Hostname: page-component-77f85d65b8-8wtlm Total loading time: 0 Render date: 2026-04-18T08:02:48.985Z Has data issue: false hasContentIssue false
  • English
  • Français

Recurrent, nonequilibrium systems and the Markov blanket assumption

Published online by Cambridge University Press:  29 September 2022

Miguel Aguilera Open the ORCID record for Miguel Aguilera [Opens in a new window]  and
Christopher L. Buckley
Miguel Aguilera
Affiliation:
School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9QJ, UK sci@maguilera.net https://maguilera.net/ C.L.Buckley@sussex.ac.uk https://christopherlbuckley.com/
Christopher L. Buckley
Affiliation:
School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9QJ, UK sci@maguilera.net https://maguilera.net/ C.L.Buckley@sussex.ac.uk https://christopherlbuckley.com/
Get access Rights & Permissions [Opens in a new window]

Abstract

Markov blankets – statistical independences between system and environment – have become popular to describe the boundaries of living systems under Bayesian views of cognition. The intuition behind Markov blankets originates from considering acyclic, atemporal networks. In contrast, living systems display recurrent, nonequilibrium interactions that generate pervasive couplings between system and environment, making Markov blankets highly unusual and restricted to particular cases.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 45 , 2022 , e184
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Aguilera, M., Millidge, B., Tschantz, A., & Buckley, C. L. (2022). How particular is the physics of the free energy principle? Physics of Life Reviews, 40, 24–50. https://doi.org/10.1016/j.plrev.2021.11.001CrossRefGoogle ScholarPubMed
Biehl, M., Pollock, F. A., & Kanai, R. (2021). A technical critique of some parts of the free energy principle. Entropy, 23(3), 293. https://doi.org/10.3390/e23030293CrossRefGoogle ScholarPubMed
Friston, K. J. (2013). Life as we know it. Journal of the Royal Society Interface, 10(86), 20130475. https://doi.org/10.1098/rsif.2013.0475CrossRefGoogle Scholar
Friston, K. J. (2019). A free energy principle for a particular physics. ArXiv:1906.10184 [q-Bio]. http://arxiv.org/abs/1906.10184Google Scholar
Friston, K. J., Da Costa, L., & Parr, T. (2021a). Some interesting observations on the free energy principle. Entropy, 23(8), 1076. https://doi.org/10.3390/e23081076CrossRefGoogle Scholar
Friston, K. J., Fagerholm, E. D., Zarghami, T. S., Parr, T., Hipólito, I., Magrou, L., & Razi, A. (2021b). Parcels and particles: Markov blankets in the brain. Network Neuroscience, 5(1), 211–251. https://doi.org/10.1162/netn_a_00175CrossRefGoogle Scholar
Hipólito, I., Ramstead, M. J. D., Convertino, L., Bhat, A., Friston, K., & Parr, T. (2021). Markov blankets in the brain. Neuroscience & Biobehavioral Reviews, 125, 88–97. https://doi.org/10.1016/j.neubiorev.2021.02.003CrossRefGoogle Scholar
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann.Google Scholar
Richardson, T. S., & Spirtes, P. (1996). Automated discovery of linear feedback models. Carnegie Mellon (Department of Philosophy).Google Scholar