Predicting Psychological Outcomes from Subscale Configurations in Psychedelic-Induced Mystical Experiences

An MEQ-30 Machine Learning Study, Corrected and Reproduced (August 15, 2026).

This AI-assisted secondary-data study examines whether MEQ-30 questionnaire items and subscale configurations predict life satisfaction better than a binary Complete Mystical Experience threshold. Using an open survey of 700 Polish adults, including 414 who reported psychedelic use, it evaluates Ridge regression, XGBoost, and LightGBM with nested cross-validation, SHAP feature attributions, and exploratory clustering.

The best model, XGBoost, achieved RMSE = 6.2673 and R² = 0.0165, indicating very limited predictive power. An accuracy audit identified and corrected an incorrect MEQ-30 subscale mapping and test-fold information leakage during hyperparameter selection. The paper includes source code and discloses AI assistance.

This is a case study in evaluating AI-assisted research, not a direct LLM benchmark. Life satisfaction is used as a proxy outcome; the cross-sectional design cannot establish causation, and the findings do not establish clinical utility. Its central evaluation lesson is the importance of valid measurements, leakage-free testing, reproducibility, and transparent reporting of weak results.