The quest to understand consciousness has spawned numerous theories, each offering unique perspectives but none achieving universal acceptance. While these theories provide valuable insights, they often serve more as guiding narratives than mechanistic frameworks for empirical validation. Here, we propose that incorporating critical brain dynamics—the idea that neural networks operate near a critical transition point—may offer a more cohesive understanding of consciousness. We term this hypothesis the ConCrit framework, combining the concepts of consciousness and criticality. By reviewing empirical evidence and computational models, we suggest that near-critical systems exhibit many properties aligned with leading theories of consciousness. Specifically, approaching a critical point enhances the complexity and richness of internal representations and heightens sensitivity to the system’s own state. Consequently, a system capable of conscious experience is more likely to exhibit consciousness near criticality and lose it as it deviates from this regime. In this paper, we review the concepts of criticality and critical brain dynamics, explore their relationship with consciousness from theoretical and empirical perspectives, and outline key implications and predictions.
Shriki et al. (Tue,) studied this question.