The Hydraulic Brain: Understanding as Constraint-Release Phase Transition in Whole-Body Resonance

TL;DR

This study reveals that whole-body resonance acts as a phase transition gate in cognition, characterized by nonlinear dynamics and binary threshold behavior, challenging traditional noise-based models of neural signals.

Contribution

It introduces a thermodynamic framework showing body-brain resonance as a discrete gate triggering nonlinear information integration, advancing understanding of embodied cognition.

Findings

High bidirectional coupling between brain and body during P300 recognition

Identification of a supercritical transition phase with state expansion

Resonance strength is uncorrelated with transition magnitude

Abstract

Current models treat physiological signals as noise corrupting neural computation. Previously, we showed that removing these "artifacts" eliminates 70% of predictive correlation, suggesting body signals functionally drive cognition. Here, we investigate the mechanism using high-density EEG (64 channels, 10 subjects, 500+ trials) during P300 target recognition. Phase Slope Index revealed zero-lag synchrony (PSI=0.000044, p=0.061) with high coherence (0.316, p<0.0001). Ridge-regularized Granger causality showed massive bidirectional coupling (F=100.53 brain-to-body, F=62.76 body-to-brain) peaking simultaneously at 78.1ms, consistent with mutually coupled resonance pairs. Time-resolved entropy analysis (200ms windows, 25ms steps) revealed triphasic dynamics: (1) constraint accumulation (0-78ms) building causal drive without entropy change (delta-S=-0.002 bits, p=0.75); (2) supercritical transition (100-600ms) triggering state expansion (58% directional increase, binomial p=0.002); (3) sustained metastability. Critically, transition magnitude was uncorrelated with resonance strength (r=-0.044, p=0.327), indicating binary threshold dynamics. Understanding emerges through a thermodynamic sequence: brain-body resonance acts as a discrete gate triggering non-linear information integration. This architecture may fundamentally distinguish biological from artificial intelligence. Keywords: embodied cognition, phase transitions, Granger causality, thermodynamics, neuromorphic computing, resonance dynamics, EEG artifacts