Bayesian Theory of Consciousness as Exchangeable Emotion-Cognition Inference
arXiv Preprint Archive May 17, 2024 Xin Li
Consciousness emerges from a cycle-consistent, affectively anchored inference process recursively structured by the interaction of emotion and cognition. Emotion acts as a low-dimensional structural prior; cognition provides specificity-instantiating updates. This emotion-cognition cycle minimizes joint uncertainty by aligning emotionally weighted priors with context-sensitive cognitive appraisals. Subjective experience arises as the informational footprint of temporally extended, affect-modulated simulation. The Exchangeable Integration Theory of Consciousness (EITC) models conscious episodes as conditionally exchangeable samples drawn from a latent affective self-model. This latent variable supports integration via a unified cause-effect structure with nonzero irreducibility and differentiation by preserving contextual specificity. The framework connects to the Bayesian theory of consciousness through Rao-Blackwellized inference, which stabilizes inference by marginalizing latent self-structure while enabling adaptive updates, preventing inference collapse and supporting goal-directed simulation.