Bianca Avanzo — The Root Frequency Theory
← Back
Bianca Avanzo

Bianca Avanzo

Independent Researcher

Background

I am an independent researcher working at the intersection of cognitive science, neurophenomenology, and the science of self. My work investigates how a coherent sense of self is maintained, and how it becomes vulnerable to fragmentation, across biological, neural, and symbolic scales of organization.

My Approach

·

Analytical Autoethnography: Root Frequency Theory (RFT) emerged from a sustained period of longitudinal first-person inquiry. I treat structured subjective observation as a rigorous source of scientific constraint to guide hypothesis formation.

·

Transdisciplinary Modeling: The framework is developed in active dialogue with contemporary neuroscience, particularly the Free Energy Principle (FEP), Integrated Information Theory (IIT), and Menon's Triple Network Model.

·

The Goal: To translate phenomenological insights into a formally investigable architecture, clarifying how experiential organization may relate to underlying mechanisms through operational tools such as the M-RFT metric.

Research Interests

·Consciousness and experiential continuity across time;
·Large-scale brain network dynamics (DMN, salience, executive networks);
·Predictive processing and active inference;
·Neurophenomenology and first-person methodology;
·Stress, psychopathology, and multiscale dysalignment;
·Coherence metrics and health-technology scaffolds.

Current Work

The Root Frequency Theory framework and its associated operational tools are currently under active development, including the M-RFT metric—a proposed measure of cross-scale alignment designed to track the precision of internal models—and the development of computational scaffolds to support systemic integration.

Efforts are also directed toward establishing R&D partnerships, academic–industry mentorship, and access to multimodal neurophysiological environments to rigorously examine the framework's core predictions and explore its potential computational translation into scalable health-technology contexts.

Interested in collaborating or learning more about the project?

Get in touch