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A Theory of Consciousness from a Theoretical Computer Science Perspective: Insights from the Conscious Turing Machine

arXiv 2107.13704 ↗First posted 29 Jul 2021cs.AI q-bio.NCcited by 22

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Abstract (arXiv)

The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. We examine consciousness from the perspective of theoretical computer science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations. In the spirit of Alan Turing's simple yet powerful definition of a computer, the Turing Machine (TM), and perspective of computational complexity theory, we formalize a modified version of the Global Workspace Theory (GWT) of consciousness originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, Jean-Pierre Changeaux and others. We are not looking for a complex model of the brain nor of cognition, but for a simple computational model of (the admittedly complex concept of) consciousness. We do this by defining the Conscious Turing Machine (CTM), also called a conscious AI, and then we define consciousness and related notions in the CTM. While these are only mathematical (TCS) definitions, we suggest why the CTM has the feeling of consciousness. The TCS perspective provides a simple formal framework to employ tools from computational complexity theory and machine learning to help us understand consciousness and related concepts. Previously we explored high level explanations for the feelings of pain and pleasure in the CTM. Here we consider three examples related to vision (blindsight, inattentional blindness, and change blindness), followed by discussions of dreams, free will, and altered states of consciousness.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 3 of its 173 references that are in the map. First posted on arXiv 29 Jul 2021.

Builds on

3 of 173 references.

References · 3

  1. 2020Deep Learning and the Global Workspace Theorycited by 35
  2. 2021A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learningcited by 27
  3. 2020A Theoretical Computer Science Perspective on Consciousnesscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 6; 2024: 6; 2025: 3; 2026: 3.

22 papers.

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  1. 2022Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questionscited by 116
  2. 2022On the link between conscious function and general intelligence in humans and machinescited by 15
  3. 2023Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledgecited by 12
  4. 2024Dissociating Artificial Intelligence from Artificial Consciousnesscited by 7
  5. 2023Eight challenges in developing theory of intelligencecited by 6
  6. 2022Brainish: Formalizing A Multimodal Language for Intelligence and Consciousnesscited by 4
  7. 2023A Categorical Framework of General Intelligencecited by 4
  8. 2024AI Consciousness is Inevitable: A Theoretical Computer Science Perspectivecited by 4
  9. 2024Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanismcited by 4
  10. 2023Survey of Consciousness Theory from Computational Perspectivecited by 3
  11. 2024ITCMA: A Generative Agent Based on a Computational Consciousness Structurecited by 3
  12. 2022A Theoretical Computer Science Perspective on Free Willcited by 2
10 more
  1. 2023On the Computational Complexity of Ethics: Moral Tractability for Minds and Machinescited by 2
  2. 2023If consciousness is dynamically relevant, artificial intelligence isn't consciouscited by 2
  3. 2024A Universal Knowledge Model and Cognitive Architecture for Prototyping AGIcited by 1
  4. 2024Report on Candidate Computational Indicators for Conscious Valenced Experiencecited by 1
  5. 2026What Capable Agents Must Know: Selection Theorems for Robust Decision-Making under Uncertaintycited by 1
  6. 2026CTM-AI: A Blueprint for General AI Inspired by a Model of Consciousnesscited by 1
  7. 2025Federated Learning Inspired Fuzzy Systems: Decentralized Rule Updating for Privacy and Scalable Decision Makingcited by 0
  8. 2025Consciousness as a Functorcited by 0
  9. 2025AI Consciousness and Existential Riskcited by 0
  10. 2026Discovering Machine Correlates of Consciousnesscited by 0