The free energy principle: it’s not about what it takes, it’s about what took you there

Abstract

Philosophical writings on the free energy principle in the life sciences often give the impression that minimising free energy is sufficient for life. But minimising free energy is not a sufficient condition for life. In fact, one can perfectly well conceive of a system that actively minimises its free energy, and for this very reason moves inexorably towards death. So, where does the assumption of this entailment relation come from? There is indeed an entailment relation, but it goes the other way around: life entails minimising free energy. Put another way, if you exist, now, under the right conditions, it is because you’ve done something like minimising your free energy. However, the question of whether you will exist tomorrow cannot be settled purely by resorting to the fact that you will minimise your free energy to get there. The simple point I make in this paper is that the free energy principle is not concerned with the sufficient conditions of existence, but rather with what must have been the case, given that you exist. It’s not about figuring out what it takes to be alive; it’s about figuring out what took you there.

This is a preview of subscription content, access via your institution.

Fig. 1

Notes

  1. 1.

    It is important to note that the FEP includes processes other than free energy minimisation. It also includes expected free energy minimisation (and generalised free energy minimisation, (Parr and Friston 2019)). While minimising free energy endows the organism with postdictive inference, minimising expected free energy endows the organism with predictive inference. This is due to the simple reason that the outcomes and states involved in the inference process under expected free energy minimisation are in the future, not the present. Effectively, this means that inferring one’s beliefs about states of the world means inferring what will most likely be seen under those beliefs, and under a given sequence of action to be engaged (i.e., action policy).

References

  1. Allen, M., Friston, K. J. (2016). From cognitivism to autopoiesis: towards a computational framework for the embodied mind. Synthese, 1–24.

  2. Badcock PB, Davey CG, Whittle S, Allen NB, Friston KJ (2017) The depressed brain: an evolutionary systems theory. Trends Cognit Sci 21(3):182–194

    Article  Google Scholar 

  3. Beal MJ (2003) Variational algorithms for approximate bayesian inference. University of London, London

    Google Scholar 

  4. Bogacz R (2017) A tutorial on the free-energy framework for modelling perception and learning. J Math Psychol 76(Pt B):198–211

    Article  Google Scholar 

  5. Buckley, C. L., Kim, C. S., McGregor, S., & Seth, A. K. (2017). The free energy principle for action and perception: A mathematical review. J Math Psychol, 81(Supplement C), 55–79.

  6. Bruineberg J, Rietveld E (2014) Self-organization, free energy minimization, and optimal grip on a field of affordances. Front Human Neurosci 8:599

    Article  Google Scholar 

  7. Clark A (2013) Whatever next? predictive brains, situated agents, and the future of cognitive science. Behav Brain Sci 36(03):181–204

    Article  Google Scholar 

  8. Clark A (2017) How to knit your own Markov blanket: resisting the second law with metamorphic minds. In Philosophy and predictive processing: 3 (eds Metzinger T, Wiese W). Frankfurt am Main, Germany: MIND Group.

  9. Cleland CE (2002) Methodological and epistemic differences between historical science and experimental science*. Phil of Sci 69(3):447–451

    Article  Google Scholar 

  10. Colombo M, Wright C (2018) First principles in the life sciences: the free-energy principle, organicism, and mechanism. Synthese. https://doi.org/10.1007/s11229-018-01932-w

    Article  Google Scholar 

  11. Constant A., Ramstead MJD, Veissière SPL, Campbell JO, Friston KJ (2018). A variational approach to niche construction. J R Soc Interface R Soc, 15(141). https://doi.org/10.1098/rsif.2017.0685

  12. Da Costa L, Parr T, Sajid N, Veselic S, Neacsu V, Friston K (2020). Active inference on discrete state-spaces: a synthesis. In arXiv [q-bio.NC]. arXiv. http://arxiv.org/abs/2001.07203

  13. Dupré J (2020) Life as process. Epistemol Phil Sci 57(2):96–113

    Article  Google Scholar 

  14. Friston KJ (2005) A theory of cortical responses. Phil Trans R Soc London Series B Biol Sci 360(1456):815–836

    Article  Google Scholar 

  15. Friston KJ (2009) The free-energy principle: a rough guide to the brain? Trends Cognit Sci 13(7):293–301

    Article  Google Scholar 

  16. Friston KJ (2010) The free-energy principle: a unified brain theory? Nat Rev Neurosci 11(2):127–138

    Article  Google Scholar 

  17. Friston KJ (2011). Embodied inference: or ``I think therefore I am, if I am what I think’'. In W. Tschacher & C. Bergomi (Eds.), The implications of embodiment: Cognition and communication (pp. 89–125). Imprint Academic.

  18. Friston KJ (2013) Life as we know it. J R Soc Interface R Socy 10(86):20130475

    Article  Google Scholar 

  19. Friston KJ, Parr T, de Vries B (2017) The graphical brain: Belief propagation and active inference. Netw Neurosci 1(4):381–414

    Article  Google Scholar 

  20. Friston KJ, Stephan KE (2007) Free-energy and the brain. Synthese 159(3):417–458

    Article  Google Scholar 

  21. Friston KJ, Thornton C, Clark A (2012) Free-energy minimization and the dark-room problem. Front Psychol 3:130

    Google Scholar 

  22. Hesp C, Ramstead MJD, Constant A., Badcock P (2019). A multi-scale view of the emergent complexity of life: a free-energy proposal. Evolution & Development. https://link.springer.com/chapter/https://doi.org/10.1007/978-3-030-00075-2_7

  23. Hohwy J (2016) The self-evidencing brain. Noûs 50(2):259–285

    Article  Google Scholar 

  24. Hohwy J (2020) Self-supervision, normativity and the free energy principle. Synthese. https://doi.org/10.1007/s11229-020-02622-2

    Article  Google Scholar 

  25. Kirchhoff M (2015) Species of realization and the free energy principle. Australas J Philos 93(4):706–723

    Article  Google Scholar 

  26. Kirchhoff M, Froese T (2017) Where There is Life There is Mind: In Support of a Strong Life-Mind Continuity Thesis. Entropy19(4): 169.

  27. Kirchhoff M, Parr T, Palacios E, Friston K, Kiverstein J (2018). The Markov blankets of life: autonomy, active inference and the free energy principle. Journal of the Royal Society, Interface / the Royal Society, 15(138) https://doi.org/10.1098/rsif.2017.0792

  28. Klein C (2018) What do predictive coders want? Synthese 195(6):2541–2557

    Article  Google Scholar 

  29. McNamara JM, Green RF, Olsson O (2006) Bayes’ theorem and its applications in animal behaviour. Oikos 112(2):243–251

    Article  Google Scholar 

  30. Okasha S (2013) The evolution of bayesian updating. Philos Sci 80(5):745–757

    Article  Google Scholar 

  31. Parr T, Friston KJ (2018) The anatomy of inference: generative models and brain structure. Front Comput Neurosci 12:90

    Article  Google Scholar 

  32. Parr T, Friston KJ (2019) Generalised free energy and active inference. Biol Cybern. https://doi.org/10.1007/s00422-019-00805-w

    Article  Google Scholar 

  33. Ramstead MJD, Badcock PB, Friston KJ (2017) Answering Schrödinger’s question: a free-energy formulation. Phys Life Rev 24:1–16

    Article  Google Scholar 

  34. Ramstead MJD, Kirchhoff MD, Friston KJ (2019). A tale of two densities: active inference is enactive inference. Adapt Behav, 1059712319862774.

  35. Ramstead MJD, Kirchhoff MD, Constant A, Friston KJ (2019) Multiscale integration: beyond internalism and externalism. Synthese. https://doi.org/10.1007/s11229-019-02115-x

    Article  Google Scholar 

  36. Richerson PJ (2018) An integrated bayesian theory of phenotypic flexibility. Behav Proc. https://doi.org/10.1016/j.beproc.2018.02.002

    Article  Google Scholar 

  37. Sella G, Hirsh AE (2005) The application of statistical physics to evolutionary biology. Proc Natl Acad Sci USA 102(27):9541–9546

    Article  Google Scholar 

  38. Smith R, Friston K, Whyte C (2021). A step-by-step tutorial on active inference and its application to empirical Data. https://doi.org/10.31234/osf.io/b4jm6

  39. Smith R, Kuplicki R, Teed A, Upshaw V, Khalsa SS (2020). Confirmatory evidence that healthy individuals can adaptively adjust prior expectations and interoceptive precision estimates. In Cold Spring Harbor Laboratory (p. 2020.08.31.275594). https://doi.org/10.1101/2020.08.31.275594

  40. Sunnåker M, Busetto AG, Numminen E, Corander J, Foll M, Dessimoz C (2013) Approximate bayesian computation. PLoS Comput Biol 9(1):e1002803

    Article  Google Scholar 

  41. Tavaré S, Balding DJ, Griffiths RC, Donnelly P (1997) Inferring coalescence times from DNA sequence data. Genetics 145(2):505–518

    Article  Google Scholar 

  42. Tschantz A, Seth AK, Buckley CL, Komarova NL (2020) Learning action-oriented models through active inference. PLOS Comput Biol 16(4):e1007805

    Article  Google Scholar 

  43. Van Es T (2020). Living models or life modelled? on the use of models in the free energy principle. Adapt Behav, 1059712320918678.

  44. Wiese W, Metzinger T (2017) Vanilla PP for Philosophers: A Primer on Predictive Processing. https://philarchive.org/rec/WIEVPF?all_versions=1

Download references

Acknowledgements

I want to thank Paul Badcock, Paul Griffiths, Mark Colyvan, Christopher Whyte, Pierrick Bourrat, Joshua Christie, Christopher Lean, Peter Takacs, Carl Brusse, Stefan Gawronski, and Walter Veit for helpful comments on earlier versions of this paper.

Funding

Work on this article was supported by the Australian Laureate Fellowship project A Philosophy of Medicine for the 21st Century (Ref: FL170100160) and by a Social Sciences and Humanities Research Council doctoral fellowship (Ref: 752–2019-0065).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Axel Constant.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Constant, A. The free energy principle: it’s not about what it takes, it’s about what took you there. Biol Philos 36, 10 (2021). https://doi.org/10.1007/s10539-021-09787-1

Download citation

Keywords

  • Free Energy Principle
  • Postdiction
  • Prediction
  • Historical sciences