the interaction between these systems can systematically lead to reasoning errors. This article explores the applicability of Kahneman’s dual-process framework to artificial learning systems, particularly in addressing phenomena such as hallucinations and inference failures in large language models. We examine the cognitive mechanisms involved in idea formation at the neural level and propose two postulates that outline the structural challenges in implementing a System 2 analogue in artificial intelligence. Given that machine learning systems rely on mathematical formalisms, we introduce a simplified mathematical model of cognitive processing. This model suggests that an axiomatic understanding of synaptic behaviour may be crucial to identifying and mitigating systematic reasoning flaws in natural language processing systems.