Co-constructing Markov blankets: tricky solutions
Authors: Thomas van Es1*, Inês Hipólito 2, 3
1 Centre for philosophical psychology, Universiteit Antwerpen, Belgium
2 Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
3 Department of Psychology, Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
Keywords: co-constructivism, historicity, hierarchical nesting, free energy, dynamical systems
Comment on 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.
In their important contribution to the free energy principle (FEP) literature, Raja et al. (2021)
point out crucial shortcomings and issues for the FEP to meet its ambitious goals, including the
provision of a unified science with specific focus on cognitive and biological sciences.
Additionally, Raja et al. criticise an FEP ambition to establish an operationally defined, objective
metaphysics or ontology through the FEP. We want to comment on these two critiques, and
explore potential ways forward.
We shall first discuss the issues with the FEP ontology, as they also have implications for
the promise of a unified science. The process of establishing a Markov blanket is fundamentally
co-constructed by the modeller, their history, sociomaterial environment and research interests, and
the real-world system in context that make up the experimental system (Hipólito and van Es
2022). This precludes a claim to objectivity, as different modellers will carve up a system
differently. However, this need not be problematic. As we have said before: “to demand an
ontology over and above what is relevant to our research interests is to demand an ontology that
is epiphenomenal to our investigations” (see Hipólito and van Es, Forthcoming).
This poses issues for realist perspectives on the FEP, which aim to use the formalism as a
proxy for the world to be understood. Under a realist reading, model-based findings would have
a direct relation to the world under study. The computations used in the model are typically
considered equivalent to the computations used by the real-world system it is modelled after; the
boundaries established in the formalism would then also map onto real boundaries in the world.
These derivations from the FEP are problematized by Raja et al.’s criticism.
Yet an ontologically more parsimonious, ‘instrumentalist’ approach of the FEP in which
the FEP is taken to be an interesting tool for investigations rather than a guide to a metaphysical
truth (see e.g. van Es 2020) is not shielded from this criticism either. It seems, following Raja et
al.’s paper, that even as a tool for investigations the FEP may be lacking. The onus is then on
FEP theorists to show how the FEP can contribute to biological and cognitive sciences.
Acknowledging the co-constructive nature of the FEP helps understanding its
contributions. With co-constructive science we mean the following. The various aspects of the
experimental system influence and construct one another and the whole dialectically. Not only
does the experimenter actively intervene in the system to be studied in its experimentation, but
they also construct it by framing it, determining the boundaries of the system of interest,
determining what is and isn’t of interest. The experimenter is also constructed in virtue of its
participation in the experimental environment. Its own behavioural repertoire is directed by the
experiment, the system to be studied, and the sociomaterial environment which includes the
research group’s aims, research conventions and so on (Hesp and Hipólito, 2022).
Co-construction is a historical, temporally thick concept: the current co-constructive
processes are themselves constructed (and continue to be constructed) historically. They also
construct the future situation. In this sense, co-construction is intended as an expansion of the
biological concept of niche construction. By these lights, the current scientific endeavours also
impact the practices of other researchers and others beyond the academy. Think of the way
publications of results open up further investigations or novel questions, spark new
methodological debates or spur on other researchers to incorporate the methodology into their
own investigations and explore the possibilities.
Given this perspective on FEP modelling, there are two fruitful, complementary
responses to the challenge by Raja et al. 1) We can look outside of the FED's ambitions for
applications of the FEP despite these limitations, and simultaneously 2) we can develop the
foundational structures of the FEP so as to overcome the limitations. In the former category,
Northoff et al. (2022) describe how connecting the FEP with a temporospatial dynamic view of
neuro-mental processes could allow us to manufacture ‘adaptive agents’ that could augment
instead of imitate human behaviours, and help patients navigate troublesome situations based on
a database of stored experiences that are flexibly put to use by the free energy minimising
system. Similarly, Da Costa et al. (2022) indicate that challenges in robotics such as robustness
and planning can be alleviated by implementing the FEP. Fields et al. (2022) use the FEP
formalisation of physical interactions as information exchange in conjunction with the
development of quantum theory as a scale-free information theory to break new ground in
quantum biology. Integration of the entropic brain hypothesis in neuropharmacology
(Carhart-Harris 2018) with the FEP has also been helpful in understanding the success of
psychedelics in mental health therapy (Hipólito et al., 2022; Carhart-Harris and Friston 2019).
These are just a few examples in which the tools and methodologies of the FEP are used and
applied beyond its initial intentions to explore novel territory for otherwise unanswered
questions. Even without solving the foundational issues that limit the FEP’s grander ambitions,
then, there remain important contributions to be found in its wider constructive impact.
There is also work that rises up to the latter, foundational issue. Unfortunately, modelling
the entire development of the course and dynamics of a living being during their lifespan remains
an impossible task due to the high level of complexity. After all, complexity is proportional to the
tractability for modelling. This is generally known as the intractability of the posterior in Bayesian
inference. This problem calls for dimensionality reduction.1As a methodological concession, this
common technique in modelling science treats a nonlinear system (i.e. a system whose output is
not proportional to the change of the input, therefore chaotic, unpredictable, or counterintuitive)
as if it were linear (i.e.the output is proportional to the input) (Hipólito and van Es, 2022).
Yet methods that better encompass complexity can be found in dynamical and complex
systems theory. Using the FEP, we can apply Markov blankets in a dynamical setting. For this, we
can restrict ourselves and look into a particular moment in time. Using nested Markov blankets,
one starts with the acknowledgment of the situatedness of variational dynamics of a behaviour
(i.e. that a system’s behaviour is situated in a history of continuous flux of interactions with their
environment). Any state in time then depends on a set of previous states: those without which
the present state would not emerge or exist in the way that it does. The present state density of a
system is thus determined by the system dynamics at a previous time (Parr et al. 2021).
Simultaneously, it relates to the future in a probabilistic manner (Parr et al. 2020; Hipólito et al.
2021). Thus, while limited to specific, well-defined situations, the FEP researchers are making
headway into accommodating historicity. Nonetheless, we concede to Raja et al. that it remains
difficult to account for larger scale dynamics and drastic changes over time that we find in many
living systems. In this comment, however, we have shown that there remains plenty for the FEP
to address, tackle, and influence within and outside of its usual boundaries.
Inês Hipólito gratefully acknowledges support from the Berlin School of Mind and Brain and the
Institute of Philosophy at the Humboldt-Universität zu Berlin. We also would like to thank
Guilherme Sanches de Oliveira for fruitful discussions that helped shape some of these ideas.
1For a survey of dimensionality reduction techniques see Sorzano (2014).
Carhart-Harris R. L. (2018). The entropic brain - revisited. Neuropharmacology,142, 167–178.
Carhart-Harris, R. L., & Friston, K. (2019). REBUS and the anarchic brain: toward a unified
model of the brain action of psychedelics.Pharmacological reviews,71(3), 316-344.
Da Costa, L., Lanillos, P., Sajid, N., Friston, K., & Khan, S. (2022). How active inference could
help revolutionise robotics. Entropy,24(3), 361.
Fields, C., Friston, K., Glazebrook, J. F., & Levin, M. (2022). A free energy principle for generic
quantum systems. Progress in Biophysics and Molecular Biology.
Friston, K. J., Fagerholm, E. D., Zarghami, T. S., Parr, T., Hipólito, I., Magrou, L., & Razi, A.
(2021). Parcels and particles: Markov blankets in the brain. Network Neuroscience,5(1), 211-251.
Hesp, C., & Hipólito, I. (2022). Living on the edge-practical information geometry for studying
the emergence and propagation of life forms: Comment on" How particular is the physics of the
free-energy principle?" by Aguilera et al. Physics of life reviews, 42, 52-55.
Hipólito, ., Mago, J., Rosas, F., & Carhart-Harris, R. (2022). Pattern Breaking: A Complex
Systems Approach to Psychedelic Medicine. Retrieved from psyarxiv.com/ydu3h
Hipólito, I. & van Es, T. (Forthcoming) Free Energy Pragmatics: Markov blankets don’t
prescribe objective ontology, and that’s okay. Behavioural Brain Sciences
Hipólito, I., & van Es, T. (2022). Enactive-Dynamic Social Cognition and Active Inference.
Frontiers in psychology,13, 855074. https://doi.org/10.3389/fpsyg.2022.855074
Hipólito, I., Ramstead, M. J., Convertino, L., Bhat, A., Friston, K., & Parr, T. (2021). Markov
blankets the brain. Neuroscience & Biobehavioral Reviews,125, 88-97.
Northoff, G., Fraser, M., Griffiths, J., Pinotsis, D. A., Panangaden, P., Moran, R., & Friston, K.
(2022). Augmenting Human Selves Through Artificial Agents - Lessons From the Brain. Frontiers
in computational neuroscience,16, 892354. https://doi.org/10.3389/fncom.2022.892354
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.
Parr, T., Da Costa, L., Heins, C., Ramstead, M. J. D., & Friston, K. J. (2021). Memory and markov
blankets. Entropy,23(9), 1105.
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,
Sorzano, C. O. S., Vargas, J., & Montano, A. P. (2014). A survey of dimensionality reduction
techniques. arXiv preprint arXiv:1403.2877.
van Es, T. (2020). Minimising prediction errors in predictive processing: from inconsistency to
non-representationalism. Phenomenology and the Cognitive Sciences,19(5), 997-1017.