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Choosing a Markov blanket.

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  • 1Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, LondonWC1N 3AR, UK. thomas.parr.12@ucl.ac.uk https://tejparr.github.io/.

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Summary
This summary is machine-generated.

This commentary explores Markov blanket definitions and how precision and salience mediate system interactions. Different perspectives on choosing a Markov blanket influence these internal-external system dynamics.

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Area of Science:

  • Computational Neuroscience
  • Information Theory
  • Systems Biology

Background:

  • Markov blankets are fundamental in defining system boundaries and information exchange.
  • Precision and salience are key factors influencing how systems interact with their environment.

Purpose of the Study:

  • To analyze the relationship between Markov blanket definitions and their role in mediating system interactions.
  • To explore how varying perspectives on selecting a Markov blanket impact internal-external system dynamics.

Main Methods:

  • Conceptual analysis of Markov blanket theory.
  • Examination of precision and salience in mediating system boundaries.
  • Exploration of different theoretical perspectives on Markov blanket selection.

Main Results:

  • The definition of a Markov blanket is contingent upon the chosen perspective.
  • Precision and salience play crucial roles in mediating the flow of information across the Markov blanket.
  • Different viewpoints lead to distinct interpretations of internal-external system interactions.

Conclusions:

  • Understanding Markov blanket definitions is essential for characterizing system boundaries.
  • The interplay of precision and salience is critical for adaptive system behavior.
  • A flexible approach to defining Markov blankets is necessary to capture diverse system dynamics.