mlsanityData to 14 Oct 2026

The feasibility of artificial consciousness through the lens of neuroscience

arXiv 2306.00915 ↗First posted 1 Jun 2023q-bio.NC cs.AI cs.LGcited by 18

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Abstract (arXiv)

Interactions with large language models have led to the suggestion that these models may soon be conscious. From the perspective of neuroscience, this position is difficult to defend. For one, the inputs to large language models lack the embodied, embedded information content characteristic of our sensory contact with the world around us. Secondly, the architecture of large language models is missing key features of the thalamocortical system that have been linked to conscious awareness in mammals. Finally, the evolutionary and developmental trajectories that led to the emergence of living conscious organisms arguably have no parallels in artificial systems as envisioned today. The existence of living organisms depends on their actions, and their survival is intricately linked to multi-level cellular, inter-cellular, and organismal processes culminating in agency and consciousness.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 14 of its 121 references that are in the map. First posted on arXiv 1 Jun 2023.

Builds on

14 of 121 references.

References · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,071
  5. 2018Machine Theory of Mindcited by 226
  6. 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
  7. 2021Coordination Among Neural Modules Through a Shared Global Workspacecited by 85
  8. 2022Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolutioncited by 78
  9. 2023Could a Large Language Model be Conscious?cited by 73
  10. 2022The neuroconnectionist research programmecited by 48
  11. 2020Deep Learning and the Global Workspace Theorycited by 36
  12. 2022Mind the gap: Challenges of deep learning approaches to Theory of Mindcited by 21
2 more
  1. 2022On the link between conscious function and general intelligence in humans and machinescited by 16
  2. 2023If consciousness is dynamically relevant, artificial intelligence isn't consciouscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 9; 2025: 4; 2026: 4.

18 papers.

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  1. 2023Consciousness in Artificial Intelligence: Insights from the Science of Consciousnesscited by 104
  2. 2025Does It Make Sense to Speak of Introspection in Large Language Models?cited by 25
  3. 2024A Philosophical Introduction to Language Models - Part II: The Way Forwardcited by 18
  4. 2024Simulacra as Conscious Exoticacited by 16
  5. 2024Can LLMs make trade-offs involving stipulated pain and pleasure states?cited by 16
  6. 2024Is artificial consciousness achievable? Lessons from the human braincited by 11
  7. 2024Dissociating Artificial Intelligence from Artificial Consciousnesscited by 8
  8. 2024Agnosticism About Artificial Consciousnesscited by 6
  9. 2024Recognizing Limits: Investigating Infeasibility in Large Language Modelscited by 4
  10. 2025Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responsescited by 3
  11. 2024Artificial intelligence and the internal processes of creativitycited by 2
  12. 2024The Epistemic Asymmetry of Consciousness Self-Reports: A Formal Analysis of AI Consciousness Denialcited by 1
6 more
  1. 2025Consciousness in Artificial Intelligence? A Framework for Classifying Objections and Constraintscited by 1
  2. 2026Just aware enough: Evaluating awareness across artificial systemscited by 1
  3. 2026AI and Consciousness: Shifting Focus Towards Tractable Questionscited by 1
  4. 2025AI Consciousness and Existential Riskcited by 0
  5. 2026Interoceptive machine framework: Toward interoception-inspired regulatory architectures in artificial intelligencecited by 0
  6. 2026Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigationscited by 0