Meditation-related changes in brain dynamics and structure were investigated by scanning experienced meditators and naive controls with MRI during rest and focused-attention meditation. A machine-learning approach showed that effective connectivity (causal relationships between brain regions) was more informative than functional or structural connectivity alone for distinguishing meditators from controls. The most informative effective-connectivity links involved several large-scale networks, predominantly in the left hemisphere. Anatomical differences were smaller but present: meditators had stronger structural connectivity between four left-hemisphere areas belonging to somatomotor, dorsal attention, subcortical, and visual networks. The findings suggest a mechanism linking brain structure and function underlying meditation.
A complexity-based morphospace with three axes—autonomous, cognitive, and social complexity—can represent both biological and synthetic conscious systems. Awareness corresponds to computational complexity and wakefulness to autonomous complexity. Consciousness is argued to function as an evolutionary game-theoretic strategy, motivating social complexity as a third dimension. The framework yields a taxonomy of four types of consciousness based on embodiment: biological, synthetic, group, and simulated. This classification aids in identifying design principles for engineering conscious machines and in comparing signatures of consciousness across domains relevant to cognitive neuroscience, AI, and biomimetics.