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Intrinsic units: identifying a system's causal grain.

William Marshall1, Graham Findlay2, Larissa Albantakis2

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This summary is machine-generated.

Integrated Information Theory (IIT) introduces a framework to compute consciousness in physical systems by identifying intrinsic units. This approach allows for macro units, potentially increasing integrated information and advancing tests for consciousness.

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Area of Science:

  • Neuroscience
  • Theoretical Physics
  • Philosophy of Mind

Background:

  • Integrated Information Theory (IIT) proposes that consciousness is a fundamental property of physical systems.
  • IIT quantifies consciousness using integrated information (Φ), representing a system's cause-effect power.
  • The theory posits that the 'grain' of system units (e.g., neurons, synapses) must maximize Φ.

Purpose of the Study:

  • To develop a computational framework for IIT that includes macro units, not just micro units.
  • To identify a system's intrinsic units – those that directly constitute its experience.
  • To extend the mathematical framework of IIT 4.0 for assessing cause-effect power across different scales (grains).

Main Methods:

  • Formalizing the physical existence postulates of IIT for macro units.
  • Extending the IIT 4.0 mathematical framework to evaluate cause-effect power across multiple grains.
  • Utilizing simple, simulated systems to compute integrated information with macro units.

Main Results:

  • Demonstrated that systems with macro units can exhibit higher integrated information (Φ) than systems composed solely of micro units.
  • Identified specific types of macro units that can enhance a system's cause-effect power.
  • Provided a method to determine the intrinsic units of a system from its intrinsic perspective.

Conclusions:

  • The developed framework enables the computation of integrated information in systems with macro units.
  • This approach offers a foundation for empirical testing and inferring consciousness within physical systems based on IIT.
  • The identification of intrinsic units provides a more nuanced understanding of how systems generate experience.