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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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System Integrated Information.

William Marshall1,2, Matteo Grasso1, William G P Mayner1,3

  • 1Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

Integrated Information Theory (IIT) defines consciousness mathematically. This study introduces a new measure for integrated information (φs) to identify consciousness in systems.

Keywords:
causationconsciousnessintegrated informationintrinsic

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Theoretical Physics

Background:

  • Integrated Information Theory (IIT) provides a mathematical framework for consciousness, starting from its essential properties (axioms).
  • IIT postulates translate these axioms into requirements for the physical substrate of consciousness (a complex).
  • The theory posits that consciousness is identical to the cause-effect structure of a maximally irreducible substrate (a Φ-structure).

Purpose of the Study:

  • To introduce a novel definition for the integrated information of a system (φs) based on IIT's core postulates.
  • To investigate the influence of determinism, degeneracy, and connectivity on system-integrated information.
  • To demonstrate that the proposed measure can identify genuine conscious complexes.

Main Methods:

  • Formulating a mathematical framework for assessing integrated information (φs) grounded in IIT's existence, intrinsicality, information, and integration postulates.
  • Analyzing the impact of system properties like determinism, degeneracy, and network topology on φs.
  • Comparing the φs of identified complexes with those of overlapping candidate systems.

Main Results:

  • A new measure for system-integrated information (φs) is defined, adhering to IIT's fundamental principles.
  • Determinism, degeneracy, and network fault lines are shown to affect the calculated φs.
  • The proposed measure successfully identifies systems with higher φs as distinct complexes compared to their constituent or overlapping parts.

Conclusions:

  • The developed measure (φs) offers a quantitative assessment of integrated information consistent with IIT.
  • This framework aids in identifying physical substrates of consciousness (complexes) within larger systems.
  • The findings contribute to a more precise, mathematically grounded understanding of consciousness in physical systems.