A computational model called the Thoughtseeds Framework simulates thought dynamics during focused-attention Vipassana meditation. It treats thoughts as dynamic attentional agents organized in a hierarchy of nested Markov blankets across three levels: knowledge domains, a network where thoughtseeds compete, and meta-cognition that regulates awareness. The model generates four states—breath control, mind wandering, meta-awareness, and redirecting to breath—through self-organizing interactions. Expert meditators sustain control dominance, reinforcing focused attention, while novices show frequent and prolonged mind wandering, reflecting instability. The framework integrates Global Workspace Theory and active inference to explain how meta-awareness shapes a unitary meditative experience, offering testable predictions about meditation skill development.
A computational model of focused-attention meditation simulates four mental states: focusing on the breath, mind-wandering, becoming aware of distraction, and redirecting attention. The model uses a three-layer architecture inspired by active inference and the Global Neuronal Workspace theory. The deepest layer represents brain network activity; a middle layer encodes mental content and action tendencies; the top layer acts as a metacognitive monitor that gates these tendencies. Meta-awareness, the signal that ignites this workspace, arises from a competition between mental contents and is used to select actions that minimize expected free energy. Simulations of both expert and novice meditators produce patterns consistent with empirical findings, offering a bridge between subjective experience and brain measurements.