New Quantum-Analogue Model Explains Brain's 'Change of Mind' Mechanism

A new theoretical framework from researchers Vasily Lubashevskiy and Ihor Lubashevsky (arXiv:2605.25214) blends neural field theory with quantum-analogue mathematics to model how the brain processes sensory information above the level of conscious awareness. Their 'cloud-function' formalism treats the spatial structure of mental representations as inheriting properties of perceived objects, while temporal evolution follows regularities of large-scale neural activity governed by polynomial nonlinearities and global phase-shift invariance. The governing equation resembles a Schrödinger-type equation with a nonlinear, non-Hermitian Hamiltonian, supplemented by Lotka-Volterra-like interaction terms—a hybrid rarely seen in neuroscience.

The paper applies this model to the vexing phenomenon of 'change of mind' during decision-making. The cloud-function captures the dynamic tension between fast preconscious sensory processing and slower conscious comparison of alternatives, explaining how initial choices can be revised mid-execution. The model aligns with experimental evidence of continuous post-decisional evidence accumulation. The authors also discuss the necessity of cloud-function self-interaction, hinting at feedback loops that could refine predictions. While purely theoretical, this work could inspire new computational models for cognitive AI and brain-computer interfaces, offering a mathematical language for phenomena that classical neural networks struggle to replicate.