Ketamine and Psilocybin Differentially Impact Sensory Learning During the Mismatch Negativity
Bibliographic record
Abstract
Abstract Ketamine and psilocybin show potential as therapies for various mental illnesses, including major depressive disorder. However, further investigation into their neural mechanisms is required to understand their effects on the brain. By combining computational modelling with electroencephalography (EEG), we examine the effects of ketamine and psilocybin on hierarchical sensory pwPE learning in the context of the auditory mismatch negativity, an event-related potential consistently shown to be reduced under psychotomimetic interventions. We employed a Bayesian framework and re-analyzed a previously acquired EEG dataset (Schmidt et al., 2012) by modelling single-trial EEG data using the Hierarchical Gaussian Filter. Using a placebo-controlled within-subject crossover design, healthy subjects were administered either S-ketamine or psilocybin during an auditory roving paradigm of pure sinusoidal tones. Our findings elucidate distinct neural impacts of ketamine and psilocybin on sensory learning: ketamine led to a larger reduction in the effect of sensory precision compared to placebo from 207 to 316 ms peaking at 277 ms in the frontal central channels, while psilocybin showed no significant effect. Both drugs reduced the expression of belief precision between 160 to 184 ms, peaking at 172 ms. For higher-level volatility pwPEs, ketamine reduced the expression at 312 ms while psilocybin had a null effect. For perception of elementary imagery, ketamine had a greater effect than psilocybin on sensory and volatility precision, while psilocybin had a greater effect on volatility pwPEs. Our findings suggest hallucinogens have distinct effects on sensory learning that could inform tailored therapies for major depression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".