Consciousness as a Compressed Internal Report: A Biological Regulatory Framework for Understanding Why Artificial Systems Do Not Produce Conscious Experience

Consciousness is best understood not as a product of computational complexity or behavioral sophistication but as a biologically constrained internal reporting interface generated by regulatory processes in living organisms. The brain produces compressed internal reports that summarize interactions between internal physiological states, environmental information, and self-referential processes, functioning as operational interfaces for coordinating perception, action, and regulation under energetic and informational constraints. Subjective experience emerges from biological regulation, meaning-related filtering, and self-referential integration, not raw computation. The concept of Meaning Cost describes the cognitive and energetic constraints of generating meaningful interpretations. Artificial systems, however sophisticated, lack the biologically grounded regulatory architecture for internally structured experiential reports, so behavioral sophistication should not be taken as evidence of machine consciousness.

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Abstract

Recent advances in artificial intelligence have renewed debates about whether increasingly sophisticated computational systems might eventually become conscious. Much of this discussion implicitly assumes that sufficient computational complexity or behavioral sophistication could give rise to subjective experience. However, this assumption remains conceptually unclear and lacks a coherent account of the biological mechanisms underlying consciousness. This study proposes an alternative perspective by conceptualizing consciousness as a biologically constrained internal reporting interface generated by regulatory processes within living organisms. According to this framework, the brain does not construct exhaustive representations of reality but instead produces compressed internal reports that summarize interactions between internal physiological states, environmental information, and self-referential processes. These reports function as operational interfaces that enable organisms to coordinate perception, action, and internal regulation under energetic and informational constraints. Within this model, subjective experience emerges not from raw computational capacity but from the interaction between biological regulation, meaning-related filtering processes, and self-referential integration. The framework also introduces the concept of Meaning Cost, which describes the cognitive and energetic constraints associated with generating meaningful interpretations of situations. From this perspective, artificial systems may generate increasingly sophisticated behavioral outputs while lacking the biologically grounded regulatory architecture required for internally structured experiential reports. Consequently, the behavioral sophistication of artificial intelligence should not be interpreted as evidence for the emergence of machine consciousness. By distinguishing between computational output and experiential architecture, this study provides a conceptual framework for clarifying debates about artificial consciousness and offers a biologically grounded perspective on the relationship between neural regulation, information compression, and subjective experience. Keywords: Consciousness; Artificial Intelligence; Subjective Experience; Meaning Cost; Internal Reporting Interface; Cognitive Architecture; Neural Integration