A composite, multilevel, and multidimensional model of consciousness is proposed as a heuristic framework to guide artificial consciousness research. The model treats consciousness as a complex phenomenon with distinct constituents and dimensions that can be operationalized for study and replication. It avoids binary thinking (conscious vs. non-conscious) and offers a structured basis for testable hypotheses. Using 'awareness' as a case study, the paper demonstrates how specific dimensions can be pragmatically analyzed and targeted for artificial instantiation. This approach aims to advance the scientific and technical understanding of artificial consciousness by breaking down conceptual intricacies and aligning them with practical research goals.
The paper examines how diverse current theories of consciousness are and whether they share common ground. It argues that logical and empirical commensurability—the ability to compare theories along specific dimensions—is necessary for identifying shared features. Comparing a subset of neuroscience-based theories reveals no single unifying model; theories that appear similar on one dimension may differ on another. The authors propose using multiple probing questions to assess overall similarities and differences between theories. Despite conflicting background definitions of consciousness, they conclude that a shared methodological approach to studying brain-consciousness relationships could eventually allow different theories to be integrated and merged, overcoming the current fragmentation.
The brain's spontaneous activity constructs its own inner time and space, and this spatiotemporal dynamics may be the missing link between neural activity and mental phenomena such as self, consciousness, and time perception. The authors propose 'Spatiotemporal Neuroscience,' which focuses on the brain's temporo-spatial dynamics—like functional connectivity and frequency fluctuations—rather than on specific cognitive or affective functions. They show how mechanisms such as spatiotemporal repertoire, integration, and speed give rise to distinct mental features. This approach treats the relationship between neuronal and mental features as intrinsic and non-causal, offering a common currency that connects brain and mind.