Network_Based_Multi_Axial_Cognitive_Framework.pdf

Note: Disorder Coordinates and Temporal Axis have been revised. Quadrant network mapping in progress. Stated networks represent core modules of larger networks currently being mapped A Three-Axis Framework for Neural Network Organization: From Cognitive Quadrants to Clinical Applications A theoretical framework proposing that neural organization follows three orthogonal dimensions: η (control direction), τ (temporal processing), and α (processing mode). These axes generate eight distinct cognitive quadrants that operate as discrete network-level systems despite underlying neural activity being continuous. Each quadrant corresponds to specific brain networks and exhibits measurable resource allocation patterns, with cognitive resources operating under conservation principles where total allocation across all quadrants equals 100% of available processing capacity. The framework introduces dynamic resource allocation constraints that create competitive dynamics between cognitive systems - increased activation in one quadrant necessarily reduces resources available to others. This enables precise clinical predictions, including specific resource allocation dysfunction patterns for psychiatric disorders: Q2 (contemplative integrator) hyperactivation consuming 40-70% of total resources in major depressive disorder, Q1-Q3 (strategic-procedural) hyperactivation consuming 60-80% in OCD, and Q7 (reactive responder) hyperactivation with Q1 (strategic analyst) hypoactivation in ADHD. The discrete quadrant model operates at the network organizational level - the appropriate scale of abstraction for understanding consciousness and clinical applications, similar to how atomic theory provides discrete models despite quantum mechanical continuity. The framework honors both the modular reality of brain network architecture (default mode, salience, executive control networks) and phenomenological experience of bounded cognitive states, clear transitions between thinking modes, and distinct resource competition patterns. Clinical ...