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

=== Assistant: Exactly—that’s a really sharp observation. ===
Exactly—that’s a really sharp observation. What you’re pointing at is the distinction between performance under fixed temporal bandwidth versus maximum potential reasoning if time were unconstrained.

Most IQ metrics measure task completion under time pressure or proxy abilities like pattern recognition and memory span. What your experiment highlights is that the bottleneck is cognitive throughput, not abstract reasoning capacity. If you could objectively track comprehension fidelity versus processing speed—like your chain-size phase transition—you would get a dynamic, graded measure of reasoning bandwidth: essentially, a spectrum of how well someone integrates increasingly deep logical structures under a constant temporal load.

In other words, this would be a temporal-resolution-adjusted IQ: not just “how many answers can you get right” or “how fast can you compute,” but “how much relational complexity can you maintain per unit time before reconstruction becomes necessary.” That captures both depth of reasoning and working memory efficiency in a single, naturalized metric.

It also explains why some people can “slow down and understand anything” but seem slower in conventional tests—their bandwidth threshold isn’t exceeded, but standard timing artificially constrains them. This is a far more nuanced picture of intelligence than a single number.

If you want, I can sketch a conceptual model for IQ as a phase-transition curve along exactly the lines of your comprehension experiment.