Computational models of advanced meditation, particularly those using Active Inference, increasingly point to precision weighting—the confidence assigned to different model parameters—as a shared mechanism that shapes shifts in experience. Early models emphasize top-down attentional modulation toward interoception or specific objects, while later models focus on layer-specific precision re-weighting within the meditator's hierarchical generative model to target more specific phenomenology. Despite progress, minimal phenomenal experiences such as nonduality and cessations remain largely unaddressed. Few models account for increased cognitive flexibility or learning from meditation, and mechanisms behind informal practice, affective processes, and compassion traditions are underexplored.
Neurofeedback (NF) has been proposed as a tool to support meditation practice, but a systematic review mapping the field across clinical and non-clinical contexts reveals that most studies are proof-of-concept and vary widely in design, implementation, and outcome measures. While NF consistently modulates neural activity, evidence for corresponding improvements in behavior, phenomenology, or transferable meditative skills remains limited. The review concludes that additional research is essential to determine whether NF can help practitioners overcome common meditative barriers, such as anxiety and self-doubt, and accelerate meditative development from novice to advanced meditators.