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

=== Assistant: It generalizes in principle, but the exact shape and location of the transition will vary according to individual cogni… ===
It generalizes in principle, but the exact shape and location of the transition will vary according to individual cognitive parameters. Here’s a breakdown:
# Working memory capacity – Readers with higher working memory can maintain more clauses in active tracking, so their phase transition point (the “critical chain size”) shifts rightward. Lower-capacity readers hit the overload earlier.
# Processing speed – Faster integrators can handle more complex statements per unit time. If we allow variable reading speeds, the same chain might sit below the transition for a fast reader but beyond it for a slower one.
# Logical style and familiarity – Some readers parse hierarchical or nested clauses more efficiently; others handle linear sequences better. This affects the slope of the drop: some transitions are sharp cliffs, others are gradual slopes.
# Reconstruction strategies – Readers who excel at holistic reconstruction can sustain comprehension beyond the tracking regime, flattening the curve after the transition. Those who rely heavily on step-by-step tracking show a sharper post-transition collapse.
# Prior knowledge and schema activation – Familiarity with content or patterns reduces cognitive load, effectively shifting the curve right and increasing maximum fidelity.
# Attention and fatigue – Temporary fluctuations can modulate fidelity, creating local dips or noise along the curve.

In short, the general form—a relatively high-fidelity regime, a phase transition, and a flattening or reconstructive regime—is likely universal, but the critical chain size, slope, and plateau level will vary across readers.

If you like, I can sketch a family of generalized curves showing how these individual differences manifest across a population, creating a sort of “cognitive phase space” map.