Informational structures: A dynamical system approach for integrated information.
Francisco J. Esteban, Javier A. Galadí, José A. Langa, José R Portillo, Fernando Soler-Toscano
PLoS Computational Biology September 1, 2018 DOI: 10.1371/journal.pcbi.1006154 (opens in new tab) via PubMed
Summary
AI-generated from the abstractIntegrated Information Theory (IIT) offers a mathematical framework for consciousness, identifying conscious experience with a conceptual structure that is composed of parts, informative, integrated, and maximally irreducible. This paper extends IIT by introducing a space-time continuous version of integrated information. Using graph and dynamical systems approaches, it defines an Informational Structure for a mechanism in a given state, associated with the system's global attractor over time. This structure determines all past and future behavior, enriches phase space points with cause-effect power via an Informational Field, and allows a measure of integrated information through invariants and transition probability matrices.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Key finding | Proposes a space-time continuous version of integrated information by defining an Informational Structure associated with the global attractor of a dynamical system, which enriches phase space points with cause-effect power and allows a measure of integrated information. |
Abstract
Integrated Information Theory (IIT) has become nowadays the most sensible general theory of consciousness. In addition to very important statements, it opens the door for an abstract (mathematical) formulation of the theory. Given a mechanism in a particular state, IIT identifies a conscious experience with a conceptual structure, an informational object which exists, is composed of identified parts, is informative, integrated and maximally irreducible. This paper introduces a space-time continuous version of the concept of integrated information. To this aim, a graph and a dynamical systems treatment is used to define, for a given mechanism in a state for which a dynamics is settled, an Informational Structure, which is associated to the global attractor at each time of the system. By definition, the informational structure determines all the past and future behavior of the system, possesses an informational nature and, moreover, enriches all the points of the phase space with cause-effect power by means of its associated Informational Field. A detailed description of its inner structure by invariants and connections between them allows to associate a transition probability matrix to each informational structure and to develop a measure for the level of integrated information of the system.