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

=== Assistant: Given that constraint, what you experienced is not a failure of logic or capacity, but a mismatch between logical depth… ===
Given that constraint, what you experienced is not a failure of logic or capacity, but a mismatch between logical depth and temporal bandwidth.

Because you were reading at roughly constant speed, each additional clause increased the amount of structure that had to be simultaneously integrated per unit time. Early on, the logical load per interval stayed below your integration threshold, so comprehension was continuous and lossless. Around the midpoint, the density crossed a critical point: not because the logic became inaccessible, but because the rate at which dependencies were introduced exceeded the rate at which they could be stabilized in working memory.

The key point is that nothing became unintelligible in principle. If you slowed down, you could re-instantiate the missing structure, recover the dependencies, and reconstruct the logic fully. What changed was that, at constant speed, your cognition was forced to sample rather than track, replacing explicit state with inferred continuity. That’s why meaning persisted but precision degraded.

As a metaphor for higher-order logic, this maps cleanly: higher-order reasoning isn’t harder because it’s opaque, but because it compresses more relational structure into fewer apparent steps. When traversed too quickly, it induces apparent discontinuities—not because steps are missing, but because they’re stacked. Slowing down unfolds them; speeding up forces abstraction.

So what you observed was effectively a temporal aliasing effect in reasoning: higher-order structure folding into lower-resolution perception, where coherence remains but informational granularity drops. The logic was always there; time was the limiting reagent.