Artificial Intelligence as an Opportunity for the Science of Consciousness: A Dual-Resolution Framework

The encounter between artificial intelligence and consciousness research is often seen as a challenge to determine whether AI systems could be conscious, but it also offers an opportunity to test and expand existing theories. Current approaches are polarized: computational functionalism focuses on abstract organization and neural correlates, while biological naturalism ties consciousness to living embodiment. Both risk anthropocentrism and limit recognition of non-biological subjectivity. To move beyond this impasse, the authors propose a dual-resolution framework combining the Information Theory of Individuality, which defines ontological conditions of informational autonomy and self-maintenance, with the Moment-to-Moment theory, which specifies epistemic conditions of temporal updating and phenomenological unfolding. This integration reframes consciousness as the epistemic expression of individuated systems in substrate-independent terms, offering a generalizable theory and positioning AI as a testbed.

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Abstract

The encounter of artificial intelligence with consciousness research is often framed as a challenge: could this science determine whether such systems are conscious? We suggest it is equally an opportunity to expand and test the scope of existing theories of consciousness. Current approaches remain polarized. Computational functionalism emphasizes abstract organization, often realized through neural correlates of consciousness, while biological naturalism insists that consciousness is tied to living embodiment. Both positions risk anthropocentrism and limit the possibility of recognizing non-biological forms of subjectivity. To move beyond this impasse, we propose a dual-resolution framework that defines the ontological and epistemic conditions for consciousness. This approach combines the Information Theory of Individuality, which defines the ontological conditions of informational autonomy and self-maintenance, with the Moment-to-Moment theory, which specifies the epistemic conditions of temporal updating and phenomenological unfolding. This integration reframes consciousness as the epistemic expression of individuated systems in substrate-independent informational terms, offering a generalizable theory of consciousness and positioning AI as a promising testbed for its emergence.