arXiv AI
Calibration and transfer in indicator-based assessments of artificial consciousness
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From S3Q Theory to Implementation: Towards an Architecture for Machine Qualia
The paper proposes a five‑layer implementation architecture for the S3Q theory of consciousness, mapping its three core conditions—grounded sensorimotor situatedness, internal simulation via a world model, and structural coherence between predictions and observations—to existing computational primitives. It integrates these components into a single pipeline that processes continuous, differentiable per‑object slot vectors, includes a developmental bootstrap sequence, and yields falsifiable predictions that cannot be produced by any subset of the architecture alone. The model further suggests that a basic sense of self emerges from linking actions to outcomes, and that behavior can be categorized into hesitation, curiosity, or avoidance based on outcome unexpectedness and valence. "whyItMatters":"The architecture provides a concrete, testable framework that unifies theoretical conditions of consciousness with practical computational components, enabling empirical investigation of machine consciousness."