Overloaded minds and machines: a cognitive load framework for human-AI symbiosis

Abstract

Human cognition falters under overload because working memory is sharply limited, as described by Cognitive Load Theory. Advanced AI systems show parallel failures when tasks exceed context windows or cause model collapse. This review synthesizes these constraints through a unifying lens, revealing shared mechanisms like bounded workspaces and chunking, alongside divergences such as human metacognition. We introduce a “bounded agent complementarity” model that proposes dynamic load-balancing for symbiotic intelligence, with implications for reasoning in domains such as education, medicine, and aviation. The framework highlights ways to mitigate these mutual limits and yields testable predictions for augmented cognition and resilient human-AI systems.