Consciousness is better understood as emerging gradually from biological organization rather than appearing suddenly in nervous systems. A graded continuum is proposed spanning five levels: passive physical order, adaptive biological organization, selectional and behavioral agency, phenomenal consciousness, and reflective self-consciousness. Nervous systems accelerated and centralized selection and coordination processes that existed before neurons. The hard problem of consciousness is located at the threshold where bounded, resource-constrained, selection-performing systems become phenomenally present. This framework has implications for non-neural biological systems, artificial intelligence, and the substrate-dependence debate, suggesting the story of consciousness may need to begin before neurons.
A computational framework called Artificial F1 introduces a gating operator that selects which signals enter a system's task space before learning or evaluation occurs, unlike standard reinforcement learning or attention-based architectures that work on fixed inputs. This framework predicts improved sample efficiency under high-dimensional noise, energy savings from conditional processing, and a discontinuity in action-selection entropy at the gating threshold. The paper also develops Organizational Phenomenology, arguing that phenomenal properties like certainty and unity arise as structural consequences of bounded selection architectures, not as additional properties. The hard problem of consciousness is located at the opacity any coherent bounded system maintains at its selection boundary.