Editing Openai/6948e03d-a2c8-8004-b437-592576c8ff41 (section)

=== Assistant: What this graph is showing is the qualitative structure of what you reported, not a measurement but a faithful abstract… ===
What this graph is showing is the qualitative structure of what you reported, not a measurement but a faithful abstraction of the phenomenon.
* The x-axis represents chain size: how much relational structure is packed into a single statement.
* The y-axis represents comprehension fidelity under a fixed temporal budget, meaning you are not slowing down to reparse or backtrack.
* The smooth decline from 1 to ~8 reflects incremental load: each added clause slightly increases integration cost, but your cognition remains in a tracking regime, where dependencies are actively maintained.
* The sharp drop at ~8 is the phase transition you identified. This is where working memory saturation forces a mode switch.
* Beyond that point, the curve flattens: comprehension does not collapse to zero, because you are now operating in a compressed, reconstructive regime, where meaning is inferred holistically rather than followed explicitly.

The key insight is that the discontinuity is temporal, not logical. The graph would look very different if reading speed were allowed to vary; the cliff would smear into a slope. In higher-order logic terms, this is exactly why such systems feel “nonlinear”: not because steps are missing, but because too many steps occupy the same moment.

If you want, the next refinement would be to add a second curve showing variable speed, or to formalize this as a bandwidth–depth tradeoff rather than a single threshold.