A Modular Theory of Subjective Consciousness for Natural and Artificial Minds
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914071645782016 |
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| author | Gillon, Michaël |
| author_facet | Gillon, Michaël |
| contents | Understanding how subjective experience arises from information processing remains a central challenge in neuroscience, cognitive science, and AI research. The Modular Consciousness Theory (MCT) proposes a biologically grounded and computationally explicit framework in which consciousness is a discrete sequence of Integrated Informational States (IISs). Each IIS is a packet of integrated information tagged with a multidimensional density vector that quantifies informational richness. Its magnitude correlates with subjective intensity, shaping memory, behavior, and continuity of experience. Inputs from body and environment are adaptively filtered, processed by modules (abstraction, narration, evaluation, self-evaluation), and integrated into an IIS. The resulting packet, tagged with its density vector, is transmitted to behavioral readiness, memory, and decision-making modules, closing the loop. This explains why strongly tagged states exert greater influence on long-term memory and action. Unlike Global Workspace Theory, Integrated Information Theory, or Higher-Order Thought, MCT specifies a full computational pipeline producing discrete informational units with quantifiable internal structure. Subjectivity is reframed as a correlate of the density-tagging signal with functional consequences. MCT generates testable predictions, such as stress enhancing memory encoding, and provides a naturalistic blueprint for both biological and artificial architectures. Consciousness, in this view, is not an irreducible essence but an evolvable, quantifiable, and constructible feature of complex information processing. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_01864 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Modular Theory of Subjective Consciousness for Natural and Artificial Minds Gillon, Michaël Neurons and Cognition Artificial Intelligence Understanding how subjective experience arises from information processing remains a central challenge in neuroscience, cognitive science, and AI research. The Modular Consciousness Theory (MCT) proposes a biologically grounded and computationally explicit framework in which consciousness is a discrete sequence of Integrated Informational States (IISs). Each IIS is a packet of integrated information tagged with a multidimensional density vector that quantifies informational richness. Its magnitude correlates with subjective intensity, shaping memory, behavior, and continuity of experience. Inputs from body and environment are adaptively filtered, processed by modules (abstraction, narration, evaluation, self-evaluation), and integrated into an IIS. The resulting packet, tagged with its density vector, is transmitted to behavioral readiness, memory, and decision-making modules, closing the loop. This explains why strongly tagged states exert greater influence on long-term memory and action. Unlike Global Workspace Theory, Integrated Information Theory, or Higher-Order Thought, MCT specifies a full computational pipeline producing discrete informational units with quantifiable internal structure. Subjectivity is reframed as a correlate of the density-tagging signal with functional consequences. MCT generates testable predictions, such as stress enhancing memory encoding, and provides a naturalistic blueprint for both biological and artificial architectures. Consciousness, in this view, is not an irreducible essence but an evolvable, quantifiable, and constructible feature of complex information processing. |
| title | A Modular Theory of Subjective Consciousness for Natural and Artificial Minds |
| topic | Neurons and Cognition Artificial Intelligence |
| url | https://arxiv.org/abs/2510.01864 |