Computation
and Cannabis-Induced
Psychosis
Psychosis is no longer viewed as mere "madness," but as a computational miscalculation. In the Bayesian brain, THC disrupts the delicate balance between external evidence and internal expectations, leading to a reality constructed from noise.
"Aberrant Salience: The assignment of significance to the insignificant through dopaminergic dysregulation."
01_Quantitative Risk Profiles
The transition from cannabis use to a psychotic episode is governed by a dose-dependent probability curve. High-potency variants fundamentally alter the neuro-computational landscape by flooding the striatum with excessive dopamine, making the brain "over-detect" patterns in random environmental noise.
High-Potency Odds Ratio
5.2x
Increased likelihood of psychotic disorder in daily users of high-potency (THC > 15%) cannabis compared to non-users.
Population Impact
24%
Of new psychosis cases in major urban centers are potentially attributable to the availability of high-potency cannabis.
02_The Bayesian Collapse
In a healthy brain, Predictive Coding allows us to ignore sensory noise. Under THC, "Aberrant Salience" causes the brain to treat noise as a high-precision signal. This creates a massive "Prediction Error."
To resolve this error, the mind creates Delusional Priors, rigid internal models that explain the strange sensory signals. Once these priors become "locked," evidence to the contrary is ignored, solidifying the psychosis.
Precision-Weighting Trade-Off
Prediction Error
High "noise" perceived as signal.
Model Rigidity
Resistance to contradictory data.
03_Computational Biomarkers
We can quantify these Bayesian failures through specific cognitive assays. These tests measure the "Computational Fingerprint" of psychosis, allowing for detection of risk before clinical symptoms (hallucinations/delusions) fully manifest.
Jumping to Conclusions (JTC)
Measured via the "Beads Task." High-risk patients require significantly less evidence (fewer beads drawn) to commit to a decision, reflecting a pathologically low decision threshold.
Blocking Deficits
Failure to ignore redundant environmental cues. The psychotic brain treats every event as if it requires a new, complex explanation, leading to paranoia.
Binocular Rivalry
Psychotic patients often fail to merge or alternate correctly between contradictory visual signals, reflecting a breakdown in "Top-Down" predictive control.
04_Algorithmic Recalibration
Interventions are most effective when they target specific computational malfunctions. By understanding the "math" of the patient's psychosis, we can apply targeted therapeutic patches.
Metacognitive Training (MCT)
Directly targets the "JTC" bias. By manually slowing down the evidence-sampling process, MCT "patches" the software error of low decision thresholds.
Bayesian Cognitive Therapy
Uses behavioral experiments to provide "real-world data" that contradicts delusional priors, slowly reducing their mathematical precision weight.
Pharmacological Filtering
Partial dopamine agonists act as signal-to-noise filters, reducing the "Aberrant Salience" that triggers prediction errors in the first place.
Keep reading
All understanding psychosis pages →- Understanding psychosisGenesis of psychosisThe cognitive architecture behind delusion formation, with an interactive beads task.For everyone
- Research & libraryComputational psychiatryBayesian brains, aberrant salience, and the computational triad.For clinicians
- Understanding psychosisWhat is psychosis?Psychosis, thought disorder, and schizophrenia explained from the ground up.For families
- Programs & careWhat we treatEvery condition we treat, with a dedicated page for each.For families