The Neurodynamic Cognitive Systems Model

A predictive control architecture for real-time cognition.

The Neurodynamic Cognitive Systems Model is a structural framework for understanding how cognition regulates itself in real time — how minds transition between functional modes, preserve coherence under uncertainty, and adapt fluidly to shifting internal and external demands. Developed at the intersection of neuroscience, control theory, and predictive processing, NCSM offers a mechanistic account of intelligence as a recursive system of dynamic regulation — not a static collection of functions.

Where conventional models compartmentalize perception, memory, and behavior into fixed modules, NCSM defines the control architecture that links them. It models cognition not as computation over content, but as an ongoing process of arbitration, timing, and reconfiguration — one that can be simulated, tested, and applied across disciplines.

At its core, NCSM integrates three interlocking systems:

The Neurocognitive Predictive Processing Engine — responsible for generating, evaluating, and precision-weighting internal models in response to environmental feedback.

The Dynamic Cognitive Systems — a five-mode attractor architecture that captures the dynamic interplay between cognitive systems over time.

The Cognitive Synchronization and Transition Protocol — a control layer that determines when and how cognitive state shifts occur, based on oscillatory, predictive, and contextual signals

Together, these components form a recursive regulatory loop — enabling minds to remain coherent, flexible, and structured under pressure.

NCSM provides a formal structure for what neuroscience measures, and a dynamic logic for what psychology observes. It bridges theories of prediction, emotion, memory, and control into a unified model of how cognition functions, and transitions in motion.

This is not just a new way of describing the mind. It is a generative architecture for modeling intelligence across biological, psychological, and artificial systems.

The Architecture

NCSM consists of three interlocking systems that together define how predictive cognition regulates itself:

1. Neurocognitive Predictive Processing Engine

A recursive inference loop that generates expectations, flags prediction errors, and dynamically regulates confidence via precision weighting.

Core components:

• Generative Model

• Error Detection System

• Precision Weighting Mechanism

2. Dynamic Cognitive Systems Model

A five-mode architecture representing quasi-stable attractor states in cognitive state space:

• Instinctual Core System: Instinctual (reflex, drive)

• Social & Enviromental Modulation System: Social-Emotional (context, inhibition)

• Executive Control & Optimization System: Executive Control (goal-setting, arbitration)

• Memory Pattern Retrival System: Memory/Schema (retrieval, inference)

• Associative Pattern Recognition System: Associative (creativity, simulation)

These systems coordinate, compete, and suppress based on contextual relevance and predictive demand.

3. Cognitive Synchronization & Transition Protocol

The mesoscopic control layer — a regulatory system that gates transitions between modes using:

• Oscillatory phase coherence

• Prediction error thresholds

• Confidence-weighted arbitration

• Neuromodulatory signals (dopamine, noradrenaline, etc.)

CSTP enables fluid cognitive reconfiguration — and predicts what happens when this regulation fails.

What Makes NCSM Different

Linking, Neuroscience, Psychology, and Real-Time Cognitive Dynamics

Cognition does not arise from isolated functions, nor is it reducible to single-region activation or task-based traits. It emerges from dynamic regulation — the continuous, recursive negotiation between competing internal systems. Yet most cognitive models stop short of explaining this: how transitions occur, how control is allocated, and what enables coherence when multiple subsystems compete.

The Neurodynamic Cognitive Systems Model (NCSM) defines this missing layer. Situated at the mesoscopic scale, between micro-level neural computation and macro-level behavior — NCSM models cognition as a real-time control system. It captures the processes by which the brain regulates shifts in attention, emotion, memory, and decision-making, aligning moment-to-moment function with context and internal demand.

This regulatory layer links multiple explanatory domains:

Psychology — reframing affect, motivation, rigidity, and conflict as patterns of transition, arbitration, or failure to reallocate internal control

Neuroscience — grounding subsystem dynamics in observable brain networks, oscillatory signatures (e.g., theta–gamma coupling), and neuromodulatory signals (e.g., dopamine, noradrenaline)

Behavioral Science — modeling how regulation of internal modes translates into action, reflection, habit, or reactive breakdown

Biological Control — aligning with principles of predictive coding, feedback stability, and systemic coherence regulation

Clinical Psychiatry — offering mode-specific explanations for dysregulation in disorders such as depression, PTSD, ADHD, OCD, and dissociative conditions

NCSM is structured not as a descriptive metaphor, but as a formally defined, simulation-ready architecture. Each component — from predictive modeling (NPPE), to subsystem competition (DCSM), to oscillatory control (CSTP) — is mechanistically specified and empirically testable.

NCSM is:

Neuroanatomically grounded — mapping to specific cortical and subcortical circuits

Clinically applicable — providing testable predictions about failure modes, coherence collapse, and resilience

Cross-compatible — integrating with predictive processing, symbolic cognition, AGI architectures, and cognitive neuroscience

If validated through ongoing empirical work, NCSM would represent the most granular, structurally unified, and computationally testable model of real-time human cognition yet constructed — a framework capable of linking psychological dynamics to neural control logic at every level of scale. Where traditional models isolate cognition into parts, NCSM unifies it into process — recursive, regulated, and fully alive in time.

Current Research Outputs

The Neurodynamic Cognitive Systems Model has advanced beyond theoretical development into a structured, testable architecture. Each subsystem — from predictive modeling to control arbitration — has been formally defined, internally validated, and aligned with empirical research standards.

The following components are complete or in active development:

CSTP Arbitration Model
Full specification of subsystem prioritization logic, transition thresholds, and gating functions based on predictive error, confidence weighting, and resource allocation.

Model Architecture Preprint
A comprehensive systems-level manuscript detailing NPPE, DCSM, and CSTP components, including neuroanatomical mappings and task-level predictions. [View on Zenodo]

Validation Roadmap
EEG, fMRI, and behavioral protocols designed to test CSTP transitions, coherence breakdown signatures, and arbitration latency under cognitive load.

Transition Protocol Pilot
A testbed for simulating mode arbitration and gating under structured perturbation. Early-stage results are informing parameter tuning and timing constraints.

Collaborative Documentation
Open-access materials outlining model assumptions, simulation logic, and empirical targets — available for reviewers, labs, and aligned research teams. These outputs form the foundation for our next phase: targeted empirical validation and strategic collaboration.

What's Next

We are currently finalizing the CSTP validation task suite — including EEG/fMRI experimental designs and behavioral paradigms targeting arbitration, transition failure, and coherence breakdown under cognitive load.

Our next phase focuses on collaborative testing and formal validation across disciplines. We are actively seeking engagement with:

Cognitive neuroscience labs — to implement EEG/fMRI protocols based on CSTP gating predictions

AGI researchers and modelers — exploring integration of dynamic arbitration and mode control in intelligent systems

PhD programs and academic advisors — aligned with cognitive architecture, systems neuroscience, or computational psychiatry

Systems theorists and independent researchers — contributing to the development of structured models of adaptive intelligence

Partnerships are open, documentation is live, and the framework is ready for field-level collaboration.

Collaborate or Connect

We’re currently open to validation partners, collaborators, and institutional alliances. To discuss research, funding, or implementation, [Contact us].

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