The Mathematica of General Intelligence: Conceptual Abstraction in Mind and Machine

We propose Conceptual Adaptation Theory (CAT) as a general framework for intelligence, unifying learning, knowledge, and consciousness under a shared mathematical formulation. Building on Predictive Processing and the Free Energy Principle, CAT introduces a set of recursive equations that define learning (L), knowledge (K), intelligence (I), and consciousness (C) in terms of conceptual restructuring in response to precision-weighted prediction error. We show that CAT explains the adaptive generalization capabilities of both human cognition and artificial systems, offering a scalable framework for dynamic, context-sensitive learning. The framework not only addresses current limitations in static machine learning architectures but also offers testable hypotheses for cognitive development and transactive consciousness. This work lays the foundation for a general theory of intelligence applicable to both minds and machines.