An Experimental Meditation using NLP (Neuro-Linguistic Programming) Approaches

June 24, 2024

This meditation is built on the idea of trance identification with a future idealised, or present higher realized, self. Deep Trance Identification is a concept derived from the fields of NLP and hypnosis, but has its origins in shamanism, spiritual and religious practices of all kinds. Shamanic practices of https://decideursnews.com/  https://www.pressamedia.com/  https://www.topbrokeri.com/  https://camround.com/  http://poradydlarodzicow.pl/  http://autoinspiracje.pl/  https://szczesliwemaluchy.pl/  https://swiatdzieciakow.pl/  https://jakieubranie.pl/  https://pojazdomania.pl/  https://modabeztajemnic.pl/  https://budowaniebeztajemnic.pl/  https://niewiedziales.pl/  identification with spirit beings and deities would appear to go back to pre-history. More recently, some forms of the mystical Christian practices of the Stations of the Cross encourage identification with Jesus at stages from his being condemned to die through to his resurrection.

In Sogyal Rinpoche’s book, The Tibetan Book of living and Dying, he describes a meditation he calls Guru Yoga. I have adapted this meditation with an emphasis on some of the submodality distinctions. I would be interested in people’s thoughts or experiences with this meditation.

It seems to me that one should be able to future pace (nlp jargon) the insights and resources from the meditation back to the future self. This then creates a feedback loop, because the resources develop in the future pacing and become increasingly expanded and available to the trance identification. So as one carries out the meditation below, one can become aware of the insights that develop in “the now” contributing to the spiritual development of the future guru self being meditated upon. This further develops the guru self and results in accelerated insights in “the now”.

I am constantly amazed by the obvious familiarity in buddhist writings with submodalities, given that they don’t appear to have an explicit model of such.