The Ontology of Cognitive Constraint Networks: Physical Conditions for Perception and Cognition From Barrier Asymmetry to Internal Models

What physical conditions must a constraint network satisfy in order to perceive its environment, form an internal model of its causal structure, and use that model to predict and act? This document --- the Ontology of Cognitive Constraint Networks (CCN) --- answers that question from first principles. The derivation begins at Barrier Asymmetry (Eb^melt Eb^form, L0 derived theorem, Constraint Dynamics v3. 1). Shape asymmetry --- the qualitative difference between narrow formation barriers and wide meltdown barriers --- establishes an asymmetric permeability at the system-environment boundary: environmental differences can cross into the system, while internal differences need not be returned to the environment on every occasion. This asymmetry is the physical root of the boundary Cut (Generative Grammar v2. 0, Rule 1) --- the first cognitive operation, defined herein as a physical process: environmental differences crossing the system boundary alter internal constraint states through physical interaction. From the Cut operation, the complete architecture of cognition unfolds. Repeated Cut events within a Hebbian time window (₇₄₁₁ t_, anchored in Time v3. 1) produce association constraints --- undirected edges A---B encoding statistical co-occurrence patterns. These association constraints are CCN's core output. They are defined in a three-layer constitutional structure: the physical layer (w₀₁ 0, 1, Hebbian co-occurrence count, CLOSED), the channel layer (C₀₁ () = C₀ (), from Information v3. 2 Channel Capacity Theorem, CLOSED), and the information layer (I (A;B) C₀₁ (), Shannon theorem, STANDARD). Association constraints are constitutively undirected --- they do not encode causal direction. The emergence of causal directionality requires the additional physical condition of intervention (Causality v1. 8, Causal Arbitrage Theorem), formalized herein as the Pre-Logical State Theorem. Repeated Encapsulate and Project operations on the association constraint graph produce a functionally differentiated three-layer internal model (Life v3. 1, 5. 3): a sensory mapping layer (Cut-coupled constraints), a state estimation layer (Encapsulate-compressed latent states), and a prediction output layer (Project-extrapolated future states). This emergence is not designed --- it is the structural consequence of GG v2. 0 operations applied to the association constraint graph under the constraints of finite energy and finite distinguishability. CCN defines the physical conditions for all seven GG v2. 0 operations, establishing the precise energy budgets (Ė₌₀₈₍, Ėₑ₄ₒ₏), conditions, and architectural prerequisites (L 4 for self-reference, derived from Hierarchical Depth v3. 1) for each operation. It provides the complete closed-loop architecture: perception (Cut) association (Hebbian formation) compression (Encapsulate) prediction (Project) prediction error detection (Cut difference detection) model update (Slide/Divide/Adjudicate-Meltdown) action (effector Cut) environmental feedback perception. Biological CCNs (human nervous systems) and AI CCNs (artificial neural networks) are shown to be two realizations of the same physical theorem on different substrates. The document further derives CCN internal dynamics (learning, memory, decision, affect, flow, intuition, volition, sleep), multi-CCN interaction (language, attachment, empathy, cooperation, trust, consensus reality), CCN pathology (the degeneration corridor, < 1 persistent locking, structural damage, chemical hijacking of regulation, progressive constraint meltdown, Cut precision loss, Divide precision anomalies), and CCN normative structure. Keywords: Cognitive constraint network; association constraint; pre-logical state; internal model; Barrier Asymmetry; Cut operation; Hebbian learning; prediction error; Encapsulate; self-reference; ratio; degeneration corridor; AI CCN