The architecture of cannabis-induced psychosis
In computational psychiatry, the brain is modeled as an inference machine. High-potency cannabis disrupts the precision-weighting of sensory evidence, causing the internal Bayesian engine to favor rigid priors over objective reality.
Use the research console to apply different protocols. Observe how the mathematical parameters and cognitive fingerprints shift in the charts below.
01 The Bayesian breakdown
This model visualizes how the brain weighs sensory evidence against internal beliefs. In the THC state, the brain fails to update beliefs based on new data, so internal priors dominate the perception cycle, the same failure that drives jumping to conclusions.
02 Neural biomarkers
This radar profile compares core computational parameters. Each protocol shifts the cognitive fingerprint, showing how therapy moves the system back toward the healthy baseline.
03 Therapeutic patches
Computational treatments target mathematical failures. By raising the decision threshold or lowering belief precision, we patch the algorithmic loop of the psychotic brain.
MCT
Metacognitive training forces the brain to accumulate more evidence before committing to a decision, raising the decision threshold that collapses in psychosis.
CBT
Cognitive behavioral therapy reduces the mathematical precision of delusional priors, making rigid beliefs revisable again.
RX
Pharmacological blockade reduces synaptic and striatal noise, restoring the sensory signal-to-noise ratio that supports accurate inference.