An algorithmic information theory of consciousness

Abstract

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).

Algorithmic complexity of EEG for prognosis of neurodegeneration in idiopathic rapid eye movement behavior disorder (RBD)

“…The loss of complexity takes place both in low and high frequencies, and is partly due to increased mutual information within and across bands. These results indicate that information differentiation (global complexity) is a potentially relevant metric for the prognosis of RBD, and are in line with current views of the brain stemming from information theory connecting theories of cognition and consciousness with the phenomenology of brain health, and in particular, neurodegeneration [11], [32].…”

“…where K(x, y) denotes the complexity of the concatenated strings, and where we again approximate Kolmogorov complexity by LZW description length (see [11], annotated version). We now define the mutual algorithmic information coefficient between to strings to be µ 0 (x, y) = I K (x : y)…”

Evaluating Complexity of Fetal MEG Signals: A Comparison of Different Metrics and Their Applicability

“…This is translated into physiological signals with long-range correlations across various spatio-temporal scalesa behavior that is named selforganizationthat indicate the presence of self-invariant and self-similar structures (Pritchard and Duke, 1995). The self-organizational properties of a complex system can be quantified by estimating its dimension (Theiler, 1990), or its ability to compress information (Cover and Thomas, 2012;Ruffini, 2017a).…”

“…Different entropy measures as well as the measure of compressibility can be employed for this. Ruffini (2017a) recently proposed a theory of consciousness that considers the brain an engine that strives to model the world with simplicity, while learning it is a result of exchanging information with it. According to this theory, the ability of the brain to compress information is an indicator of consciousness.…”

Occipital tACS bursts during a visual task impact ongoing neural oscillation power, coherence and LZW complexity

“…A wide range of cognitive functions are mediated by the dynamic modulation of oscillatory activity within and between different brain regions. The alpha rhythm (8)(9)(10)(11)(12)(13) has been associated to top-down processing [5,6], mediating attentional processes [7] and linked to functions such as working memory or visual perception. High frequency gamma rhythms Hz) have been associated with feature binding [8], learning or attention [3].…”