The Grammar of Cognition: A Multiaxial Framework for Structured-Adaptive Intelligence

The project, The Grammar of Cognition: A Multiaxial Framework for Structured-Adaptive Intelligence, addresses the persistent vulnerability of human decision-making to systematic cognitive biases and arbitrary noise, which leads to widespread fallibility across expert domains. This paper introduces a novel, multiaxial framework designed to enhance bias-resilient and well-calibrated intelligence by uniquely integrating two major, yet historically separate, streams of cognitive enhancement: procedural sequencing and predictive processing. The core of the framework is the synthesis of the structured, procedural rigor of Linear Sequential Unmasking–Expanded (LSU-E), which serves as the cognitive syntax regulating the order of information intake, with the dynamic, error-sensitive mechanism of Minimization of Prediction Error (MPE), which serves as the adaptive semantics responsible for updating internal models. This integrated architecture, known as the Grammar of Cognition, regulates intelligence across four critical dimensions: Temporal Structuring (driven by LSU-E to manage the order of information to prevent prior knowledge contamination); Error Sensitivity (driven by MPE to sharpen the accuracy of internal models through dynamic error correction); Contextual Modulation (regulating how context affects both sequencing and error thresholds); and Cognitive Load Management (optimizing resource allocation for dual-process mitigation). By enforcing optimal information sequencing while continually sharpening the accuracy of predictive models, the Grammar of Cognition offers a powerful, generalized antidote to cognitive overfitting, order effects, and bias contamination. This work holds crucial implications for improving decision-making, judgment, and learning across critical domains, including forensics, clinical diagnostics, policy design, and education. The document provides the full theoretical foundation and detailed outlines for pathways toward empirical validation.