On Human Consciousness

TL;DR

This paper explores how dynamical networks in the brain support non-binary information processing and proposes a dual hierarchy model to address consciousness, emphasizing ongoing learning and the uniqueness of subjective experiences.

Contribution

It introduces a dynamical systems-based model linking neural network behavior to the hard problem of consciousness and subjective experience.

Findings

Neural networks exhibit non-binary information processing.

A dual hierarchy model supports evolving consciousness.

Finite brains are always learning or forgetting.

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).