How high learning rate can lead to depression

Thanks @Ariel Cheng for helping a lot in refining the idea, with her thorough understanding of FEP

Epistemic status: An attempt at a synthesis of the cholinergic theory of depression and the role of acetylcholine in the Active Inference theory of the brain, by a neuroscience layperson. My understanding of the math behind FEP is also incomplete, but it seems to me that it's worth writing out a potentially mathematically mistaken idea, rather than delaying shipping by continually getting sidetracked by all the existing FEP literature.

I am not claiming to explain depression fully by the theory, it is a probably wrong mechanistic model explaining maybe just a tiny fraction of depression etiology, there are many more biological explanations that may apply better to many cases.

Intro:

Depression is often (usually implicitly) conceived of as "fixed priors" on the state of oneself and the world, with an overly pessimistic bias. Depressed people's views are considered to be a mere product of a "chemical imbalance" (which chemical? serotonin almost certainly not^[1]). The standard psychotherapeutic treatment of depression, CBT, is based on this idea; Your problems are cognitive distortions, and by getting into a better epistemic state about them, they diminish.

However, depressive realism seems to hold for at least some cognitive tasks, and increased activity of the same neurotransmitter appears to mediate both the effects of many "cognitive enhancers" (nootropics) and depression. This may be explained by depression being an attractor state achieved by pathologically increased learning rate.

In this text, I propose a theory of the mechanism behind this connection, using mostly an Active Inference model of the mind.

TL;DR (by GPT-5.1):

Claim. Major depression is not a state of fixed priors, but a miscalibrated learning regime: high precision on ascending prediction errors (↑ACh) and relatively low precision on deep priors/policies (↓catecholamines). In short: a pathologically high learning rate.
Mechanism. Acetylcholine (ACh) up-weights sensory (or internally generated) prediction errors, forcing aggressive bottom‑up updating; monoamines (esp. dopamine) down, high‑level priors (goals, self‑model) lose precision. The agent becomes exquisitely sensitive to surprises and revises beliefs rapidly—especially in the negative dir

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quila's Shortform

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quila's Shortform

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