New Complex Brain Hypothesis resolves entropy-content paradox in consciousness research
Friston et al. show brain entropy alone can't explain deep meditation vs. psychedelics...
The Entropic Brain Hypothesis (EBH) has long tied high neurophysiological entropy to rich phenomenal experience, as seen in psychedelic states. But recent neuroimaging studies of Minimal Phenomenal Experiences (MPEs)—such as deep meditative states and possibly 5-MeO-DMT intoxication—reveal a paradox: these states also exhibit increased entropy despite their subjective 'contentless' awareness. In a new arXiv preprint (2605.16146), Jonas Mago, Karl Friston, and coauthors present the Complex Brain Hypothesis (CBH) to resolve this conundrum.
The CBH proposes that the richness of experience is better indexed by **complexity** rather than entropy alone. It argues that the brain's inferential 'grain'—the scale at which it resolves uncertainty—modulates phenomenal content. High-content psychedelic experiences (HCPEs) arise from a fine-grained regime where loosened constraints amplify fluctuations into proliferating content. In contrast, MPEs arise from a coarse-grained regime where a simpler model dissolves variety into pure awareness. Both regimes elevate entropy, but their complexity signatures diverge. This framework refines the EBH and positions MPEs as critical test cases for computational theories of consciousness, with implications for meditation research, psychedelic therapy, and neurophenomenology.
- New paper by Mago, Friston, and colleagues (arXiv 2026) introduces the Complex Brain Hypothesis to resolve entropy-content paradox in consciousness studies.
- Both Minimal Phenomenal Experiences (e.g., deep meditation) and high-content psychedelic states show elevated brain entropy but opposite subjective richness.
- CBH argues complexity—modulated by coarse vs. fine-grained inferential regimes—better accounts for phenomenal content than entropy alone.
Why It Matters
Shifts consciousness research from entropy to complexity, offering a unified framework for altered states and computational testing.