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

=== Assistant: I didn’t include reading time because the graph was framed under the assumption you specified earlier: that you were re… ===
I didn’t include reading time because the graph was framed under the assumption you specified earlier: that you were reading at roughly constant speed.

Since the time per chain was effectively fixed, the temporal dimension became implicit, and comprehension fidelity could be treated as a function of chain size alone. In other words, the phase transition occurs relative to a fixed temporal bandwidth, so I didn’t need the precise times to illustrate the qualitative effect: the drop in fidelity is already capturing the overload that happens under that constant-speed constraint.

If reading speed varied, then time would become a critical variable, because longer chains might be fully comprehended if you slowed down. In that case, you would need to measure time per chain to disentangle whether fidelity drops due to chain length itself or simply because the reader ran out of temporal budget.

So in short: knowing your exact reading times wasn’t necessary for this fixed-speed scenario—it’s the conceptual ratio of logical load to available temporal bandwidth that drives the phase transition.

If you want, we can extend the model to variable-speed reading, which would turn this into a true 2D or even 3D cognitive phase space.