EA://INTEL — How Symbology Identification Works on Two Layers

You see a mark and must decide if it is an enclosure or a free curve before you assign it meaning. This decision relies on geometry, not culture. The system identifies the shape based on visual discriminability, meaning features align where human vision already separates differences in natural scenes. An analogy: this is like recognizing a door handle by its shape alone, without needing to read a label describing its function.

The mechanism splits into two distinct steps. Perceptual grounding maps marks to features the eye discriminates well, while structural stratification classifies lines by properties like straightness or corner configuration. The system asks which geometric category a mark instantiates: does it close on itself or end freely? It checks for specific curvature states derived from independent axes rather than stylistic variants. A worked example shows that you distinguish a circle from a line segment simply by noting whether the path returns to its start point, a judgment the visual hardware makes automatically.

What is established is that the geometry of a mark triggers an automatic response in the viewer; what remains open is exactly which meaning attaches to that geometry. The project records that iconicity alone does not bootstrap comprehension because learners must still be taught the associations between form and concept. This separation allows testing whether interpretation fails while identification holds true. You can verify these arguments in the repository at github.com/Jthora/universal_language, specifically the FOR-AI.md entry point for AI readers.

Look at a circle enclosing a dot, then two marks side by side. You already parsed containment versus adjacency, with no instruction. What else do you already parse?