LSD microdosing attenuates the impact of temporal priors in time perception
Bibliographic record
Abstract
Abstract Recent theoretical work embedded within the predictive processing framework has proposed that the neurocognitive and therapeutic effects of psychedelics are driven by the modulation of priors (Carhart-Harris & Friston, 2019). We conducted pre-registered re-analyses of previous research (Yanakieva et al., 2019) to examine whether microdoses of lysergic acid diethylamide (LSD) alleviate the temporal reproduction bias introduced by priors, as predicted by this theoretical framework. In a between-groups design, participants were randomly assigned to one of four groups receiving LSD (5, 10, or 20 μg) or placebo (0 μg) and completed a visual temporal reproduction task spanning subsecond to suprasecond intervals (0.8 to 4 sec). Using mixed-effects modelling, we evaluated the impact of the treatment group, and of the overall history of stimulus intervals ( global priors) and the local stimulus history ( local priors), weighted by their respective precision weights (inverse of variance), on temporal reproduction. Our principal finding was that the precision-weighted local priors and their precision weights reduced the under-reproduction bias observed under LSD in the original research. Furthermore, controlling for the precision- weighted local prior eliminated the reduced temporal reproduction bias under LSD, indicating that LSD microdosing mitigated the temporal under-reproduction by reducing the relative weighting of priors. These results suggest that LSD microdosing alters human time perception by decreasing the influence of local temporal priors.
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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 | 0.001 |
| 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".