Curvature-Based Intuition: A Fractal-Ricci Framework for Understanding Neurodivergent Cognition and Mental Health

This theoretical paper establishes a geometric foundation for intuition through fractal-Ricci curvature dynamics on cognitive manifolds. We extend the Fractal-Ricci Intelligence Framework to formalize intuition as a non-linear field resonance phenomenon emerging from multiscale curvature gradients [1, 2]. The model provides a mathematical basis for understanding neurodivergent cognition patterns as variations in fractal-dimensional topology. Mental health phenomena are reinterpreted as disruptions in curvature-stabilized phase-amplitude coupling. Our purely geometric approach offers novel theoretical pathways for computational psychiatry. Parameters are abstract and not derived from human data. AcknowledgementsThe author acknowledges the use of OpenAI's ChatGPT and DeepSeek as assistive tools in the development of ideas, refinement of theoretical models, and support in drafting and editing the manuscript. The AI was employed for brainstorming, language polishing, diagram suggestions, and exploring mathematical formulations, with all conceptual direction and final interpretations guided by the author. Note: This is a Work In Progress Paper and the experimental setup is just mentioned as a placeholder for future experimentations. No experiments have so far being conducted to check the performance of the model. It is at a early stages of theoretical work and is positioned here just for the DOI.