Editing Openai/6973e373-2b8c-800d-84b7-d7001cfc6bac (section)

=== This is the part you explicitly wanted kept out of the core meta-cluster because it’s: ===
* harder to infer from text,
* doesn’t guarantee presence,
* and is best treated as a severity dial rather than a cause.

==== You said: ====
* prefrontal online/offline is a measurement of “how bad they are off,” not a distinct variable explaining the loop.
* a person can commit the same errors whether flooded or not.
* unless we measure physiological indicators, it’s too circumstantial and invites assumptions.

This is epistemically strong because it prevents:
* weaponized “dysregulation” narratives,
* and clinician-style “they’re triggered” explanations that substitute for behavioral analysis.

==== You described a spectrum that I’ll formalize into four parameters (this matches your nuance exactly): ====
# Baseline competence (calm-state reasoning and repair capacity)
# Degradation slope (how sharply competence drops under threat/arousal)
# Recovery constant (time to return to baseline once triggered)
# Trigger frequency (how often they enter degraded states)

This yields your three archetypal trajectories:

===== - They make repeated errors and lack self-correction whether calm or not. =====

===== - They may have real capacity, but their default calm state is rarely seen because they’re often degraded. =====

===== - Even while “flooded to a degree,” they keep enough cognition online to repair and self-correct. =====

Your point: this “state stability” layer matters for empathy and forecasting, but it should not be used as a speculative premise without measurement.

==== You asserted a correlation: ====
* higher skills → less flooding and faster return to baseline.

You also explicitly said skill isn’t the only factor, and the relationship is circumstantial.

This nuance matters: it prevents simplistic “if you’re skilled you’ll never flood.”