ArticlePDF Available

Abstract and Figures

[A heavily rewritten version of this paper has been published in BBS in 2021] Markov blankets have been used to settle disputes central to philosophy of mind and cognition. Their development from a technical concept in Bayesian inference to a central concept within the free-energy principle is analysed. We propose to distinguish between instrumental Pearl blankets and realist Friston blankets. Pearl blankets are substantiated by the empirical literature but can do limited philosophical work. Friston blankets can do philosophical work, but require strong theoretical assumptions. Both are conflated in the current literature on the free-energy principle. Consequently, we propose that distinguishing between an instrumental and a realist research program will help clarify the literature.
Content may be subject to copyright.
Update: The Emperor’s New Markov Blankets October 22nd, 2021
This is the version of the paper that was pre-printed on December 1st, 2020. A heavily
rewritten version of the paper has been accepted for publication in Behavioral and Brain
Sciences in October 2021. The 2021 version is currently available as a preprint on the
website of the journal:
Eventually, this version of the paper will be published together with the commentaries and
our reply to commentaries.
In the 2021 version, we cut some of the more technical material and extended the
philosophical argumentation. For this reason, we think it is worthwhile to keep the 2020
version of the paper available as a resource on-line.
In general, please cite the new, 2021, version of the paper:
Bruineberg, Jelle and Dolega, Krzysztof and Dewhurst, Joe and Baltieri, Manuel (2021) The
Emperor’s New Markov Blankets. Behavioral and Brain Sciences 1-63. [Preprint]
If, however, you would like to cite the 2020 version of the paper, please clearly indicate that
you are citing the PhilSciArchive preprint:
Bruineberg, Jelle and Dolega, Krzysztof and Dewhurst, Joe and Baltieri, Manuel (2020) The
Emperor’s New Markov Blankets. PhilSciArchive [Preprint]
Best wishes,
The authors
The Emperor’s New Markov Blankets
Jelle Bruineberg (corresponding author)
Department of Philosophy, Macquarie University, Sydney, Australia
Krzysztof Dolega
Institut fr Philosophie 2, Ruhr-Universitt Bochum, Bochum, Germany
Joe Dewhurst
Munich Center for Mathematical Philosophy, Ludwig-Maximilians-Universitt Mnchen,
Manuel Baltieri (corresponding author)
Laboratory for Neural Computation and Adaptation, RIKEN Centre for Brain Science, Wako
City, Japan
Markov blankets have been used to settle disputes central to philosophy of mind and
cognition. Their development from a technical concept in Bayesian inference to a central
concept within the free-energy principle is analysed. We propose to distinguish between
instrumental Pearl blankets and realist Friston blankets. Pearl blankets are substantiated by
the empirical literature but can do limited philosophical work. Friston blankets can do
philosophical work, but require strong theoretical assumptions. Both are conflated in the
current literature on the free-energy principle. Consequently, we propose that distinguishing
between an instrumental and a realist research program will help clarify the literature.
1 Introduction
The formal concept of a Markov blanket plays a central role in many recent formulations of
the Free Energy Principle (FEP), and to applications of the FEP to the study of life and
cognition within the active inference framework. Our aim in this paper is to present an
overview of the history and development of this concept in the context of Bayesian inference,
and then to argue that its use in the active inference framework has stretched the concept
too far beyond its formal origins. This new use of the Markov blanket concept allows the
proponents of active inference to draw (we think) unwarranted metaphysical conclusions
on the basis of a purely mathematical formalism. We conclude that those wishing to use
Markov blankets for these purposes are faced with a dilemma: either they stick to the
original innocuous-but-metaphysically-uninteresting formulation; or they bolster it with
novel metaphysical premises. However, in the latter case it is the additional premises and
not the mathematical construct itself that carries out most of the theoretical work leading to
novel conclusions, undermining any claim that these conclusions simply follow from the
original Markov blanket formalism.
The FEP is a mathematical framework, developed by Karl Friston and colleagues (Friston,
Kilner, and Harrison 2006; Friston, Daunizeau, et al. 2010; Friston 2010; Friston, FitzGerald,
et al. 2017; Friston 2019), which specifies an objective function that self-organizing systems
need to minimize in order to ensure adaptive exchanges with their environment. This
minimization is made possible through variational inference, a machine learning technique
previously developed by (Neal and Hinton 1998). One major appeal of the FEP is that it aims
for (and seems to deliver) an unprecedented integration of the life sciences (including
psychology, neuroscience, and theoretical biology). The difference between the FEP and
earlier inferential theories (e.g., (Gregory 1970)) is that not only perceptual processes, but
also other cognitive functions such as learning, attention, and action planning (Friston 2010;
Friston, FitzGerald, et al. 2017), can be subsumed under one single principle, the
minimization of free energy, through the active inference framework. Furthermore, it is
claimed that this principle applies not only to human and other cognitive agents, but also
self-organizing systems more generally, offering a unified approach to the life sciences
(Friston 2013b; Friston, Levin, et al. 2015).
Another appealing claim made by proponents of the FEP and active inference is that it can
be used to settle fundamental metaphysical questions in a formally motivated and
mathematically grounded manner. The FEP has been used to (supposedly) resolve debates
about the boundaries of the mind (Hohwy 2017; Clark 2017; Kirchhoff and Kiverstein 2019),
the boundaries of living systems (Friston 2013b; Kirchhoff 2018; Kirchhoff et al. 2018), the
relationship between mind and matter (Friston, Wiese, and Hobson 2020), has been
proposed as an ordering principle by which the spatial and temporal scales of mind, life, and
society are linked (Ramstead, Badcock, and Friston 2018; Veissière et al. 2020), and has been
applied to the Earth’s climate system in support of the Gaia hypothesis (Rubin et al 2020). In
some places, FEP formalisms are explicitly presented as replacing (perhaps outdated)
philosophical arguments (Ramstead et al. 2019; Ramstead, Friston, and Hipólito 2020). A
complicating factor here is that the core of the FEP rests upon an intertwined web of
mathematical constructs borrowed from physics, computer science, computational
neuroscience, and machine learning. This web of formalisms is developing at an impressively
fast pace and the constructs it describes are often assigned a slightly unconventional
meaning whose full implications are not always obvious.
While this might ironically explain
some of its appeal, as it can seem to the layperson to be steeped in unassailable mathematical
justification, it also risks the possibility of ’smuggling in’ unwarranted metaphysical
assumptions. To its critics the FEP can appear like a moving target, each time introducing
new constructs that make the previous criticism inapplicable (see for example the exchange
between (Sun and Firestone 2020b; Seth et al. 2020; Van de Cruys, J. Friston, and Clark 2020)
and (Sun and Firestone 2020a)). Here we want to focus on just one of these formal
constructs, the concept of a Markov blanket that comes originally from the literature on
Some of the newest additions are non-equilibrium steady states (Friston 2019), dual information geometry
(Parr, Costa, and Friston 2020), each requiring detailed knowledge of non-equilibrium thermodynamics and
differential geometry.
Bayesian inference and graphical modelling, and demonstrate how it is now being used in
ways that stretch it far beyond its innocuous formal definition.
We think there are a set of issues arising from the (mis)use of Markov blankets that threaten
the possibility of the FEP doing the metaphysical work that its proponents expect it to carry
out. In our view the FEP literature consistently fails to clearly distinguish between the ‘map’
(a representation of reality) and the ‘territory’ (reality itself).
This slippage becomes most
apparent in their treatment of the concept of a Markov blanket. In statistics and machine
learning, Markov Blankets are a formal property of nodes in a Bayesian network. They
designate a set of nodes that essentially shield a random variable (or a set of variables) from
the rest of the variables (Pearl 1988; Bishop 2006; Murphy 2012). Bayesian networks are
typically used as useful abstractions of complex phenomena. By contrast, in the FEP
literature Markov blankets are frequently assigned a status as worldly boundaries with a
variety of different roles: they belong to the territory. This discrepancy in the use of Markov
blankets is indicative of a broader tendency within the FEP literature, in which mathematical
abstractions are treated as worldly entities with causal powers. By focusing here on the case
of Markov blankets, we hope to give a specific diagnosis of this problem, and a suggested
solution, but our analysis does have potentially wider implications for the general use of
formal constructs within the FEP literature.
The aim of this paper is twofold. First, we want to explain how it has been possible for such
an innocuous technical concept as a Markov blanket to come to be used in order to settle
central debates in philosophy of biology and cognition. We will trace the development of
Markov Blankets starting from their standard application in Bayesian networks, through the
role they play in variational inference, to their use in the literature on active inference. We
will argue that in the course of this transition (Friston 2012, 2013b; Friston 2019) a new and
largely independent theoretical construct has emerged (Friston, Da Costa, and Parr 2020;
Biehl, Pollock, and Kanai 2020; Rosas et al. 2020), one that is more closely aligned with
notions of sensorimotor loops and agent-environment boundaries (Tishby and Polani 2011;
Ay and Zahedi 2014). For this reason, we propose to distinguish between ‘Pearl blankets’ to
refer to the standard use of Markov blankets and ‘Friston blankets’
to refer to the new
construct. While Pearl blankets are unambiguously part of the map, Friston blankets are best
understood as part of the territory. Since these are different formal constructs with different
metaphysical implications, the scientific credibility of Pearl blankets should not
automatically be extended to Friston blankets.
The second aim of this paper is to use the above distinction between Pearl blankets and
Friston blankets in order to critically assess claims resting on the application of Markov
blankets to philosophical problems. We find that in many cases map and territory are not
clearly differentiated, thereby conflating Friston and Pearl blankets to draw potentially
unwarranted conclusions. We suggest that this literature would do well in differentiating
between two different research programs, which we call ‘inference with a model’ and
Arguments loosely along these lines have been developed in Andrews (2020) and van Es (2020).
The authors wish to credit Martin Biehl for this name, after first pointing out to some of them the novelties
introduced by Friston in his use of Markov blankets.
‘inference within a model’. These two approaches differ not only in how they interpret
Markov Blankets, but also in their overall goals:
‘Inference with a model’ assumes that a system in the world can be usefully described using
the tools of Bayesian probability theory, for example in the form of graphical models. Markov
blankets might then be utilised in these models as constructs for describing conditional
independencies among variables, but both the blankets and the models exist only as tools for
the scientist performing inference with a model, not as ontological truths about the intrinsic
nature of a system ‘out there in the world’. Another dominant assumption in the literature is
that agents themselves use a generative model of their environment to perform inference.
Understood in this way, the explanatory project for cognitive neuroscience is to discover
what generative model an agent is using to infer the states of its environment, but here also
Markov blankets are understood instrumentally, as properties of an agent’s model of the
world, not as real properties of the world itself.
‘Inference within a model’, on the other hand, seeks to understand inference as it is physically
implemented in a system, and places literal Markov blankets at the boundary between the
system and its environment. The ‘model’ within which these Markov blankets are used is
usually understood ontologically: here the map is the territory the system performing
inference is itself a model of its environment, and its boundary is demarcated by Markov
blankets. This ontological understanding of Markov blankets (unlike the above instrumental
understanding) cannot simply be justified by pointing to the mathematical formalisms
involved, nor can it be justified by pointing to the previous successes of inference with a
model and Pearl blankets more generally. The resulting approach is quite far removed from
an empirical and naturalistic research program, and might be better seen as a branch of
formal metaphysics applied to a scientific framework. We will argue that although this
approach might have interesting philosophical consequences, it is dependent upon
additional metaphysical assumptions that are not themselves contained withing the Markov
blanket construct.
In section 1 we introduce the formal machinery required for variational Bayesian inference,
in order to lay the groundwork for our discussion in section 2 of the traditional role played
by Markov blankets in probabilistic inference. In section 3 we present the active inference
framework and different roles played by Markov blankets within this framework, which we
suggest has ended up stretching the original concept beyond its original formal purpose
(here we distinguish between the original ’Pearl’ blankets and the novel ’Friston’ blankets).
In section 4 we expand on this suggestion, focusing specifically on the role now played by
Markov blankets in distinguishing the sensorimotor boundaries of organisms, which we
argue stretches the original notion of a Markov blanket in a philosophically unprincipled
manner. Finally, in section 5 we consider some of the theoretical consequence of conflating
these two different uses of the Markov blanket concept, and conclude that it would be more
usefull and productive to keep the two clearly distinct from one another when discussing
active inference and the FEP.
2 Variational Bayesian inference
The last twenty years in cognitive science have been marked by what can be called ‘a
Bayesian turn’, with an emerging number of theories and methodological approaches
appealing to or making use of Bayesian methods (Dayan et al. 1995; Knill and Richards 1996;
Rao and Ballard 1999; Knill and Pouget 2004; Friston, Kilner, and Harrison 2006; Doya 2007;
Clark 2013, 2015; Hohwy 2013). In particular, the application of Bayesian formulations to
the study of perception and other processes described as problems of inference has
generated a huge literature, highlighting a large interest in Bayesian probability theory for
the study of brains and minds. In this section we will review the formal background to
variational Bayesian inference to lay the foundations for what will follow.
2.1 Bayes theorem
In statistics, inference is the process by which one can estimate some hidden property,
usually the state or a parameter, of a system given some (often uncertain and limited)
evidence. For instance, how do we determine if a watermelon is ripe by knocking on it? Or
how can a cognitive system estimate a presence of some object on the basis of the state of its
receptors alone? From the perspective of Bayesian reasoning (Robert 2007; Berger 2013),
one can approach these kinds of inferential problems by applying Bayes theorem to
determine the optimal solution. Bayes theorem normally takes the following form:
   
  
This formula is a recipe for calculating the posterior probability,  , of a
hypothesis/hidden state given observation . The probability  captures a priori
knowledge about state (i.e., a prior probability), while  describes the likelihood of
observing when is assumed. The remaining term, , represents the likelihood of
observing independently of the hidden state and is usually referred to as the marginal
likelihood or model evidence, and plays the role of a normalising factor that ensures that the
posterior is expressed on the  interval and sums up to . In other words, the posterior
probability   represents the Bayes optimal combination of prior information
represented by  (e.g., what we know about ripe watermelons, before we get to knock on
the one in front of us) and a likelihood model   of how observations are generated in
the first place (e.g., how different (ripe or not) watermelons give rise to different sounds,
including the observed ), normalised by the knowledge about the observations integrated
over all possible hidden variables,  (e.g., how watermelons may sound, regardless of the
specific maturation stage).
To simplify the notation, we follow the convention used by standard treatments such as (Blei, Kucukelbir,
and McAuliffe 2017), where we denote both variables and their value assignments using lowercase letters
(i.e., X=x is assumed) while bold letters are used to denote vectors of variables (e.g., ).
Although this scheme offers a powerful tool for probabilistic inference, it is mostly limited to
simple, low-dimensional, often discrete or analytically tractable problems. This can be easily
seen when we consider the model evidence  as a normalisation term, computed as a
marginal likelihood, i.e., a likelihood integrated over all possible hidden variables :
   
In practice, computing the exact model evidence is rarely feasible. The process is, in fact,
often analytically intractable (i.e., no closed-form solution for the posterior) or
computationally too expensive (i.e., a large of infinite number of hidden states ) (MacKay
2003; Beal 2003; Bishop 2006). To obviate some of the limitations of exact Bayesian
inference schemes, different approximations can be deployed, which rely on either
stochastic or deterministic methods. Stochastic approximations of Bayesian inference are
based for example on Monte Carlo sampling (e.g., Markov Chain Monte Carlo/particle
filtering approaches (Chen 2003; Bishop 2006; Murphy 2012)) and while very effective, they
can be computationally expensive and in some cases may not offer the best analogy to
describe brains and biological systems in their more natural, dynamic and fast-paced
Deterministic approximations are often less precise but can arguably be
more easily used as models of biologically plausible implementations. In this context,
variational methods (Hinton and Zemel 1994; Jordan et al. 1999; MacKay 2003; Beal 2003;
Bishop 2006; Blei, Kucukelbir, and McAuliffe 2017; Zhang et al. 2018) are a popular choice,
including for the FEP framework discussed in this paper.
2.2 Variational inference
The main idea behind variational inference is that the problem of inferring the posterior
probability of some latent or hidden variables from a set of observations (i.e., the posterior
 ) can be transformed into an optimisation problem. Roughly speaking, the method
involves stipulating a family of probability densities over the latent variables, such that
each    is a possible approximation to the exact posterior. The goal of variational
inference then is to find an optimal distribution  which is closest to the true posterior.
The candidate distribution is often called the recognition or variational density, because the
methods used employ variational calculus, i.e., functions  are varied with respect to
some partition of the latent variables in order to achieve the best approximation of  .
In variational Bayes, the problem is stated using a common measure of dissimilarity between
two probability distributions, the Kullback-Leibler or KL divergence (here denoted by ):
  󰅻   
By using this definition of KL divergence, one can obtain the following equation:
See however (Sanborn and Chater 2016) for a different perspective.
      
   
 
  
where denotes expectations with respect to the variational density .
The trick of variational Bayes consists in letting go of trying to minimise the KL divergence
in equation (3) directly, shifting the objective to optimising a different functional which
bounds the model evidence. Since  is constant with respect to , the KL divergence
from the previous equation can be restated as:
      
   
is usually referred to as the evidence lower bound (ELBO) because it constitutes a lower-
bound on the model evidence, such that    for any  (this follows from
equation (5) and the fact that the KL divergence is always non-negative (Bishop 2006)). This
lower bound is also commonly referred to, by analogy with free energy in statistical physics,
as negative variational free energy (Hinton and Zemel 1994; Beal 2003; Murphy 2012). One
can in fact see negative ELBO  as the difference between an (expected) energy term
and a (Shannon) entropy term
        
  
as an internal energy term (Murphy 2012). Crucially, minimising the free energy , or
maximising the ELBO, implies a minimisation of the KL divergence in equation (3) while,
importantly, leaving the log-model evidence  unchanged. This then implies that
  󰅻
In the next section we will then look into how the problem stated in equation (9) is effectively
solved by variational inference.
2.3 The mean-field approximation
One of the most crucial components of variational inference is the choice of a variational
family . Typically, one proceeds by either introducing a parametrized family  with
parameters optimised to find an approximate posterior (e.g., might be the mean and
covariance matrix for a family of Gaussian distributions (Opper and Archambeau 2009)) or
by applying a mean field approximation commonly adopted in statistical physics (Parisi
1988), which considers partitions of hidden variables   
such that
  
 
Using the calculus of variations (Beal 2003; Bishop 2006; Friston, Trujillo-Barreto, and
Daunizeau 2008), one can then show that the optimal  (i.e., the one that maximises the
ELBO or minimises variational free energy) is also a product of terms , which are
(marginally) independent and can be expressed in the form of:
 
where   is a partition function (i.e., a normalising factor) and
 denotes an expectation with respect to all partitions of variational density ,  
, excluding , i.e., each partition  is averaged with respect to all other
partitions 
. To simplify some calculations and express this quantity in a more familiar
form, most practical applications restate the above equation in terms of logarithms, so that:
   
By resting on a product of independent partitions, as expressed by equation (10), the mean-
field approximation introduces a strong assumption on the relationships between different
hidden variables  , essentially stating that different partitions of do not exert a strong
influence on each other and can thus assumed to be marginally (or unconditionally)
independent. In particular, this means that the interactions of a partition with other
partitions are assumed to be mediated only by their mean-field effects, i.e., their interactions
correspond to the expected, or average, effects over all other partitions  (Jordan et al.
1999; Fox and Roberts 2012), see equation (11). This constitutes a drastic simplification of
the effective interactions between variables (Bishop 2006; Zhang et al. 2018), but it is often
effective due to the mathematical tractability achieved with the simplified version of the
inference problem, and the fact that in some (simplified) cases, mean-field effects can even
exactly describe the solutions to some problems (Jordan et al. 1999). It is however crucial to
Notice that , with M=N corresponding to a fully factorised variational density (Zhang et al. 2018), and
M<N to the case where different partitions contain more than one element of x (Bishop 2006).
Another way to see this is by rewriting equation (10) as ) to denote the variational density
q(x) in terms of the product of one partition, q(xi), and all the remaining ones, .
highlight that the mean-field assumption operates only on the variational density , and
therefore does not encode the ‘real’ set of dependencies that may in fact exist among
variables  . As we shall see briefly, when one considers the ‘real’ set of dependencies in
the joint probability  utilised to infer via the posterior  , it is possible to
further simplify the inference problem by defining more specifically which partitions 
should be used to build the average , i.e., all elements with   ? Or only some? If so,
which ones?
As it turns out, when there are relations of marginal or unconditional independence between
variables   such that, for instance
  
these should be taken into account, so that marginally independent variables can be excluded
from the set of variables with    and thus from the computation of the average in
equation (11). At the same time, it is interesting to note that another type of relation, i.e.,
conditional independence, can play a similar and in some cases even more impactful role (at
least in terms of simplifying an inference problem). Two variables, and , are said to be
conditionally independent given a third one, if
     
This corresponds, intuitively, to the idea that effectively ‘shields’ (or d-separates (Pearl
2009)) from , and from . As we will see in the next section, where this idea is
unpacked in more detail, can also be said to be a ‘Markov blanket’ (in the Pearl sense (Pearl
1988)) for and , such that no information about can improve estimates of or vice
versa, when is known. Crucially, this implies that conditionally independent variables will
also be excluded from the set    and thus from the average in equation (11). In practice,
this means that the factorised distributions  left in equation (11) essentially
constitute a ‘shield’ for the the partition .
While this description may be quite intuitive for a small number of variables (e.g., ‘we will
not consider when we compute the average effects on given the fact that ‘shields’
from ’), describing analytically these dependence relations, and especially their effects on
problems of variational inference, is not trivial. To overcome this impracticality, one is often
inclined to look for alternative representations that can more easily expresses the
relationships between variables  , and the ensuing simplifications for the computation
of optimal , while maintaining a rigorous mathematical formalism. In the next section
we will introduce probabilistic graphical models as one way to accomplish this
simplification, and then use them to make clear the concept of a Markov blanket as it first
appeared in the FEP literature.
3 Markov blankets and probalistic graphical models
A common way to represent probabilistic models and their typical algebraic manipulations
comes in the form of probabilistic graphical models. Probabilistic graphical models are a
family of mathematical representations describing relationships between random variables
using diagrams (Pearl 1988; Bishop 2006; Murphy 2012). Random variables are drawn as
nodes in a graph, with shaded nodes usually representing variables that are observed, or
empty ones used for variables that are latent, or hidden. The (probabilistic) relationships
between such random variables are then expressed using edges connecting the nodes. These
connections can be directed, conventionally depicted as arrows, or undirected, in which case
simple lines are used. Although these relationships are formally defined in terms of basic
manipulations on probability distributions (including the two fundamental operations of
marginalization and conditioninalization (Bishop 2006)), graphical models provide some
practical advantages in reasoning about these formal properties, presenting a clear and
easily interpreted depiction of the relationships between variables.
For the purposes of the present manuscript we will focus on graphs with directed links,
which provide the basis for Bayesian networks, and play a crucial role in the context of active
inference ( Friston, Parr, and Vries 2017). Standard introductions to these models and other
types of graphical representations such as Markov random fields and factor graphs can be
found in, for instance, (Pearl 1988; Bishop 2006; Murphy 2012).
3.1 Bayesian networks
Formally, a Bayesian network is defined as:
   
where     is a directed acyclic graph (DAG) consisting of a set of variables, vertices or
nodes and edges among them , and is a collection of tables containing dependencies
between these variables as a set of stochastic matrices, i.e., matrices where all entries  are
nonnegative real numbers    , and each row represents a probability density such
that   . The graph is often represented by an adjacency matrix , such that
   for each edge    of nodes in the graph , i.e., the matrix contains ones in
positions  when there is a connection between node and node in , and zeros for all
missing connections in the graph. The tables then contain the specific factorisation of a
joint probability distribution over the variables characterised, for a DAG, by the following
equation (Murphy 2012):
   
  
where  is the set of variables depends on. This dependence relation is visually
illustrated with connections in the graph , using arrows originating from variables in the
set  and terminating in . Such relationships between the variables are often
described using genealogical terms, with  being the parents, or ‘ancestors’, of their
child, or ‘descendant’, node .
A simple Bayesian network can be found in Fig. 1, where variables  are
variously connected to exemplify different types of dependencies. Algebraically, this model
can be expressed as
     
Graphically, the same relations can be represented in a model where the node , which is
completely disconnected from the rest of the network, is unconditionally independent from
all other variables. The remaining variables then express the three canonical examples of
(in)dependencies among 3-node graphs, constituting the basis for a general notion of d-
separation (separation in directed acyclic graphs, or directed separation) provided in (Pearl
and are marginally independent but only conditionally dependent if is observed
(i.e., when becomes a shaded node), a case technically known also as head-to-head
and are marginally dependent but conditionally independent if is observed, also
known as head-to-tail,
and are marginally dependent but conditionally independent if is observed, also
known as tail-to-tail.
For the sake of the topics discussed in this manuscript, it is worth stressing that, unlike other
kinds of graphical models, e.g., the undirected Markov random fields, Bayesian networks can
only be acyclic, meaning that no closed path can be followed from an initial node to go back
to the same node.
Figure 1: An example of a Bayesian network with different dependence relations among nodes. This
network represents the statistical relationships of six random variables. Here there is no observed (i.e., shaded)
node, assuming that instances from different variables may be measured at different points in time. The edges
Note, however, that for many applications of cyclical models, the networks can be unfolded by indexing the
state of each variable in time and forming an acyclic graph that serves as as a ’snapshot’ of what is going on in
the original model. For the application of this idea to the work on active inference and the FEP introduced in
the next section, see (Kwisthout, Bekkering, and Van Rooij 2017).
take the form of arrows that indicate directed dependencies among variables. Marginal, or unconditional
independencies can be seen, for instance, between the random variable and all other variables in the graphs
(i.e., no connections with other nodes). Conditional dependence instead appears in between and given , and
between and given . On the other hand, and are marginally independent (no direct path between the
two), but not conditionally given (e.g., knowing and measuring will tell us something about ).
3.2 Markov blankets in Bayesian networks, or Pearl blankets
Bayesian networks play an especially prominent role in the visualisation of marginal and
conditional independence relations introduced in the previous section, with the former
represented by the lack of direct connections between two nodes, and the latter defined in
terms of a set of nodes ‘shielding’ one variable (or set of variables) from all others. Shielding
is usually cashed out using the notion of d-separation (Pearl 1988), heuristically defined
above in terms of the three fundamental types of graphical connections that can be used to
determine conditional independencies in any Bayesian network. Thus, the concept of (d-
)separation can be used to describe the minimal set of nodes that renders a particular node
conditionally independent of all other nodes in a Bayesian graph, also known as the Markov
blanket of the node rendered conditionally independent (Pearl 1988) As the concept was
first introduced by Judea Pearl, we will refer to Markov blankets in this traditional sense as
Pearl blankets throughout the rest of the paper, in order to keep them distinct from the
Friston blankets introduced in section 4.
Pearl blankets are especially relevant when it comes to visualising, understanding, and
simplifying networks of considerable size. Thus, while trying not to overcomplicate our
current presentation, we introduce a slightly bigger graph in Fig. 2 to showcase the presence
of a shielding set of nodes in a graphical and hopefully more intuitive way. This network
represents a joint density  with 2 observations and 18 hidden states variously
connected. For example, we can define the Pearl blanket for node  (dashed border) as the
set of nodes
  
These nodes are highlighted in Fig. 2 using thick borders, a slight abuse of notation but
sufficient for our purposes. Here the Pearl blanket includes the parents of , {, its
children , and the so-called co-parents of its children, , i.e., all other nodes
that the children of  depend on. Formally, a Pearl blanket for a set of variables is thus
equivalent to
   
where  corresponds to the parents of ,  to the children and  to the co-
parents of respectively.
Figure 2: A Bayesian network describing the dependence of some observable variables on hidden
variables following    . Thick lines are used for nodes constituting the Pearl blanket for a
selected node , depicted here with a dashed border.
Importantly, the specification of conditional independencies, presented here in terms of
Pearl blankets, can be used to simplify the approximation of the posterior when conducting
variational inference, as we mentioned briefly at the end of the previous section. In
particular, following the mean-field approximation introduced earlier, once the minimal set
of conditional dependencies for a node is defined, the problem of finding the optimal
variational density , given by the product of independent factors specified in
equation (12), can be simplified (following (Bishop 2006; Fox and Roberts 2012)) to the
computation of
   
Here the mean-field effects, or expectations over all other independent factorisations
, are now replaced by expectations with respect to the variables known to
form the Pearl blanket of a node , and the normalisation factor
is replaced by , representing a new partition function calculated without the
conditionally independent variables excluded using the Pearl blanket definition.
To see a practical application of this result, let’s consider our example network in Fig. 2. For
a given factorisation of the variational density  into 18 components , with  
 (i.e., one for each random variable forming the network), to compute  initially
we would have computed, following equation (12), a series of components
  
  
  
  
   
for all   , where the average of each factor is taken on the remaining 17 factors. However,
thanks to the marginal independence between different groups of variables, e.g., between
the ‘left’ and ‘right’ hand side sub-networks in Fig. 2, which are completely disconnected, and
the conditional independence of other variables given their Pearl blanket, one gets
  󰅸󰅸
  󰅸󰅸
  󰅸󰅸
  󰅸󰅸
 󰅸󰅸
This not only likely improves the inference process by excluding average effects from parts
of the network that are completely disconnected from each other, but also further decreases
the number of nodes used to calculate expectations to only the ones forming the Pearl
blanket for each node. In larger networks the advantages of using Pearl blankets become
even more obvious, considering for example graphs with hundreds or thousands of variables
where mean-fields averages can now be computed using only a handful of nodes.
Having presented the basics of conducting Bayesian inference using probabilistic graphical
models, and the way in which Pearl blankets can be deployed for variational inference, we
now turn to the discussion of how the Markov blanket concept is used in the active inference
framework, an approach to the study of biological and cognitive systems inspired by the FEP.
4 Markov blankets and active inference
Active inference is a process theory derived from the application of variational inference to
the study of biological and cognitive systems (Friston, Daunizeau, et al. 2010; Friston 2013b;
Friston, Rigoli, et al. 2015; Friston, FitzGerald, et al. 2017; Friston 2019). The core
assumption underlying active inference is that living organisms can be thought of as systems
whose fundamental imperative is to minimise free energy (this constitutes the so called ‘free
energy principle’ (Friston 2010; Friston 2019)). Active inference attempts to explain action,
perception, and other aspects of cognition under the umbrella of variational (and expected)
free energy minimization (Friston, Daunizeau, et al. 2010; Feldman and Friston 2010;
Friston, FitzGerald, et al. 2017)). From this perspective, perception can be understood as a
process of optimising a variational bound on surprisal, as advocated by standard methods in
approximate Bayesian inference applied in the context of perceptual science (see for
instance (Dayan et al. 1995; Knill and Richards 1996; Rao and Ballard 1999; Lee and
Mumford 2003; Friston 2005)). At the same time, action is conceptualised as a process that
allows a system to create its own new observations, while casting motor control as a form of
inference (Attias 2003; Kappen, Gómez, and Opper 2012), with agents changing the world to
better meet their expectations. Active inference integrates a more general framework where
minimising expected free energy
accounts for more complex processes of action and policy
selection (Friston, Rigoli, et al. 2015; Friston, FitzGerald, et al. 2017; Tschantz, Seth, and
Buckley 2020). While a full treatment of active inference remains beyond the scope of this
, here we wish to highlight the formal connections between this framework and
the use of variational Bayes in standard treatments of approximate probabilistic inference
(as described in the previous two sections). More specifically, we can ask what role Pearl
blankets might play in active inference.
4.1 Pearl blankets in active inference
First we need to identify some of the formal notation used by active inference, which is
related to the variational approaches described previously. Here we use the notation
previously adopted in equation (9) to formulate perception and action as variational
problems in active inference, specifying perception as the minimization
  󰅻
based on a process that generates an optimal bound on the posterior   (see
equation (3), and characterizing action in terms of policies (i.e., sequences of actions)
 󰅻
This describes action selection as a minimisation of expected free energy, , based on
beliefs about future and unseen observations , up to a time . In doing so, we immediately
notice that equation (23) essentially mirrors the previously defined equation (9), with the
important caveat that in active inference, sequences of actions (i.e., policies ) are now a part
of the free energy . In a closed loop of action and perception, policies can effectively
modify the state of the world, generating new observations , something that classical
formulations of variational inference in statistics and machine learning do not consider,
instead assuming fixed observations or data (MacKay 2003; Beal 2003; Bishop 2006).
Some formulations of active inference, especially the earlier ones (Friston et al. 2007;
Friston, Trujillo-Barreto, and Daunizeau 2008; Friston 2008)), have thus explicitly relied on
a set of assumptions similar to the ones highlighted in the previous section: a mean-field
approximation and the use of Pearl blankets. The latter were seen as an integral part of the
The free energy expected in the future for unknown (i.e., yet to be seen) observations, combining a trade-off
between negative instrumental and negative epistemic values.
For some technical treatments and reviews, see e.g., (Bogacz 2017; Buckley et al. 2017; Friston, FitzGerald,
et al. 2017; Biehl et al. 2018; Sajid, Ball, and Friston 2019; Da Costa et al. 2020).
standard variational inference toolkit, where they are used to simplify the minimisation of
variational free energy (or maximisation of the ELBO) by specifying which variables need to
be considered for mean-field averages via appropriate constraints of conditional
independence (see Fig. 2). The only noticeable difference in these formulations is in the very
definition of the mean-field assumption, here implemented as ‘structured’, in the sense that
variables are partitioned in three independent sets (  ): hidden states and inputs,
parameters, and hyper-parameters. In this case, the use of Pearl blankets is entirely
consistent with existing literature and definitions of conditional independence in graphical
models, if not slightly overzealous given the typical focus on a relatively low number of
partitions. Indeed, it is not entirely clear what Pearl blankets actually add to this formulation,
since it is often claimed that given a partition of variables ‘the Markov [= Pearl] blanket
contains all [other] subsets, apart from the subset in question’ (Friston 2013b, 2008; Friston
et al. 2007; Friston, Trujillo-Barreto, and Daunizeau 2008), where all other sets”
corresponds to   . However, in more recent formulations of active inference the concept
has been applied in a slightly different way, as more than just a formal tool.
4.2 From Pearl blankets to Friston blankets
In a number of recent theoretical and philosophical treatments based on ideas from active
inference and the FEP, Markov blankets have been assigned a much more prominent role
that cannot be explained just in terms of the formal properties of Pearl blankets. In some
formulations of active inference, starting with (Friston and Ao 2012; Friston 2013b; Friston,
Sengupta, and Auletta 2014), Markov blankets are in fact introduced as a tool to describe a
specific form of conditional independence between a dynamical system and its environment,
serving as a kind of boundary between organism and world.
As an emblematic example of this transition, we’ll focus first on just one paper, Friston’s ‘Life
as we know it" (Friston 2013b), where he presents a proof-of-principle simulation for
conditions claimed to be relevant for the origins of life. This paper is often used as an example
of how to extend the relevance of Markov blankets beyond the realm of probabilistic
inference and into cognitive (neuro)science and philosophy of mind. The paper aims to show
how Markov blankets spontaneously form in a (simulated) ‘primordial soup’. This simulation
consists of a number of particles that are moving through a viscous fluid. The interaction
between the particles is governed by Newtonian and electrochemical forces, both only
working at short-range. This, in turn, means that one third of the particles is then prevented
from exerting any electrochemical force on the others. The result of running the simulation
is something resembling a blob of particles (Fig. 3).
Figure 3: The ‘primordial soup’ simulated in (Friston 2013b). The larger (cyan) dots represent the location of
each particle. There are three smaller (blue) dots associated with each particle, representing the electrochemical
state of that particle
Using the model adopted in the simulations (for details please refer to (Friston 2013b)), one
can then plot an adjacency matrix based on the coupling (i.e., dependencies) between
different particles at a final (simulation) time , representing the particles in a ‘steady-state’
(under the strong assumption that the system has evolved towards and achieved its final
steady state at time , when the simulation is stopped). The adjacency matrix is itself a
representation of the electrochemical interactions between particles, but can be interpreted
as an abstract depiction of a Bayesian network. A dark square in the adjacency matrix at
element  indicates that two particles are electrochemically coupled, and hence we could
imagine that there is a directed edge from node to node (see notation in section 3.1). In
this work, the directed edge is drawn if and only if particle electrochemically affects particle
(Fig. 4). Because of the way the simulation is set up, the network will not be symmetrical
(since a third of the randomly selected particles will not electrochemically affect the
remaining ones).
Figure 4: The adjacency matrix of the simulated soup at steady-state. Element  has value 1 (a dark square)
if and only if subsystem electrochemically affects subsystem . The four grey squares from top left to bottom right
represent the hidden states, the sensory states, the active states and the internal states respectively.
Spectral graph theory is then used to identify the 8 most densely coupled nodes, which are
defined as the ‘internal’ states. Given these internal states, the Markov blanket is then found
through tracing the parents, children and co-parents of children in the network (see Eq. 18
in (Friston 2013b)). As an extra interpretive step, the nodes in this Markov blanket can be
further separated into ‘sensory’ and ‘active’ states. The ‘sensory states’ correspond to the
parents of the internal states. The ‘active states’ correspond to the children of the internal
states and their co-parents.
States that are not internal states and part of the Markov
blanket are then called ‘external states’. This procedure thus delivers four sets:
: external states
: internal states
: active states
: sensory states
Applied to the primordial soup simulation, each particle can be coloured to indicate which
of these sets it has been assigned to (see Fig. 5). Given the dominance of short-range
interactions and the density of particles, it should not come as a surprise that the particles
that are labeled as active and sensory states form a spatial boundary around the states that
are labelled as internal states. Given their placement in the simulated state space, this gives
the impression that the active and sensory form a structure similar to a cell membrane.
See section 5.1 for a discussion on the role of co-parents.
Figure 5: The Markov blanket of the simulated soup at steady-state in (Friston 2013b). Figure reproduced
using the code provided with (Friston 2013b). Similarly to Fig. 3 particles are indicated by larger dots. Particles
which belong to the set of sensory states are in magenta, active states are in red, while internal states are in dark
blue. A ‘blanket’ of active and sensory cells surrounding the internal particles can be seen.
The ‘Markov blanket formalism’ advocated by Friston (2013b) and described formally above
does most of the work in the FEP literature when it comes to identifying internal, sensory,
active, and external states It is important to note that the partitioning of the primordial soup
is not done directly, but requires a formal representation of the system. This formalizing step
requires a number of additional assumptions that Friston does not provide any justification
for. For example, it is unclear why only electrochemical interactions are used to construct
the adjacency matrix while other forms of influence included in the simulation (such as
Newtonian forces) are ignored. The demarcations made by analysing the adjacency matrix
are than used to label the nodes in the original system (as in Fig. 5).
The simulation assumes that by viewing the system through the Markov blanket formalism,
plus some additional assumptions about the separation of its states into different sets of
variables, it is possible to uncover hidden properties of the target system which, in some
sense, ’instantiates’ or ‘possesses’ a Markov blanket. This procedure of attributing to the
territory (the dynamical system) what is a property of the map (the Bayesian network) is a
clear example of the reification fallacy: treating something abstract as something concrete
(without any further justification). At the very least, we think that this way of using the
formalism goes beyond the merely epistemic role that Markov blankets (in the Pearl sense)
were originally intended to carry out. As we will show, we think that many of FEP’s
proponents are using the blanket formalism in a much more metaphysically robust sense,
one whose details cannot simply be assumed to follow from the formal properties of Markov
blankets. Therefore, we propose to distinguish between ‘Pearl blankets’ to refer to the
standard ‘epistemic’ use of Markov blankets and ‘Friston Blankets’ to refer to this new
‘metaphysical’ construct. While Pearl blankets are unambiguously part of the map (i.e., the
graphical model), Friston blankets are best understood as parts of the territory (i.e., the
system being studied). We will now look in more detail at some of the philosophical claims
about agent-environment boundaries that Friston blankets have been taken to support,
following the claims made by Friston in “Life as we know it".
4.3 Friston blankets as agent-environment boundaries
Why and how have Markov blankets been reified to act as parts of the target system, e.g., by
delineating its spatiotemporal boundaries, rather than merely formal tools intended for
scientific representation and statistical analysis? When did the map become conflated with
the territory? Here we aim to answer this question by presenting a series of different
treatments inspired by Friston’s use of Markov blankets in “Life as we know it". In doing so
we can see how what was once an abstract mathematical construct used to describe
conditional independence in graphical models came to be seen as a physical entity that
somehow causes conditional independence. This latter interpretation has potentially
interesting philosophical implications, but does not follow straightforwardly from the
former mathematical construct. Perhaps surprisingly, many authors in the field are
seemingly not aware of this process of reification, and this has led to the conflation of several
different kinds of boundaries in the literature: Markov blankets are characterized
alternatively as statistical boundaries, causal boundaries, spatial boundaries, epistemic
boundaries, and autopoietic boundaries, and each characterisation is treated as somehow
equivalent to (and interchangeable with) the others.
For instance, Allen and Friston (2018) write rather uncontroversially:
The boundary (e.g., between internal and external states of the system) can be
described as a Markov blanket. The blanket separates external (hidden) from the
internal states of an organism, where the blanket per se can be divided into sensory
(caused by external) and active (caused by internal) states. (p. 2474, our italics)
It is possible to read this passage in an entirely instrumentalist way. That the boundary ‘can
be described’ using a blanket suggests the system can be modeled as having a blanket. This
way of applying the Markov blanket is in line with the standard use of the notion introduced
by Pearl and explained in the first part of this paper. On the other hand, this instrumentalist
reading is put under pressure on the very next page:
In short, the very existence of a system depends upon conserving its boundary,
known technically as a Markov blanket, so that it remains distinguishable from its
environmentinto which it would otherwise dissipate. The computational
‘function’ of the organism is here fundamentally and inescapably bound up into the
kind of living being the organism is, and the kinds of neighbourhoods it must
inhabit. (p. 2475)
where the Markov blanket is exactly equated with the physical boundary in the world.
Markov blankets here function to distinguish a system from its environment, much in the
way a cell membrane does: the loss of a Markov blanket is equated with the loss of systemic
integrity. This removes the distinction between the model of the system and the system itself.
Map and territory have become indistinguishable, conflating what we are calling Friston
blankets with the original Pearl blankets.
Other works seem to maintain a slightly more neutral perspective. Clark (2017), for example,
carefully distinguishes between the causal process (the territory) and the Bayesian network
(the map):
Notice that the mere fact that some creature (a simple feed-forward robot, for
example) is not engaging in active online prediction error minimization in no way
renders the appeal to a Markov blanket unexplanatory with respect to that
creature. The discovery of a Markov blanket indicates the presence of some kind of
boundary responsible for those statistical independencies. The crucial thing to notice,
however, is that those boundaries are often both malleable (over time) and
multiple (at a given time), as we shall see. (p.4, our italics)
Here the discovery of a Markov blanket, perhaps only in our model of the system, serves to
indicate the presence of a physical boundary in the system itself. Clark seems to hold that
Markov blankets are discovered within the modelling domain, and that this discovery
indicates the presence of something important (“some kind of boundary”) in the target
domain. While it is perhaps relatively unobjectionable, this move seems to presuppose a
tight (and hence non-arbitrary) relation between the model and its target domain of an agent
and its environment, with potentially crucial consequences for our understanding of
cognitive systems (cf. Clark’s previous work on ‘cognitive extension’ (Clark and Chalmers
In a similar fashion, other works enforce the perspective that Markov blankets are a useful
indicator to look for when attempting to define the boundaries of a system of interest.
Kirchhoff et al. (2018), for example, write that:
A Markov blanket defines the boundaries of a system (e.g., a cell or a multi-cellular
organism) in a statistical sense.
They then go on to say, with much stronger implications, that
[A] teleological (Bayesian) interpretation of dynamical behaviour in terms of
optimization allows us to think about any system that possesses a Markov blanket
as some rudimentary (or possibly sophisticated) ‘agent’ that is optimizing
something; namely, the evidence for its own existence.
It is however never made explicit in the rest of their paper how to conceive specifically of a
‘boundary in a statistical sense’, perhaps indirectly relying on the inflated version of a
Markov blanket proposed in (Friston and Ao 2012; Friston 2013b).
Hohwy (2017) also equates the internal states identified by the Markov blanket formalism
with the agent:
The free energy agent maps onto the Markov blanket in the following way. The
internal, blanketed states constitute the model. The children of the model are the
active states that drive action through prediction error minimization in active
inference, and the sensory states are the parents of the model, driving inference. If
the system minimizes free energy or the long-term average prediction error
then the hidden causes beyond the blanket are inferred. (pp. 3-4)
For Hohwy, the Markov blanket is not just a statistical boundary, but also an epistemic one.
Because the external states are conditionally independent from the internal states (given the
Markov blanket), the agent needs to infer the value of the external states (the ‘hidden
causes’) based upon the information it is receiving ‘at’ its Markov blanket, i.e., the sensory
surface. Hohwy even goes as far as to define the philosophical position of epistemic
internalism in terms of a Markov blanket:
A better answer is provided by the notion of Markov blankets and self-evidencing
through approximation to Bayesian inference. Here there is a principled distinction
between the internal, known causes as they are inferred by the model and the
external, hidden causes on the other side of the Markov blanket. This seems a clear
way to define internalism as a view of the mind according to which perceptual and
cognitive processing all happen within the internal model, or, equivalently, within
the Markov blanket. This is then what non-internalist views must deny.
In other words, Markov blankets ‘epistemically seal-off’ agents from their environment. In
the same paper, Hohwy, like Allen and Friston above, seems to equate an agent’s physical
boundary with the Markov Blanket:
Crucially, self-evidencing means we can understand the formation of a well-
evidenced model, in terms of the existence of its Markov blanket: if the Markov
blanket breaks down, the model is destroyed (there literally ceases to be evidence
for its existence), and the agent disappears. (p.4)
Finally, in a similar vein Ramstead, Badcock, and Friston (2018) characterize Markov
blankets as at once statistical, epistemic, and systemic boundaries:
Markov blankets establish a conditional independence between internal and
external states that renders the inside open to the outside, but only in a conditional
sense (i.e., the internal states only ‘see’ the external states through the ‘veil’ of the
Markov blanket; [32,42]). [...] With these conditional independencies in place, we
now have a well-defined (statistical) separation between the internal and external
states of any system. A Markov blanket can be thought of as the surface of a cell, the
states of our sensory epithelia, or carefully chosen nodes of the World Wide Web
surrounding a particular province.
We can see now how Markov blankets have moved from a rather simple statistical tool used
for specifying a particular structure of conditional independence within abstract random
variables, to structures in the world that cause conditional independence, that separate an
organism from its environment, and that epistemically seal off agents from their
environment. These characterizations would sound bizarre to the average computer
scientist, about the only people aware of Markov blankets before 2012-2013, who are
familiar only with the original ‘Pearl blanket’ formulation. In the next section we will
consider the novel construct of a ‘Friston blanket’ in more detail, and highlight a number of
additional assumptions that are necessary for Markov blankets to do the kind of
philosophical work they have been proposed to do by the authors quoted above.
5 Friston blankets and sensorimotor loops in active inference
The more recent formulations of active inference, starting with (Friston and Ao 2012; Friston
2013b; Friston, Sengupta, and Auletta 2014), have effectively attempted to use Markov
blankets as a tool to characterize a specific form of conditional independence in systems that
can be understood as being composed of an agent and its environment (given the blanket,
c.f. (Hipolito et al. 2020)). In particular, this use of Markov blankets assumes that the
networks of interest can be meaningfully partitioned
into four distinct classes of variables,
mapping to constructs usually stipulated for the purpose of defining sensorimotor loops, i.e.,
the action-reaction cycles between an organism and its ecological niche (see for instance
(Tishby and Polani 2011; Ay and Zahedi 2014; Montúfar, Ghazi-Zahedi, and Ay 2015; Biehl
2017) for explicit connections to the Bayesian networks formalism). These four sets include,
as highlighted in section 4.2 (and here repeated as a reminder): external states, internal
states, active states, and sensory states.
Active inference assumes that the sequences of actions used to update observations form a
closed loop with perceptual inference, such that any action taken will have a subsequent
effect on perceptual inference, which in turn drives the generation of novel actions. When
taken together with the partitioning of the system into internal/external and
perceptual/active states, Friston’s conceptualization of agent-environment systems under
the FEP begins to resemble previous Bayesian treatments of sensorimotor loops. In
sensorimotor theory, external states refer to world variables that generate observations ,
sensed by a system whose internal states determine actions that can affect the state of
the environment in a causally circular closed loop (see the black arrows in Fig. 6). However,
unlike other Bayesian treatments of sensorimotor loops, Friston and colleagues also assume
a rather general set of connections (depicted in grey, see for instance (Friston 2013b; Friston
2019)), often eschewed by other authors (Tishby and Polani 2011; Ay and Zahedi 2014;
Montúfar, Ghazi-Zahedi, and Ay 2015). These connections include bidirectional effects
between sensors and actuators, ways in which sensors may influence external states, and
ways in which actuators may influence internal states. Considering all the connections (black
and grey alike) in Fig. 6 leads to the emergence of an ‘interactional asymmetry’ in the agent-
environment coupling (Barandiaran, Di Paolo, and Rohde 2009), due to the lack of directed
connections from internal states to sensors (meaning peripheral observations are not
directly affected by the internal state of a system) and from external states to actuators
At least at (nonequilibrium) steady-state, as this now appears to be one of the new assumptions packaged
with the more explicit definition of a Friston blanket (Friston, Da Costa, and Parr 2020; Friston, Fagerholm, et
al. 2020).
(meaning that states defined as external cannot directly affect actuators).
We will now turn
to the novel role played by Markov blankets or rather Friston blankets in determining
the sensorimotor boundaries of such systems, and highlight some ways in which this role
differs from the role typically played by the more traditional Pearl blankets in Bayesian
Figure 6: A sensorimotor loop. A diagram representing possible dependences between different components of
interest: sensors, internal states, actuators and external states. Notice that although this figure uses directed edges
to signify causal influence (Ay and Zahedi 2014), it is not strictly a Bayesian graph, as it depicts cyclic sets of
circular dependencies (some between pairs of components, and an overall loop including all components).
5.1 Friston blankets as sensorimotor boundaries
To bring the novel role played by Friston blankets into full view we will first apply the four-
way partitioning of random variables proposed by Friston and colleagues to the arbitrary
Bayesian network that we introduced in Fig. 2. We hope that this schematic example will
demonstrate that the partitioning cannot simply be applied to any graphical model without
first making some additional assumptions. For instance, here we will label a node, say 
from Fig. 2, as an ‘internal’ state (signified by a teal colour as in Fig. 6), which is conditionally
separated from all the remaining ‘external’ variables (in lavender) by a set of nodes
constituting its Friston blanket (see Fig. 7a). Following Friston’s proposal, the nodes in this
Friston blanket can be further separated into ‘sensory’ (magenta) and ‘active’ (blue) states,
the former corresponding to the parents of the internal state (i.e., node ) and the latter
including its children. Picking a different node, such , to be labelled ‘internal’ generates a
correspondingly different blanket (see Fig. 7b). It is clear here that the Friston blanket does
Notably, the diagram in Fig. 6 is not a Bayesian network, nor is it intended to approximate one. However,
even though the sensorimotor loop assumes cycles (due to both the bidirectional connections between
different components and the overall circular causality imposed by the very definition of such a loop) it can
be mapped to an acyclic directed graph by explicitly representing sets of nodes indexed by an appropriate
temporal notation that removes the apparent cyclicality, see for instance (Ay and Zahedi 2014), and (Friston,
Parr, and Vries 2017) more specifically for active inference.
not define what is inside and what is outside (or at least not without further assumptions),
but can rather only be identified once we have already made this choice (by labelling one
node as ‘internal’).
Figure 7: The Bayesian network described in Fig. 2, labelled with the Friston blanket notation. Example
sensorimotor blankets for different (internal) states,  and respectively, with labels removed for clarity.
External states in lavender, sensory states in magenta, internal states in teal ( in (a) and in (b)), active states
in blue, and putative co-parents in mustard. Notice that the partitions obtained here do not map to the separation
of internal states (hidden states Fig. 2) and world states (observations Fig. 2). This suggests that sensorimotor
blankets are Markov blankets applied under a specific set of assumptions that cannot be traced to standard uses
of Markov blankets in variational inference (Jordan et al. 1999; Fox and Roberts 2012).
The status of co-parents (labelled in mustard) in this model is also somewhat ambiguous, as
while they can influence active states, they presumably should not be counted as ‘internal’
to the Fristonian agent defined by the sensorimotor boundary (Friston 2013b; Friston, Levin,
et al. 2015; Hohwy and Michael 2017; Kirchhoff et al. 2018; Ramstead, Friston, and Hipólito
2020; Hipolito et al. 2020). On this account, Friston (2013b) writes that:
[...] the Markov blanket can itself be partitioned into two sets that are, and are not,
children of external states. We will refer to these as a surface or sensory states and
active states, respectively. (p. 2, emphasis added)
This means that co-parents should be seen as sensory or active states depending on whether
they are themselves dependent on external (i.e., not belonging to either blanket or internal)
states, as suggested explicitly, for instance, in Figure 1. of (Ramstead, Friston, and Hipólito
2020) or Figure 1. of (Hipolito et al. 2020). However, in other works, co-parents are
sometimes discussed implicitly, with Friston, Levin, et al. (2015) for example writing:
External states cause sensory states that influencebut are not influenced by
internal states, whereas internal states cause active states that influencebut are
not influenced byexternal states [...] (p. 3, emphasis added)
or Kirchhoff et al. (2018) observing:
The partitioning rule governing Markov blankets illustrates that external states
which are ‘hidden’ behind the Markov blanket—cause sensory states, which
influence, but are not themselves influenced by, internal states, while internal states
cause active states, which influence, but are not themselves influenced by, external
states [7]. (emphasis added)
In other cases, their role is on the other hand largely ignored. Demekas, Parr, and Friston
(2020) for example state
The parents of internal states are the sensory states that mediate the influence of
the outside world, and their children are the active states that mediate their
influence on the outside world. (p. 2, our italics)
Kirchhoff and Kiverstein (2019) similarly write that
The internal states of an agent can be shown to be formally equivalent to the
internal states of the model [...]. The children of the model can be mapped onto the
active states that cause actions [...]. The parents of the model are in turn a
formalisation of the sensory states that influence the dynamics of internal states so
as to further guide and inform action. (pp. 5-6, our italics, note that co-parents are
not classified or even considered in this paper)
while Palacios et al. (2020) describe active states as
Active states       states of action on the world (e.g.,
exocytosis of signalling molecules) that depend upon sensory and internal states.
(Table 1, again there is no mention of coparents, which could also be seen as active
states in the classification proposed by (Friston 2013b))
Another example comes from Hohwy and Michael (2017), who remark that
The key is that active states are the downstream effects of what we have called
deeply hidden endogenous states, and these endogenous states are the
downstream effects of sensory states.
It follows that the part of the model that is involved in active inference is the self:
this part of the model (the active states and their more deeply hidden causes) are
the very endogenous causes that can be inferred in perceptual inference, which
therefore become part of the self-model that in turn, in a dynamic downstream
manner, shape active inference.
In several of these works, co-parents are essentially glossed over in the definition of Friston
blankets, perhaps in line with other work on Bayesian networks for sensorimotor loops,
where no variables playing this role are usually mentioned (Tishby and Polani 2011; Ay and
Zahedi 2014; Montúfar, Ghazi-Zahedi, and Ay 2015; Biehl 2017). However, this omission is
not immediately obvious and often not stated explicitly (if active states are ‘caused’ only by
primary internal ones, we can only assume that the authors mean there’s no room for co-
parents), and most importantly sheds light on an important formal difference between
Friston blankets and Pearl blankets.
To bring this difference into full view, consider how the conditions which lead up to and
modulate the patellar reflex (or knee-jerk reaction) could be illustrated using a Bayesian
graph. This a common example of a mono-synaptic reflex arc in which a movement of the leg
can be caused by mechanically stretching the quadriceps leg muscle by striking it with a
small hammer. The stretch produces a sensory signal sent directly to motor neurons in the
spinal cord which, in turn, produce an efferent signal that triggers a contraction of the
quadriceps femoris muscle (or what is observed more familiarly as a jerking leg movement).
If we project these conditions onto the left arm of our sample network Fig. 2, we get
something like Fig. 8.
Figure 8: Conditions leading up to the knee-jerk reflex. On the left, a Bayesian network where and denote
the motor intentions of the doctor and the patient respectively. indicates the medical intervention with a
hammer, while stands for the cortical motor command sent to the which denotes the spinal neurons which are
directly responsible for causing the kicking movement . stands for an independent way of moving the patient’s
leg, e.g., by someone else kicking it. In the middle and on the right, the same network partitioned using a ‘naive’
Friston blanket with different choices of internal states, and respectively.
This simple network allows us to illustrate several problems with interpreting the co-
parents in Friston blankets. Take , i.e., the activation of the cortical motor neurons, as the
node of interest. As the graph makes clear, the activation of these neurons can be explained
away by either a strike of a medical hammer into the tendon () or a motor command from
the central nervous system (). This reflects the fact that the patellary reflex can also be
modulated by the motor intentions of the patient. Under one possible interpretation of
Friston blankets, the spinal signal which causes the movement would be an active state,
meaning that the motor command would be interpreted as an internal state of the patient.
However, this leads to a puzzle about the way in which we should interpret , which would
then fall into the Friston blanket of (see Fig. 8b), but stands for an external condition
influencing the spinal neurons . One could object that our example delineates internal states
in the wrong way, and that should be considered an internal state, as in Fig. 8c, while it is
the bodily movement that should be considered as an active state. Notice, however, that
this would not help in any way, since there is always some possible external intervention
that could lead to the same kind of bodily movement, and has exactly the same formal
properties as any putatively ‘internal’ cause of the movement. This example points back to
the problem of differentiating between effects produced by an agent (internal states) and
those brought about by nodes not constitutive of an agent (co-parents). The state of a node
is not simply the joint product of its co-parents, as completely separate causal chains (the
doctor’s intention vs. the patient’s intention) can produce the same outcome (i.e., spinal
neuron activation). Hence the partitioning of states into internal/external by means of a
Markov blanket does not necessarily equate with the boundary between agent and
environment found in sensorimotor loops. If Friston blankets are to serve the role of
demarcating this boundary, they will require some additional assumptions that cannot
simply be read off the original Markov blanket formalism (i.e., what we have been calling
Pearl blankets).
A similar problem arises when we turn to the sense in which some states can be called ‘active’
or ‘sensory’, given that they may not uniquely map onto the internal and external states of
the system (or the observed and unobserved variables in a graph) that we are interested in
studying. This issue can be demonstrated by the fact that under the Friston blanket
formulation, the location of a node in the graph layout is not sufficient to identify whether or
not it is an ’internal’ state in Friston’s sense (recall that we had to start by arbitrarily selecting
an internal state when formulating the graphs in Fig. 7). If we decide to interpret the
active/sensory distinction by exclusively following Friston’s use of the sensorimotor
analogy, we will soon realise that using a Friston blanket as if it was a Pearl blanket falls short
of explaining (and in some cases may be directly inconsistent with) the general identification
of active and sensory states in agent-environment coupled systems. In particular, states
described as ‘sensory’ are typically associated with observations made by an agent (Friston
explicates their role by talking about ‘the agent’s sensations’ (Friston 2013b)), but the formal
definition of a Markov blanket does not guarantee that, given some arbitrary partition like
in Fig. 2, these states will correspond with the observable variables in a Bayesian network.
In our example neither of the ‘sensory’ states of the two Markov blankets overlap with the
two nodes, and , that constitute observations in the network. Somewhat paradoxically,
a naive use of Friston’s understanding of the Markov blanket formalism results in treating
these observed variables as external states for the teal-coloured internal nodes used in our
example ( and ). Markov blankets provide the conceptual tools to deal with statistical
(in)dependence and causal mediation, but under the Friston blanket application they are
employed to account for epistemic mediation. This might seem intuitive for systems that we
consider to be sentient or epistemic agents anyway (such as a cell or a larger organism), but
becomes wildly implausible when applied more generally. Do the spinal neurons infer the
doctor’s intentions given the presence of the hammer? This would be a highly unusual kind
of agent, and it seems like applying the Markov blanket formalism to this case stretches it far
beyond its original purpose.
A further, and perhaps even more substantial, problem is that conditional independence is
itself model-relative. One possible objection to the patellar reflex network presented above
is that the conditions making up the graph are not fine grained enough, i.e., that the model is
too simple. After all, the hammer does not directly intervene on the neurons in the spinal
column, but rather on the tendon that causes the contraction of the muscle, which is
responsible for the afferent signal that is the true proximal cause of the activation of the
spinal motor neurons. However, just as it is difficult (and potentially ill-defined) to identify
the most proximate cause of the knee-jerk, it is difficult to identify the most proximate cause
and consequence of any internal state. Since the very distinction between sensory and active
states (the sensorimotor boundary) and external states (the rest of the world) hangs upon
the distinction between ‘most proximate cause’ and ‘causes further removed’, the
identifiability of such a cause is crucial. This point is well made by (Anderson 2017) who
writes on the identifiability of the proximal cause:
An obvious candidate answer would be that I have access only to the last link in the
causal chain; the links prior are increasingly distal. But I do not believe that
identifying our access with the cause most proximal to the brain can be made to
work, here, because I don’t see a way to avoid the path that leads to our access being
restricted to the chemicals at the nearest synapse, or the ions at the last gate. There
is always a cause even “closer” to the brain than the world next to the retina or
A possible solution to this granularity problem would be to give up entirely on the idea of
having some ‘privileged’ sense in which observations can be defined in the first place, i.e.,
each variable can become an observation, or a measurement, for any other variable (Friston
2019; Palacios et al. 2020). For example, the way in which some states count as ‘sensory’
could be understood in a way that has little to do with everyday agentic language, but rather
is more metaphorical, analogous to the way that for a physicist, electrons might be
‘observing’ protons and ‘acting’ by rotating around a nucleus or jumping energy levels. This
interpretation would imply that the distinction between sensory/active states should be
understood along the lines of a ‘causal’ interpretation in which some states that are causally
influenced by variables outside of the blanket are considered ‘reactive’, while active states
are just those that exert causal influences on the variables external to the blanket. Although
Friston and his collaborators have recently come to propose this non-literal interpretation
(Friston 2019; Hipolito et al. 2020), it is important to note that interpreting their notion of a
Markov blanket in this causal way does not solve all the ambiguities brought on by their
refinements. It may even introduce new problems, especially for the study of cognitive
agents, and is unlikely to be popular among early adopters of the approach who hoped for a
more literal interpretation of Friston blankets as sensorimotor boundaries. If the formalism
can be legitimately applied as widely as it is now suggested, it is no longer clear that it will
have anything interesting to say about cognition in particular, as the ‘literalists’ might be
hoping for (see (Baltieri, Buckley, and Bruineberg 2020) for a similar discussion).
While we do not want to try and solve all of these issues at this stage, we do think that they
point to the importance of recognising that the notion of a Friston blanket as employed in
the active inference literature is intended to carry out a very different role from the standard
definition of a Pearl blanket used in the formal modelling literature. The open question here
is whether Bayesian networks and Markov blankets are really the right kinds of conceptual
tools to delineate the sensorimotor boundaries of agents and living organism, or whether
there are really two different kinds of project going on here, each of which deserves its own
set of formal tools and assumptions.
6 Two (very) different tools for two (very) different projects
So far, we have presented the conceptual journey on which Markov blankets have been
taken. They started out as an auxiliary construct in the probabilistic inference literature
(Pearl blankets), and have ended up at as a tool to distinguish agents from their environment
(Friston blankets). The analysis above already showed the deep differences between Pearl
blankets and Friston blankets, both in terms of their more technical assumptions and of the
general aims of these two constructs. However, in the literature on the FEP and active
inference, the two have not yet really been distinguished. Even in very recent work there is
an obvious conflation of Pearl and Friston blankets, using the former to try and define, justify,
or explain the latter. For example, see the figures presented in (Kirchhoff et al. 2018;
Ramstead, Friston, and Hipólito 2020; Sims 2020) and (Hipolito et al. 2020), where Bayesian
networks are used to describe what we would call Friston blankets. However, there are a
series of extra assumptions that are necessary to move from Pearl blankets to Friston
blankets, and these are rarely (if ever) explicitly stated or argued for. Some of these
assumptions were implicitly touched upon in (Friston 2013b), where Friston blankets were
defined by looking at the adjacency matrix of a set of particles simulated via a (random)
dynamical system, which is assumed to be ergodic after it heuristically appeared to have
reached a steady-state, and crucially, after arbitrarily assuming the number of clusters of
particles that ought to be forming the ‘internal states’. More recently, after Biehl, Pollock, and
Kanai (2020) questioned some of the technical assumptions underlying the use of Markov
blankets by Friston, the idea of Friston blankets being understood as a distinct construct has
gained traction in the literature.
In a recent paper, Friston blankets are formalised in terms of constraints on sparse coupling
of dynamics (or with arbitrary thresholds for non-sparse couplings), and identified via the
non-zero components of the Hessian of the non-equilibrium steady state density represented
in Langevin form using Ao’s decomposition (Friston, Da Costa, and Parr 2020; Friston,
Fagerholm, et al. 2020) taking the construct far away from its Pearl blanket origins.
Importantly, these points serve to highlight a pervasive confusion with the use of the Friston
blanket construct as adopted so far (i.e., at least up until work such as (Friston, Da Costa, and
Parr 2020)). For example, Kirchhoff and Kiverstein (2019) simply assume that the Markov
blanket construct can be transposed from the formal to the physical domain, writing:
The notion of a Markov blanket is taken from the literature on causal Bayesian
networks (Pearl 1998). Transposed to the realm of living systems, the Markov
blanket allows for a statistical partitioning of internal states (e.g., neuronal states)
from external states (e.g., environmental states) via a third set of states: active and
sensory states. The Markov blanket formalism can be used to define a boundary for
living systems that both segregates internal from external states and couples them
through active and sensory states. (p. 2, our italics)
Such a transposition is not at all straightforward, and the phrasing ‘transposed to the realm
of living systems’ covers up a great explanatory leap from the merely formal Pearl blanket
construct to the metahpysically-laden Friston blanket construct. It remains unclear what
additional assumptions are being made in order to support the claim that the Markov blanket
formalism can settle philosophically relevant questions. Another example that illustrates the
ambitiousness of the philosophical prospects of the Friston blanket construct is again
provided by Kirchhoff and Kiverstein (2019):
We employ the Markov blanket formalism to propose precise criteria for
demarcating the boundaries of the mind that unlike other rival candidates for
“marks of the cognitive” avoids begging the question in the extended mind debate.
Based on what we have presented above however, the philosophical validity of using Friston
blankets to draw sensorimotor boundaries cannot simply be assumed from the formal
credibility of the original Pearl blanket construct. In order to evaluate the plausibility of
using the Markov blanket formalism as a model of life and mind, we will now take a brief
foray into some of the philosophical literature on models and modelling.
6.1 Models and modelling
As a general rule, one should not mistake the map described by a model for the territory it is
describing: a model of the sun is not itself hot, a model of an organism is not itself alive, and
so on. Scientific models (‘maps’) are typically understood as representations of some part of
the world (‘territory’) that we can use to better understand something about that part of the
world. No model is an entirely precise representation of its target system (if that were true
then the model would cease to be any more useful than an exact replica of its target domain,
reminiscent of Borge’s one-to-one map of the world (Borges 1946)). It always involves some
degree of abstraction or approximation, ideally in a way that draws attention to or otherwise
clarifies some relevant aspect of what is being modelled. Moreover, a scientific model is
always used by some researcher or research community for a specific purpose (Weisberg
2007, 2013), and only has meaning relative to that research context. The form a model takes
is dependent on our own epistemic capacities. As we saw in section 3.1, Bayesian networks
(and more generally all graphical models in the statistic and machine learning literature) are
a visual way of representing probability distributions that makes conditional dependencies
easily visible and intuitive. Nothing more, nothing less. The only formal difference between,
e.g., equation (17) and Fig. 1, is the mode of presentation, i.e., they are two maps of the same
territory. All of this is relatively uncontroversial, although of course there are many more
fine-grained disputes about the exact nature and function of scientific models (Downes
Zooming back in on Bayesian networks (for example Fig. 2), the hidden states are scientific
unobservables. There is a broader debate in philosophy of science about whether it is
justified to believe in the reality of scientific unobservables (‘realism’) or whether they are
auxiliary constructs helpful for explaining scientific observables (‘instrumentalism’).
Without going into this debate in detail, we think that the answer to this question is partially
dependent on the modeling tools used. So what about Bayesian networks? In general,
Bayesian networks are not tied to a particular level of abstraction: their power lies in the fact
that they remain agnostic about the relationship between the random variables they
represent. This method works well for complex phenomena in the medical and social
sciences (see for instance (Pourret, Naı̈m, and Marcot 2008)), where no clear causal
pathways are available and multiple levels need to be integrated into one model. In the knee-
jerk example introduced above Fig. 8, we drew a direct arrow between the doctor’s intention
and the hammer hitting the patient’s knee, disguising lots of (for our purposes) unnecessary
fine-grained detail.
What then decides what makes a good Bayesian network? The dominant assumption in the
literature is that the best model is one that accounts for the data in the most parsimonious
way ((Stephan et al. 2009; Friston, FitzGerald, et al. 2017)). This intuition can be formalised
via a process of model comparison, using different criteria, for example the Akaike
information criterion (AIC), the Bayesian information criterion (BIC), or, as we presented in
Section 1, variational free energy (via the maximisation of model evidence, equivalent to the
minimisation of surprisal). In the case of variational free energy, one can then take into
account a trade-off between the complexity of a model and the accuracy with which it is able
to predict the data, or observations. When minimizing free energy using a range of different
models, the one with the lowest free energy is thus taken to be the one that accounts for the
data in the most parsimonious way (cf. the Occam factor (MacKay 2003; Bishop 2006; Friston
2010; Daunizeau 2017).
This means that the graphical model itself is already an abstraction, reflecting (at least
partially) a choice by the modeller about which observations to include. When used to
describe neural behaviour, such models will typically cluster groups of neurons into single
nodes, but even if each node represented a single neuron it could still be further decomposed,
revealing the internal structure of each neuron, and so on (Friston, Fagerholm, et al. 2020;
Hipolito et al. 2020). However, as long as the data does not necessitate such complexity, a
model in which groups of neurons are clustered will be selected. In other words, the
epistemic aim (even for the models used in the context of active inference) is not to arrive at
a complete model of the world, but rather to obtain the most parsimonious model that
captures the relevant relations (Baltieri and Buckley 2019).
What does this imply for the philosophical prospects of the Friston blanket construct serving
as a sensorimotor boundary? Simply put, where Friston blankets are located in a model
depends (at least partially) on modeling choices, i.e., Friston blankets cannot simply be
‘detected’ in some objective way and then used to determine the boundary of a system. This
can be easily seen by the fact that Markov blankets are defined only in relation to a set of
conditional (in)dependencies, or the equivalent graphical models (in either static systems
(Pearl 1988), or dynamic regimes at steady-state (Friston, Da Costa, and Parr 2020)). The
choice of a particular graphical model is then usually enforced by Bayesian model selection,
which is in turn dependent on the data used (e.g., one cannot hope to model the firing activity
of neurons, given as data fMRI recordings that already measure only at the grain of voxels).
These considerations point, in our opinion, to a strongly instrumentalist understanding of
Bayesian networks, and hence of Markov blankets, which would not justify the kinds of
strong philosophical conclusions drawn by some from the idea of a Friston blanket (see e.g.,
(Hohwy 2016; Friston, Wiese, and Hobson 2020)).
There is a second conceptual issue latent in the current discussion. We started our paper
with the parallel between perceptual inference and scientific inference. Both use a
previously learned model and a set of observations to infer the causal structure of the
unobserved outside world. This parallel puts (model-based) cognitive neuroscience in a
rather special place: it makes models of how animals model their environment. A cognitive
neuroscientist uses both behavioral and neural data to infer the most likely model that the
agent’s brain implements. For example, Parr et al. (2019) use both MEG and eye-tracking to
disambiguate a number of causal models for active vision. These putative models correspond
in a fairly straightforward way to a neural network and make concrete predictions about
both neural dynamics as well as oculomotor behavior. By scoring these models based on
their accuracy in predicting neural dynamics and oculomotor behavior, weighted by the
complexity of those models the most ‘likely’ causal model is selected (i.e., the one that best
explain the data in the most parsimonious way). In other words, the agent implements a
causal model of its environment, and the scientist uses another causal model to infer which
particular model the agent implements.
There is nothing wrong with this doubling up of modeling relations as long as one is
conceptually careful: one needs to distinguish between properties of the environment,
properties of the agent’s model of the environment and properties of the scientist’s model of
the agent modelling its environment. Considering these different modeling relations
provides a new lens to analyse the different between Pearl and Friston blankets: Pearl
blankets are boundaries drawn on the scientist’s map of the agent-environment system (in
the form of a Bayesian network). The question is whether Friston blankets are similarly
drawn on the scientist’s map or whether they are boundaries in the agent-environment
system itself. The former option is rather uncontroversial: it makes Friston blankets (with
the exception of the issue of the co-parents of children, and a few more technical constraints
highlighted in (Friston, Da Costa, and Parr 2020)) closely akin to Pearl blankets, but unlikely
to be of much philosophical interest (at least when it comes to the question of boundaries
between agents and environments). The latter option might do interesting philosophical
work, but requires a number of substantial metaphysical commitments, and cannot simply
be assumed to follow from the previous success and formal validity of Pearl blankets. We
will further describe how these two projects could be developed in the next section.
6.2 Inference with a model and inference within a model
If Pearl blankets play a fundamentally instrumental role in assigning probabilities to
different outcomes, based on different modelling choices, spatial and temporal coarse-
grainings, etc. can the same be said about Friston blankets? In an ambitious projects started
perhaps with (Friston 2013b) and currently under development with recent works such as
(Friston 2019; Friston, Wiese, and Hobson 2020), what seems to emerge is a desire to use
Friston blankets as the basis for a philosophical distinction between agent and environment,
other core constructs in the life and the social sciences (Ramstead, Badcock, and Friston
2018; Veissière et al. 2020), and perhaps even a metaphysical characterization of what it is
to be any kind of system (Friston 2019). This suggests that Friston blankets are to be
understood as something more than a merely instrumental statistical construct, i.e., as an
actual thing out there in the world that can be identified and theorised about. We would like
now to highlight what we take to be one of the fundamental differences between the notions
of Friston and Pearl blankets. In the previous sections we drew attention to the fact that the
notion of a Friston blanket rests on the assumption that there is some kind of agent at the
centre of the blanket (i.e., the internal states ought to constitute an agent, see e.g., (Friston
2013b; Hohwy 2016; Kirchhoff et al. 2018)), and that this agent is separated by some kind of
(statistical) boundary from the rest of its environment (i.e., external states of the graphical
model are now equated to a real environment). As we have seen, this talk in terms of agents
and environment is not present in, and thus not justified by, the original notion of a Pearl
blanket. The distinction between the two blanket constructs (Pearl and Friston) can then be
easily identified once we look at who appears to be performing the inference and what
system that inference is performed on, when each kind of blanket is deployed. To do so,
however, it is important that we first distinguish among potentially four kinds of (Bayesian)
networks that are sometimes used in the literature to describe processes of (approximate)
1. a generative process  capturing the actual causal structure of the
environment where hidden states generate observations .
2. a generative model  representing our best (epistemic) understanding 
of how some given data/observations are generated from real hidden states  for a
given environment.
3. a posterior density  encoding the inversion scheme on a generative model,
i.e., the most likely state of the environment given the observations, using either exact
or approximate methods.
4. a variational or recognition density  (for variational inference schemes) used to
determine the best approximation of the (usually uncomputable) posterior
  given a series of constraints on .
These four kinds of network can then be combined in different ways, leading to two quite
different kinds of research program, which we refer to as, respectively, ‘inference with a
model’ and ‘inference within a model’. We will now discuss each in turn.
6.2.1 Inference with a model
As mentioned in the section above on models, the reason why model-based inference is used
in science is because the causal structure of the world is not directly given to us. Typically,
we want to know the state of some aspect of the world  while only having access to some
observations . For example, using fMRI we can observe the change in the magnetic field
surrounding the head due to blood-oxygen-level dependent (BOLD) contrast, and based on
this activity wish to study some cognitive processes. Applying our terminology to this
example, we can say that unobservable cognitive processes  cause (in an indirect and
complex way) observable changes in the magnetic field . The process (1)  by
which the environment (including cognitive processes) generates observations is assumed
to exist, but is beyond our reach. Our current assumptions about this generative process are
represented in the generative model (2). The network drawn in Fig. 2 could be a schematic
version of such a model. One can think of node as the observed magnetic field and think
of node  as representing the cognitive process we want to investigate. The cognitive
process is then intermeshed in a causal web that ultimately generates our observations, and
thus allows us to build a model inferring its structure.
It is important to note that in order to to effectively perform inference on this network, we
would have to draw a second network that reverses the information flow to represent the
calculation of a posterior      as the most likely
explanation of a given set of observations (see for example Figure 8.37 and following figures
in (Bishop 2006)). In such a case, we would talk of the posterior  (3) as an
‘inverse’ model, in the sense that it is derived from an inversion of the generative model (2).
Finally, as we already explained in the first sections of this manuscript, should this inference
problem prove to be too complex for calculating the posterior directly, we could instead use
an approximation by introducing a variational or recognition density . In this case (4),
yet another Bayesian network, could be drawn to describe , e.g., using the mean-field
assumption as described in section 2.2, so that methods like variational message passing
(Bishop 2006) could be used to approximate the posterior  .
In all of the above cases, Pearl blankets simply capture relations of conditional independence
between variables in the model, regardless of the different roles that random variables play
in each network. In other words, the above cases say nothing about what any postulated
Pearl blankets might correspond to in the external world, especially in scenarios where the
generative model and the generative process are not assumed to be identical. In this sense,
Pearl blankets are simply a tool for the experimenter, who sits outside of some system of
interest, trying to peer into that system by performing inference with a (perhaps graphical)
model. Generative models should here be seen as epistemic tools to formulate predictions
about the world that are useful to the experimenter, rather than ontological statements
about some objective truth to be found out there in the world. Nobody using Pearl blankets
to assist them in performing variational Bayesian inference believes that those blankets
correspond to literal boundaries in the systems that they are studying.
As mentioned above, an important motivation for the free-energy principle is the parallel
between scientific inference and perceptual inference. Like the scientist, the agent wants to
know and control the state of some aspect of the world (1)  while only having access to
some observations . The agent can solve this problem via a generative model (2) of its
environment. The agent uses (or appears to use) variational inference to obtain a recognition
density (4) which approximates the posterior density (3). Like in scientific inference, Pearl
blankets might appear in the agent’s own model, but again they would be a tool used by the
agent, not a literal feature of either the agent or its environment (or indeed, the boundary
between the two).
In model-based cognitive neuroscience, the two approaches are stacked together. The
explanatory project is to infer what generative model an agent is using to infer the states of
its environment. This, to us, seems to be one of the strongest empirical applications of the
FEP, as can be seen in the influential work of the likes of (Parr et al. 2019; Adams et al. 2013;
Pezzulo, Rigoli, and Friston 2018) and reflects a more general explanatory strategy in
cognitive neuroscience (Lee and Mumford 2003). As an instrumental modeling strategy, we
take no issue with treating the brain as functioning analogous to a scientist, but as a more
realist claim it has a number of problems. Most notably, the FEP denies the distinction
between scientist and model: an agent does not have a model of its environment that it uses
using to perform inference, but rather an agent is a model of its environment (Friston 2013a;
Bruineberg, Kiverstein, and Rietveld 2018; Friston 2019; Baltieri and Buckley 2019). There
is no separate entity that uses a generative model to perform inference, instead the agent
performs (or appears to perform) inference, and it is at once scientist and model. It might be
that for this reason some theorists have turned away from ‘inference with a model’ towards
a different (and perhaps even more ambitious) explanatory project, which we will call
‘inference within a model’.
6.2.2 Inference within a model.
The ‘primordial soup simulation’ that we presented in 4.2 presents a very different research
direction for the FEP and active inference framework. This simulation starts out with a soup
of coupled particles and aims to show how a distinction between ‘agent’ and ‘environment’
emerges naturally as the dynamics of the system reach equilibrium. Here we will use the
example of Fig. 7, where the lavender nodes represent external states, coupled to a set of
internal nodes which, under the Friston blanket interpretation, is claimed to imply that the
system represented by these internal nodes is an agent performing inference on the external
states (Friston 2013b; Friston 2019). Effectively, this Bayesian network integrates and
characterises different processes that are usually (graphically) represented separately: (1)
a generative process  in the form of external states producing sensory states, and
(4) a variational density 
or (3) an exact posterior (in the first part of (Friston 2019))
encoded in a set of internal states that ‘use’ sensory information to ‘produce’ actions affecting
the environment in the future. Because of the presence of both these blocks within the same
network, one gets to describe both data generation (the evolution of the generative process
 as a stochastic process), and the (active) inversion scheme (based on the
approximate posterior  and the recognition dynamics that optimise its sufficient
statistics (Friston 2013b; Friston 2019)) in a single graphical model, where the system
performing inference is postulated given a set of initial assumptions (weakly mixing random
dynamical systems, sparse dynamical coupling, and the ensuing Friston blanket,
nonequilibrium steady-state) and explicitly drawn within a graphical Bayesian network
(Friston 2019; Friston, Da Costa, and Parr 2020).
Here the constructed Friston blanket is not one that describes which variables are
probabilistically shielded from which other variables, for example in the computation of
mean-field averages for an internal node, but is rather posited as the statistical boundary
that conditionally separates an assumed or postulated set of internal states (via sparsity
constraints or arbitrary thresholding) from another set of states postulated or assumed to
be external to the ‘agent’. These two sets of states are then interpreted as an agent-
environment coupled system given an arbitrary ‘context-dependent’ partition that
determines, for example, how the Friston blanket for a whole cell characterises something
qualitatively different from the Friston blanket for half of the same cell. Thus, the main
Under a mean-field formulation (Bishop 2006), a variational Gaussian approximation (Opper and
Archambeau 2009; Friston 2013b) or a mix of the two (Friston, Trujillo-Barreto, and Daunizeau 2008))
feature of Friston blankets, and what differentiates them from Pearl blankets, is that they are
not simply used by a scientist to perform inference but rather explain inference itself, by
explaining the existence of a system distinct from its environment (i.e., an agent). In other
words, the explanatory project here is to start with a Bayesian network or a random
dynamical system and ask questions like: ‘under what conditions does one part of the model
(the agent) come to infer the states of another part of the model (the environment)’.
It should be clear now that the philosophical bounty here is potentially large. The project of
‘inference within a model’ is to define in mathematical terms what it is to be a system
(Ramstead, Badcock, and Friston 2018), where to draw the boundary between agent and
environment (Kirchhoff and Kiverstein 2019), what it is to be a sentient and conscious being
(Friston, Wiese, and Hobson 2020), and what is required for an agent to have a
representation (Ramstead, Friston, and Hipólito 2020), all hotly contested philosophical
questions. Clearly, researchers working with the FEP tradition want to draw metaphysical
conclusions out of the Friston blanket construct. But metaphysical consequences require
metaphysical premises, and cannot simply be read off the formal model itself (i.e., from
previous work on Pearl blankets). One obvious consideration here is that ‘inference within a
model’ is performed on an idealized mathematical structure, either a random dynamical
systems or a Bayesian network, not the physical world itself. The question is then whether
the mathematical structures posited by the FEP are merely a map of self-organizing systems
(in which case the non-metaphysical Pearl blanket construct), or are themselves the
territory. In the latter case the FEP framework might constitute something like an
‘information ontology’, perhaps an appealing picture for some but certainly not something
that comes without any further metaphysical commitments. Menary and Gillett (2020)
suggest something like this when they write "Our point here in drawing these connections is
to highlight the strong Platonist and Pythagorean metaphysical attitudes that are implicit in
Ramstead and colleagues’ formal ontology approach" (p. 24). Such an approach could be
valid and interesting, but it would certainly not be metaphysically innocent!
Another route to go would be to see the ‘primordial soup’ simulation as a mathematical
formalization demonstrating the emergence of a sensorimotor boundary (Friston blanket)
in a highly idealized domain. In a similar vein, concepts in theoretical biology have been
formalized in the idealized domain of the Game of Life (Beer 2004, 2014, 2020). This might
be an interesting way of modelling emergent processes in complex systems, but it would not
support any metaphysical claims about Friston blankets. We will not pursue this idea any
further here, but offer it as a more modest, perhaps ‘instrumental’ interpretation that some
proponents of the FEP and active inference might be inclined to adopt in order to avoid any
stronger commitments.
Perhaps the clearest expression of the metaphysical commitments implied by the use of
Friston blankets is provided by Ramstead et al. (2019), who write:
The claims we are making about the boundaries of cognitive systems are
ontological. We are using a mathematical formalism to answer questions that are
traditionally those of the discipline of ontology, but crucially, we are not deciding
any of the ontological questions in an a priori manner. The Markov blankets are a
result of the system’s dynamics. In a sense, we are letting the biological systems
carve out their own boundaries in applying this formalism. Hence, we are
endorsing a dynamic and self-organising ontology of systemic boundaries. (p. 3)
where the claim seems to be that the answers to these ontological questions can be simply
assumed by ‘doing the maths’ and then checking where the Markov blanket lies. If by Markov
blanket they mean here the traditional Pearl blanket, then something like this might be
possible, but it will not have the desired ontological consequences. If, however, they mean
Friston blanket (and we assume that they do), then the ontological consequences might
follow, but not without further metaphysical premises. This is this is the dilemma faced by
the proponent of Friston blankets within the active inference framework. Later in the same
paper, they write:
By placing our Markov blanket around Homo sapiens, we necessarily encapsulate
all of the dynamic, lower-level processes responsible for producing every
phenotype, while imposing a clear upper limit on the complex adaptive system
under scrutiny. Although the human Markov blanket is nested within the broader
dynamics of other global Markov blankets that extend out into the universe, these
lie beyond the limits of the system that this ecobiopsychosocial framework
endeavours to explain. (p. 13)
Here the Markov blankets are ‘placed’ instead of being a ‘result of the system’s dynamics’,
and can indeed be placed at a multitude of different points, resulting in a nested hierarchy of
blankets from the smallest cell out into the widest reaches of the universe. The picture is one
of ‘Markov blankets all the way down’, but if this is the case then the boundaries demarcated
by such blankets can no longer do any interesting work. If blankets can be ‘placed’ so as to
cut any of the joints of any modeled system, then they are effectively nothing more than a
purely instrumental construct, useful perhaps for studying these systems, but not to be
understood as anything ‘real’ out there in the world. To be clear, we think that Markov
blankets understood in this instrumental Pearl blanket sense are extremely valuable tools,
we just don’t think that they license any of the metaphysical claims made by those using
Friston blankets to demarcate the boundaries of systems.
In sum, the main difference between the concepts of Pearl blankets and Friston blankets is
that while the former describes a property of statistical models that can be used for different
purposes ((1) to (4) above), the latter is a particular interpretation of that property for the
purpose of studying agent-environment systems (broadly construed, but based on drawing
networks where a generative model (2) precisely equates with the generative process (1)
and a second entity, statistically separated by a Friston blanket, is drawn that performs exact
(3) or approximate (4) inference within the model). Any interpretation of a classical Pearl
blanket beyond the statistical one depends on the researchers’ goals, interests, and
metaphysical assumptions. In the case of Friston blankets, the interpretation is already in
some important ways fixed, because the very concept of a Friston blanket depends on the
assumption that the system of interest is an agent who is in some way bounded from its
external milieu, and performs activities that can be conceptualised as inferences about the
state of that milieu. Classical Pearl blankets are formal tools that are used to make inferences
about some system, using a model of that system, while Friston blankets assign a
sensorimotor interpretation of the model and assume that the system of interest is itself
performing inferences. Conflating these two notions, or assuming that the latter follows
uncontroversially from the former, can too easily lead one to draw some unwarranted
philosophical conclusions, and for this reason we encourage caution and conceptual hygiene
when using Markov blankets of either kind.
7 Conclusion
The free energy principle and active inference framework have recently gained traction in
the fields of neuroscience and biology due to their ambitious claims regarding a definition of
a unifying principle that ought to characterise living and cognitive systems, and their
functions and behaviours (Friston 2010; Friston, FitzGerald, et al. 2017; Hesp et al. 2019;
Friston 2019; Kuchling et al. 2020). Under the umbrella term of predictive processing, they
have also gained popularity in philosophy of mind and cognitive science, where they appear
to play the role of a new thinking tool that could settle centuries-long disputes in the most
disparate areas of mind and life (Clark 2013, 2015, 2020; Hohwy 2013; Friston, Wiese, and
Hobson 2020). At the same time, different parts of the FEP and its associated process
theories, in the form of prediction error minimization, hierarchical predictive coding or
active inference, have raised some important, and in some cases yet-to-be-answered,
scientific and philosophical questions. Some of them have to do with the capacity of the
framework to account for traditional folk psychological distinctions between belief and
desire (see e.g., (Klein 2018; Yon, Heyes, and Press 2020)), although its defenders have
argued that it can either account for desire in a novel way (Wilkinson et al. 2019), or that it
is a mistake to expect neuroscientific theories to account for folk psychological constructs at
all (Dewhurst 2017). Another, very common, kind of critique is that the framework either
does not, or cannot even in principle, enjoy any empirical support, and should at best be
considered a theoretical redescription of our existing data (see e.g., (Colombo, Elkin, and
Hartmann 2018; Liwtin and Miłkowski 2020; Cao 2020)). Yet another kind of critique argues
that there is no significant connection between the (a priori) FEP formalism on the one hand,
and the (empirical) process theories it is intended to support on the other ((Colombo and
Wright 2018; Williams 2020), or that it presents a false equivocation between probability
and adaptive value (Colombo 2020). Finally, Andrews (2020) and van Es (2020) have
recently argued against a realist interpretation of the mathematical models described by free
energy principle, which are claimed to be better interpreted instrumentally. Along the same
lines, Baltieri, Buckley, and Bruineberg (2020) provided a worked-out example of this
instrumentalist view, where an engine coupled to a Watt (centrifugal) governor is shown to
perform active inference as an example of ‘pan-(active-)inferentionalism’, asking what can
possibly be gained by thinking of the behaviour of a coupled engine-mechanical governor
system in terms of perception-action loops under the banner of free energy minimisation.
These last three works come closest, at least in spirit, to the topics discussed in this paper,
which have to do with a disconnect between the formal properties of Markov blankets and
the way these are deployed in the arguments used to support the metaphysical claims made
by free energy principle. More specifically, in this paper we have addressed some possible
concerns regarding the use of Markov blankets (Pearl 1988) within the free energy principle.
After having been initially adopted in the context of (variational) inference problems, as a
tool to simplify the calculations of approximate posteriors by taking advantage of relations
of conditional independence (Bishop 2006; Murphy 2012), in the context of the free energy
principle these tools have ultimately been used to perform work that claims to clarify
boundaries of the mind (Hohwy 2017; Clark 2017; Kirchhoff and Kiverstein 2019), of living
(Friston 2013b; Kirchhoff 2018; Kirchhoff et al. 2018) and even social systems (Ramstead,
Badcock, and Friston 2018; Veissière et al. 2020). What is interesting here is that mere
(statistical) divisions made within a Bayesian network representing relations of
independence of a generative model define what it is to be a system. In other words, the
Bayesian network takes precedence over the physical world that it is supposed to model. In
some passages it even appears that the world is taken to be a Bayesian network, with the
Markov blankets defining what it is to be a ‘thing’ (Friston 2013b; Kirchhoff et al. 2018;
Friston 2019; Hipolito et al. 2020). This then brought us to some possible issues, namely the
question of whether Bayesian networks are merely an instrumental modelling tool for the
free energy principle framework and consequent adoptions in cognitive science and
philosophy of mind, or whether the framework presupposes some kind of more fundamental
Bayesian graphical ontology.
As we mentioned in section 6, all of this points towards a fundamental dilemma for anyone
wanting to use Markov blankets to make substantial philosophical claims about biological
and cognitive systems, which is what we take proponents of free energy principle to be
wanting to do. On the one hand, Markov blankets can be used instrumentally in their original
Pearl blanket guise, as a formal mathematical construct for inference on a generative model,
for example in the form of a Bayesian network. This usage is philosophically innocent, but
cannot, without further assumptions that need to be stated explicitly, justify the kinds of
conclusions that it is sometimes used for in the literature based on the FEP (Hohwy 2017;
Kirchhoff et al. 2018; Kirchhoff and Kiverstein 2019). On the other hand, Markov blankets
can be used in a more realist fashion, which we have called Friston blankets, as an ontological
construct demarcating actual boundaries ‘out there in the world’, such that inferential
processes also become also something to be see seen ‘out there in the (physical) world’
(Friston 2019). This is surely a more exciting application of the Markov blanket formalism,
but it cannot be simply or innocently ‘read off the mathematics’ of the more standard usage
advocated in statistics and machine learning (Pearl 1988), and requires some additional
technical (Friston 2019; Biehl, Pollock, and Kanai 2020; Friston, Da Costa, and Parr 2020)
and philosophical (Ramstead, Badcock, and Friston 2018; Friston, Wiese, and Hobson 2020;
Hipolito et al. 2020) assumptions, that may in the end be doing all of the interesting work
The difference between inference with and inference within a model, here roughly
corresponding to the use of Pearl and Friston blankets, shows why the potential payoff of the
latter construct is much larger than the former. In inference with a model, the graphical
model is an epistemic tool for a scientist to perform inference. In inference within a model
the scientist disappears from the scene, becoming a mere spectator of the unravelling
inference show before their eyes. Here the (Friston) blanket specifies the anatomy of
inference: it is a formalization of what it is to be a cognising living system, and defines the
boundary between this system and its environment.
Ultimately, the considerations presented in this paper leaves the free energy theorist with a
dilemma. One can accept a rather innocent conception of Markov blankets that merely
licenses an instrumental interpretation, one under which any system can be treated ‘as-if’ it
had a blanket, and which is admittedly scientifically useful but that has not lead to much
more philosophically interesting conclusions so far; or, one can import a number of stronger
metaphysical assumptions about the mathematical structure of reality to support a realist
reading where the blanket becomes a literal boundary between agent and world. At any rate,
such a strong realist reading cannot be justified by ‘just following from the mathematics’, but
needs to be independently argued for, and such an argument has not yet been offered.
The authors would like to thank Mel Andrews, Martin Biehl, Daniel Dennett, Richard Menary,
Fernando Rosas, Filippo Torresan, Nina Poth and other members of Tobias Schlicht’s
research group for insightful discussions and timely feedback on previous versions of the
manuscript. MB is a JSPS International Research Fellow supported by a JSPS Grant-in-Aid for
Scientific Research (No. 19F19809). KD’s work is funded by the Volkswagen Stiftung grant
no. 87 105.
Adams, Rick A, Klaas Stephan, Harriet Brown, Christopher Frith, and Karl J Friston. 2013.
“The Computational Anatomy of Psychosis.” Frontiers in Psychiatry 4: 47.
Allen, Micah, and Karl J Friston. 2018. “From Cognitivism to Autopoiesis: Towards a
Computational Framework for the Embodied Mind.” Synthese 195 (6): 245982.
Anderson, Michael L. 2017. “Of Bayes and Bullets: An Embodied, Situated, Targeting-Based
Account of Predictive Processing.” In In Philosophy and Predictive Processing: 3, edited by
Wanja Wiese and Thomas K Metzinger, 6073. Frankfurt am Main, Germany: MIND Group.
Attias, Hagai. 2003. “Planning by Probabilistic Inference.” In AISTATS. Citeseer.
Andrews, Mel. 2020. The Math is not the Territory: Navigating the Free Energy Principle.
[Preprint] URL: (accessed 2020-11-30).
Ay, Nihat, and Keyan Zahedi. 2014. “On the Causal Structure of the Sensorimotor Loop.” In
Guided Self-Organization: Inception, 26194. Springer.
Baltieri, Manuel, and Christopher L. Buckley. 2019. “Generative Models as Parsimonious
Descriptions of Sensorimotor Loops.” Behavioral and Brain Sciences 42: e218.
Baltieri, Manuel, Christopher L Buckley, and Jelle Bruineberg. 2020. “Predictions in the Eye
of the Beholder: An Active Inference Account of Watt Governors.” arXiv Preprint
Barandiaran, Xabier E, Ezequiel Alejandro Di Paolo, and Marieke Rohde. 2009. “Defining
Agency: Individuality, Normativity, Asymmetry, and Spatio-Temporality in Action.” Adaptive
Behavior 17 (5): 36786.
Beal, Matthew J. 2003. Variational Algorithms for Approximate Bayesian Inference. University
of London London.
Beer, Randall D. 2004. “Autopoiesis and Cognition in the Game of Life.” Artificial Life 10 (3):
———. 2014. “The Cognitive Domain of a Glider in the Game of Life.” Artificial Life 20 (2):
———. 2020. “An Investigation into the Origin of Autopoiesis.” Artificial Life 26 (1): 522.
Berger, James O. 2013. Statistical Decision Theory and Bayesian Analysis. Springer Science &
Business Media.
Biehl, Martin. 2017. “Formal Approaches to a Definition of Agents.” arXiv Preprint
Biehl, Martin, Christian Guckelsberger, Christoph Salge, Simón C. Smith, and Daniel Polani.
2018. “Expanding the Active Inference Landscape: More Intrinsic Motivations in the
Perception-Action Loop.” Frontiers in Neurorobotics 12: 45.
Biehl, Martin, Felix A Pollock, and Ryota Kanai. 2020. “A Technical Critique of the Free Energy
Principle as Presented in" Life as We Know It" and Related Works.” arXiv, arXiv2001.
Bishop, Christopher M. 2006. Pattern Recognition and Machine Learning. Springer-Verlag
New York.
Blei, David M, Alp Kucukelbir, and Jon D McAuliffe. 2017. “Variational Inference: A Review
for Statisticians.” Journal of the American Statistical Association 112 (518): 85977.
Bogacz, Rafal. 2017. “A Tutorial on the Free-Energy Framework for Modelling Perception
and Learning.” Journal of Mathematical Psychology 76: 198211.
Borges, JL. 1946. “On Exactitude in Science (a. Hurley, Trans.).” Collected Fictions. New York:
Viking Penguin.
Bruineberg, Jelle, Julian Kiverstein, and Erik Rietveld. 2018. “The Anticipating Brain Is Not a
Scientist: The Free-Energy Principle from an Ecological-Enactive Perspective.” Synthese 195
(6): 241744.
Buckley, Christopher L, Chang Sub Kim, Simon McGregor, and Anil K Seth. 2017. “The Free
Energy Principle for Action and Perception: A Mathematical Review.” Journal of
Mathematical Psychology 14: 5579.
Cao, Rosa. 2020. “New Labels for Old Ideas: Predictive Processing and the Interpretation of
Neural Signals.” Review of Philosophy and Psychology 11 (3): 51746.
Chen, Zhe. 2003. “Bayesian Filtering: From Kalman Filters to Particle Filters, and Beyond.”
Statistics 182 (1): 169.
Clark, Andy. 2013. “Whatever Next? Predictive Brains, Situated Agents, and the Future of
Cognitive Science.” Behavioral and Brain Sciences 36 (03): 181204.
———. 2015. Surfing Uncertainty: Prediction, Action, and the Embodied Mind. Oxford
University Press.
———. 2017. “How to Knit Your Own Markov Blanket.” In In Philosophy and Predictive
Processing: 3, edited by Thomas K Metzinger and Wanja Wiese. Open MIND. Frankfurt am
Main: MIND Group.
———. 2020. “Beyond Desire? Agency, Choice, and the Predictive Mind.” Australasian
Journal of Philosophy 98 (1): 115.
Clark, Andy, and David Chalmers. 1998. “The Extended Mind.” Analysis 58 (1): 719.
Colombo, Matteo. 2020. Maladaptive social norms, cultural progress, and the free-energy
principle.” Behavioral and Brain Sciences 43: e100.
Colombo, Matteo, Lee Elkin, and Stephan Hartmann. 2018. “Being Realist about Bayes, and
the Predictive Processing Theory of Mind.” The British Journal for the Philosophy of Science,
Colombo, Matteo, and Cory Wright. 2018. “First Principles in the Life Sciences: The Free-
Energy Principle, Organicism, and Mechanism.” Synthese.
Da Costa, Lancelot, Thomas Parr, Noor Sajid, Sebastijan Veselic, Victorita Neacsu, and Karl J
Friston. 2020. “Active Inference on Discrete State-Spaces: A Synthesis.” arXiv Preprint
Daunizeau, Jean. 2017. “The Variational Laplace Approach to Approximate Bayesian
Inference.” arXiv Preprint arXiv:1703.02089.
Dayan, Peter, Geoffrey E Hinton, Radford M Neal, and Richard S Zemel. 1995. “The Helmholtz
Machine.” Neural Computation 7 (5): 889904.
Demekas, Daphne, Thomas Parr, and Karl J Friston. 2020. “An Investigation of the Free
Energy Principle for Emotion Recognition.” Frontiers in Computational Neuroscience 14.
Dewhurst, Joe. 2017. “Folk Psychology and the Bayesian Brain.” In Philosophy and Predictive
Processing. Frankfurt am Main: MIND Group.
Downes, Stephen M. 2020. Models and Modeling in the Sciences a Philosophical Introduction.
Doya, Kenji. 2007. Bayesian Brain: Probabilistic Approaches to Neural Coding. MIT press.
Es, Thomas van. 2020. “Living Models or Life Modelled? On the Use of Models in the Free
Energy Principle.” Adaptive Behavior.
Feldman, Harriet, and Karl J Friston. 2010. “Attention, Uncertainty, and Free-Energy.”
Frontiers in Human Neuroscience 4: 215.
Fox, Charles W, and Stephen J Roberts. 2012. “A Tutorial on Variational Bayesian Inference.”
Artificial Intelligence Review 38 (2): 8595.
Friston, Karl, Lancelot Da Costa, and Thomas Parr. 2020. “Some Interesting Observations on
the Free Energy Principle.” arXiv Preprint arXiv:2002.04501.
Friston, Karl J. 2005. “A theory of cortical responses.” Philosophical Transactions of the Royal
Society of London. Series B, Biological Sciences 360 (1456): 81536.
———. 2008. “Hierarchical models in the brain.” PLoS Computational Biology 4 (11).
———. 2010. “The free-energy principle: a unified brain theory?” Nature Reviews.
Neuroscience 11 (2): 12738.
———. 2012. “A Free Energy Principle for Biological Systems.” Entropy 14 (11): 21002121.
———. 2013a. “Active Inference and Free Energy.” Behavioral and Brain Sciences 36 (03):
———. 2013b. “Life as We Know It.” Journal of the Royal Society Interface 10 (86): 20130475.
———. 2019. “A Free Energy Principle for a Particular Physics.” arXiv Preprint
Friston, Karl J, and Ping Ao. 2012. “Free Energy, Value, and Attractors.” Computational and
Mathematical Methods in Medicine 2012.
Friston, Karl J, Jean Daunizeau, James Kilner, and Stefan J. Kiebel. 2010. “Action and behavior:
A free-energy formulation.” Biological Cybernetics 102 (3): 22760.
Friston, Karl J, Erik D Fagerholm, Tahereh S Zarghami, Thomas Parr, Inês Hipólito, Loı̈c
Magrou, and Adeel Razi. 2020. “Parcels and Particles: Markov Blankets in the Brain.” arXiv
Preprint arXiv:2007.09704.
Friston, Karl J, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, and Giovanni
Pezzulo. 2017. “Active Inference: A Process Theory.” Neural Computation 29 (1): 149.
Friston, Karl J, James Kilner, and Lee Harrison. 2006. “A Free Energy Principle for the Brain.”
Journal of Physiology-Paris 100 (1): 7087.
Friston, Karl J, Michael Levin, Biswa Sengupta, and Giovanni Pezzulo. 2015. “Knowing One’s
Place: A Free-Energy Approach to Pattern Regulation.” Journal of the Royal Society Interface
12 (105): 20141383.
Friston, Karl J, Jérémie Mattout, Nelson Trujillo-Barreto, John Ashburner, and Will Penny.
2007. “Variational Free Energy and the Laplace Approximation.” Neuroimage 34 (1): 220
Friston, Karl J, Thomas Parr, and Bert de Vries. 2017. “The Graphical Brain: Belief
Propagation and Active Inference.” Network Neuroscience 1 (4): 381414.
Friston, Karl J, Francesco Rigoli, Dimitri Ognibene, Christoph Mathys, Thomas Fitzgerald, and
Giovanni Pezzulo. 2015. “Active inference and epistemic value.” Cognitive Neuroscience, 1
Friston, Karl J, N. Trujillo-Barreto, and J. Daunizeau. 2008. “DEM: A variational treatment of
dynamic systems.” NeuroImage 41 (3): 84985.
Friston, Karl J, Wanja Wiese, and J Allan Hobson. 2020. “Sentience and the Origins of
Consciousness: From Cartesian Duality to Markovian Monism.” Entropy 22 (5): 516.
Friston, Karl, Biswa Sengupta, and Gennaro Auletta. 2014. “Cognitive Dynamics: From
Attractors to Active Inference.” Proceedings of the IEEE 102 (4): 42745.
Gregory, Richard. 1970. The Intelligent Eye. Weidenfeld; Nicolson.
Hesp, Casper, Maxwell Ramstead, Axel Constant, Paul Badcock, Michael D Kirchhoff, and Karl
J Friston. 2019. “A Multi-Scale View of the Emergent Complexity of Life: A Free-Energy
Proposal.” In Evolution, Development, and Complexity: Multiscale Models in Complex Adaptive
Systems. 1st Ed., Ch. 7. Springer Proceedings in Complexity.
Hinton, Geoffrey E, and Richard S Zemel. 1994. “Autoencoders, Minimum Description Length
and Helmholtz Free Energy.” In Advances in Neural Information Processing Systems, 310.
Hipolito, Ines, Maxwell Ramstead, Laura Convertino, Anjali Bhat, Karl Friston, and Thomas
Parr. 2020. “Markov Blankets in the Brain.” arXiv Preprint arXiv:2006.02741.
Hohwy, Jakob. 2013. The Predictive Mind. OUP Oxford.
———. 2016. “The Self-Evidencing Brain.” Noûs 50 (2): 25985.
———. 2017. “How to Entrain Your Evil Demon.” In In Philosophy and Predictive Processing:
2, edited by Thomas K Metzinger and Wanja Wiese. Open MIND. Frankfurt am Main: MIND
Hohwy, Jakob, and John Michael. 2017. “Why Should Any Body Have a Self?” In The Subject’s
Matter: Self-Consciousness and the Body, 363. MIT Press.
Jordan, Michael I, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul. 1999. “An
Introduction to Variational Methods for Graphical Models.” Machine Learning 37 (2): 183
Kappen, Hilbert J, Vicenç Gómez, and Manfred Opper. 2012. “Optimal Control as a Graphical
Model Inference Problem.” Machine Learning 87 (2): 15982.
Kirchhoff, Michael D. 2018. “Autopoiesis, Free Energy, and the Life–Mind Continuity Thesis.”
Synthese 195 (6): 251940.
Kirchhoff, Michael D, and Julian Kiverstein. 2019. “How to Determine the Boundaries of the
Mind: A Markov Blanket Proposal.” Synthese, 120.
Kirchhoff, Michael, Thomas Parr, Ensor Palacios, Karl J Friston, and Julian Kiverstein. 2018.
“The Markov Blankets of Life: Autonomy, Active Inference and the Free Energy Principle.”
Journal of the Royal Society Interface 15 (138): 20170792.
Klein, Colin. 2018. “What Do Predictive Coders Want?” Synthese 195 (6): 254157.
Knill, David C, and Alexandre Pouget. 2004. “The Bayesian Brain: The Role of Uncertainty in
Neural Coding and Computation.” Trends in Neurosciences 27 (12): 71219.
Knill, David C, and Whitman Richards. 1996. Perception as Bayesian Inference. Cambridge
University Press.
Kuchling, Franz, Karl J Friston, Georgi Georgiev, and Michael Levin. 2020. “Morphogenesis as
Bayesian Inference: A Variational Approach to Pattern Formation and Control in Complex
Biological Systems.” Phys Life Rev 33: 88108.
Kwisthout, Johan, Harold Bekkering, and Iris Van Rooij. 2017. “To Be Precise, the Details
Don’t Matter: On Predictive Processing, Precision, and Level of Detail of Predictions.” Brain
and Cognition 112: 8491.
Lee, Tai Sing, and David Mumford. 2003. “Hierarchical Bayesian Inference in the Visual
Cortex.” JOSA A 20 (7): 143448.
Liwtin, Piotr, and Marcin Miłkowski. 2020. “Unification by Fiat: Arrested Development of
Predictive Processing.” Cognitive Science 44.
MacKay, David JC. 2003. Information Theory, Inference and Learning Algorithms. Cambridge
university press.
Menary, Richard, and Alexander J. Gillett. 2020. “Are Markov Blankets Real and Does It
Matter?” In The Philosophy and Science of Predictive Processing, edited by Dina Mendonça,
Manuel Curado, and Steven S. Gouveia. Bloomsbury Academic.
Montúfar, Guido, Keyan Ghazi-Zahedi, and Nihat Ay. 2015. “A Theory of Cheap Control in
Embodied Systems.” PLoS Comput Biol 11 (9): e1004427.
Murphy, Kevin P. 2012. Machine Learning: A Probabilistic Perspective. MIT press.
Neal, Radford M, and Geoffrey E Hinton. 1998. “A View of the Em Algorithm That Justifies
Incremental, Sparse, and Other Variants.” In Learning in Graphical Models, 35568. Springer.
Opper, Manfred, and Cédric Archambeau. 2009. The Variational Gaussian Approximation
Revisited.” Neural Computation 21 (3): 78692.
Palacios, Ensor Rafael, Adeel Razi, Thomas Parr, Michael Kirchhoff, and Karl Friston. 2020.
“On Markov Blankets and Hierarchical Self-Organisation.” Journal of Theoretical Biology 486:
Parisi, Giorgio. 1988. Statistical Field Theory. Addison-Wesley.
Parr, Thomas, Lancelot Da Costa, and Karl Friston. 2020. “Markov Blankets, Information
Geometry and Stochastic Thermodynamics.” Philosophical Transactions of the Royal Society
A 378 (2164): 20190159.
Parr, Thomas, M Berk Mirza, Hayriye Cagnan, and Karl J Friston. 2019. “Dynamic Causal
Modelling of Active Vision.” Journal of Neuroscience 39 (32): 626575.
Pearl, Judea. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible
Inference. Morgan Kaufmann.
———. 2009. “Causal Inference in Statistics: An Overview.” Statistics Surveys 3: 96146.
Pezzulo, Giovanni, Francesco Rigoli, and Karl J Friston. 2018. “Hierarchical Active Inference:
A Theory of Motivated Control.” Trends in Cognitive Sciences 22 (4): 294306.
Pourret, Olivier, Patrick Naı̈m, and Bruce Marcot. 2008. Bayesian Networks: A Practical Guide
to Applications. John Wiley & Sons.
Ramstead, Maxwell James Désormeau, Paul Benjamin Badcock, and Karl John Friston. 2018.
“Answering Schrödinger’s Question: A Free-Energy Formulation.” Physics of Life Reviews 24:
Ramstead, Maxwell JD, Karl J Friston, and Inês Hipólito. 2020. “Is the Free-Energy Principle
a Formal Theory of Semantics? From Variational Density Dynamics to Neural and Phenotypic
Representations.” Entropy 22 (8): 889.
Ramstead, Maxwell JD, Michael D Kirchhoff, Axel Constant, and Karl J Friston. 2019.
“Multiscale Integration: Beyond Internalism and Externalism.” Synthese.
Rao, Rajesh PN, and Dana H Ballard. 1999. “Predictive Coding in the Visual Cortex: A
Functional Interpretation of Some Extra-Classical Receptive-Field Effects.” Nature
Neuroscience 2 (1): 7987.
Robert, Christian. 2007. The Bayesian Choice: From Decision-Theoretic Foundations to
Computational Implementation. Springer Science & Business Media.
Rosas, Fernando E, Pedro AM Mediano, Martin Biehl, Shamil Chandaria, and Daniel Polani.
2020. “Causal Blankets: Theory and Algorithmic Framework.” arXiv Preprint
Rubin, Sergio, Thomas Parr, Lancelot De Costa, and Karl Friston. 2020. Future climates:
Markov blankets and active inference in the biosphere.” Journal of the Royal Society Interface
17: 20200503.
Sajid, Noor, Philip J Ball, and Karl J Friston. 2019. “Active Inference: Demystified and
Compared.” arXiv Preprint arXiv:1909.10863.
Sanborn, Adam N, and Nick Chater. 2016. “Bayesian Brains Without Probabilities.” Trends in
Cognitive Sciences 20 (12): 88393.
Seth, Anil, Beren Millidge, Christopher L Buckley, and Alexander Tschantz. 2020. “Curious
Inferences: Reply to Sun and Firestone on the Dark Room Problem.” Trends in Cognitive
Sciences 24 (9): 68183.
Sims, Matthew. 2020. “How to Count Biological Minds: Symbiosis, the Free Energy Principle,
and Reciprocal Multiscale Integration.” Synthese, 123.
Stephan, Klaas Enno, Will D Penny, Jean Daunizeau, Rosalyn J Moran, and Karl J Friston. 2009.
“Bayesian Model Selection for Group Studies.” Neuromiage 46 (4): 100417.
Sun, Zekun, and Chaz Firestone. 2020a. “Optimism and Pessimism in the Predictive Brain.”
Trends Cogn. Sci. 24: 68385.
———. 2020b. “The Dark Room Problem.” Trends Cogn. Sci. 24: 34648.
Tishby, Naftali, and Daniel Polani. 2011. “Information Theory of Decisions and Actions.” In
Perception-Action Cycle, 60136. Springer.
Tschantz, Alexander, Anil K Seth, and Christopher L Buckley. 2020. “Learning Action-
Oriented Models Through Active Inference.” PLOS Computational Biology 16 (4): e1007805.
Van de Cruys, Sander, Karl J. Friston, and Andy Clark. 2020. “Controlled Optimism: Reply to
Sun and Firestone on the Dark Room Problem.” Trends Cogn. Sci. 24 (9): 68081.
Veissière, Samuel PL, Axel Constant, Maxwell JD Ramstead, Karl J Friston, and Laurence J
Kirmayer. 2020. “Thinking Through Other Minds: A Variational Approach to Cognition and
Culture.” Behavioral and Brain Sciences 43.
Weisberg, Michael. 2013. Simulation and Similarity: Using Models to Understand the World.
Oxford University Press.
Weisberg, Michael. 2007. “Who Is a Modeler?” The British Journal for the Philosophy of Science
58 (2): 20733.
Wilkinson, Sam, George Deane, Kathryn Nave, and Andy Clark. 2019. “Getting Warmer:
Predictive Processing and the Nature of Emotion.” In The Value of Emotions for Knowledge,
10119. Palgrave Macmillan.
Williams, Daniel. 2020. “Is the Brain an Organ for Prediction Error Minimization?” A Preprint.
Yon, Daniel, Cecilia Heyes, and Clare Press. 2020. “Beliefs and Desires in the Predictive
Brain.” Nature Communications 11: 4404.
Zhang, Cheng, Judith Bütepage, Hedvig Kjellström, and Stephan Mandt. 2018. “Advances in
Variational Inference.” IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (8):
... For example, Hohwy's claim that the blanket is best placed around the brain is a conditional arrangement related to, 46 This means that the understanding of Markov's blankets in PP and the active inference framework is far beyond Pearl's approach, in which there is no separation into active and passive states. 47 It should be noticed that there is a divergence between ontic interpretations of Markov blankets in terms of Pearl "instrumental" blankets and Friston "realist" blankets (Bruineberg et al., 2020). Pearl interpretation does not go beyond pure formalism and Markov blankets themselves turn out to be a mathematical construct used for making inferences about the e.g. ...
... living organisms). Such an approach is not "philosophically innocent", as convincingly demonstrated by Bruineberg et al., 2020. among others, the explanatory strategy, the research interests, and the phenomenon under study. ...
Full-text available
The purpose of this paper is to provide a systematic review of the Predictive Processing framework (hereinafter PP) and to identify its basic theoretical difficulties. For this reason, it is, primarily, polemic-critical and, secondarily, historical. I discuss the main concepts, positions and research issues present within this framework (§1-2). Next, I present the Bayesian-brain thesis (§3) and the difficulty associated with it (§4). In §5, I compare the conservative and radical approach to PP and discuss the internalist nature of the generative model in the context of Markov blankets. The possibility of linking PP with the free energy principle (hereinafter FEP) and the homeostatic nature of predictive mechanisms is discussed in §6. This is followed by the presentation of PP's difficulties with solving the dark room problem and the exploration-exploitation trade-off (§7). I emphasize the need to integrate PP with other models and research frameworks within cognitive science. Thus, this review not only discusses PP, but also provides an assessment of the condition of this research framework in the light of the hopes placed on it by many researchers. The Conclusions section discuss further research challenges and the epistemological significance of PP.
... Since writing this paper, we have been encouraged by the enthusiasm with which these issues have been discussed in the literature. For readers interested in delving further into these exchanges, recent papers include [46][47][48][49]. In concluding, we would like to thank Biehl et al. for a thorough and useful deconstruction of [2]. ...
Full-text available
Biehl et al. (2021) present some interesting observations on an early formulation of the free energy principle. We use these observations to scaffold a discussion of the technical arguments that underwrite the free energy principle. This discussion focuses on solenoidal coupling between various (subsets of) states in sparsely coupled systems that possess a Markov blanket—and the distinction between exact and approximate Bayesian inference, implied by the ensuing Bayesian mechanics.
... The Free Energy Principle (FEP) (Friston, 2019a;Friston & Ao, 2012a;Friston, Kilner, & Harrison, 2006; is an emerging theory in theoretical neuroscience which aims to tackle an extremely deep and fundamental question -can one characterise necessary behaviour of any system that maintains a statistical separation from its environment (Bruineberg, Dolega, Dewhurst, & Baltieri, 2020;Friston, 2019a;? Specifically, it argues that any such system can be seen as performing an elemental kind of Bayesian inference where the dynamics of the internal states of such a system can be interpreted as minimizing a variational free energy functional (Beal, 2003) 1 , and thus performing approximate (variational) ...
In this PhD thesis, we explore and apply methods inspired by the free energy principle to two important areas in machine learning and neuroscience. The free energy principle is a general mathematical theory of the necessary information-theoretic behaviours of systems that maintain a separation from their environment. A core postulate of the theory is that complex systems can be seen as performing variational Bayesian inference and minimizing an information-theoretic quantity called the variational free energy. The thesis is structured into three independent sections. Firstly, we focus on predictive coding, a neurobiologically plausible process theory derived from the free energy principle which argues that the primary function of the brain is to minimize prediction errors, showing how predictive coding can be scaled up and extended to be more biologically plausible, and elucidating its close links with other methods such as Kalman Filtering. Secondly, we study active inference, a neurobiologically grounded account of action through variational message passing, and investigate how these methods can be scaled up to match the performance of deep reinforcement learning methods. We additionally provide a detailed mathematical understanding of the nature and origin of the information-theoretic objectives that underlie exploratory behaviour. Finally, we investigate biologically plausible methods of credit assignment in the brain. We first demonstrate a close link between predictive coding and the backpropagation of error algorithm. We go on to propose novel and simpler algorithms which allow for backprop to be implemented in purely local, biologically plausible computations.
... We find that, in the class of linear systems explored, the answer to this question is that the statistical structure required by the FEP only arises in a very narrow class of systems, requiring stringent conditions such as fully symmetric agent-environment interactions that we can in general not expect from systems displaying agency [9]. The generality of the FEP has been questioned in the past due to conceptual issues [22,23], or the existence of counterexamples challenging that sensorimotor interfaces, Markov blankets and solenoidal decoupling follow from each other [11]. However, our study is the first to our knowledge that shows that the assumptions of the FEP do not hold for a very broad class of systems, namely linear, weakly coupled systems, except for the limited case of fully symmetric agent-environment interaction. ...
The Free Energy Principle (FEP) states that any dynamical system can be interpreted as performing Bayesian inference upon its surrounding environment. Although the FEP applies in theory to a wide variety of systems, there has been almost no direct exploration of the principle in concrete systems. In this paper, we examine in depth the assumptions required to derive the FEP in the simplest possible set of systems - weakly-coupled non-equilibrium linear stochastic systems. Specifically, we explore (i) how general are the requirements imposed on the statistical structure of a system and (ii) how informative the FEP is about the behaviour of such systems. We find that this structure, known as a Markov blanket (i.e. a boundary precluding direct coupling between internal and external states) and stringent restrictions on its solenoidal flows, both required by the FEP, make it challenging to find systems that fulfil the required assumptions. Suitable systems require an absence of asymmetries in sensorimotor interactions that are highly unusual for living systems. Moreover, we find that a core step in the argument relating the behaviour of a system to variational inference relies on an implicit equivalence between the dynamics of the average states with the average of the dynamics. This equivalence does not hold in general even for linear systems as it requires an effective decoupling from the system's history of interactions. These observations are critical for evaluating the generality and applicability of the FEP and point to potential issues in its current form. These issues make the FEP, as it stands, not straightforwardly applicable to the simple linear systems studied here and suggests more development is needed before it could be applied to the kind of complex systems which describe living and cognitive processes.
Full-text available
The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity in recent years as a useful approach for modeling neurocognitive processes. This framework is highly general and flexible in its ability to be customized to model any cognitive process, as well as simulate predicted neuronal responses based on its accompanying neural process theory. It also affords both simulation experiments for proof of principle and behavioral modeling for empirical studies. However, there are limited resources that explain how to build and run these models in practice, which limits their widespread use. Most introductions assume a technical background in programming, mathematics, and machine learning. In this paper we offer a step-by-step tutorial on how to build POMDPs, run simulations using standard MATLAB routines, and fit these models to empirical data. We assume a minimal background in programming and mathematics, thoroughly explain all equations, and provide exemplar scripts that can be customized for both theoretical and empirical studies. Our goal is to provide the reader with the requisite background knowledge and practical tools to apply active inference to their own research. We also provide optional technical sections and multiple appendices, which offer the interested reader additional technical details. This tutorial should provide the reader with all the tools necessary to use these models and to follow emerging advances in active inference research.
Full-text available
The goal of the paper is to review existing work on consciousness within the frameworks of Predictive Processing, Active Inference, and Free Energy Principle. The emphasis is put on the role played by the precision and complexity of the internal generative model. In the light of those proposals, these two properties appear to be the minimal necessary components for the emergence of conscious experience-A Minimal Unifying Model of consciousness. © 2021 The Author(s) 2021. Published by Oxford University Press.
Full-text available
The extended mind thesis claims that a subject’s mind sometimes encompasses the environmental props the subject interacts with while solving cognitive tasks. Recently, the debate over the extended mind has been focused on Markov Blankets: the statistical boundaries separating biological systems from the environment. Here, I argue such a focus is mistaken, because Markov Blankets neither adjudicate, nor help us adjudicate, whether the extended mind thesis is true. To do so, I briefly introduce Markov Blankets and the free energy principle in section 2. I then turn from exposition to criticism. In section 3, I argue that using Markov Blankets to determine whether the mind extends either begs the question against the extended mind or provides us an answer based on circular reasoning. In section 4, I consider whether Markov Blankets help us perspicuously frame the debate over the extended mind, answering in the negative. This is because resorting to Markov Blankets to determine whether the mind extends yields extensionally inadequate conclusions which violate the parity principle. In section 5, I argue that resorting to Markov Blankets makes internalism about the mind vacuously true, preventing any substantial inquiry over the extended mind. A brief concluding paragraph follows.
Conference Paper
Full-text available
This presentation will deal with the epistemological problem of the relationship between the subject and the object of knowledge in social science. We will try to show how the epistemological framework of Michel Foucault can be used to tackle this problem. Our point will be that Foucault’s methodology solves this problem by envisioning the research process as a practice of an intervention into the object. This intervention is envisioned as a form of criticism of the researched object by showing its contingent and arbitrary nature.
Full-text available
The core hypothesis of this paper is that neuropsychoanalysis provides a new paradigm for artificial general intelligence (AGI). The AGI agenda could be greatly advanced if it were grounded in affective neuroscience and neuropsychoanalysis rather than cognitive science. Research in AGI has so far remained too cortical-centric; that is, it has privileged the activities of the cerebral cortex, the outermost part of our brain, and the main cognitive functions. Neuropsychoanalysis and affective neuroscience, on the other hand, affirm the centrality of emotions and affects—i.e., the subcortical area that represents the deepest and most ancient part of the brain in psychic life. The aim of this paper is to define some general design principles of an AGI system based on the brain/mind relationship model formulated in the works of Mark Solms and Jaak Panksepp. In particular, the paper analyzes Panksepp’s seven effective systems and how they can be embedded into an AGI system through Judea Pearl’s causal analysis. In the conclusions, the author explains why building a sub-cortical AGI is the best way to solve the problem of AI control. This paper is intended to be an original contribution to the discussion on AGI by elaborating positive arguments in favor of it.