mlsanityData to 7 Oct 2026

Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms

arXiv 2212.14787 ↗First posted 30 Dec 2022q-bio.NCcited by 21

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Abstract (arXiv)

This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the properties of experience in physical (operational) terms. It identifies the essential properties of experience (axioms), infers the necessary and sufficient properties that its substrate must satisfy (postulates), and expresses them in mathematical terms. In principle, the postulates can be applied to any system of units in a state to determine whether it is conscious, to what degree, and in what way. IIT offers a parsimonious explanation of empirical evidence, makes testable predictions, and permits inferences and extrapolations. IIT 4.0 incorporates several developments of the past ten years, including a more accurate translation of axioms into postulates and mathematical expressions, the introduction of a unique measure of intrinsic information that is consistent with the postulates, and an explicit assessment of causal relations. By fully unfolding a system's irreducible cause-effect power, the distinctions and relations specified by a substrate can account for the quality of experience.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Dec 2022.

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Citing papers per year: 2023: 2; 2024: 5; 2025: 5; 2026: 9.

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  1. 2023Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approachcited by 14
  2. 2024Is artificial consciousness achievable? Lessons from the human braincited by 11
  3. 2024Dissociating Artificial Intelligence from Artificial Consciousnesscited by 7
  4. 2023Eight challenges in developing theory of intelligencecited by 6
  5. 2024Preliminaries to artificial consciousness: a multidimensional heuristic approachcited by 4
  6. 2026Minimal Computational Preconditions for Subjective Perspective in Artificial Agentscited by 3
  7. 2025A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousnesscited by 2
  8. 2024Report on Candidate Computational Indicators for Conscious Valenced Experiencecited by 1
  9. 2024The Epistemic Asymmetry of Consciousness Self-Reports: A Formal Analysis of AI Consciousness Denialcited by 1
  10. 2025Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysiscited by 1
  11. 2025Artificial Consciousness as Interface Representationcited by 1
  12. 2025Consciousness in Artificial Intelligence? A Framework for Classifying Objections and Constraintscited by 1
9 more
  1. 2026Body-Grounded Perspective Formation and Conative Attunement in Artificial Agentscited by 1
  2. 2025AI and Consciousnesscited by 0
  3. 2026Toward IIT-Inspired Consciousness in LLMs: A Reward-Based Learning Frameworkcited by 0
  4. 2026Hidden Coalitions in Multi-Agent AI: A Spectral Diagnostic from Internal Representationscited by 0
  5. 2026Intrinsic Computational Functionalism and Simulated Consciousnesscited by 0
  6. 2026Perspective Latents as an Architectural Condition for Causal Emergence in Active Inference Agentscited by 0
  7. 2026Categorical AI phenomenology: A first-person approachcited by 0
  8. 2026Gradland: On Phenomenal Experience, Differentiated Across Many Dimensionscited by 0
  9. 2026A Mathematical Model of Motivated Emotional Mind - Cognitive Embodied Systemcited by 0