Shared neural mechanisms of trait mindfulness and hypnotic susceptibility: A scoping review toward a unifying predictive coding framework.
Qiang Chen, Yating Zhang, Chenrui Liu, Yu Fu, Quan Gan, Zhuangfei Chen
Brain Research Bulletin June 27, 2026 DOI: 10.1016/j.brainresbull.2026.112021 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Scoping review Peer reviewed |
|---|---|
| Topics | Meditation |
| Keywords | Attention EEG Hypnosis susceptibility Interoception Metacognitive awareness Neuroimaging Predictive coding |
| Key findings | Trait mindfulness and hypnosis susceptibility share neural mechanisms involving the prefrontal cortex, anterior cingulate cortex, enhanced theta and alpha EEG rhythms, and salience and central executive network interactions, with differential regulation via predictive coding. |
Abstract
This scoping review systematically compares the neural mechanisms underlying trait mindfulness and hypnosis susceptibility, focusing on three key dimensions: brain region activation, electroencephalographic (EEG) signals, and core brain networks. The aim is to elucidate potential shared mechanisms involved in conscious state modulation. Through a comprehensive review of existing brain imaging and EEG studies, we found that both phenomena involve the prefrontal cortex and anterior cingulate cortex, enhanced theta and alpha EEG rhythms, and are closely related to the dynamic interaction between the salience network and the central executive network. These findings suggest their potential role in attentional regulation. Guided by the theory of predictive coding for interoception, we propose an integrative framework: trait mindfulness and hypnosis susceptibility jointly influence cognitive processes and alter self-conscious states through differential regulation. Specifically, trait mindfulness enhances perceptual inference and metacognitive awareness, whereas hypnosis susceptibility facilitates the acceptance of high-precision prior cues (suggestions) and promotes active inference. This framework provides a unified perspective for understanding the foundation of the two constructs and identifies directions for future research and clinical translation (e.g., anxiety interventions).