When the Model Locks In
Flexibility is expensive.
Efficiency is seductive.
When the model locks in
Early predictive rules begin as adaptive shortcuts.
They reduce uncertainty.
They lower computational load.
They allow faster response.
But repetition narrows possibility.
When a particular expectation repeatedly minimizes error, the system samples alternatives less often. Competing interpretations lose weight. The predictive landscape compresses.
Efficiency increases.
Exploration decreases.
Over time, the model becomes less provisional and more assumed. It shifts from hypothesis to background condition.
What once functioned as a flexible adjustment becomes structural constraint.
The organism does not experience this as narrowing. It experiences it as normality.
Lock-in does not require trauma. It requires consistency.
The more stable the feedback loop, the less incentive the system has to revise. And because revision introduces temporary instability, the system resists it.
Prediction that once served adaptation can become limitation.
The model does not announce when it has hardened.
It simply stops considering alternatives.
From:
Minds Built Between Us
PART I — The Predictive Organism
01 The First Predictions We Ever Make
Subsection: When the model locks in
Translated from English ; minor errors may occur.