# Preliminaries to artificial consciousness: a multidimensional heuristic approach

arXiv: https://arxiv.org/abs/2403.20177 · HTML: https://mlsanity.com/paper/2403.20177/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 94 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Could a Large Language Model be Conscious?](https://mlsanity.com/paper/2303.07103/) (2023)
- [Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms](https://mlsanity.com/paper/2212.14787/) (2022)
- [AI Consciousness is Inevitable: A Theoretical Computer Science Perspective](https://mlsanity.com/paper/2403.17101/) (2024)
- [Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside''](https://mlsanity.com/paper/2402.09030/) (2024)

## Built on by (most cited first, 4 of 4)

- [Just aware enough: Evaluating awareness across artificial systems](https://mlsanity.com/paper/2601.14901/) (2026)
- [AI Consciousness and Existential Risk](https://mlsanity.com/paper/2511.19115/) (2025)
- [Behaviora - A Conceptual Architecture for External and Internal Behavior of Robots and Agents](https://mlsanity.com/paper/2609.27536/) (2026)
- [Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations](https://mlsanity.com/paper/2609.38486/) (2026)

## 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.