Leveraging psychedelic neuroscience to boost human creativity using artificial intelligence
Bibliographic record
Abstract
Psychedelics, such as LSD and psilocybin, disrupt entrenched cognitive patterns by facilitating novel insights and new associations. This paper considers how AI can potentially mimic these psychedelic-induced cognitive disruptions to augment and enhance human creativity. Psychedelics likely enhance creativity by altering brain function, notably the activity of the Default Mode Network, which leads to changes in cognition. Psychologically, they may reduce latent inhibition, increase divergent thinking, and promote implicit learning. Similarly, AI systems can replicate these creative enhancements by introducing novel associations, reframing familiar information, and facilitating unconscious cognitive shifts. The risks associated with AI use are also compared to psychedelics, including dependency, ethical concerns, and homogenization of outputs due to bias. Integrating the cognitive mechanisms activated by psychedelics into AI design provides promising pathways for creativity enhancement. Carefully designed AI could act as a cognitive catalyst, fostering innovative thought processes and adaptive problem-solving while addressing identified ethical and practical concerns.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".