Prompt Theory: A Unified Framework for Understanding AI Prompting and Human Cognition
Preprint
GitHub
Abstract
We introduce Prompt Theory, a foundational framework that establishes structural isomorphisms between artificial intelligence prompting systems and human neurobiological input-output mechanisms. By formalizing the parallels between prompt design and cognitive input processing, we reveal shared emergent properties, failure modes, and optimization pathways across both domains. Our contribution is threefold: (1) a formal mapping between AI prompting and human cognitive processing stages, from input encoding to output generation; (2) identification of recursive patterns that govern both systems, including attention allocation, context management, and drift phenomena; and (3) a unified mathematical framework for optimizing prompts based on neurobiologically-inspired principles. Experimental results demonstrate that prompt designs informed by this framework yield significant improvements in AI system performance across multiple benchmarks. We posit that prompt engineering should be reconceptualized as a fundamental locus of intelligence amplification at the human-AI boundary, with implications for cognitive science, AI alignment, and human-computer interaction.
Keywords: Prompt Theory, Large Language Models, Cognitive Science, Human-AI Interaction, Attention Mechanisms, Recursive Systems, Emergence
