On Human Consciousness
arXiv Preprint Archive September 11, 2016 via arXiv
Summary
AI-generated from the abstractMathematical 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.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Topics | Philosophy of mind |
| Keywords | Q-bio.nc Neuroscience Cognitive science Neural networks |
| Key finding | Non-binary information processing in small-scale neural networks supports a dual hierarchy model of consciousness, implying that finite brains must always learn or forget and that subjective feelings with countably infinite facets cannot be fully learned by automata. |
Abstract
We consider the implications of the mathematical analysis of neurone-to-neurone dynamical complex networks. We show how the dynamical behaviour of small scale strongly connected networks lead naturally to non-binary information processing and thus multiple hypothesis decision making, even at the very lowest level of the brain's architecture. In turn we build on these ideas to address the hard problem of consciousness. We discuss how a proposed "dual hierarchy model", made up form of both external perceived, physical, elements of increasing complexity, and internal mental elements (experiences), may support a leaning and evolving consciousness. We discuss the idea that a human brain ought to be able to re-conjure subjective mental feelings at will and thus these cannot depend on internal nose (chatter) or internal instability-driven activity. An immediate consequence of this model, grounded in dynamical systems and non-binary information processing, is that finite human brains must always be learning or forgetteing and that any possible subjective internal feeling that may be idealised with a countable infinity of facets, can never be learned by zombies or automata: though it can be experienced more and more fully by an evolving brain (yet never in totality, not even in a lifetime).