The Application of the “Banchenko's Mnemonic Dream Synchronization Method” for the Joint Synchronization of Dream Elements (Including Lucid Dreams), within the Framework of “Blokhin's Dreaming Cell Concept,” and the Analysis of the Obtained Results Using the “Kapustin's AI Dream Matching Model.”
International Internal Medicine Journal January 29, 2024 DOI: 10.33140/iimj.02.01.06 (opens in new tab)
Summary
AI-generated from the abstractSynchronizing waking activities—such as watching a TV series together and discussing key concepts—can influence dream content, introducing shared symbols and themes. In a study with eight participants from different countries, dream journals were kept for a month, then an AI model analyzed dreams before and after a 16-day synchronization period using the Banchenko technique. Results showed that memes and tag-memes appeared in dreams, and in-depth discussions fostered unique group symbols. Lucid dream frequency remained stable, suggesting limited effectiveness of synchronization alone for inducing lucid dreams. Systematic dream journaling improved dream recall. The AI model identified common dream objects, but human verification was still needed.
Study at a glance
| Characteristics | Observational study with pre-post analysis Peer reviewed |
|---|---|
| Sample size | 8 |
| Population | Eight participants from different countries |
| Intervention | Mnemonic Dream Synchronization Technique of Banchenko |
| Duration | 30 days before synchronization and 30 days after start of synchronization; 16-day synchronization period |
| Key finding | Synchronization during wakefulness influences dream content, introducing memes and tag-memes, but does not increase lucid dream frequency. |
Abstract
Purpose: The research is aimed at studying the potential for dream synchronization, including lucid dreams, among a group of participants using the concepts of "memes" and "tag-memes" within the framework of the "Banchenko Algorithm" and the "Mnemonic Dream Synchronization Technique of Banchenko." Additionally, it aims to assess the effectiveness of a new artificial intelligence model built and trained by Mykhailo Kapustin for identifying correlations in the dreams of the participants [1]. Methods: In the study, eight participants from different countries kept dream journals for a month. Afterward, the artificial intelligence model developed by Kapustin was trained to analyze these dreams. Following this, over the course of 16 days, synchronization among the group of participants was conducted using the "Mnemonic Dream Synchronization Technique of Banchenko," which included activities like collectively watching the TV series "The OA. Synchronization took into account the difference in time zones. Additionally, synchronous discussions of the viewing were carried out on the Telegram messenger, along with additional discussions related to key concepts and "tag-memes." Data analysis covered 30 days before synchronization and 30 days after the start of synchronization, using Kapustin's model with subsequent manual verification [2].Results: The data obtained for two periods confirm the influence of synchronization during wakefulness on dreams and highlight the emergence of "memes" and "tag-memes" in dream content, as well as objects and scenes unrelated to the elements of discussion or collective viewings. In-depth discussions during wakefulness contribute to the development of unique "tag-memes" within the group. The novelty of the research is based on the fact that it's suggested that electromagnetic fields may serve as mediators in intercellular communication, facilitating the exchange of information between cells. The systematic keeping of dream journals may influence the content of dreams. The frequency of lucid dreams remained stable, which may indicate the limited effectiveness of isolated methods of joint tuning without additional methods for inducing lucid dreams. Conclusions: The research highlights the impact of wakeful-state synchronization on dream content. "Memes" and "tag-memes" play their roles, but systematic dream journaling is also important for improving dream recall. The frequency of lucid dreams suggests the need for further investigation into isolated practices. A computational model excels at identifying common dream objects, but human evaluation remains integral during algorithm refinement.