Neuroscience of Consciousness
January 1, 2016
Erik Hoel, Larissa Albantakis, William Marshall et al.
153 citations
Causal interactions in complex systems like the brain can be examined at different spatiotemporal levels. While it is often assumed that the micro level is causally complete, this work shows that causal power can be stronger at macro levels. Using a measure called ΦMax, developed within integrated information theory, the authors systematically evaluated causal power at micro and macro levels in simplified neuronal-like systems. For systems with indeterminism or degeneracy, ΦMax peaked at a macro level when coarse-graining micro elements produced macro mechanisms with high irreducible causal selectivity.
Entropy
March 18, 2021
Leonardo S. Barbosa, William Marshall, Larissa Albantakis et al.
74 citations
Integrated Information Theory (IIT) begins with essential properties of consciousness and translates them into postulates that any physical system must satisfy to specify the physical substrate of consciousness. A recently introduced information measure captures three of these postulates—existence, intrinsicality, and information—and is unique. This work shows that the measure also satisfies the remaining postulates of integration and exclusion, creating a framework that identifies maximally irreducible mechanisms. These mechanisms can form maximally irreducible systems, which then specify the physical substrate of conscious experience.
Entropy
February 11, 2023
William Marshall, Matteo Grasso, William G. P. Mayner et al.
19 citations
Integrated information theory (IIT) proposes that consciousness is identical to the cause-effect structure generated by a maximally irreducible substrate (a Φ-structure). This work introduces a definition for system-integrated information (φs) grounded in IIT's postulates of existence, intrinsicality, information, and integration. It examines how determinism, degeneracy, and connectivity fault lines affect system-integrated information. The proposed measure identifies complexes as systems whose φs exceeds that of any overlapping candidate systems.