Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-28T16:44:38.986Z Has data issue: false hasContentIssue false
  • English
  • Français

The Emperor's New Markov Blankets

Published online by Cambridge University Press:  22 October 2021

Jelle Bruineberg Open the ORCID record for Jelle Bruineberg [Opens in a new window] ,
Krzysztof Dołęga Open the ORCID record for Krzysztof Dołęga [Opens in a new window] ,
Joe Dewhurst Open the ORCID record for Joe Dewhurst [Opens in a new window]  and
Manuel Baltieri Open the ORCID record for Manuel Baltieri [Opens in a new window]
Jelle Bruineberg
Affiliation:
Department of Philosophy, Macquarie University, Sydney, NSW 2109, Australia [email protected]
Krzysztof Dołęga
Affiliation:
Institut für Philosophie II, Fakultät für Philosophie und Erziehungswissenschaft, Ruhr-Universität Bochum, 44801 Bochum, Germany [email protected]
Joe Dewhurst
Affiliation:
Fakultät für Philosophie, Wissenschaftstheorieund Religionswissenschaft, Munich Center for Mathematical Philosophy, Ludwig-Maximilians-Universität München, 80539 Munich, Germany [email protected]
Manuel Baltieri
Affiliation:
Laboratory for Neural Computation and Adaptation, RIKEN Centre for Brain Science, 351-0106 Wako City, Japan [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The free energy principle, an influential framework in computational neuroscience and theoretical neurobiology, starts from the assumption that living systems ensure adaptive exchanges with their environment by minimizing the objective function of variational free energy. Following this premise, it claims to deliver a promising integration of the life sciences. In recent work, Markov blankets, one of the central constructs of the free energy principle, have been applied to resolve debates central to philosophy (such as demarcating the boundaries of the mind). The aim of this paper is twofold. First, we trace the development of Markov blankets starting from their standard application in Bayesian networks, via variational inference, to their use in the literature on active inference. We then identify a persistent confusion in the literature between the formal use of Markov blankets as an epistemic tool for Bayesian inference, and their novel metaphysical use in the free energy framework to demarcate the physical boundary between an agent and its environment. Consequently, we propose to distinguish between “Pearl blankets” to refer to the original epistemic use of Markov blankets and “Friston blankets” to refer to the new metaphysical construct. Second, we use this distinction to critically assess claims resting on the application of Markov blankets to philosophical problems. We suggest that this literature would do well in differentiating between two different research programmes: “inference with a model” and “inference within a model.” Only the latter is capable of doing metaphysical work with Markov blankets, but requires additional philosophical premises and cannot be justified by an appeal to the success of the mathematical framework alone.

Keywords

active inferenceBayesian inferencefree energy principleMarkov blanketsscientific realism
Type
Target Article
Information
Behavioral and Brain Sciences , Volume 45 , 2022 , e183
Check for updates
Copyright
Copyright © The Author(s), 2021. 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.)

References

Adams, R. A., Stephan, K., Brown, H., Frith, C., & Friston, K. J. (2013). The computational anatomy of psychosis. Frontiers in Psychiatry, 4, 47.CrossRefGoogle ScholarPubMed
Aguilera, M., Millidge, B., Tschantz, A., & Buckley, C. L. (2021). How particular is the physics of the free energy principle? arXiv preprint arXiv:2105.11203.Google ScholarPubMed
Allen, M., & Friston, K. J. (2018). From cognitivism to autopoiesis: Towards a computational framework for the embodied mind. Synthese, 195(6), 2459–2482.CrossRefGoogle ScholarPubMed
Anderson, M. L. (2017). Of Bayes and bullets: An embodied, situated, targeting-based account of predictive processing. In Wiese, W. & Metzinger, T. K. (Eds.), Philosophy and predictive processing (Vol. 4, pp. 1–14). MINDGroup.Google Scholar
Andrews, M. (2020). The math is not the territory: Navigating the free energy principle. [Preprint]. http://philsci-archive.pitt.edu/18315Google Scholar
Attias, H. (2003). Planning by probabilistic inference. In Bishop, C. M., & Frey, B. J. (Eds.), Proc. of the 9th Int. Workshop on artificial intelligence and statistics, 2003 (pp. 9–16). PMLR.Google Scholar
Baltieri, M., & Buckley, C. L. (2019). Generative models as parsimonious descriptions of sensorimotor loops. Behavioral and Brain Sciences, 42, e218.CrossRefGoogle ScholarPubMed
Baltieri, M., Buckley, C. L., & Bruineberg, J. (2020). Predictions in the eye of the beholder: An active inference account of Watt governors. Artificial life conference proceedings (pp. 121–129). MIT Press.Google Scholar
Barandiaran, X. E., Di Paolo, E., & Rohde, M. (2009). Defining agency: Individuality, normativity, asymmetry, and spatio-temporality in action. Adaptive Behavior, 17(5), 367–386.CrossRefGoogle Scholar
Beal, M. J. (2003). Variational algorithms for approximate Bayesian inference. Doctoral dissertation, UCL (University College London).Google Scholar
Beer, R. D. (2004). Autopoiesis and cognition in the game of life. Artificial Life, 10(3), 309–326.CrossRefGoogle ScholarPubMed
Beer, R. D. (2014). The cognitive domain of a glider in the game of life. Artificial Life, 20(2), 183–206.CrossRefGoogle ScholarPubMed
Beer, R. D. (2020). An investigation into the origin of autopoiesis. Artificial Life, 26(1), 5–22.CrossRefGoogle ScholarPubMed
Beni, M. D. (2021). A critical analysis of Markovian monism. Synthese, 199, 6407–6427. https://doi.org/10.1007/s11229-021-03075-xCrossRefGoogle ScholarPubMed
Biehl, M. (2017). Formal approaches to a definition of agents. Doctoral dissertation, University of Hertfordshire.Google Scholar
Biehl, M., Guckelsberger, C., Salge, C., Smith, S. C., & Polani, D. (2018). Expanding the active inference landscape: More intrinsic motivations in the perception-action loop. Frontiers in Neurorobotics, 12, 45.CrossRefGoogle ScholarPubMed
Biehl, M., Pollock, F. A., & Kanai, R. (2021). A technical critique of some parts of the free energy principle. Entropy, 23(3), 293.CrossRefGoogle ScholarPubMed
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer-Verlag.Google Scholar
Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859–877.CrossRefGoogle Scholar
Bogacz, R. (2017). A tutorial on the free energy framework for modelling perception and learning. Journal of Mathematical Psychology, 76, 198–211.CrossRefGoogle ScholarPubMed
Boik, J. C. (2021). Science-driven societal transformation, part III: Design. Sustainability, 13(2), 726.CrossRefGoogle Scholar
Bruineberg, J., Kiverstein, J., & Rietveld, E. (2018). The anticipating brain is not a scientist: The free energy principle from an ecological-enactive perspective. Synthese, 195(6), 2417–2444.CrossRefGoogle Scholar
Buckley, C. L., Kim, C. S., McGregor, S., & Seth, A. K. (2017). The free energy principle for action and perception: A mathematical review. Journal of Mathematical Psychology, 14, 55–79.CrossRefGoogle Scholar
Cao, R. (2020). New labels for old ideas: Predictive processing and the interpretation of neural signals. Review of Philosophy and Psychology, 11(3), 517–546.CrossRefGoogle Scholar
Chakravartty, A. (2017). Scientific realism. In Zalta, E. N. (Ed.), The Stanford encyclopedia of philosophy (summer 2017 edition). https://plato.stanford.edu/archives/sum2017/entries/scientific-realism/Google Scholar
Ciaunica, A., Constant, A., Preissl, H., & Fotopoulou, K. (2021). The first prior: From co-embodiment to co-homeostasis in early life. Consciousness and Cognition, 91, 103117.CrossRefGoogle ScholarPubMed
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and BrainSciences, 36(3), 181–204.Google ScholarPubMed
Clark, A. (2015a). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford University Press.Google Scholar
Clark, A. (2015b). Radical predictive processing. The Southern Journal of Philosophy, 53, 3–27.CrossRefGoogle Scholar
Clark, A. (2017). How to knit your own Markov blanket. In Metzinger, T. K. & Wiese, W. (Eds.), Philosophy and predictive processing: 3. Open MIND (pp. 1–19). MIND Group.Google Scholar
Clark, A. (2020). Beyond desire? Agency, choice, and the predictive mind. Australasian Journal of Philosophy, 98(1), 1–15.CrossRefGoogle Scholar
Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7–19.CrossRefGoogle Scholar
Colombo, M. (2020). Maladaptive social norms, cultural progress, and the free-energy principle. Behavioral and Brain Sciences, 43, e100.CrossRefGoogle ScholarPubMed
Colombo, M., Elkin, L., & Hartmann, S. (2018). Being realist about Bayes, and the predictive processing theory of mind. The British Journal for Philosophy of Science, 72(1).Google Scholar
Colombo, M., & Palacios, P. (2021). Non-equilibrium thermodynamics and the free energy principle in biology. Biology & Philosophy, 36(5), 1–26.CrossRefGoogle Scholar
Colombo, M., & Seriès, P. (2012). Bayes in the brain – on Bayesian modelling in neuroscience. The British Journal for the Philosophy of Science, 63, 697–723.CrossRefGoogle Scholar
Colombo, M., & Wright, C. (2021). First principles in the life sciences: The free-energy principle, organism, and mechanism. Synthese, 198(14), 3463–3488.CrossRefGoogle Scholar
Da Costa, L., Friston, K., Heins, C., & Pavliotis, G. A. (2021). Bayesian mechanics for stationary processes. Proceedings of the Royal Society A, 477(2256), 20210518.CrossRefGoogle ScholarPubMed
Da Costa, L., Parr, T., Sajid, N., Veselic, S., Neacsu, V., & Friston, K. (2020). Active inference on discrete state-spaces: A synthesis. Journal of Mathematical Psychology, 99, 102447.CrossRefGoogle ScholarPubMed
Daunizeau, J. (2017). The variational Laplace approach to approximate Bayesian inference. [preprint] arXiv:1703.02089.Google Scholar
Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995). The Helmholtz machine. Neural computation, 7(5), 889–904.CrossRefGoogle ScholarPubMed
Dewhurst, J. (2017). Folk psychology and the Bayesian brain. In Philosophy and predictive processing (pp. 1–13). MIND Group.Google Scholar
Di Paolo, E., Thompson, E., & Beer, R. D. (2021). Laying down a forking path: Incompatibilities between enaction and the free energy principle.CrossRefGoogle Scholar
Dołęga, K. (2017). Moderate predictive processing. In T. K. Metzinger, & W. Wiese, (Eds.), Philosophy and predictive processing (pp. 1–19). MIND Group.Google Scholar
Fausto-Sterling, A. (2021). A dynamic systems framework for gender/sex development: From sensory input in infancy to subjective certainty in toddlerhood. Frontiers in Human Neuroscience, 15, 150.CrossRefGoogle ScholarPubMed
Feldman, H., & Friston, K. J. (2010). Attention, uncertainty, and free energy. Frontiers in Human Neuroscience, 4, 215.CrossRefGoogle ScholarPubMed
Fox, S. (2021). Active inference: Applicability to different types of social organization explained through reference to industrial engineering and quality management. Entropy, 23(2), 198.CrossRefGoogle ScholarPubMed
Friston, K., Sengupta, B., & Auletta, G. (2014). Cognitive dynamics: From attractors to active inference. Proceedings of the IEEE, 102(4), 427–445.CrossRefGoogle Scholar
Friston, K. J. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society of London. Series B, Biologicalsciences, 360(1456), 815–836.CrossRefGoogle ScholarPubMed
Friston, K. J. (2008). Hierarchical models in the brain. PLoS Computational Biology, 4(11).CrossRefGoogle ScholarPubMed
Friston, K. J. (2010). The free energy principle: A unified brain theory? Nature Reviews. Neuroscience, 11(2), 127–138.CrossRefGoogle ScholarPubMed
Friston, K. J. (2012). A free energy principle for biological systems. Entropy, 2012(14), 2100–2121.Google Scholar
Friston, K. J. (2013). Life as we know it. Journal of the Royal Society Interface, 10(86), 20130475.CrossRefGoogle ScholarPubMed
Friston, K. J. (2019). A free energy principle for a particular physics. [preprint] arXiv:1906.10184.Google Scholar
Friston, K. J., & Ao, P. (2012). Free energy, value, and attractors. Computational and mathematical methods in medicine, Volume 2012, Article ID 937860.CrossRefGoogle Scholar
Friston, K. J., Da Costa, L., & Parr, T. (2021a). Some interesting observations on the free energy principle. Entropy, 2021(23), 1076. https://doi.org/10.3390/e23081076CrossRefGoogle Scholar
Friston, K. J., Daunizeau, J., Kilner, J., & Kiebel, S. J. (2010). Action and behavior: A free energy formulation. Biological Cybernetics, 102(3), 227–260.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Friston, K. J., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017a). Active inference: A process theory. Neural Computation, 29(1), 1–49.CrossRefGoogle ScholarPubMed
Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. Neuroimage, 19(4), 1273–1302.CrossRefGoogle ScholarPubMed
Friston, K. J., Kilner, J., & Harrison, L. (2006). A free energy principle for the brain. Journal of Physiology-Paris, 100(1), 70–87.CrossRefGoogle ScholarPubMed
Friston, K. J., Levin, M., Sengupta, B., & Pezzulo, G. (2015a). Knowing one's place: A free energy approach to pattern regulation. Journal of The Royal Society Interface, 12(105), 20141383.CrossRefGoogle Scholar
Friston, K. J., Mattout, J., Trujillo-Barreto, N., Ashburner, J., & Penny, W. (2007). Variational free energy and the Laplace approximation. Neuroimage, 34(1), 220–234.CrossRefGoogle ScholarPubMed
Friston, K. J., Parr, T., & de Vries, B. (2017b). The graphical brain: Belief propagation and active inference. Network Neuroscience, 1(4), 381–414.CrossRefGoogle ScholarPubMed
Friston, K. J., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015b). Active inference and epistemic value. Cognitive Neuroscience, 6(4), 187–214.CrossRefGoogle ScholarPubMed
Friston, K. J., Trujillo-Barreto, N., & Daunizeau, J. (2008). DEM: A variational treatment of dynamic systems. NeuroImage, 41(3), 849–885.CrossRefGoogle Scholar
Friston, K. J., Wiese, W., & Hobson, J. A. (2020). Sentience and the origins of consciousness: From Cartesian duality to Markovian monism. Entropy, 22(5), 516.CrossRefGoogle ScholarPubMed
Gardner, M. (1970). Mathematical games: The fantastic combinations of John Conway's new solitaire game “life.” Scientific American, 223, 120–123.CrossRefGoogle Scholar
Gładziejewski, P. (2016). Predictive coding and representationalism. Synthese, 193(2), 559–582.CrossRefGoogle Scholar
Gregory, R. L. (1980). Perceptions as hypotheses. Philosophical Transactions of the Royal Society of London. B, Biological Sciences, 290(1038), 181–197.Google ScholarPubMed
Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17(9), 767–773.CrossRefGoogle ScholarPubMed
Grossberg, S. (1980). How does a brain build a cognitive code? Psychological Review, 87(1), 1–51.CrossRefGoogle ScholarPubMed
Hafner, V. V., Loviken, P., Villalpando, A. P., & Schillaci, G. (2020). Prerequisites for an artificial self. Frontiers in Neurorobotics, 14, 1–10.CrossRefGoogle ScholarPubMed
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 Evolution, development and complexity (pp. 195–227). Springer.CrossRefGoogle Scholar
Hinton, G. E., & Zemel, R. S. (1994). Autoencoders, minimum description length, and Helmholtz free energy. In Advances in neural information processing systems (pp. 3–10). Morgan Kaufmann.Google Scholar
Hipólito, I., Ramstead, M. J. D., Convertino, L., Bhat, A., Friston, K. J., & Parr, T. (2021). Markov blankets in the brain. Neuroscience and Biobehavioral Reviews, 125, 88–97.CrossRefGoogle ScholarPubMed
Hohwy, J. (2013). The predictive mind. Oxford University Press.CrossRefGoogle Scholar
Hohwy, J. (2016). The self-evidencing brain. Noûs, 50(2), 259–285.CrossRefGoogle Scholar
Hohwy, J. (2017). How to entrain your evil demon. In Metzinger, T. K. & Wiese, W. (Eds.), Philosophy and predictive processing: 2. Open MIND (pp. 1–15). MIND Group.Google Scholar
Jefferys, W. H., & Berger, J. O. (1991). Sharpening Occams razor on a Bayesian strop. Bulletin of the Astronomical Society, 23(3), 1259.Google Scholar
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233.CrossRefGoogle Scholar
Kappen, H. J., Gómez, V., & Opper, M. (2012). Optimal control as a graphical model inference problem. Machine Learning, 87(2), 159–182.CrossRefGoogle Scholar
Khezri, D. B. (2021). Free energy governance-sensing, sensemaking, and strategic renewal-surprise-minimization and firm survival. Doctoral dissertation, Universität St. Gallen.Google Scholar
Kiefer, A., & Hohwy, J. (2018). Content and misrepresentation in hierarchical generative models. Synthese, 195, 2387–2415.CrossRefGoogle Scholar
Kiefer, A. B. (2020). Psychophysical identity and free energy. Journal of The Royal Society Interface, 17(169), 20200370. http://dx.doi.org/10.1098/rsif.2020.0370CrossRefGoogle ScholarPubMed
Kirchhoff, M. D. (2018). Autopoiesis, free energy, and the life–mind continuity thesis. Synthese, 195(6), 2519–2540.CrossRefGoogle Scholar
Kirchhoff, M. D., & Kiverstein, J. (2019). Extended consciousness and predictive processing: A third-wave view. Routledge.CrossRefGoogle Scholar
Kirchhoff, M. D., & Kiverstein, J. (2021). How to determine the boundaries of the mind: A Markov blanket proposal. Synthese, 198(5), 4791–4810. https://doi.org/10.1007/s11229-019-02370-yCrossRefGoogle Scholar
Kirchhoff, M. D., Parr, T., Palacios, E., Friston, K. J., & Kiverstein, J. (2018). The Markov blankets of life: Autonomy, active inference and the free energy principle. Journal of The Royal Society Interface, 15(138), 20170792.CrossRefGoogle ScholarPubMed
Kirchhoff, M. D., & Robertson, I. (2018). Enactivism and predictive processing: A non-representational view. Philosophical Explorations, 21(2), 264–281.CrossRefGoogle Scholar
Kirchhoff, M. D., & van Es, T. (2021). A universal ethology challenge to the free energy principle: Species of inference and good regulators. Biology & Philosophy, 36, 8.CrossRefGoogle Scholar
Kiverstein, J., Kirchhoff, M., & Thacker, M. (2021). Why pain experience is not a controlled hallucination of the body. [preprint]. http://philsci-archive.pitt.edu/18770/Google Scholar
Klein, C. (2018). What do predictive coders want? Synthese, 195(6), 2541–2557.CrossRefGoogle Scholar
Knill, D. C., & Pouget, A. (2004). The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences, 27(12), 712–719.CrossRefGoogle ScholarPubMed
Knill, D. C., & Richards, W. (1996). Perception as Bayesian inference. Cambridge University Press.CrossRefGoogle Scholar
Körding, K., & Wolpert, D. (2004). Bayesian integration in sensorimotor learning. Nature, 427, 244–247.CrossRefGoogle ScholarPubMed
Kuchling, F., Friston, K. J., Georgiev, G., & Levin, M. (2020). Morphogenesis as Bayesian inference: A variational approach to pattern formation and control in complex biological systems. Physics of Life Reviews, 33, 88–108.CrossRefGoogle ScholarPubMed
Lee, T. S., & Mumford, D. (2003). Hierarchical Bayesian inference in the visual cortex. JOSA A, 20(7), 1434–1448.CrossRefGoogle ScholarPubMed
Litwin, P., & Miłkowski, M. (2020). Unification by fiat: Arrested development of predictive processing. Cognitive Science, 44, e12867.CrossRefGoogle ScholarPubMed
MacKay, D. J. (2003). Information theory, inference and learning algorithms. CUP.Google Scholar
Maturana, H. R., & Varela, F. J. (1972). Autopoiesis and cognition: The realization of the living (Vol. 42). Springer Science & Business Media.Google Scholar
Menary, R., & Gillett, A. J. (2020). Are Markov blankets real and does it matter? In Mendonca, D., Curado, M., & Gouveia, S. S. (Eds.), The philosophy and science of predictive processing (pp. 39–58). Bloomsbury Academic.Google Scholar
Millidge, B., Tschantz, A., Seth, A. K., & Buckley, C. L. (2020). On the relationship between active inference and control as inference. International workshop on active inference (pp. 3–11). Springer.CrossRefGoogle Scholar
Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT press.Google Scholar
Oaksford, M., & Chater, N. (2001). The probabilistic approach to human reasoning. Trends in Cognitive Sciences, 5(8), 349–357.CrossRefGoogle ScholarPubMed
Opper, M., & Archambeau, C. (2009). The variational Gaussian approximation revisited. Neural computation, 21(3), 786–792.CrossRefGoogle ScholarPubMed
Palacios, E. R., Razi, A., Parr, T., Kirchhoff, M., & Friston, K. (2020). On Markov blankets and hierarchical self-organisation. Journal of Theoretical Biology, 486, 110089.CrossRefGoogle ScholarPubMed
Parisi, G. (1988). Statistical field theory. Addison-Wesley.Google Scholar
Parr, T. (2021). Message passing and metabolism. Entropy, 23(5), 606.CrossRefGoogle ScholarPubMed
Parr, T., Da Costa, L., & Friston, K. (2020). Markov blankets, information geometry and stochastic thermodynamics. Philosophical Transactions of the Royal Society A, 378(2164), 20190159.CrossRefGoogle ScholarPubMed
Parr, T., Da Costa, L., Heins, C., Ramstead, M. J. D., & Friston, K. J. (2021). Memory and Markov blankets. Entropy, 23(9), 1105.CrossRefGoogle ScholarPubMed
Parr, T., Mirza, M. B., Cagnan, H., & Friston, K. J. (2019). Dynamic causal modelling of active vision. Journal of Neuroscience, 39(32), 6265–6275.CrossRefGoogle ScholarPubMed
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann.Google Scholar
Pearl, J. (2009). Causality. Cambridge University Press.CrossRefGoogle Scholar
Penny, W. D., Friston, K. J., Ashburner, J., Kiebel, S., & Nichols, T. (Eds.) (2011). Statistical parametric mapping: The analysis of functional brain images. Elsevier.Google Scholar
Pezzulo, G., Rigoli, F., & Friston, K. J. (2018). Hierarchical active inference: A theory of motivated control. Trends in Cognitive Sciences, 22(4), 294–306.CrossRefGoogle ScholarPubMed
Poirier, P., Faucher, L., & Bourdon, J. N. (2021). Cultural blankets: Epistemological pluralism in the evolutionary epistemology of mechanisms. Journal for General Philosophy of Science, 52(2), 335–350.CrossRefGoogle Scholar
Raja, V., Valluri, D., Baggs, E., Chemero, A., & Anderson, M. L. (2021). The Markov blanket trick: On the scope of the free energy principle and active inference. Physics of Life Reviews, 39(2), 49–72. doi:10.1016/j.plrev.2021.09.001CrossRefGoogle Scholar
Ramstead, M. J., Friston, K. J., & Hipólito, I. (2020a). Is the free energy principle a formal theory of semantics? From variational density dynamics to neural and phenotypic representations. Entropy, 22(8), 889.CrossRefGoogle ScholarPubMed
Ramstead, M. J., Hesp, C., Tschantz, A., Smith, R., Constant, A., & Friston, K. (2021). Neural and phenotypic representation under the free-energy principle. Neuroscience & Biobehavioral Reviews, 120, 109–122. https://www.sciencedirect.com/science/article/pii/S0149763420306643CrossRefGoogle ScholarPubMed
Ramstead, M. J., Kirchhoff, M. D., Constant, A., & Friston, K. J. (2019). Multiscale integration: Beyond internalism and externalism. Synthese, 198, 41–70.CrossRefGoogle ScholarPubMed
Ramstead, M. J., Kirchhoff, M. D., & Friston, K. J. (2020b). A tale of two densities: Active inference is enactive inference. Adaptive Behavior, 28(4), 225–239.CrossRefGoogle ScholarPubMed
Ramstead, M. J. D., Badcock, P. B., & Friston, K. J. (2018). Answering Schrödinger's question: A free energy formulation. Physics of Life Reviews, 24, 1–16.CrossRefGoogle ScholarPubMed
Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87.CrossRefGoogle ScholarPubMed
Rosas, F. E., Mediano, P. A. M., Biehl, M., Chandaria, S., & Polani, D. (2020). Causal blankets: Theory and algorithmic framework. In Verbelen, T., Lanillos, P., Buckley, C. L., & De Boom, C. (Eds.), Active Inference. IWAI 2020. Communications in Computer and Information Science (Vol. 1326, pp. 187–198). Springer.Google Scholar
Rubin, S., Parr, T., Da Costa, L., & Friston, K. J. (2020). Future climates: Markov blankets and active inference in the biosphere. Journal of the Royal Society Interface, 17, 20200503.CrossRefGoogle ScholarPubMed
Sajid, N., Ball, P. J., Parr, T., & Friston, K. J. (2021). Active inference: Demystified and compared. Neural Computation, 33(3), 674–712.CrossRefGoogle ScholarPubMed
Sánchez-Cañizares, J. (2021). The free energy principle: Good science and questionable philosophy in a grand unifying theory. Entropy, 23(2), 238. https://doi.org/10.3390/e23020238CrossRefGoogle Scholar
Seth, A., Millidge, B., Buckley, C. L., & Tschantz, A. (2020). Curious inferences: Reply to Sun and Firestone on the dark room problem. Trends in Cognitive Sciences, 24(9), 681–683.CrossRefGoogle Scholar
Sims, M. (2020). How to count biological minds: Symbiosis, the free energy principle, and reciprocal multiscale integration. Synthese, 199, 2157–2179. https://doi.org/10.1007/s11229-020-02876-wCrossRefGoogle Scholar
Stephan, K. E., Penny, D., Daunizeau, W. J., Moran, R. J., & Friston, K. J. (2009). Bayesian model selection for group studies. NeuroImage, 46(4), 1004–1017.CrossRefGoogle ScholarPubMed
Stephan, K. E., Penny, W. D., Moran, R. J., den Ouden, H. E., Daunizeau, J., & Friston, K. J. (2010). Ten simple rules for dynamic causal modeling. Neuroimage, 49(4), 3099–3109.CrossRefGoogle ScholarPubMed
Sun, Z., & Firestone, C. (2020a). The dark room problem. Trends in Cognitive Sciences, 24, 346–348.CrossRefGoogle ScholarPubMed
Sun, Z., & Firestone, C. (2020b). Optimism and pessimism in the predictive brain. Trends in Cognitive Sciences, 24, 683–685.CrossRefGoogle ScholarPubMed
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science (New York, N.Y.), 331(6022), 1279–1285.CrossRefGoogle Scholar
Tishby, N., & Polani, D. (2011). Information theory of decisions and actions. Perception-action cycle (pp. 601–636). Springer.CrossRefGoogle Scholar
Tschantz, A., Seth, A. K., & Buckley, C. L. (2020). Learning action-oriented models through active inference. PLoS Computational Biology, 16(4), e1007805.CrossRefGoogle ScholarPubMed
Van de Cruys, S., Friston, K. J., & Clark, A. (2020). Controlled optimism: Reply to Sun and Firestone on the dark room problem. Trends in Cognitive Science, 24(9), 680–681.CrossRefGoogle Scholar
van Es, T. (2021). Living models or life modelled? On the use of models in the free energy principle. Adaptive Behavior, 29(3), 315–329. https://doi.org/10.1177/1059712320918678CrossRefGoogle Scholar
van Es, T., & Kirchhoff, M. D. (2021). Between pebbles and organisms: Weaving autonomy into the Markov blanket. Synthese, 199, 6623–6644. https://doi.org/10.1007/s11229-021-03084-wCrossRefGoogle Scholar
Veissière, S. P., Constant, A., Ramstead, M. J., Friston, K. J., & Kirmayer, L. J. (2020). Thinking through other minds: A variational approach to cognition and culture. Behavioral and Brain Sciences, 43, 1–21.Google ScholarPubMed
Vowels, M. J., Camgoz, N. C., & Bowden, R. (2021). D'ya like DAGs? A survey on structure learning and causal discovery. arXiv preprint arXiv:2103.02582, 1–35.Google Scholar
Wiese, W., & Friston, K. J. (2021). Examining the continuity between life and mind: Is there a continuity between autopoietic intentionality and representationality? Philosophies, 6(1), 18. https://doi.org/10.3390/philosophies6010018CrossRefGoogle Scholar
Wilkinson, S., Deane, G., Nave, K., & Clark, A. (2019). Getting warmer: Predictive processing and the nature of emotion. The value of emotions for knowledge (pp. 101–119). Palgrave Macmillan.Google Scholar
Williams, D. (2018). Predictive processing and the representation wars. Minds and Machines, 28, 141–172CrossRefGoogle ScholarPubMed
Williams, D. (2021). Is the brain an organ for free energy minimisation? Philosophical Studies, 195, 2459. http://dx.doi.org/10.1007/s11098-021-01722-0Google Scholar
Woodward, J. (2003). Making things happen. Oxford University Press.Google Scholar
Yon, D., Heyes, C., & Press, C. (2020). Beliefs and desires in the predictive brain. Nature Communications, 11, 4404.CrossRefGoogle ScholarPubMed
Zhang, C., Bütepage, J., Kjellström, H., & Mandt, S. (2018). Advances in variational inference. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(8), 2008–2026.CrossRefGoogle ScholarPubMed