Root Lab
A call for integrative collaboration
Investigating how biological regulation, neural dynamics, and self-narrative integrate to sustain a coherent sense of self — and what happens, across scales, when they don't.
The Problem
More than one billion people are living with a mental health condition,1 yet the gap between what science understands about the breakdown of mind-body regulation and what clinical tools can actually measure continues to widen. Across anxiety, depression, dissociation, and chronic stress, a common pattern is emerging: fragmentation. Not simply distress, but a loss of coordination between physiological regulation, neural dynamics, and self-narrative.2,3 This breakdown may begin before symptoms become clinically visible.4
Current tools remain siloed. Interoceptive accuracy is measured in the lab.5 HRV is tracked in clinical or consumer health settings.6 Narrative coherence is studied through phenomenology and psychology.7 But no system integrates these layers longitudinally, outside the scanner, in the actual flow of a person's life. At the same time, AI systems are becoming embedded in that life. Most are optimized for speed, personalization, and engagement. But from a predictive processing perspective, engagement is not the same as coherence. In many cases, it may amplify prediction error, attention fragmentation, and narrative instability.8
What this program investigates
Root Lab is an independent research program organized around one central question:
Can cross-level coherence — the alignment between physiological regulation, neural dynamics, and self-representation — be formally measured, tracked over time, and supported through coherence-centered human-AI interaction?
- Securing a PhD position with a research group working on interoception, computational psychiatry, or neuroscience-AI — to bring the empirical infrastructure this work requires.
- Validating the M-RFT coherence metric through multimodal lab protocols combining HRV, interoceptive accuracy tasks, and neuroimaging.
- Longitudinal tracking of coherence disruption in clinical populations — panic disorder, addiction, ADHD, and anxiety — conditions characterized by spatiotemporal misalignment, distorted self-narrative, and ruminative thought patterns.
- Expanding the current N=1 ecological study toward a formal clinical validation trial with N=20–40 participants.
- Investigating whether coherence in human-AI interaction is measurable and whether AI-mediated epistemic scaffolding produces detectable changes in inner coherence — not just behavioral outcomes.
The foundational framework. C0–C4 nested architecture. Experiential continuity as a systemic property.
A formal index of cross-level coordination. Pre-empirical. Designed for lab validation with neuroimaging partners.
Human-AI interaction as coherence scaffolding. The integration layer between information systems and lived experience.
321 sessions across 23 active days. Coherence tracked via Apple Watch HRV, interoceptive task, and narrative scoring. Preliminary data complete.
If coherence can be formally measured, several things become possible:
- Detecting fragmentation before it becomes a diagnosable condition — intervening in the pre-symptomatic window rather than after clinical threshold.
- A unified metric that allows comparison across studies currently siloed by modality — interoception, HRV, and narrative coherence measured together for the first time.
- Testing whether structured epistemic scaffolding — not symptom suppression — can restore cross-level integration longitudinally.
- Designing AI systems that calibrate their output to the coherence state of the person receiving it, rather than optimizing for engagement.
- Providing researchers with ecological, longitudinal data on how autonomic flexibility, interoceptive accuracy, and narrative organization co-vary in real life outside the laboratory.
- Advancing a privacy-preserving architecture for personal health AI — where raw data stays on-device and inference happens locally.
- 1. GBD 2019 Mental Disorders Collaborators. (2022). Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019. The Lancet Psychiatry, 9(2), 137–150.
- 2. Northoff, G. (2016). Spatiotemporal psychopathology I: No rest for the brain's resting state activity in depression? Journal of Affective Disorders, 190, 854–866.
- 3. Critchley, H. D., & Garfinkel, S. N. (2017). Interoception and emotion. Current Opinion in Psychology, 17, 7–14.
- 4. Seth, A. K., Suzuki, K., & Critchley, H. D. (2012). An interoceptive predictive coding model of conscious presence. Frontiers in Psychology, 2, 395.
- 5. Garfinkel, S. N., Seth, A. K., Barrett, A. B., Suzuki, K., & Critchley, H. D. (2015). Knowing your own heart: Distinguishing interoceptive accuracy from interoceptive awareness. Biological Psychology, 104, 65–74.
- 6. Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61(3), 201–216.
- 7. Varela, F. J. (1996). Neurophenomenology: A methodological remedy for the hard problem. Journal of Consciousness Studies, 3(4), 330–349.
- 8. Avanzo, B. (2026). Root Knowledge: Embodied knowledge as a foundation for coherent human–AI interaction. Zenodo. https://doi.org/10.5281/zenodo.20060589