Research
Core Research Questions
- Can percolation theory model cognitive fragmentation in digital trace data?
- How does the Observer-Operator bridge degrade under stress?
- Can DBSCAN behavioral clustering predict intervention thresholds?
- What is the relationship between fragmentation score and task performance?
N=1 Proof-of-Concept Results
Preliminary internal validation shows:
- Fragmentation scores between 0.42% (stabilized) and 15% (pre-intervention) across test cases.
- Executive bridges are 1.5× more likely to fail than internal cluster edges under stress.
- DBSCAN identifies irregular clusters that correspond to self-reported "stuck" states.
Computational Psychiatry Framework
The platform is designed as a transparent, reproducible tool for computational psychiatry. It does not provide clinical diagnoses but offers a model for understanding how cognitive networks break down and reorganize.
Planned Publications
- Percolation-Theoretic Analysis of Executive Function Fragmentation (in preparation)
- Observer-Operator Dynamics: A Framework for AI Alignment (target: NeurIPS 2025)
- DBSCAN Behavioral Modes in Digital Trace Data (collaboration with computational biology partners)
Collaborators
We welcome collaboration from researchers in:
- Computational psychiatry
- Network science / percolation theory
- AI safety and alignment
- Neurodiversity and executive function
See Participate for how to get involved.
Open Source
The core engine is available for audit and contribution: