VALIDATION PATHWAY

From Architecture to Empirical Testing.

The Neurodynamic Cognitive Systems Model (NCSM) is a rigorously defined framework for modeling real-time cognition. Developed at the intersection of neuroscience, control theory, and predictive processing, it offers a structured, testable account of how cognitive systems regulate transitions, allocate control, and preserve coherence under pressure.

The core architecture — including the NPPE, DCSM, and CSTP — is complete and internally consistent. Each component is designed for falsifiability, with predictions aligned to measurable neural and behavioral signatures.

We are now advancing through simulation, task design, and protocol development. Current work includes implementing arbitration logic in code, modeling coherence breakdown, and designing EEG/fMRI experiments to test CSTP’s predictions.

This validation pathway is ongoing, with collaboration across neuroscience and cognitive science actively sought. The long-term goal is to establish NCSM as a general architecture for cognition — one that is theoretically grounded, empirically verified, and applicable across clinical, technological, and adaptive domains.

PHASE I
CORE ARCHITECTURE FINALIZED (Complete)

Milestones achieved:

• Full model manuscript published (Zenodo)

• Structural components defined: NPPE, DCSM, CSTP

• Arbitration logic and transition equations formalized

• Glossary, neuroanatomical mapping, and testable predictions written

• Project infrastructure launched: website, documentation, OpenCollective

• Seed funding secured (Emergent Ventures, LTFF)

Status: The theoretical framework is complete and internally coherent. The model is now positioned for implementation and testing.

PHASE II
CSTP SIMULATION DEVELOPMENT (In Progress)

Objectives:

• Translate arbitration logic into executable pseudocode

• Build simulations of cognitive mode transitions and collapse conditions

• Prototype control parameters (prediction error, confidence weighting, system thresholds)

• Visualize subsystem arbitration dynamics in synthetic environments

Current Work:
CSTP simulation loop is under development in Python. Initial focus is on validating the internal consistency of arbitration outputs under varying ΔE, ΔC, and ΔR conditions.

PHASE III
VALIDATION TASK DESIGN (Design Underway)

Deliverables in preparation:

• EEG/fMRI prediction maps derived from CSTP structure

• Behavioral task paradigms targeting transition delay, arbitration conflict, and coherence breakdown

• Physiological correlates mapped: theta–gamma coupling, frontal midline theta, pupil dynamics

• Open protocol documentation for use in lab-based experiments

Goal: Align the model’s internal logic with measurable neural and behavioral signatures.

PHASE IV
EMPIRICAL COLLABORATION (Open Call)

Next steps:

• Engage with cognitive neuroscience labs for protocol deployment

• Execute EEG and/or fMRI tasks based on CSTP gating predictions

• Collect data on transition timing, arbitration success/failure, and oscillatory coherence

• Refine model parameters based on empirical findings

Target collaborators:

• Labs working in cognitive control, decision-making, attention switching

• Researchers focused on predictive processing, active inference, and control theory

PHASE V
CROSS-DOMAIN APPLICATION

Planned integrations:

• Clinical neuroscience: Model regulatory failure in depression, PTSD, OCD

• AGI architecture: Implement arbitration and transition logic to prevent coherence breakdown

• Neuroadaptive interfaces: Enable real-time detection and modulation of cognitive state transitions

Long-term goal:
Establish NCSM as a foundational framework for understanding cognition in human systems — unifying predictive processing, control dynamics, and functional mode transitions into a single, testable architecture.

This includes:

• A generalizable theory of real-time cognitive regulation

• A platform for diagnostics and psychiatric modeling

• A systems-level control framework applicable to both biological and synthetic cognition

NCSM is built to scale with the complexity of the systems it models. As research deepens, collaborations grow, and validation expands, our focus remains clear: to develop a coherent, testable architecture for cognition that can hold under pressure — in minds, machines, and institutions alike.

The work has begun. We invite those building the next layer of understanding to join us.