Understanding individual differences in non-ordinary state of consciousness: Relationship between phenomenological experiences and autonomic nervous system

Fig. 1. Experimental Procedure. Participants completed five randomized conditions: 'Rest', 'Imagination', 'Rest-Auditory', 'AICT', and 'AICT-Auditory'. For each condition, data collection included a global questionnaire, EEG recording using a 256-channel EGI system, and physiological measures (respiration and ECG) using the EGI polygraph input box. Additionally, participants provided phenomenological data through free recall and self-reporting after each condition. The timeline of the protocol is depicted on the left, showing the sequential steps for each condition.

Fig. 2. Change in phenomenological self-rating at rest and during auto-induced cognitive trance (AICT). a. The left panel shows the mean self-reported scores for each feature across all subjects, with 'AICT' in red and 'Rest' in blue. b. The right panel displays feature importances derived from a multivariate classification performed using a random forest classifier to distinguish between 'Rest' and 'AICT', with a decoding accuracy of 86 % (p≤.001).

Fig. 3. Linear mixed model reports changes between 'Rest' and 'AICT' conditions for each phenomenological feature. Only models showing significant coefficients between intercept and slope (i.e., outside the 95 % confidence interval) from Table 2 were plotted. A median split (black line) was used to separate individuals into high ratings at rest (red dotted line) and low ratings at rest (blue dotted line).

Fig. 4. Change and inter-individual variability in heart rate variability (HRV) at rest and during auto-induced cognitive trance (AICT). a. The left panel shows the mean self-reported scores for each feature across all subjects, with 'AICT' in red and 'Rest' in blue. b. The right panel displays feature importances derived from a multivariate classification performed using a random forest classifier to distinguish between 'Rest' and 'AICT', with a decoding accuracy of 73 % (p≤.001).

Fig. 5. Linear mixed model reports changes between 'Rest' and 'AICT' conditions for each HRV feature. Only models showing a significant coefficient between intercept and slope (i.e., outside the 95 % confidence interval) from Table 3 were plotted. A median split was used to separate individuals into high ratings at rest (red) and low ratings at rest (blue).

Fig. 6. Interrelation between self-reported phenomenological experiences and HRV measures during auto-induced cognitive trance (AICT). a. All principal component analyses (PCA) obtained after dimensionality reduction, accounting for 90 % of the explained variance with all Δ self-reported phenomenological features, b. Contribution of each self-reported phenomenological feature (or weighted matrix) ordered by value for PCA1, the first principal component (23 % explained variance) c. Regression between the variation (Δ) of self-reported phenomenological experiences during AICT (PCA1) and the variation (Δ) of HFn (HRV) during AICT. The regression results indicate R²=0.31, which remains significant after FDR correction (p≤.05).

Fig. 7. Interrelation between parasympathic nervous system and self-reported phenomenological experiences during auto-induced cognitive trance (AICT). Results for regressions between variation (Δ) of HFn (HRV) and variation (Δ) of self-reported phenomenological experiences during AICT; external thought (R²=0.30; p=.003), inner thought (R²=0.29; p=.003), emotion (R²=0.33: p=.001), absorption (R²=0.18; p=.02), dissociation (R²=0.18; p=.02), and trance intensity (R²=0.17, p=.03). After FDR correction, external thought, inner thought, and emotion remained significant (p≤.05), while absorption, dissociation, and trance intensity were not significant (p=.09).