mlsanityData to 7 Oct 2026

Preliminaries to artificial consciousness: a multidimensional heuristic approach

arXiv 2403.20177 ↗First posted 29 Mar 2024cs.AI cs.RO q-bio.NCcited by 4

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Abstract (arXiv)

The pursuit of artificial consciousness requires conceptual clarity to navigate its theoretical and empirical challenges. This paper introduces a composite, multilevel, and multidimensional model of consciousness as a heuristic framework to guide research in this field. Consciousness is treated as a complex phenomenon, with distinct constituents and dimensions that can be operationalized for study and for evaluating their replication. We argue that this model provides a balanced approach to artificial consciousness research by avoiding binary thinking (e.g., conscious vs. non-conscious) and offering a structured basis for testable hypotheses. To illustrate its utility, we focus on "awareness" as a case study, demonstrating how specific dimensions of consciousness can be pragmatically analyzed and targeted for potential artificial instantiation. By breaking down the conceptual intricacies of consciousness and aligning them with practical research goals, this paper lays the groundwork for a robust strategy to advance the scientific and technical understanding of artificial consciousness.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 5 of its 94 references that are in the map. First posted on arXiv 29 Mar 2024.

Builds on

5 of 94 references.

References · 5

  1. 2023Consciousness in Artificial Intelligence: Insights from the Science of Consciousnesscited by 99
  2. 2023Could a Large Language Model be Conscious?cited by 70
  3. 2022Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical termscited by 21
  4. 2024AI Consciousness is Inevitable: A Theoretical Computer Science Perspectivecited by 4
  5. 2024Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside''cited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.