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An algorithmic information theory of consciousness
1Starlab Barcelona, Avda. Tibidabo 47bis, 08035 Barcelona, Spain and Neuroelectrics Corporation, 210 Broadway, Cambridge, MA 02139, USA.
Neuroscience of Consciousness
|July 26, 2018
Summary
Algorithmic information theory offers a framework to quantify consciousness by viewing experience as structured information processing. This theory, based on Kolmogorov complexity, links brain activity to conscious states.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Cognitive Science
Background:
- Objective metrics for conscious states are crucial in neurology and AI.
- Previous work proposed reality as a mental construct from information compression.
Purpose of the Study:
- To propose mechanisms for structured conscious experience.
- To demonstrate algorithmic information theory as a framework for quantifying consciousness.
Main Methods:
- Utilizing algorithmic information theory and Kolmogorov complexity (KT theory).
- Hypothesizing that compressive models in cognitive systems enable structured experience.
- Examining brain activity via input streams, spontaneous states, brain perturbation, and behavioral quantification.
Main Results:
- Algorithmic information theory provides a natural framework for consciousness research.
- Self-awareness arises from bidirectional interaction with the environment.
- Brain activity can generate complex, hierarchically organized data through model tracking.
Conclusions:
- The KT theory offers a novel perspective on consciousness rooted in information processing.
- Proposed methods can identify correlates of conscious states in brain activity and behavior.
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