Mathematical modeling of neuron-to-neuron dynamical networks shows that even small-scale strongly connected networks perform nonbinary information processing, enabling multiple-hypothesis decision-making at the brain's lowest architectural level. This framework addresses aspects of the hard problem of consciousness, proposing a dual hierarchy model composed of externally perceived physical elements of increasing complexity and internally experienced mental elements (feelings). The model implies that finite human brains must always be learning and forgetting; any subjective feeling that could be fully idealized with a countable infinity of facets could never be learned completely by automata. Mental elements act like latent variables in processing and decision-making, conferring an evolutionary fast-thinking advantage.
Mathematical analysis of small-scale strongly connected neural networks shows they naturally perform non-binary information processing, enabling multiple hypothesis decision-making at the brain's lowest architectural level. Building on this, a proposed "dual hierarchy model"—comprising external physical elements of increasing complexity and internal mental experiences—supports a learning, evolving consciousness. Because the brain can re-conjure subjective feelings at will, these feelings cannot depend on internal noise or instability-driven activity. A consequence is that finite human brains must always be learning or forgetting, and any subjective feeling with a countable infinity of facets can never be learned by zombies or automata, though an evolving brain can experience it increasingly fully, never in totality.