Information Flow Theory (IFT): A General Theory of Biologic and Artificial Consciousness
Benjamin Bleier
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Key points | Proposes that Information Flow Theory explains consciousness by prioritizing the direction of information flow over computation, arguing that basic evolutionary processes produce the perception of consciousness, that predicted neural circuits exist in humans and may be identifiable in non-human nervous systems, and that the framework separates artificial intelligence from artificial consciousness while predicting an architecture for creating consciousness in neural networks. |
Abstract
Strategies exploring the top-down mechanisms of conscious thought within the human brain have been unable to produce a generalized explanatory theory that scales through evolution and can be applied to non-human systems. Information Flow Theory (IFT) provides a novel framework for understanding both the development and nature of consciousness in any system capable of processing information. In prioritizing the direction of information flow over information computation, IFT explains how basic evolutionary processes produce the perception of consciousness and offers multiple experimental avenues to confirm its validity. The neural correlates of the information circuits predicted by IFT exist in humans and are theoretically identifiable in non-human biologic nervous systems. Perhaps most importantly, IFT disentangles artificial intelligence from artificial consciousness and predicts a specific architecture which may be used to create consciousness within neural networks.