Bayesian Theory of Consciousness as Exchangeable Emotion-Cognition Inference
This paper proposes a unified framework in which consciousness emerges as a cycle-consistent, affectively anchored inference process, recursively structured by the interaction of emotion and cognition. Drawing from information theory, optimal transport, and the Bayesian brain hypothesis, we formalize emotion as a low-dimensional structural prior and cognition as a specificity-instantiating update. This emotion-cognition cycle minimizes joint uncertainty by aligning emotionally weighted priors with context-sensitive cognitive appraisals. Subjective experience thus arises as the informational footprint of temporally extended, affect-modulated simulation. We introduce the Exchangeable Integration Theory of Consciousness (EITC), modeling 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 across episodes. We connect this architecture to the Bayesian theory of consciousness through Rao-Blackwellized inference, which stabilizes inference by marginalizing latent self-structure while enabling adaptive updates. This mechanism ensures coherence, prevents inference collapse, and supports goal-directed simulation. The formal framework builds on De Finetti's exchangeability theorem, integrated information theory, and KL-regularized optimal transport. Overall, consciousness is reframed as a recursive inference process, shaped by emotion, refined by cognition, stabilized through exchangeability, and unified through a latent self-model that integrates experience across time.
Code (0)
등록된 구현이 없습니다.
Tasks
HippocampusNavigateSpecificityMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Modeling Layered Consciousness with Multi-Agent Large Language Models
We propose a multi-agent framework for modeling artificial consciousness in large language models (LLMs), grounded in psychoanalytic theory. Our \textbf{Psychodynamic Model} simulates self-awareness, preconsciousness, an…
parameter-efficient fine-tuningHumanoid Artificial Consciousness Designed with Large Language Model Based on Psychoanalysis and Personality Theory
Human consciousness is still a concept hard to define with current scientific understanding. Although Large Language Models (LLMs) have recently demonstrated significant advancements across various domains including tran…
Quantum-like model for unconscious-conscious interaction and emotional coloring of perceptions and other conscious experiences
Quantum measurement theory is applied to quantum-like modeling of coherent generation of perceptions and emotions and generally for emotional coloring of conscious experiences. In quantum theory, a system should be separ…
On the Minimal Theory of Consciousness Implicit in Active Inference
The multifaceted nature of subjective experience poses a challenge to the study of consciousness. Traditional neuroscientific approaches often concentrate on isolated facets, such as perceptual awareness or the global st…
Bayesian InferenceNarrative-Centered Emotional Reflection: Scaffolding Autonomous Emotional Literacy with AI
Reflexion is an AI-powered platform designed to enable structured emotional self-reflection at scale. By integrating real-time emotion detection, layered reflective prompting, and metaphorical storytelling generation, Re…