Insights on Psychedelics: A Systematic Review of Therapeutic Effects
crossref（2024）
Abstract
Insights are a common and powerful subjective experience that frequently occur during psychedelic experiences, and are often considered by clinicians and patients to be central to the therapeutic value of psychedelics, but this has not been systematically assessed. Insight refers to a sudden acquisition of knowledge or understanding that is strongly felt to be true or real. In this systematic review, we analysed data from 98 studies reporting insight following a psychoactive dose of a classic-psychedelic, to elucidate several aspects of psychedelic-catalysed insight (PCI), including its prevalence, relationship to dose, time-course, and relationship to therapeutic outcomes in the context of mental health and wellbeing. Experiences of PCI were positively correlated with dose, and reached statistical significance in 43 of 46 (93%) studies that compared psychedelic and placebo conditions. Crucially, 25 of 29 studies (86%) found that PCI was associated with positive therapeutic outcomes, and this relationship was often stronger than mystical-type experiences, which have received more research attention. This systematic review contributes to our understanding of the aspects of subjective experiences that drive psychedelic-related outcomes, and suggests that PCI is an important topic for the clinical practice of psychedelic therapies and for understanding the mechanisms of psychedelic therapy more broadly.
MoreTranslated text
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined