Spin-Based Modeling of Perception as Emergent from contextualized Internal Evaluation

TL;DR

This paper presents a spin-based microscopic model of perception, demonstrating how internal evaluative interactions give rise to perceptual states and how neutral evaluations influence perceptual sensitivity and entropy.

Contribution

It introduces a novel spin model framework that links microscopic evaluative processes to macroscopic perception, incorporating neutral states to explain perceptual phenomena.

Findings

Neutral evaluative states increase perceptual sensitivity.

Inclusion of neutral states lowers the critical threshold for perception.

The model predicts altered perceptual landscapes in mental disorders.

Abstract

We develop a microscopic model of perception of an interoceptive sensation in which spin-like variables encode an organism's internal evaluation of embodied vital norms associated with the sensation. Spins can take positive, negative, or neutral values. These local valorizations interact on a lattice embedded in the environmental context, and their collective configuration gives rise to a macroscopic perceptual state. By applying a coarse-graining procedure to a family of symmetric spin models, we derive a macroscopic Landau-type functional that makes explicit the mechanism by which key phenomenological features of perception, emerge from microscopic evaluative interactions. A central result is that the inclusion of a neutral evaluative state fundamentally alters the structure of the perceptual landscape, enhancing entropy, lowering the critical threshold, and increasing sensitivity to contextual input. These results establish a principled link between microscopic evaluative processes and large-scale perceptual organization, offering a flexible framework for integrating perceptual regulation and neurobiological modeling. The model integrates notions of neuroscience and cognitive science using the formalism of condensed matter field theory and providing novel theoretical insights and experimental predictions for conditions such as mental disorders and chronic pain.