Temporal Ratio Model (TRM): A General Framework for Ratio-Based Cognitive and Physical Inference

This paper introduces and formalizes the Temporal Ratio Model (TRM), a unifying mathematical and cognitive framework describing how systems—biological, perceptual, computational, and physical—infer structure using ratios of temporal or sequential change. TRM proposes that stable perception and prediction in humans and other systems arise not from absolute magnitudes, but from relative proportional shifts, enabling robustness under uncertainty, noise, and incomplete information.

Building on ratio-based cognition, recursive inference, and dynamical stability principles, TRM v2 expands the core model with examples from probability updating, sensorimotor timing, Bayesian ratio fusion, error-minimizing decision cycles, and time-normalized environmental sampling. The paper positions TRM as a candidate general law of temporal inference applicable across psychology, neuroscience, machine learning, and physical system modeling.