Music as hypnosis? A comparative network analysis of entrancing states of mind

Abstract

Music has often been linked to the capacity to induce trance-like states, with listeners sometimes describing their experiences as “hypnotic-like”. However, it is still unclear whether music can induce effects similar to those found in response to a hypnotic induction, or whether these references rest on a metaphor. Using matched samples (n=266), this exploratory study compared the subjective experience of hypnosis and music by contrasting phenomenology of consciousness dimension scores in response to listening to self-chosen music and following a hypnotic induction. We estimated regularized Gaussian graphical models and corresponding centrality indices to detect the most central experiential features in the networks for both groups music and hypnosis. We also examined the interconnectedness between communities of nodes, and compared the two networks using the Network Comparison Test (NCT). The intensity of listening to music was found to be slightly higher compared to the hypnotic state, and differed especially in emotions and visual imagery. The NCT revealed that the networks were similar in density, as was the average variance of dimensions explained by the other dimensions included in the network. Although the adjacency matrices correlation was high, the overall network structures for music and hypnosis were nonetheless significantly different. Post-hoc tests further indicated that connections were significantly different for two pairs of dimensions: rationality – volitional control and self-awareness – volitional control. Altogether, these findings suggest that music and an applied hypnotic induction evoke different states of mind. Advocating a family-resemblances approach, more research is required to identify shared mechanisms of trance-like experiences.

Keywords: Music listening, hypnosis, consciousness, phenomenology, network analysis

Here is the link to the preprint: https://osf.io/8xahj/

the Supplementary Materials: https://osf.io/pz7vh

and the link to the R code: https://osf.io/w2xs7