On Human Consciousness
Peter Grindrod
arXiv Preprint Archive September 11, 2016 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Topics | Philosophy of mind |
| Keywords | Q-bio.nc Cognitive science Neural networks |
| Key points | Proposes that non-binary information processing in small-scale neural networks leads to multiple-hypothesis decision-making, and that a "dual hierarchy model" of physical and mental elements supports an evolving consciousness that cannot be fully replicated 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).
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