This paper proposes a phenomenology‑first methodology for artificial consciousness, redefining consciousness as the subjective experience enacted through an agent’s interface with the world. The focus shifts to first‑person structures, which are modeled by categories derived from Q‑networks to capture actions and phenomenological invariants. In this view, Q‑networks serve as relational interfaces encoding agent‑world interaction, analogous to how a computer’s dynamical state depends on sensory inputs, previous states, and actions. Using this framework we formulate a rigorous “interface consciousness” description that embeds information processing into phenomenological structure. The approach aligns with 4E cognition by emphasizing enactive, embedded, and extended aspects of experience. Consequently, the paper offers a relational and phenomenological account of artificial phenomenology grounded in categorical mathematics.
$Q(s,a)=\mathbb{E}[r+\gamma \max_{a'} Q(s',a')]$
Blogger's Review: Linking reinforcement‑learning Q‑networks with phenomenology provides a fresh mathematical angle on artificial consciousness, and the proposal merits further empirical exploration.