A dual-receptor model of serotonergic psychedelics
Bibliographic record
Abstract
Abstract Serotonergic psychedelics have been identified as promising next-generation therapeutic agents in the treatment of mood and anxiety disorders. While their efficacy has been increasingly validated, the mechanism by which they exert a therapeutic effect is still debated. A popular theoretical account is that excessive 5-HT2a agonism disrupts cortical dynamics, relaxing the precision of maladaptive high-level beliefs and making them more malleable and open to revision. We extend this perspective by developing a simple energy-based model of cortical dynamics based on predictive processing which incorporates effects of neuromodulation. Using this model, we propose and simulate hypothetical computational mechanisms for both 5-HT2a and 5-HT1a agonism. Results from our model are able to account for a number of existing empirical observations concerning serotonergic psychedelics effects on cognition and affect. Using the findings of our model, we provide a theoretically-grounded hypothesis for the clinical success of LSD, psilocybin, and DMT, as well as identify the design space of biased 5-HT1a agonist psychedelics such as 5-MeO-DMT as potentially fruitful in the development of more effective and tolerable psychotherapeutic agents in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".