Neurocomputational evidence of sustained Self-Other mergence after psychedelics
Bibliographic record
Abstract
Abstract Mental illness is often characterised by a maladaptive sense of self . The neurobiological basis of Self-Other distinction may provide targets for therapeutic interventions. Psychedelics alter the experience of selfhood, but the neurocomputational mechanism is unclear. We used a computationally-informed behavioural assay to investigate whether psychedelics disrupt Self-Other boundaries in belief formation. In a double-blind, crossover design, 22 participants received placebo, psilocybin or 2C-B (2,5-dimethoxy-4-bromophenethylamine). The next day, we fitted reinforcement learning models to probabilistic false-belief task behaviour, yielding objective Self-Other distinction measures. Compared to placebo, psychedelics induced a state of Self-Other mergence, associated with a multivariate signal of sustained psychosocial wellbeing. Effective-connectivity estimates from resting-state fMRI showed that this behavioural change was associated with reduced inhibitory tone from right temporoparietal junction to dorsomedial prefrontal cortex. We show that psychedelics quantifiably act on the neural basis of Self-Other distinction, offering potential routes to precision therapeutics in psychedelic psychiatry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".