Skip to main content
SpringerLink
Log in
Book cover

Evolution, Development and Complexity pp 195–227Cite as

A Multi-scale View of the Emergent Complexity of Life: A Free-Energy Proposal

  • Conference paper
  • First Online:

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

Abstract

We review some of the main implications of the free-energy principle (FEP) for the study of the self-organization of living systems – and how the FEP can help us to understand (and model) biotic self-organization across the many temporal and spatial scales over which life exists. In order to maintain its integrity as a bounded system, any biological system – from single cells to complex organisms and societies – has to limit the disorder or dispersion (i.e., the long-run entropy) of its constituent states. We review how this can be achieved by living systems that minimize their variational free energy. Variational free energy is an information-theoretic construct, originally introduced into theoretical neuroscience and biology to explain perception, action, and learning. It has since been extended to explain the evolution, development, form, and function of entire organisms, providing a principled model of biotic self-organization and autopoiesis. It has provided insights into biological systems across spatiotemporal scales, ranging from microscales (e.g., sub- and multicellular dynamics), to intermediate scales (e.g., groups of interacting animals and culture), through to macroscale phenomena (the evolution of entire species). A crucial corollary of the FEP is that an organism just is (i.e., embodies or entails) an implicit model of its environment. As such, organisms come to embody causal relationships of their ecological niche, which, in turn, is influenced by their resulting behaviors. Crucially, free-energy minimization can be shown to be equivalent to the maximization of Bayesian model evidence. This allows us to cast evolution (i.e., natural selection) in terms of Bayesian model selection, providing a robust theoretical account of how organisms come to match or accommodate the spatiotemporal complexity of their surrounding niche. In line with the theme of this volume, namely, biological complexity and self-organization, this chapter will examine a variational approach to self-organization across multiple dynamical scales.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    The term free energy has been used with and without hyphenation in the literature. Throughout this chapter, we write “free energy” when used as a noun (e.g., organisms minimize free energy) and “free-energy” when used as an adjective (e.g., free-energy principle and free-energy minimization).

  2. 2.

    From a technical point of view, the extensive nature of free energy means that the sum of the free energy of the parts is equal to the free energy of the sum; so what is “good” locally is good globally. This extensive characteristic implies that minimizing free energy over time is analogous to the Hamiltonian principle of least action – because action is the integral of energy over time.

References

  • Adams, R. A., Bauer, M., Pinotsis, D., & Friston, K. J. (2016). Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG. NeuroImage, 132, 175–189.

    Article  Google Scholar 

  • Ao, P. (2008). Emerging of Stochastic Dynamical Equalities and Steady State Thermodynamics. Commun. Theor. Phys. (Beijing, China), 49, 1073–1090.

    Google Scholar 

  • Arnal, L. H., Wyart, V., & Giraud, A.-L. (2011). Transitions in neural oscillations reflect prediction errors generated in audiovisual speech. Nature Neuroscience, 14(6), 797–801.

    Article  Google Scholar 

  • Badcock, P. B. (2012). Evolutionary systems theory: A unifying meta-theory of psychological science. Review of General Psychology: Journal of Division 1, of the American Psychological Association, 16(1), 10–23.

    Article  Google Scholar 

  • Badcock, P. B., Davey, C. G., Whittle, S., Allen, N. B., & Friston, K. J. (2017). The Depressed Brain: An Evolutionary Systems Theory. Trends in Cognitive Sciences, 21(3), 182–194.

    Article  Google Scholar 

  • Badcock, P. B., Ploeger, A., & Allen, N. B. (2016). After phrenology: Time for a paradigm shift in cognitive science. The Behavioral and Brain Sciences, 39, e121.

    Article  Google Scholar 

  • Barandiaran, X., & Moreno, A. (2008). Adaptivity: From Metabolism to Behavior. Adaptive Behavior, 16(5), 325–344.

    Article  Google Scholar 

  • Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012, November 21). Canonical Microcircuits for Predictive Coding. Neuron.

    Google Scholar 

  • Berger, P. L. & T. Luckmann. (1966). The Social Construction of Reality: A Treatise in the Sociology of Knowledge. Garden City, NY: Anchor Books.

    Google Scholar 

  • Branco, T., Clark, B. A., & Häusser, M. (2010). Dendritic discrimination of temporal input sequences in cortical neurons. Science, 329(5999), 1671–1675.

    Article  ADS  Google Scholar 

  • Bruineberg, J. (2018). Anticipating affordances: Intentionality in self-organizing brain-body-environment systems (Doctoral dissertation). Retrieved from UvA-DARE.

    Google Scholar 

  • Bruineberg, J., & Rietveld, E. (2014). Self-organization, free energy minimization, and optimal grip on a field of affordances. Frontiers in Human Neuroscience, 8, 599.

    Article  Google Scholar 

  • Campbell, J. O. (2016). Universal Darwinism As a Process of Bayesian Inference. Frontiers in Systems Neuroscience, 10, 49.

    Article  Google Scholar 

  • Chemero, A. (2009). Radical embodied cognition. Cambridge, MA: MIT Press.

    Book  Google Scholar 

  • Clark, A. (2015). Surfing uncertainty: prediction, action, and the embodied mind. New York, N.Y.: Oxford University Press.

    Google Scholar 

  • Colombo, M. (2014). Explaining social norm compliance. A plea for neural representations. Phenomenol. Cogn. Sci. 13, 217–238.

    Article  Google Scholar 

  • Constant, A., Bervoets, J., Hens, K., & Van de Cruys, S. (2018a). Precise Worlds for Certain Minds: An Ecological Perspective on the Relational Self in Autism. Topoi. An International Review of Philosophy, 1–13.

    Google Scholar 

  • Constant, A., Ramstead, M. J. D., Veissière, S. P. L., Campbell, J. O., & Friston, K. J. (2018b). A variational approach to niche construction. Journal of the Royal Society, Interface, 15(141).

    Google Scholar 

  • Cook, R., Bird, G., Catmur, C., Press, C., & Heyes, C. (2014). Mirror neurons: From origin to function. Behavioral and Brain Sciences, 37(2), 177–192.

    Article  Google Scholar 

  • Dawkins, R. (1976). The Selfish Gene, New York: Oxford University Press.

    Google Scholar 

  • Edelman, G. M. (1987). The Theory of Neuronal Group Selection. New York: Basic Books.

    Google Scholar 

  • Engel, A. K., Friston, K. J., & Kragic, D. (2016). The Pragmatic Turn: Toward Action-Oriented Views in Cognitive Science. MIT Press.

    Google Scholar 

  • Fragaszy, D. M. (2011). Community Resources for Learning: How Capuchin Monkeys Construct Technical Traditions. Biological Theory, 6(3), 231–240. https://doi.org/10.1007/s13752-012-0032-8

    Article  Google Scholar 

  • Fragaszy, D. M., Eshchar, Y., Visalberghi, E., Resende, B., Laity, K., & Izar, P. (2017). Synchronized practice helps bearded capuchin monkeys learn to extend attention while learning a tradition. Proceedings of the National Academy of Sciences, 114(30), 7798–7805.

    Article  Google Scholar 

  • Friston, K. J. (2010). The free-energy principle: a unified brain theory? Nature Reviews. Neuroscience, 11(2), 127–138.

    Article  Google Scholar 

  • Friston, K., Levin, M., Sengupta, B., & Pezzulo, G. (2015). Knowing one’s place: A free-energy approach to pattern regulation. Journal of the Royal Society Interface, 12(105), 20141383.

    Article  Google Scholar 

  • Friston, K. J. (2013). Life as we know it. Journal of the Royal Society, Interface / the Royal Society, 10(86), 20130475.

    Article  Google Scholar 

  • Friston, K., & Ao, P. (2012). Free-energy, value and attractors. Computational and mathematical methods in medicine, 937860.

    MATH  Google Scholar 

  • Friston, K. J., Daunizeau, J., & Kiebel, S. J. (2009). Reinforcement learning or active inference? PloS One, 4(7), e6421.

    Article  ADS  Google Scholar 

  • Friston, K. J., & Frith, C. D. (2015). Active inference, communication and hermeneutics. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 68, 129–43.

    Article  Google Scholar 

  • Friston, K., Mattout, J., & Kilner, J. (2011). Action understanding and active inference. Biological Cybernetics, 104(1–2), 137–160.

    Article  MathSciNet  MATH  Google Scholar 

  • Friston, K., Thornton, C., & Clark, A. (2012). Free-energy minimization and the dark-room problem. Frontiers in Psychology, 3, 130.

    Google Scholar 

  • Gibson, J. J. (1979). The ecological approach to visual perception: classic edition. Psychology Press.

    Google Scholar 

  • Godfrey-Smith, P. (1996). Complexity and the Function of Mind in Nature. Cambridge University Press.

    Google Scholar 

  • Gulledge, A. T., Kampa, B. M., & Stuart, G. J. (2005). Synaptic integration in dendritic trees. Journal of Neurobiology, 64(1), 75–90.

    Article  Google Scholar 

  • Hamilton, W. D. (1964). The genetical evolution of social behaviour. I. Journal of Theoretical Biology, 7(1), 1–16.

    Article  MathSciNet  Google Scholar 

  • Hickok, G. (2010). The role of mirror neurons in speech perception and action word semantics. Language and Cognitive Processes, 25:6, 749–776.

    Article  Google Scholar 

  • Hickok, G. (2013). Predictive coding? Yes, but from what source? The Behavioral and Brain Sciences, 36(4), 358.

    Article  Google Scholar 

  • Hohwy, J. (2013). The predictive mind. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Hohwy, J. (2016), The Self-Evidencing Brain. Noûs, 50: 259–285.

    Article  Google Scholar 

  • Huygens, C. (1673). Horologium oscillatorium. France: Parisiis.

    Google Scholar 

  • Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008). A hierarchy of time-scales and the brain. PLoS Computational Biology, 4(11), e1000209.

    Article  ADS  Google Scholar 

  • Kiebel, S. J., & Friston, K. J. (2011). Free energy and dendritic self-organization. Frontiers in Systems Neuroscience, 5, 80.

    Article  Google Scholar 

  • Kilner, J. M., Friston, K. J., & Frith, C. D. (2007). Predictive coding: an account of the mirror neuron system. Cognitive Processing, 8(3), 159–166.

    Article  Google Scholar 

  • Kirchhoff, M. (2017a). Predictive brains and embodied, enactive cognition: an introduction to the special issue. Synthese, 1–12.

    Google Scholar 

  • 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

  • Kirchhoff, M.D. (2017b). Predictive processing, perceiving and imagining: Is to perceive to imagine, or something close to it? Philosophical Studies, 1–17, doi: https://doi.org/10.1007/s11098-017-0891-8.

  • Kirchhoff, M.D. (2015). Species of realization and the Free Energy Principle. The Australasian Journal of Philosophy, 93(4), 706–723.

    Article  Google Scholar 

  • Lendvai, B., Stern, E. A., Chen, B., and Svoboda, K. (2000). Experience-dependent plasticity of dendritic spines in the developing rat barrel cortex in vivo. Nature 404, 876–881.

    Article  ADS  Google Scholar 

  • Manneville, P. (1995). Dissipative Structures and Weak Turbulence. Springer Lecture Notes in Physics, 457, 257–272.

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • Maynard Smith, J. (1964). Group selection and kin selection. Nature, 201(4924), 1145–1147.

    Article  Google Scholar 

  • McKinley, J. (2015). Critical Argument and Writer Identity: Social Constructivism as a Theoretical Framework for EFL Academic Writing. Critical Inquiry in Language Studies, 12(3), 184–207.

    Article  Google Scholar 

  • Mirza, M. B., Adams, R. A., Mathys, C. D., & Friston, K. J. (2016). Scene Construction, Visual Foraging, and Active Inference. Frontiers in Computational Neuroscience, 10, 56.

    Article  Google Scholar 

  • Naiman, R. J., Johnston, C. A., & Kelley, J. C. (1988). Alteration of North American Streams by Beaver: The structure and dynamics of streams are changing as beaver recolonize their historic habitat. Bioscience, 38(11), 753–762.

    Article  Google Scholar 

  • Noë, A. (2004). Action in Perception. MIT Press.

    Google Scholar 

  • Odling-Smee, F. J., & Laland, K. N. (2000). Niche Construction and Gene-Culture Coevolution: An Evolutionary Basis for the Human. In T. N. S. Tonneau F. (Ed.), Perspectives in Ethology (Vol. Sciences Perspecties in Ethology, 13). Boston, MA: Springer.

    Google Scholar 

  • Odling-Smee, J., Erwin, D. H., Palkovacs, E. P., Feldman, M. W., & Laland, K. N. (2013). Niche construction theory: a practical guide for ecologists. The Quarterly Review of Biology, 88(1), 4–28.

    Article  Google Scholar 

  • Odling-Smee, J., Laland, K. N., & Feldman, M. W. (2003). Niche Construction: The Neglected Process in Evolution. Princeton University Press.

    Google Scholar 

  • Palacios, E. R., Razi, A., Parr, T., Kirchhoff, M., & Friston, K. (2017, November 30). Biological Self-organisation and Markov blankets. bioRxiv. bioRxiv.

    Google Scholar 

  • Orgel, L. E., & Crick, F. H. C. (1980). Selfish DNA: the ultimate parasite. Nature, 284(5757), 604–607.

    Article  ADS  Google Scholar 

  • Parr, T., & Friston, K. J. (2018). Active inference and the anatomy of oculomotion. Neuropsychologia, 111, 334–343.

    Article  Google Scholar 

  • Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann.

    MATH  Google Scholar 

  • Ramstead, M. J. D., Badcock, P. B., & Friston, K. J. (2017). Answering Schrödinger’s question: A free-energy formulation. Physics of Life Reviews. https://doi.org/10.1016/j.plrev.2017.09.001

  • Ramstead, M. J. D., Badcock, P. B., & Friston, K. J. (2018). Variational neuroethology: Answering further questions: Reply to comments on “Answering Schrödinger’s question: A free-energy formulation.” Physics of Life Reviews, 24, 59–66.

    Google Scholar 

  • Ramstead, M. J. D., Veissière, S. P. L., & Kirmayer, L. J. (2016). Cultural affordances: scaffolding local worlds through shared intentionality and regimes of attention. Frontiers in Psychology, 7, 1090.

    Article  Google Scholar 

  • Seifert, U. (2012). Stochastic thermodynamics, fluctuation theorems and molecular machines. Rep Prog Phys, 75(12), 126001. doi: https://doi.org/10.1088/0034-4885/75/12/126001

    Article  ADS  Google Scholar 

  • Sengupta, B., Stemmler, M. B., & Friston, K. J. (2013). Information and Efficiency in the Nervous System – A Synthesis. PLoS Computational Biology, 9(7).

    Google Scholar 

  • Sengupta, B., Tozzi, A., Cooray, G. K., Douglas, P. K., & Friston, K. J. (2016). Towards a Neuronal Gauge Theory. PLoS Biology, 14(3), e1002400.

    Article  Google Scholar 

  • Sgrò, C. M., Lowe, A. J. and Hoffmann, A. A. (2011), Building evolutionary resilience for conserving biodiversity under climate change. Evolutionary Applications, 4: 326–337.

    Article  Google Scholar 

  • Stotz, K. (2017). Why developmental niche construction is not selective niche construction: and why it matters. Interface Focus, 7(5), 20160157.

    Article  Google Scholar 

  • Sun, Y., Gomez, F., #252, & Schmidhuber, R. (2011). Planning to be surprised: optimal Bayesian exploration in dynamic environments. Paper presented at the Proceedings of the 4th international conference on Artificial general intelligence, Mountain View, CA.

    Google Scholar 

  • Thompson, E. (2007). Mind in life: biology, phenomenology, and the sciences of mind. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Torben-Nielsen, B., & Stiefel, K. M. (2009). Systematic mapping between dendritic function and structure. Network, 20(2), 69–105.

    Article  Google Scholar 

  • van Dijk, L., Withagen, R., & Bongers, R. M. (2015). Information without content: A Gibsonian reply to enactivists’ worries. Cognition, 134, 210–214.

    Article  Google Scholar 

  • Varela, F. G., Maturana, H. R., & Uribe, R. (1974). Autopoiesis: the organization of living systems, its characterization and a model. Currents in Modern Biology, 5(4), 187–96.

    Google Scholar 

  • Varela, F. J., Thompson, E., & Rosch, E. (2017). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.

    Google Scholar 

  • Weiss, L., Brandl, P., & Frynta, D. (2015). Fear reactions to snakes in naïve mouse lemurs and pig-tailed macaques. Primates, 56(3), 279–284.

    Article  Google Scholar 

  • Yi, S., Wierstra, D., Schaul, T., & Schmidhuber, J. (2009). Stochastic search using the natural gradient. In Proceedings of the 26th Annual International Conference on Machine Learning – ICML ‘09 (pp. 1–8). New York, New York, USA: ACM Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amsterdam Centre for Brain and Cognition, University of Amsterdam, Amsterdam, Noord-Holland, Netherlands

    Casper Hesp & Axel Constant

  2. Department of Philosophy, McGill University, Montreal, QC, Canada

    Maxwell Ramstead

  3. Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia

    Paul Badcock

  4. Department of Philosophy, University of Wollongong, Wollongong, Australia

    Michael Kirchhoff

  5. Wellcome Centre for Neuroimaging, University College London, London, UK

    Karl Friston

Authors
  1. Casper Hesp
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maxwell Ramstead
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Axel Constant
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paul Badcock
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael Kirchhoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Karl Friston
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Casper Hesp .

Editor information

Editors and Affiliations

  1. Department of Physics, Worcester Polytechnic Institute, Worcester, MA, USA

    Georgi Yordanov Georgiev

  2. Acceleration Studies Foundation, Los Gatos, CA, USA

    John M. Smart

  3. Department of Biology, Institute of Zoology, Molecular Evolutionary Biology, University of Hamburg, Hamburg, Germany

    Claudio L. Flores Martinez

  4. Department of Life Sciences, Centre for Culture and Evolution, Brunel University London, Uxbridge, UK

    Michael E. Price

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hesp, C., Ramstead, M., Constant, A., Badcock, P., Kirchhoff, M., Friston, K. (2019). A Multi-scale View of the Emergent Complexity of Life: A Free-Energy Proposal. In: Georgiev, G., Smart, J., Flores Martinez, C., Price, M. (eds) Evolution, Development and Complexity. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-00075-2_7

Download citation

Publish with us

Policies and ethics

Search

Navigation