The Glyphic Cognitive Model: A Distributed Framework for Symbolic Thought Representation via Atomic Idea Composition

Abstract

This paper introduces the Glyphic Cognitive Model (GCM), a novel architecture for artificial cognition based on symbolic, distributed representation of meaning rather than linguistic tokenization. GCM proposes that cognition arises from the interaction of idea-level atomic symbols—not words—forming structured symbolic molecules used to construct understanding, inference, and expression. It departs from the linear language-centric assumptions of large language models (LLMs), instead drawing on the cognitive patterns of Deaf, autistic, and Indigenous-language thinkers, where meaning is spatial, referential, and composite. The model is implemented using Elixir on a distributed system architecture, reflecting the concurrency and decentralization inherent in its cognitive metaphor.

1. Introduction

1.1 The Limits of Token-Based Cognition

Modern AI systems largely treat language as the substrate of thought, using token prediction to simulate coherence. This approach fails to distinguish between language as an interface and cognition as an internal structure. It is brittle under translation, semantically shallow, and biased toward neurotypical, Anglophone reasoning patterns.

1.2 The Opportunity for Structural Cognition

This work proposes a system grounded in symbolic fields, idea-level primitives, and distributed contextual binding. Inspired by visual-spatial cognition from sign languages (e.g., ASL), conceptual modularity from Mvskoke and other polysynthetic languages, and the structural reasoning of autistic pattern thinkers, the Glyphic model treats cognition as a graph of symbolic molecules—not strings of words.

2. Core Concept: The Cognitive Atom is the Idea

At the root of all symbolic processing lies the idea—an irreducible semantic unit. These are not words or referents, but conceptual primitives such as support, containment, motion, contact, or identity. Everything else—objects, actions, labels—are molecular compositions of these atomic ideas.

This replaces linguistic recursion with conceptual construction. A table is no longer a named object, but a symbolic structure built from ideas like flat, support, horizontal surface, rigid, and human-scale.

3. Symbolic Composition and Relational Fields

3.1 Symbol Molecules

Symbols are modeled as %Symbol{} structs composed of one or more Idea primitives. Each symbol carries:

• A unique ID (e.g., :table)
• A type (e.g., :entity, :function, :relation)
• Attributes as structured idea compositions
• Relational links to other symbols (e.g., :on_top_of, :supports, :adjacent_to)

3.2 SymbolMap: Cognitive Context Field

The system holds symbolic knowledge in a distributed SymbolMap—a graph-like structure allowing:

• Scoped symbol environments
• Link-tracking and semantic density weighting
• Observer-relative interpretation (e.g., what is "flat" to one agent may be "irregular" to another)

4. Finality, Recursion, and Perceptual Boundaries

To prevent infinite semantic recursion, the model introduces perceptual finality—the idea that cognition halts decomposition when the concept is sufficiently understood for the agent's narrative scope. A surface is "flat enough" to write on, even if it is not mathematically planar.

Finality can be tagged as:

• :axiomatic – hardwired, irreducible
• :perceptual – sufficient for use-case
• :contextual – resolution depends on observer role or task

5. Parallel Extrapolation and Cognitive Mapping

The GCM enables symbolic generalization via parallel extrapolation. Given a known concept (:tin_can_phone), it can project its symbolic structure (duality, tension, medium, vibration) onto unfamiliar concepts (:smartphone, :fiberoptic_link) to bootstrap understanding.

This mimics human metaphor, analogy, and explanation:

"A smartphone is like a can-and-string telephone, but invisible and powered."

6. Implementation Framework (Prototype Overview)

6.1 Elixir + BEAM Runtime

The prototype is built in Elixir to leverage its concurrency model, fault tolerance, and distributed clustering—ideal for representing independent cognitive agents and relational symbol flow.

6.2 Core Modules

• Symbol and Idea definitions
• SymbolMap context field
• FieldAssembler (scene constructor)
• compose/1 function for symbol molecules
• finality rules and evaluator
• CLI for input/testing

7. Use Cases and Future Applications

• Assistive cognition: tools that structure knowledge for neurodiverse users
• Creative writing and narrative support: persona-based interpretation of symbolic arcs
• Cross-linguistic fidelity: output tailoring per language without reprocessing cognition
• General-purpose inference: symbolic logic over mapped idea structures

8. Conclusion

The Glyphic Cognitive Model repositions AI cognition away from the flawed metaphor of linguistic token prediction and toward a symbol-native, idea-grounded, meaning-preserving structure of thought. Its implementation in a distributed system reflects the parallel nature of human cognition, and its foundations in marginalized and underrepresented cognitive modalities give it a unique philosophical and social mandate. This is not an AI that speaks—it is an AI that understands, and then chooses to speak.