Phenomenology of auto-induced cognitive trance using text mining: a prospective and exploratory group study

Auto-induced cognitive trance (AICT) is a modified state of consciousness derived from shamanic tradition that can be practised by individuals after specific training. The aim of this work was to characterize the phenomenological experiences of AICT, using text mining analysis. Free recalls of subjective experiences were audio-recorded in 27 participants after five pseudo-randomized experimental sessions: ordinary conscious resting state, with auditory stimulation and with an imaginary mental task, as well as during AICT with and without auditory stimulation. Recordings were transcribed, normalized total word counts were calculated for each condition, and analyses of content were performed using IRaMuTeQ software. Results showed that the length of the participants' reports was higher for AICT compared to the other conditions, and that the content could be categorized into four classes of discourse: AICT memory, AICT, ordinary conscious states, and AICT with and without stimulation. AICT was also characterized by specific content compared to rest, auditory stimulation, and imagination conditions. Content analysis of the narrative revealed nine categories encompassing the presence of nature, people, animals, positive and negative features, sensory perceptions, body modifications, metacognition, and difficulty of describing thoughts. Among these categories, AICT is specifically characterized by reports related to the presence of nature, animals, body modifications, as well as the difficulty of describing thoughts. These results suggest that a richer phenomenology was reported during AICT, compared to the other conditions, and that AICT constitutes a class of discourse on its own, with a clear dissociation from the other conditions.