Neuro-Symbiotic Loops: Trust Calibration and Adaptive Synchronization in Human-AI Decision Making

Abstract

The integration of Large Language Models (LLMs) into decision-support loops introduces a critical challenge: the opacity of AI reason-ing creates a gap between system capability and human trust. While Explainable AI (XAI) attempts to bridge this gap, static explanations often overwhelm users under stress or provide insufficient detail during calm phases. This paper introduces the Neuro-Symbiotic Synchro-nization (NSS) Protocol, a novel framework that utilizes closed-loop physiological feedback to dynamically calibrate trust and modulate the complexity of AI explanations in real-time.

We propose a control-theoretic model where human cognitive load acts as a regulating signal for the AI’s output channel. We mathematically formulate a “Trust Oscillator” that updates trust estimates based on performance discrepancies and penalizes cognitive overload. Through a simulation study involving a Smart Grid emergency management sce-nario, we demonstrate that the NSS framework significantly improves decision accuracy (by 7.4%) and reduces cognitive workload (NASA-TLX) by 35% compared to static interfaces. Our results suggest that symbiotic Human-AI collaboration requires biologicallyinformed adap-tation rather than purely logic-driven transparency.