A Testable Computational Framework for Cognitive Enhancement: Information Input, Understanding Emergence, and Bounded Error Dynamics

The prevalent “bandwidth-dominant” paradigm in cognitive enhancement assumes that learning efficiency scales linearly with sensory input speed. This paper challenges that assumption by distinguishing exogenous information reception from endogenous understanding emergence. We propose a falsifiable computational framework in which cognitive load is modeled as a bounded accumulation process. At low input speeds, error rate grows approximately linearly; beyond an overload threshold, we hypothesize a power-law transition driven by working memory overflow. We formalize this as a phenomenological dynamical equation with explicitly stated provisional parameters, and implement a recurrent neural agent simulation that qualitatively reproduces the predicted nonlinearity. We also outline a behavioral experiment to test the predicted transition in humans. Additionally, we propose a continuous-spectrum multimodal coordination-competition model and a constrained latent alignment pipeline for human-AI structured knowledge transfer. The framework aims to provide testable boundaries for safe cognitive enhancement and BCI-based cognitive regulation, while acknowledging that current parameter values are subject to empirical calibration.