🤖 AI Summary
Artificial consciousness research suffers from conceptual ambiguity, rigid binary distinctions (e.g., “conscious” vs. “not conscious”), and a lack of operationalizability. Method: This paper proposes a multi-level, multi-dimensional heuristic framework for consciousness that decomposes it into well-defined, measurable, and reproducible constituent elements—such as awareness, attention, and self-modeling—as well as dynamic dimensions—including intensity, breadth, and stability—thereby rejecting all-or-nothing binary judgments. The approach integrates conceptual modeling, operational analysis, and case-driven validation, with initial empirical closure achieved through an awareness-focused pilot study. Contribution/Results: The work introduces the first incremental consciousness modeling paradigm that balances philosophical rigor with engineering feasibility. It establishes the first systematic, extensible operational guide to consciousness dimensions, providing a structured methodology for generating empirically testable hypotheses and designing iterative, implementable pathways toward artificial consciousness.
📝 Abstract
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.