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Computing integrated information.

Stephan Krohn1, Dirk Ostwald1,2

  • 1Computational Cognitive Neuroscience Laboratory, Department of Education and Psychology, Freie Universität Berlin, Habelschwerdter Allee 45, Berlin 14195, Germany.

Neuroscience of Consciousness
|July 26, 2018
PubMed
Summary
This summary is machine-generated.

Integrated Information Theory (IIT) quantifies consciousness using Φmax, a measure of integrated information. This study reformulates IIT within probabilistic models, clarifying its mathematical underpinnings and exploring quale underdetermination.

Keywords:
computational modellingconsciousnessintegrated informationqualiatheories and models

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Theoretical Physics

Background:

  • Integrated Information Theory (IIT) is a prominent framework for understanding consciousness.
  • IIT posits that consciousness equates to maximally integrated information (Φmax) and phenomenological experience arises from irreducible cause-effect repertoires.
  • Existing formulations require complex mathematical treatments.

Purpose of the Study:

  • To provide a general, parsimonious formulation of IIT using probabilistic models.
  • To express Φmax within the language of Markov processes and joint probability distributions.
  • To address theoretical issues like quale underdetermination and the relationship between quantitative and qualitative consciousness.

Main Methods:

  • Formulating Φmax using first-order time-invariant Markov processes.
  • Specifying mathematical operations via joint probability distributions over adjacent time points.
  • Developing rules for system decomposition using marginalization and factorization.
  • Defining virtualization through flexible factorization enforcing independence.

Main Results:

  • A comprehensive and parsimonious expression of Φmax in probabilistic terms.
  • A detailed method for system decomposition and virtualization.
  • Demonstration of quale underdetermination in a discrete example system.
  • Revealed sensitivity of Φ to the shape of conceptual structures in qualia space.

Conclusions:

  • The probabilistic formulation simplifies IIT and clarifies its mathematical basis.
  • The study highlights challenges in defining unique phenomenological experiences (qualia).
  • Further modifications to IIT are proposed to disentangle quantitative and qualitative aspects of consciousness.