Dimension Convergence Model: An Information-Based Interpretation of Perceived Dimensionality

Abstract

The Dimension Convergence Model (DCM) proposes an information-based framework for understanding how perceived dimensionality emerges from the structure of available information. The model formalizes the conditions under which high-dimensional representations collapse into lower-dimensional percepts, introducing a convergence threshold governed by information density and inter-dimensional coupling. Simulation results (box-counting and mismatch tasks) are included as supplementary data. This preprint is submitted as part of an independent theoretical research program and is prepared for arXiv submission (cs.IT / q-bio.NC).