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2017 ; 2017
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An algorithmic information theory of consciousness
#MMPMID30042851
Ruffini G
Neurosci Conscious
2017[]; 2017
(1
): nix019
PMID30042851
show ga
Providing objective metrics of conscious state is of great interest across
multiple research and clinical fields-from neurology to artificial intelligence.
Here we approach this challenge by proposing plausible mechanisms for the
phenomenon of structured experience. In earlier work, we argued that the
experience we call reality is a mental construct derived from information
compression. Here we show that algorithmic information theory provides a natural
framework to study and quantify consciousness from neurophysiological or
neuroimaging data, given the premise that the primary role of the brain is
information processing. We take as an axiom that "there is consciousness" and
focus on the requirements for structured experience: we hypothesize that the
existence and use of compressive models by cognitive systems, e.g. in biological
recurrent neural networks, enables and provides the structure to phenomenal
experience. Self-awareness is seen to arise naturally (as part of a better model)
in cognitive systems interacting bidirectionally with the external world.
Furthermore, we argue that by running such models to track data, brains can give
rise to apparently complex (entropic but hierarchically organized) data. We
compare this theory, named KT for its basis on the mathematical theory of
Kolmogorov complexity, to other information-centric theories of consciousness. We
then describe methods to study the complexity of the brain's output streams or of
brain state as correlates of conscious state: we review methods such as (i)
probing the brain through its input streams (e.g. event-related potentials in
oddball paradigms or mutual algorithmic information between world and brain),
(ii) analyzing spontaneous brain state, (iii) perturbing the brain by
non-invasive transcranial stimulation, and (iv) quantifying behavior (e.g. eye
movements or body sway).
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