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Bayesian theories of consciousness: A review in search for a minimal unifying model



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.
Neuroscience of Consciousness, 2021, 7(2), 1–14
Review Article
Special Issue: Consciousness science and its theories
Bayesian theories of consciousness: a review in search
for a minimal unifying model
Wiktor Rorot*,
Faculty of Philosophy and Faculty of Psychology, University of Warsaw, ul. Krakowskie Przedmie´
scie 3, 00-927, Stawki 5/7, Warsaw 00-183, Poland
Wiktor Rorot,
*Correspondence address. Faculty of Psychology, University of Warsaw, ul. Stawki 5/7, Warsaw 00-183, Poland. E-mail:
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.
Keywords: consciousness; free energy principle; active inference; predictive processing; computational explanation; minimal
unifying model
[T]his is not mysterious, it is just a mathematical statement of
the way things are. (Friston et al. 2020, 11)
The goal of this paper is to provide an overview of the research
on consciousness—with specic focus on phenomenal conscious-
ness (Block 1995)1—that has been so far carried out within the
formal schema of the Free Energy Principle (FEP) (e.g. Friston 2009,
Over the past 15 years, the FEP—together with related frame-
works stemming from the same theoretical core, namely the
Predictive Processing (PP; e.g. Hohwy 2013;Clark 2016;Wiese
and Metzinger 2017) and Active Inference Framework (e.g. Friston
et al. 2017a)2—has become one of the central approaches in
1The distinction between phenomenal and access consciousness is well
established but widely disputed (e.g. Kriegel 2007;Overgaard 2018); hence, I
use it throughout the paper only for the purpose of framing the discussion.
2While it is important to note both the different epistemic status of FEP and
PP and Active Inference Framework and some philosophical as well as math-
ematical discrepancies between these approaches, for the purpose of current
discussion, these differences may be safely disregarded as I will be address-
ing their very theoretical core. Some of the more important differences will be
marked throughout the text as necessary. An important point to note here is
that, while the notion of “active inference” is applied variously in the literature
to describe either the process of active inference (discussed in the next section),
a theory of motor control (e.g. Adams et al. 2013), as well as a framework
for modeling decision-making, perception, and action selection with partially
cognitive neuroscience, neurobiology, and philosophy of mind
and an increasingly important approach to articial intelli-
gence, specically reinforcement learning (see e.g. Ueltzh¨
2018;Millidge 2019). I will refer to these theories with the
name “Bayesian cognitive science, coined by Ramstead et al.
(2020), despite the fact that the FEP does not exhaust all pos-
sibilities for “Bayesian” theories of cognition and conscious-
ness. Although there is much debate about the general viability
and the de facto meaning of this framework (see e.g. Colombo
and Wright 2018;Litwin and Miłkowski 2020;Andrews 2021),
it currently seems that it is here to stay for the foreseeable
In a recent paper, Hohwy (2020) discusses the current focus
of the FEP, as well as emerging new areas of research within
this framework. Among them, he lists the growing body of work
on consciousness. This work spans several, mutually exclusive,
philosophical perspectives on subjective experience, as well as
touches on issues common to many of them, such as the problem
of identifying neural correlates of consciousness (Hohwy and Seth
observable Markov decision processes, in continuous, discrete, and mixed-state
spaces, with somewhat similar goals to the reinforcement learning approach
in machine learning [see e.g. Sajid et al. (2021) for a review of applications in
discrete cases and Friston et al. (2017b) for a discussion of the continuous and
mixed cases]. In this paper, I use “active inference” to denote the rst mean-
ing and “Active Inference Framework” to denote the last. I am grateful to an
anonymous reviewer for pressing me to clarify this.
Received: 8 January 2021; Revised: 10 September 2021; Accepted: 22 September 2021
© The Author(s) 2021. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which
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2020). In this work, the FEP exhibits its “attractive duality” (Hohwy
2020, 11)—i.e. its orthogonality to extant lines of argument.
Surprisingly, despite the disparities, all those proposals are
based on similar elements of the FEP formalism - in particular, the
concepts of “precision” and “complexity” (in the technical sense
of those two terms, described in the next section). Wanja Wiese
(2020) has advanced the notion of a “minimal unifying model” as
a framework for building a consensus between various accounts
of consciousness. In this paper, I will review the existing body of
work on consciousness in the Bayesian cognitive science. Based
on this review, I will argue that these two properties of inter-
nal models—precision and complexity—do in fact constitute a
minimal unifying model at least for Bayesian cognitive science of
consciousness. In this way, this body of work can be interpreted as
making coherent (albeit limited), empirically testable predictions
about the neural mechanisms of consciousness.
In what follows, in the “A very short introduction to the FEP”
section, I will provide a very short introduction to the FEP, focusing
on the common elements of the three frameworks of Bayesiancog-
nitive science. Next, in the “Conscious experience under the FEP”
section, I will provide an overview of the theories of conscious-
ness that employ these frameworks. In the “Discussion” section, I
will discuss the Bayesian theories of consciousness in general, try-
ing to gesture towards a common approach within those different
accounts. Finally, in the “Precision and complexity as a Minimal
Unifying Model” section, I will discuss the interpretation of the
core of this common approach in terms of a minimal unifying
model, which I believe to be the most informative.
A very short introduction to the FEP
The FEP (Friston 2019) states a simple tautology: any living system,
as long as it stays alive, can be described as minimizing the dis-
persion of its constituent states. For the purpose of this denition,
“living system” is understood as one that is in a nonequilibrium
steady state (NESS), and staying alive means maintaining this
status. To do this, the system has to remain far from the thermo-
dynamic equilibrium in a (relatively) low entropy state (hence the
tautology). Furthermore, the FEP cashes out the Good Regulator
theorem (Conant and Ross Ashby 1970), which states that every
good regulator of a system must be isomorphic with that system
(i.e. must be a model of that system). As long as it remains far from
thermodynamic equilibrium, the system behaves as if it employed
or instantiated3agenerative model, an internal statistical repre-
sentation of the target NESS density (and the worldly inuences).
Effectively, the system can be described as capable of anticipating
the changes it undergoes and of adaptive behavior. The princi-
ple has been applied to a wide range of self-organizing systems
(e.g. Friston and Stephan 2007;Friston 2013;Friston et al. 2015a;
Kirchhoff and Kiverstein 2018;Fields and Levin 2019). However,
from the philosophy of science perspective, it is best regarded
as a modeling framework rather than a model per se [Litwin
and Miłkowski (2020) and Andrews (2021) make a compelling
argument in this direction].
With regard to cognition, the “Bayesian cognitive science”
paints the picture of the brain as a hierarchical “prediction
3Depending on the accepted stance in the internalist/externalist debates,
various authors differently describe the generative model as being employed
or encoded by the system [in the case of the neurocentric version of PP, e.g.
see Kiefer and Hohwy (2018)] or embodied or instantiated by the system (in the
case of the embodied and enactive version of PP and in the case of FEP at large,
e.g. see Ramstead et al. 2020). For the discussion of this distinction, see van Es
machine.4Using its information about the state of the world,
specically about the hidden causes of sensory input—formally
represented by probability distributions referred to as the recogni-
tion density and the generative model—the brain (or the embodied
cognitive system) attempts to predict the incoming stimuli. The
mismatch between prediction and input, the prediction error, is
then passed from the bottom up along the neural hierarchy and
is used to bring the recognition density closer to the actual dis-
tribution of causes either via an update of the internal model
itself—perceptual inference—or through active involvement with
the world—active inference—which attempts to bring the hidden
causes closer to the generative density (one can think of it as a
self-fullling prophecy). The idea of the interplay between top-
down prediction and bottom-up prediction error is at the core of
the applications of the framework to actual neural circuits, as is
the case with e.g. the predictive coding model of vision (Rao and
Ballard 1999).
Prediction errors indicate surprisal (in the technical sense of
information theory, mathematically equivalent to entropy), and
this indicates that the system is leaving the NESS. Hence, the min-
imization of surprisal follows strictly from the formulation of the
FEP. However, since this value is intractable, an approximation
must be used. The (variational) free energy provides an upper bound
on surprisal by measuring the difference between recognition and
generative density.
This free energy functional can be formally decomposed into
two equivalent formulations (see e.g. Friston 2009), corresponding
to the aforementioned perceptual and active inference, which can
be symbolically depicted as:
F=Divergence +Surprise
F=Complexity Accuracy
The latter decomposition is crucial for our current purposes.
These terms are dened as follows:
Complexity denotes the amount of information required to
reconstruct the generative density, given a full knowledge
of the recognition density (quantied with a Kullback–Leibler
divergence between the two densities).
Accuracy denotes how close are the predictions generated by
the internal model to the actual distribution of probabilities of
observations, given the hidden causes.
Thus, the process of active inference can be understood as
the process of selectively sampling the world to bring sensations
closer to what is expected (note that, throughout the paper, the
terms “precision” and “complexity” are used only in the technical
sense described here and below.)
However, the increase in accuracy is bound to an increase in
complexity and to the risk of overtting the internal model. This
4Formal parts of this review are based on Buckleyet al. (2017). Furthermore,
it is important to note that the FEP is undergoing a continuous development
and improvement and, since this brief introduction is aimed at introducing key
elements of the formalism, there might be some discrepancies between what
is presented here and formal parts of the works reviewed below, which have,
in some cases, been done at a different stage of the framework. This intro-
duction attempts to capture the most recent version of FEP, making use of the
“generalized free energy” term while at the same time ignoring some of the
(important) differences between FEP, PP, and Active Inference Framework (see
e.g. Ramstead et al. 2020) as already mentioned.
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Bayesian theories of consciousness 3
fact comes simply from the irreducible uncertainty and random-
ness of the world. To take it into account, the model has to give
up some accuracy (and complexity) in exchange for robustness.
Before I move to the discussion of how the Bayesian cognitive
science tries to explain consciousness, I will provide an exam-
ple of an explanation of attention employing the FEP (roughly
falling under the PP label), showcasing crucial components of
this framework and introducing further notions that will turn out
central later on.5
Attention under the FEP
PP explains attention in terms of the control of precision assigned
to top-down hypotheses and bottom-up prediction error signals
(Feldman and Friston 2010;Hohwy 2012,2013). Formally speak-
ing, precision is the inverse variance of a probability distribution.6
Precision, most notably describes the reliability of the prediction
error signals (sensory noise) and the hypothesis generated from
the generative density (neural noise). Despite both cases being for-
mally dened in terms of inverse variance, precision assignments
are independent and, as such, have to be independently tracked
by the cognitive system.7In the more complex case of active infer-
ence, Parr and Friston (2017a,b) identify three types of precision
assignments, most importantly sensory and expected free energy
precision (see also Smith et al. 2021),8linked to different neuro-
transmitter systems in the brain and responsible for mechanisms
of attention (Parr and Friston 2017b).
Since precisions are not given and need to be inferred from the
available data, they are also subject to control from the system
itself—especially since they are volatile between context and sen-
sory modalities. As we learn from the Good Regulator theorem,
if the brain is supposed to manage precision expectations (on a
sub-personal level), then they have to be somehow modeled—
either directly, in the form of precision expectations (as Hohwy
2012 argues), or indirectly, in the form of the hyperparameters
of the generative density (Buckley et al. 2017). This process can
also be regarded as somewhat disjointed and separate and hence
interpreted as a specialized model for managing precisions [this
point is leveraged by Dołe
˛ga and Dewhurst (2019) as we will
see below].
Under this description, exogenous attention—the experience
of a stimulus “with a large spatial and/or temporal contrast
(abrupt onset)” (Hohwy 2012, 6)—arises due to the expectation
that stronger signals should have a larger signal to noise ratio, and
hence a better (sensory) precision. On the other hand, endoge-
nous attention—the top-down, internally governed assignment
5The “great absent” of this discussion is the notion of a Markov blanket that
is crucial for the contemporary version of FEP. However, most of the accounts
discussed here do not make any reference to this concept; hence, I have decided
to omit it to keep the discussion simple. It is briey introduced in the section
discussing MM below, and an interested reader is directed to papers that focus
on developing this concept, e.g. Kirchhoff et al. (2018) and Parr et al. (2020), and
to the critical discussion in Bruineberg et al. (2020).
6This applies to the standard approach of Bayesian cognitive science,
which employs continuous probability densities, which is the main focus of
this paper. In different, equivalent formulations, e.g. based on categorical dis-
tributions (see Kwisthout and van Rooij 2015;Friston et al. 2015b;Kwisthout
et al. 2017), the formal denition of precision can differ, although the role it
plays in the formalism remains comparable. For example, when categorical
distributions are involved, precision is described in terms of entropy.
7I am grateful to an anonymous reviewer for pressing this issue.
8Smith et al. (2021) replace the term “precision over policies,” proposed and
used by Parr and Friston, with “expected free energy precision” to highlight the
fact that this factor modulates the contribution of the expected free energy
term to the posterior distribution over policies. This further allows to avoid con-
fusion with terminology used in reinforcement learning. For this reason, in this
paper, I will follow Smith and colleagues’ terminology in this regard.
of focus—operates by directly modulating the responses of neu-
ronal units encoding beliefs about (expected free energy) precision
(Hohwy 2012, 7), effectively assigning higher precision to the
relevant spatiotemporal regions.
Hohwy (2012) discusses several examples of inattentional
blindness to show in detail how this account of attention can
explain them. Here, I will briey recount his account of change
blindness to make the somewhat formal discussion above more
down to earth.
Consider an experiment where the participant is presented two
similar images of an airplane. The second image differs in that
it is covered in mudsplashes, as well as the airplane depicted
misses an engine. Since the distractor—mudsplashes—offers a
strong signal and is a relatively more visible (larger or more
salient) change than the missing engine, it will “grab” the atten-
tion, i.e. high precision will be assigned to the corresponding
parts of the signal. In result, this change will activate a spe-
cic hypothesis about the world—that the participant is presented
with a transient occlusion or a change to the photo, with a strong
prior belief that the photo beneath the mudsplashes remains
the same. Only after this hypothesis is accepted as the model,
and the prediction errors corresponding to unexpected appear-
ance of mudsplashes are explained away, the subtle change
corresponding to the absence of the engine can be assigned a
higher precision and thus—can be brought to the participant’s
Conscious experience under the FEP
There are several competing models that attempt to account for
conscious experience—specically for the phenomenal
consciousness—employing tools provided by the Bayesian cogni-
tive science. These are:
1. Bayesian implementation of Graziano’s Attention Schema
Theory (B-AST) (Graziano 2013;Dołe
˛ga and Dewhurst 2019,
2. higher-order order state space approach (HOSS) (Fleming
3. the “winning hypothesis” (WH) account (Hohwy 2012,2013);
4. Predictive Global Neuronal Workspace theory (PGNW)
(Hohwy 2013,2015;Whyte 2019;Whyte and Smith 2021);
5. the “generative entanglement” (GE) account (Clark 2018,
2019;Clark et al. 2019);
6. Projective Consciousness Model (PCM) (Rudrauf et al. 2017,
2020;Williford et al. 2018);
7. Integrated World Modeling Theory (IWMT) (Safron 2020);
8. Dual Aspect Monism (DAM) (Solms and Friston 2018;Solms
2019; see also Chanes and Barrett 2016);
9. Markovian Monism (MM) (Friston et al. 2020).9
The list above is organized roughly along the axis of the degree
of realism of the accounts—as I have previously mentioned, the
FEP research on consciousness is characterized by “attractive
9This is an attempt at an exhaustive list of accounts of consciousness
within the Bayesian cognitive science. It is based on the works collected by
Hohwy (2020, Supplemental Table S1) with some newer publications added.
As the eld is dynamically developing, I apologize in advance for omitting any
proposal. It is important to mark here that I am well aware of a strong bias
with regard to the authors of the views discussed here. I have taken several
steps to avoid it (thorough research and crowdsourcing the list of publications
to be discussed), which suggests that the bias is not an artifact of this review
but rather—unfortunately—exists in the eld.
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duality” in that it is orthogonal to extant distinctions.10 Space con-
siderations do not allow for a full discussion of all the accounts
listed above. I will, however, attempt to provide the gist of each.
It is important to note that these accounts address different
aspects of phenomenal consciousness, so they are not entirely
competing with one another. However, many of them overlap in
some respects providing incoherent solutions to similar problems.
Finally, an important caveat: many of the theories of conscious-
ness discussed below follow a realist interpretation of the FEP,
arguing that brains (or embodied cognitive systems) actually do
perform free energy (FE) minimization—e.g. Williford et al. (2018)
uses the claim that “some quantity is being optimized” to argue for
realism about the computational theory of mind. In some cases,
this is justied by the authors’ reliance on specic process the-
ories of FEP (PP and Active Inference Framework; see Andrews
2021); however, in general, such an approach does not stand up to
scrutiny [as Andrews (2021) and van Es (2020) convincingly argue].
Bayesian Attention Schema Theory (AST)
AST has been originally proposed by Graziano (2013). Under this
view, consciousness is subjective awareness, and it results from
the subject attending to a given stimulus. More precisely, access
consciousness is realized by attention, while the structure of rst-
person experience with associated phenomenology is explained
by the properties of the process of control of attention. Control
of attention falls upon an internal model called the “attention
schema,” conceived of in analogy to the internal model of the
body, namely—the body schema. This means that the subject has
access to a simplied model of how attention is deployed, which
it can look into whenever a report of what is it conscious of is
required. And, according to AST, whenever we claim to be con-
scious of something, we are in fact using higher-order cognition
to “introspect” our attention schema and report what information
it holds. This proposal ts well with PP explanation of attention,
described above, and, hence, a Bayesian implementation of AST
has been recently proposed (Dołe
˛ga and Dewhurst 2019,2020).
A crucial part of both original AST and its PP formalization is
the account of the source of the “special” character of phenom-
enal consciousness. Attention schema represents the attentional
processes for the agent in a sparse manner, as its task is to con-
trol perceptual and cognitive systems, which does not require
fully detailed representation. Dołe
˛ga and Dewhurst point out that
this “frugality” is in fact a necessary requirement of a PP model
of cognition—it ensures a proper balance between complexity
and accuracy. According to the AST, this sparsity results in the
“user illusion” (Dennett 1991) of there being something special,
nonfunctional, and irreducible about phenomenal experience.
Against the WH account (discussed below), they further incor-
porate elements of Daniel Dennett’s multiple drafts view of con-
sciousness (Dennett 1991), arguing that it is not simply the best
hypothesis that becomes the contents of conscious perception.
10 It could be objected here that the exibility of Bayesian cognitive sci-
ence is in fact problematic and that postulated models of consciousness boil
down to a simple redescription of existing accounts with post hoc adjustments.
This objection refers to broader arguments brought up against the Bayesian
cognitive science: the objection of the tautological nature of the FEP (but see
Andrews 2021) and of the ad hoc and redescriptive nature of at least some of
the explanations within this framework (raised by Litwin and Miłkowski 2020;
Cao 2020). In case of consciousness, however, this is not true. Even the theories
that are closest to be seen as mere redescriptions (e.g. PGNW and B-AST) offer
novel predictions and algorithms, which enable to distinguish them from their
non-Bayesian “ancestors.” At the same time, most of the theories puts forward
novel theoretical ideas, even if they (in fact quite often) stem from combining
approaches hitherto considered opposed. Hence, this objection does not seem
to be a serious argument against the approaches discussed here.
First, it is necessary for active inference to reduce the space of pos-
sibilities and then for attention to actually probe the hypotheses.
This ties the account of “fame in the brain” (Dołe
˛ga and Dewhurst
2020) back to the AST, providing a mature deationary theory of
consciousness that treats the illusion of phenomenality seriously.
The higher-order state space approach
The HOSS proposed by Stephen M. Fleming11 addresses directly
the issue of reportability of the contents of subjective experience.
Fleming proposes a computational model that accounts for the
decision to report “I am aware of X” versus “I am unaware of
X.” His central claim is that “awareness is a higher-order state
in a generative model of perceptual contents” (Fleming 2020, 2).
This means that awareness is explicitly included as a one-
dimensional (discrete or continuous, if we allow for degrees
of awareness) internal random variable in the generative
model of a higher order than the variable tracking perceptual con-
tents of experience. Simulations of Fleming’s remarkably simple
model show its ability to account for phenomena of global igni-
tion associated with awareness (an important element of different
global workspace theories; see below).
Fleming’s model directly includes an asymmetry between
being aware and unaware. The author claims that while being
aware of an apple and of a hammer are two distinct states, both
phenomenally and functionally, being unaware of apple and of
hammer are very similar. This has consequences for prediction
error in the PP account of perception: the hypothesis that the
system is aware allows for much larger amount of prediction
error since it invokes large belief updates within the generative
model. Fleming’s simulations show these predictions to be correct
and his interpretation takes the large Kullback–Leibler divergence,
appearing when the system accepts the hypothesis “seen” ver-
sus “unseen” to correspond to the ignition pattern in the brain
associated with reports of being aware.12
Fleming further suggests a connection to precision in a pos-
sible extension of his model. He points out that inference about
the state of awareness can be aided by beliefs about attention and
other states of the perceptual system, which he suggests can be
implemented as beliefs about precision. In this way, he links his
proposal with Graziano’s AST (see previous section), even though
according to HOSS attention only provides “input into resolving
ambiguity” (Fleming 2020, 7, italics original), and is not sufcient
for determining awareness, which is a signicant incongruency
between the theories.
Fleming’s account, inasmuch as it can be expanded beyond the
model of awareness it focuses on, builds on higher-order theo-
ries of consciousness (e.g. Lau 2007). This distinguishes this pro-
posal from other Bayesian accounts of consciousness discussed
here. HOSS takes consciousness to be a higher-order process of
inference, which may take place without any active cognitive
access to this information, e.g. in working memory. This provides
limitations on the kind of cognitive architectures that allow for
11 I am grateful to anonymous reviewers for pointing me towards Fleming’s
12 An objection should be noted, however. Fleming’s interpretation of large
error as corresponding to ignition hinges on the implementation of PP algo-
rithms within the brain, the issue which Fleming does not elaborate upon.
Standard views on the implementation of the PP algorithms in the brain assume
the existence of specialized error neurons that encode prediction error (see e.g.
Bastos et al. 2012;Spratling 2017,2019). If that is the case, a large amount of
prediction error provides only an indirect explanation of global ignition, since
it implies only activity of this one type of neuron, which need not lead to
global ignition of the type Fleming envisages. Nonetheless, Fleming’s model
deserves attention, as it provides a very elegant explanation of a widely studied
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Bayesian theories of consciousness 5
conscious processes to occur: they must allow for factorization
along at least two lines (presence/absence and the contents of
perception) of a rich multidimensional state space. This factor-
ization may be computationally demanding and resultingly limit
the kinds of cognitive systems that could be considered conscious.
It also introduces complexity into HOSS, which can be taken to
control how this factorization is performed by imposing an upper
bound of the amount of states distinguished, although Fleming
does not make this connection.
The winning hypothesis account
The WH account of conscious perceptual experience has been
originally hinted at in Jakob Hohwy and collaborators’ seminal
paper on binocular rivalry (Hohwy et al. 2008; further devel-
oped in Hohwy 2012,2013). It focuses on access consciousness
(Block 1995) and does not discuss mental processes underlying
phenomenal experience.
The main claim is that the contents of conscious experience are
determined by the hypothesis about the state of the world, which
has currently the highest posterior probability, i.e. the brain’s
best guess. Each hypothesis about the state of the world can be
assessed along two separate dimensions, with regard to rst- and
second-order statistics of the inference—namely, it can be both
accurate and precise. Hohwy (2012) claims that, in order to con-
stitute conscious perception, the hypothesis must score relatively
high on both of them (see Fig. 1 in Hohwy 2012), hence—since pre-
cisions in PP framework reect attention, as described above—the
percept must be actively attended by the subject.
This is best visible in the PP story of binocular rivalry, a percep-
tual phenomenon that occurs when subjects are presented with
two different visual stimuli to each eye, e.g. a picture of a house
and of a face. What subjects consciously perceive at any moment
is either a face or a house and, over time, contents of perception
uctuate between the two images. PP (Hohwy et al. 2008) explains
this phenomenon by indicating that both stimuli correspond to
two separate hypotheses about the state of the world. Moreover,
the hypotheses are mutually exclusive (due to strong prior expec-
tations that no two objects can occupy the same space and that
faces and houses are distinct objects). The brain must accept one
of the guesses, leaving large amount of prediction error. This pre-
diction error makes the inference unstable and nally results in a
switch to a second best guess, which, however, does not account
for all the inputs and remains unstable as well.
This account has been extended by Parr et al. (2019a) and
applied to the phenomenon of Troxler fading, where static xation
leads to the experience of fading of peripheral stimuli. The choice
of the WH is closely related to action and active inference (as dis-
cussed in more detail in the section on the GE account below). Parr
and colleagues highlight the fact that binocular rivalry appears
similar to Troxler fading, where perceptual switches are due to
eyes shifting around the image—an overt action. In the former
phenomenon, the shifts in percepts might be considered results
of covert action, namely of attention shifting. This underscores
the role of the control of precision in the WH account, creating a
potential link not only to the GE approach but also to B-AST.
Predictive Global Neuronal Workspace theory
The WH proposal has been further extended and integrated with
the Global Neuronal Workspace theory [Dehaene and Changeux
2011; PGNW is developed by Hohwy (2013;2015),Whyte (2019),
and Whyte and Smith (2021)]. The two accounts are discussed sep-
arately, as the WH has also appeared throughout the literature as
a distinct proposal, without reference to the larger apparatus of
global workspace theory.
Global workspace is a proposition of a mechanism for informa-
tion exchange between otherwise isolated brain regions and pro-
cesses. The theory states that to become conscious, information
must enter this global workspace implemented “in a network of
densely connected pyramidal neurons possessing long-range exci-
tatory axons connecting prefrontal and parietal cortices” (Whyte
2019, 4; see Dehaene and Changeux 2011). Serial information pro-
cessing (resulting in experience of a “stream” of consciousness)
comes from the inhibition of competing input processes. The cen-
tral prediction of the model is that initial, local activity in the
sensory cortices corresponds to unconscious stimulus processing,
while conscious access appears in a relatively late time window
relying on global activation in the prefronto-parietal network.
Dehaene (2008) proposed that the late ignition corresponds to
the accumulation of evidence for a hypothesis about the state of
the world. Hohwy (2013) further supplements this proposal with
the role of action. In his account, ignition corresponds to the point
at which a given hypothesis has accumulated sufcient amount
of evidence to warrant a shift from perceptual to active inference
(see also Whyte 2019). Selecting the best available policy of action
requires that the hypothesis about the state of the world be held
xed. This indicates also that there should be top-down connec-
tions from the global workspace to sensory inputs, which carry
hypotheses about the contents of consciousness. This approach
offers a distinct hypothesis about the mechanism behind global
ignition from the proposal of HOSS, and direct empirical compari-
son would be benecial. One possible approach would be through
designing an experiment with a ne control of granularity of state
space of stimuli so that prediction of presence and absence would
lead to equal amount of prediction error. In this case, accord-
ing to HOSS, ignition would not in fact arise. However, given that
the stimuli were chosen so that both their presence and absence
elicits a demand for action—and active inference—if PGNW is on
track, ignition would still arise, as if the brain xed its best guess
about the state of the world.
In an important study, Whyte and Smith (2021) provide sim-
ulations of visual consciousness with an implementation of the
PGNW based on a recent version of the Active Inference Frame-
work (Parr et al. 2019b). Their model simulates the electrophys-
iological and behavioral results from phenomenal masking and
inattentional blindness experiments. This provides initial empir-
ical support for the ideas behind the PGNW, as the results of
simulations t well with empirical data collected from human
participants. In the paper, the authors also develop further the
role of precisions in PGNW, showing that different levels of signal
strength (precision of sensory likelihood a.k.a. sensory precision)
and attention (precision of the hypothesis a.k.a. expected free
energy precision) correspond to the standard taxonomy of factors
inuencing conscious access proposed within the GNW (Dehaene
et al. 2006). Moreover, the authors expand the PGNW model by
discussing the role of temporal depth, which is necessary in their
account for the report of conscious experience (see also the Inte-
grated World Modeling Theory section). In this way, they are able
to provide a denition of conscious access in terms of an infer-
ential process occurring at a sufciently deep temporal level to
allow for the integration and contextualization of information
processing at less deep levels (Whyte and Smith 2021, 13).
Not only is the PGNW consistent with the neuroscientic evi-
dence for the non-predictive version but it also provides sev-
eral unique hypotheses that: (i) conscious representation should
be continuous with processing at lower levels of the hierarchy,
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(ii) consistency of expectation inuences the amplitude of P3
event-related potential component associated with subjective
report (Whyte and Smith 2021, 12), and (iii) depending on the
uncertainty about the stimuli, the dynamics of activity between
prefrontal and parietal regions will be signicantly different, cor-
responding to precisions assigned to either predictions (closer to
the front of the brain) or prediction errors (in parietal regions)
(Whyte 2019).
The generative entanglement account
Andy Clark has developed a separate perspective on the problem
of phenomenal consciousness (Clark 2018,2019;Clark et al. 2019).
Clark’s account shares certain supercial features with the WH
account in that the contents of consciousness depend on the cog-
nitive systems’ “best hypothesis” about the state of the world.
However, what sets this approach apart is the view of GE (Clark
2019) as the source of phenomenality. Given the task of predict-
ing the state of the environment in which the subject—cognitive
system itself—plays a signicant causal role, there arises a need
for the system to maintain a self-model able to predict its own
states, “reactive complexes,” or “dispositions” (Clark 2019, 653) in
response to certain structured patterns of sensory ows, in which
interoceptive, exteroceptive, and proprioceptive inputs remain
entangled. Because of this entanglement and resulting com-
plexity, the reactive dispositions are modeled as coarse-grained,
abbreviations or latent variables for those inows. In other terms:
they constitute the system’s mid-level hypothesis about the state
of the world (Clark et al. 2019).
These “qualia” are puzzling to the agent for several reasons.
First of all, these mid-level inferences underdetermine the actual
state of the world while remaining highly certain to the system
(i.e. they have high precisions assigned). In result, they are treated
as mere “appearances” (Clark et al. 2019, 29), which nonetheless
cannot be easily “shaken off.” Second of all, since these hypothe-
ses model system’s own states, there is no need for a higher-level
model explaining how these reactive dispositions came about—
and in fact there is no such model (Clark 2019, 655). In result, the
system has access only to the nal estimation. “Qualia” are ele-
ments of a simplied self-model, which aims to be both accurate
and concise in order to guide future choice and action.
Positing the contents of phenomenal consciousness on an
intermediate or mid-level of the information processing hierarchy
has a longer history, as it has been initially proposed by Jackendoff
(1987) and later repeated by Prinz (2012). This view has often been
criticized due to the difculties with delineating the postulated
intermediate level according to the criteria proposed (Marchi and
Newen 2016). However, Marchi and Hohwy (2020) show that the
notion of active inference provides clear-cut identication of the
scope of phenomenal consciousness, which, in case of humans,
happens to be in fact intermediate. This idea connects Clark’s
GE back to Hohwy’s WH account. The contents of consciousness
depend on the best hypothesis, which is held xed for the pur-
pose of active inference. At the same time, each organism has
a privileged spatiotemporal scale on which the active inference
operates, depending on the organization of the organism’s behav-
ioral dispositions. In case of humans, Marchi and Hohwy identify
this privileged scale with that of intuitively “basic” actions: reach-
ing, grasping, taking a step, turning, crouching, etc. Hence, for
active inference to be able to provide the organism with an optimal
policy, the hypothesis specifying the contents of conscious experi-
ence has also to be spelled out on this spatiotemporal level. In case
of humans, this in fact corresponds to the intermediate level, and
the Marr’s “2.5d sketch” (Marr 1982; see Marchi and Hohwy 2020).
A slightly different proposal that ts with the scheme of the
GE account has been earlier put forward by Je
nska (2017).
nska uses elements of the PP framework—in the version
developed by Clark—to argue for a possibility of providing a novel,
expanded theory of conscious experience through an intersection
of Global Workspace Theory (in the version of Baars 1997) with
the Sensorimotor Theory (as developed by O’Regan and Noë 2001).
This account is, however, lacking details, and its main input boils
down to underscoring the importance of the ongoing integration
of data from different sensory modalities in the brain and its role
in guiding action.
Projective Consciousness Model
The PCM was originally conceived by David Rudrauf with several
collaborators—most signicantly Kenneth Williford and Daniel
Bennequin (Rudrauf et al. 2017,2020;Williford et al. 2018). While
the model strongly relies on the view of mind advanced by the FEP,
the central part of the explanation of consciousness it proposes
comes from geometrical considerations.
The PCM accepts the view of mind advanced by the
Active Inference Framework and its explanations of uncon-
scious processing, control of behavior, and the functional role
of consciousness—which is taken to be facilitation of FE min-
imization. However, the standard account of Active Inference
Framework is expanded with discussions of the internal geometry
of the generative model.
The PCM follows the program of (neuro)phenomenology
in identifying phenomenal invariants of conscious experience
(Williford et al. 2018, 3) and postulating them as axioms—as well
as explananda—of the theory. In this way, the model accepts a
conceptualization of the problem of consciousness focusing on
the perspectival (in the spatial sense) and intentional elements
of conscious experience. This perspectivality leads Rudrauf et al.
(2017) to accept that the lived space is non-Euclidean and bet-
ter described by projective geometry—a non-Euclidean geometry
initially developed by Renaissance architects and painters and
currently important in e.g. virtual reality and computer graphics
research. Thus, according to the PCM, for the generative model
to be capable to support conscious experience, it has to have
the form prescribed by the projective geometry. More formally,
they offer the concept of a “Field of Consciousness,” a three-
dimensional projective space dened by a particular vector space
corresponding to the point of view. The mental processes of per-
spective taking thus correspond to projective transformations (i.e.
geometrical transformations of the space to a different point of
view and its associated vector space), and the authors explicitly
claim (Williford et al. 2018) that such transformations are com-
puted by the brain (specically by posterior cortical and subcor-
tical structures; Rudrauf et al. 2017). The authors discuss several
examples (e.g. visual illusions, such as Necker cube and out-of-
body experiences) of application of the framework and support its
plausibility with computer simulations.
This account declaratively connects the elements of global
workspace theory as it assumes the central availability of con-
scious information with elements of the integrated information
view (Rudrauf et al. 2017;Williford et al. 2018). Furthermore, it
is compatible with some other accounts of consciousness within
the Bayesian cognitive science, most specically with the WH
account, as Rudrauf et al. (2017) use the WH account to explain
the uniqueness of the subject’s perspective (i.e. it is the one that
is capable of explaining away the largest amount of the prediction
error). The authors themselves identify “the thesis that projec-
tive transformations and projective frames necessarily subtend
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Bayesian theories of consciousness 7
the appearance and workings of consciousness” (Williford et al.
2018, 15) as a key novel contribution of their model.
Integrated World Modeling Theory
Adam Safron’s IWMT (Safron 2020) builds on the foundations
provided by the Active Inference Framework and Integrated Infor-
mation Theory (IIT) (e.g. Tononi et al. 2016), with—again—a strong
inuence of the Global Neuronal Workspace Theory (e.g. Dehaene
2014), as well as several others accounts of consciousness, includ-
ing those discussed in this paper. Safron shows that IIT and Active
Inference Framework are largely coherent and, specically, that a
union of the two accounts helps to solve some of the outstand-
ing problems of each, such as e.g. the panpsychist consequences
of integrated information view. The IWMT is a far-reaching, com-
plex account of consciousness and, due to space considerations, I
will have to omit some details.
To summarize this proposal, IWMT sees the essence of con-
sciousness in a process “capable of generating a world model with
spatial, temporal, and causal coherence with respect to the sys-
tem and its causal inter-relations with its environment” (Safron
2020, 4). The postulated “integrative world models,” which sit at
the core of this account connect postulates from Active Infer-
ence Framework, integrated information, and global neuronal
workspace: they consist of generative models, with sufcient
temporal and counterfactual depth, which are generated by com-
plexes of integrated information and by global workspaces (under-
stood as implementing Bayesian model selection). The integrative
aspect refers to the spatial, temporal, causal, and agentive coher-
ence, which are understood as ways of categorizing experience
(giving the view a somewhat Kantian avor).
On the algorithmic level, the key proposal of the IWMT are Self-
Organizing Harmonic Modes (SOHMs), a mechanism for imple-
menting complexes of integrated information, as well as global
workspaces within the brain. The harmonic modes are synchro-
nizations of activity of neural regions, providing effective con-
nectivity between the said regions and potentially implementing
self-evidencing [in the sense of Hohwy (2016)]. At the same time,
according to the IWMT, they can explain both how particular per-
ceptions cross the threshold of consciousness, as well as how uni-
tary experiences emerge from probabilistic world models. Safron
refers SOHMs to neural rhythms, with different functions—and
experiential consequences—depending on the frequency bands.
Another mechanistic postulate of the theory is the imple-
mentation of PP in the cortex in the form of (folded) variational
autoencoders13 in which the internal representation corresponds
to inferred hidden causes. This somewhat standard account of
neural implementation of PP is expanded through a reference
to the turbo coding theory—a technical procedure of encoding
signal to ensure maximal channel capacity through an intro-
duction of redundant encoding (and decoding) of the signal by
at least two encoders/decoders. The redundancy of information
enables a feedback loop between the decoders, which provides
the system with an error correction mechanism. In case of the
cortex, turbo codes enable a connection between several circuits
13 Autoencoder (see e.g. Goodfellow et al. 2016, 493–516) is a type of an arti-
cial neural network that is trained in an unsupervised fashion to learn to copy
its input into its output through an internal representation of different (usually
lower) dimensionality than the input. In this way, autoencoders are capable
of learning efcient coding of inputs. Variational autoencoders employ gen-
erative models to this goal. “Folded” in this context describes the topology of
the articial neural network implementing the autoencoder, which is folded
at the internal representation so that the neurons implementing encoding and
decoding parts of the processing are aligned (see gure 2 in Safron 2020).
(modeled as folded autoencoders), providing them with a shared
latent space that may be used e.g. for multimodal sensory inte-
gration. Although Safron does not discuss it directly, the reliance
of IWMT on autoencoding—and the dimensionality reduction at
the core of this algorithm—means that an essential feature of a
generation of the world models is the reduction of the complexity
of probability densities corresponding to them.
Phenomenologically speaking, IWMT ascribes to a form of a
“non-Cartesian” theater, positing an internal, supposedly nonho-
muncular, observer as a subject of the experiences. It envisages
phenomenal experience as real, implemented by the state of the
current maximal SOHM in the posteromedial cortices.
Dual Aspect Monism
Solms (2019), building on earlier work with Friston (Solms and
Friston 2018), offers yet a different take. According to the author,
Chalmers’ hard problem of consciousness (Chalmers 1996) cor-
rectly points out that phenomenal consciousness cannot be
reduced to function of vision or memory. However, for Solms, it
does not mean that phenomenality itself is not functional or that
it cannot be explained in functional terms.
His main point can be reconstructed as follows: both sub-
jective experience of consciousness and physiological processes
in the brain are different appearances of some other, abstract,
functional process in the same way that lightning and thunder
are different appearances of an electrical discharge. The case of
children suffering from hydranencephaly, i.e. completely decor-
ticated, in which case patients remain waking and their wakeful-
ness has a certain qualitative—phenomenal—aspect, indicated by
the fact that they are capable of affective states, leads Solms to
identify consciousness with the function of “feeling” and locate
it in subcortical structures that remain intact in the decorticated
Solms argues that the function of feeling is to enable homeo-
static control in unpredicted contexts, and states that “conscious-
ness is felt uncertainty” (Solms 2019, 7). As this monitoring is related
to survival, it is inherently value laden and classies as good those
actions and states that lead to survival (increase organism’s t-
ness). This leads both to subjectivity (i.e. availability only from
the rst-person perspective) as only the system itself can monitor
its own internal states and to the qualitative nature of experi-
ence, which results from the compartmentalization of states of
the organism into categorical variables, enabling the system to
deal with the increasing complexity of its self-model. Finally, as
conscious experience is inuenced by contextual factors, preci-
sion weighting is necessary to control the prediction errors, and,
hence, it is the mechanism implementing conscious access. Note,
however, that, in Solms’ account, precisions are not identical with
attention but rather implement both attention and motivation
(Solms 2019, 12).
The crucial role played by the homeostatic sources of expe-
rience in Solms’ model makes an interesting connection to a
previous attempt to connect PP framework to the Global Neuronal
Workspace theory; the proposal of the “limbic workspace” (Chanes
and Barrett 2016; as Whyte 2019 points out this proposal differs
from PGNW discussed above with regard to postulated model and
its predictions). Chanes and Barrett (2016) focus on the role that
limbic cortices (e.g. anterior insula, parahippocampal gyrus, and
cingulate cortex) play in cortical processing and conscious access.
The authors suggest that limbic cortices select between repre-
sentations, factoring in the organism’s homeostatic needs and
preferences. Moreover, since these brain areas have bidirectional
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connections to subcortical structures, as well as other cortical
regions, they seem to be a plausible extension of Solms’ model,
connecting his explanation of “feeling” to the processing in brain
cortex, showing how mental images could be imbued with a “what
it’s like.” If such a connection would turn out to be plausible, it
would signicantly expand the scope of Solms’ original account.
Markovian Monism
MM (Friston et al. 2020) is advanced from the rst princi-
ples (Colombo and Wright 2018) as a theory of the origin of
consciousness. It does not provide any mechanistic nor imple-
mentational details for the actual realization of consciousness,
but rather it shows how sentience (in a weak sense of respond-
ing to sensory impressions) is entailed by the tenets of the FEP.
Specically, it claims that sentience follows from the existence
of the Markov blanket. Furthermore, it is an explicitly metaphysi-
cal account of phenomenality (but see the criticism of Beni 2021).
While it differs in scope and approach from other papers discussed
here, it provides several important insights into the general pic-
ture of consciousness sketched by the Bayesian cognitive science
and, as such, requires some discussion.
In this review, I will focus on two features of this account: the
Cartesian duality of mind with its description in terms of infor-
mation geometry and Friston and collaborators’ reference to the
central role of precision for consciousness.
The Cartesian duality of the mind can be cast in terms of a pos-
sibility of describing the cognitive system from two perspectives:
a third-person perspective of psychology, neuroscience, etc., as
well as from the rst-person perspective of subjective experience.
According to the FEP, this division appears when we consider any
system as delineated by a Markov blanket—i.e. the set of states or
variables that render the internal states of the system condition-
ally independent from anything else. While Friston argues [e.g. in
his monograph (Friston 2019); but see Bruineberg et al. (2020) for
critical analysis of this proposal] that the existence of the Markov
blanket is necessary for the existence of every “thing” as far as
it can be distinguished from everything else, a key insight here
comes from the fact that the delineated system can be described
in terms of gradient ow on self-information or surprisal (roughly
speaking, this is possible as it is the steady-state solution to the
Fokker–Planck equation, a standard description of time evolu-
tion of dynamical systems). Together with the very existence of
the Markov blanket, this provides the modeler with the possibil-
ity of describing the internal states as modeling external states,
precisely in the manner described in the introduction to the FEP
above. If we equip these statistical manifolds with a Fisher infor-
mation metric (which quanties the distance between two points
by accumulating the Kullback–Leibler divergence between distri-
butions encoded on the manifold en route from one point to the
other), we impose an information geometry onto the Markov blan-
ket delineated system; but as we have two independent densities,
we, in fact, need to dene two information geometries—intrinsic,
describing autonomous (i.e. internal and active) states, param-
eterized by time, and extrinsic, describing external states and
parameterized by beliefs—i.e. internal states.
While this is only an account of why conscious experience
appears in principle, omitting its structure and implementation
in the case of actual human experience, Friston et al. (2020, 21–22)
argue that not only does it show the origin of sentience and
account for the private, subjective character of phenomenal qual-
ities but it also entails several of the criteria of consciousness, as
characterized by the IIT (e.g. uniqueness and unity of conscious
More specic details of neural implementation of conscious-
ness are only briey discussed. The authors point out that the
Fisher information metric on the internal statistical manifold is
the curvature of the variational free energy. Furthermore, they
claim that this is the same parameter as the precision or con-
dence of beliefs about external states. This parallel is not worked
out in formal detail but rather attributed to the work on gauge
theories (Sengupta et al. 2016); however, even the informal def-
inition of Fisher information metric provided above should give
the reader the general idea of the considerations showing this
equivalence. This makes a direct reference between the work on
MM and the work on DAM described above, as well as several
other papers claiming e.g. that precision plays a key role in opacity
or transparency of phenomenal beliefs (Limanowski and Friston
Bayesian cognitive science offers a hodgepodge of approaches to
the study of consciousness, which differ in the denition of the
problem, the extent of phenomena covered, the level of formal
and/or implementational detail, as well as in metaphysical com-
mitments of the views. These differences are briey summarized
in Table 1. This picture ts well with the “orthogonality” of PP
approaches with regard to current debates in neuroscience and
philosophy of mind, as stated by Jakob Hohwy (Hohwy 2020, 11).
Furthermore, this duality is actively exploited by researchers, who
not only attempt to connect PP accounts with extant prevailing
theories of consciousness, such as global neuronal workspace the-
ory, or IIT,14 but also believe that FEP can build bridges between
those, somewhat inconsistent, accounts.
I will now turn to the discussion of the “larger picture” that
the Bayesian cognitive scientists working on consciousness paint,
some problems it encounters, and its possible interpretation in
terms of a minimal unifying model.
Bayesian cognitive science of consciousness
What becomes clear once we discuss all of these accounts
together is that, despite their differences, their formal solutions
to the specic phenomena of consciousness are more or less the
same between the accounts. Specically, they all agree that pre-
cision (or precision optimization) and complexity of the internal
model are responsible for access and phenomenal consciousness.
The general picture is the following:
1. The contents of conscious perception depend upon the pos-
terior probability assigned to different hypotheses about the
state of the world (B-AST, WH, PGNW, GE, and PCM) and
2. the landscape of possibilities is reduced to serial processing
due to the exigencies of action (WH and GE),
a. however, access to this selected hypothesis is managed
by precision weighting of different processing streams,
which can take the form of attention (B-AST, HOSS, and
PGNW) and possibly happens post hoc, i.e.—at the point of
storage (B-AST and DAM).
3. In order to efciently manage precisions, a higher-order
model for precision optimization is required. It can take the
form of a meta-model (WH), a self-model (GE and DAM),
14 While IIT has been less represented among the views discussed here
than other competing theories, a recent preprint (Waade et al. 2020) discusses
another Bayesian approach to consciousness currently under development,
which is likely to strengthen the position of IIT in this context.
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Bayesian theories of consciousness 9
Table 1. The table summarizes the scope of the theories discussed in this review
Theory Access Phenomenal Function Origins Algorithm Implementation
B-AST X X X ? ?
HOSS ? X ?
GE X X X ?
“X” marks whether the theory provides (or at least attempts to provide) an account of: access consciousness, phenomenal consciousness, function of
consciousness, origins of consciousness (understood in terms of a possible “evolutionary trajectory” of forms of sentience of increasing complexity), the
algorithmic description of postulated processes, and a description of possible implementation of this algorithm (roughly in the standard sense of Marr 1982). In
problematic cases, when one of those explananda is not directly engaged with but rather hinted at, question mark was used.
an attention schema (B-AST), or a higher-order state in the
model (HOSS).
4. The contents of this model are maintained on an intermedi-
ate level, which balances out precision and complexity and
allows active inference to provide optimal policies (B-AST,
GE, IWMT, and DAM). At the same time, the model is not
accessed by any kind of higher level process and, as such,
remains at the top level of information processing hierarchy
(B-AST, HOSS, GE, and IWMT) even if its contents are passed
on for further processing.
5. The structure of this model is the source of the “special,”
“ineffable” character of phenomenal experience (B-AST, GE,
and IWMT),
a. however, it does not necessarily diffuse the metaphys-
ical reality of the predictions of this model, taking the
form of qualia (GE) or feelings (DAM), which are as real as
perceptions of objects such as “cats” or “computers” (GE).
There are obviously points of important disagreement. To note
only the most extensive:
The PCM does not assign the existence of phenomenal
consciousness to the meta-model of precision but rather to the
generative density itself (it dissents with regard to points 3–5).
The “special,” “ineffable,” and perspectival character of experi-
ence comes from the geometrical structure of the model. Note,
however, that it is formally possible that one of the effects of
the geometrical transformations postulated by PCM is a reduc-
tion of complexity of the model as these transformations focus
on what could be called the “syntactic” structure of perception
while eschewing some level of detail. A possible argument for
this interpretation would focus on the fact that the projective
transformations postulated by the PCM do not necessarily pre-
serve the identity of transformed objects. Furthermore, to support
this idea, one could argue that the transformation postulated
by this model is not directly computed in the brain, but rather
approximated, using a representation with reduced complexity.
Following this argument is, however, outside the scope of this
paper, and furthermore—this dissension of projective conscious-
ness has its (conceptual) advantages for the Bayesian theories of
consciousness at large.
The IWMT does not explicitly connect access consciousness
to attention and precision, positing the self-organizing harmonic
modes as the key mechanism (it dissents with regard to points
1–2). While Safron’s considerations—especially the hypotheses
about the role of specic frequencies of neural rhythms—enable
us to directly connect self-organizing harmonic modes with atten-
tional processing (mainly because of the relationship between the
harmonic modes and alpha and beta EEG frequency bands, which
are associated with attentional processing in the literature; see
e.g. Klimesch 2012), the relationship between these “metastable
synchronous complexes of effective connectivity” and precision
optimization is more convoluted. Functionally, it seems plausi-
ble to regard the self-organizing harmonic modes as akin to an
attention schema as they “may act as systemic causes in select-
ing specic dynamics through synchronous signal amplication”
(Safron 2020, 14). However, since, formally, they are dened in
terms of attractors for phase space description of neural activity,
it is difcult to see how they could be deconstructed into specic
variables of the said neural system, including precision, due to
the interdependencies of variables in the system. Hence, precision
is not usually explicitly modeled by the self-organizing harmonic
modes, making the IWMT distinct from the other FEP approaches
to attention. Instead, precision optimization is performed in a dis-
tributed fashion, as a by-product of multiple neural processes:
both an “as if,” implicit management of attention, and explicit,
conscious and unconscious, direct “attentional shifts.” What fol-
lows is that access to consciousness is not directly implemented
by attentional biasing but rather by a more generic process. This
also bears some similarities to the HOSS, where Fleming (2020)
underscores that precision optimization is not the only mecha-
nism responsible for awareness. This view is in fact more in line
with the current state-of-the-art research on the role of attention
for consciousness, which underscores the importance of differen-
tiating between different types of attentional processing (see e.g.
Pitts et al. 2018).15
The MM is the great absent of the list above, as it remains silent
on these issues, focusing on providing formal foundations for the
more detailed models.
There are of course other similarities shared less widely by only
two or three of the proposals discussed here. Their limited scope
does not warrant inclusion in the general picture painted above;
however, it is important to note some of the emerging proponents
like temporal depth (related to complexity), whose importance
has been underscored e.g. in recent developments of the PGNW
(Whyte and Smith 2021) and in IWMT.
Finally, it is important to note that if one were to restrict to only
a subset of approaches within Bayesian cognitive science and to
15 I am greatly indebted to Adam Safron for the discussion of some of the
ner details of the IWMT related to the relationship between self-organizing
harmonic modes, attention, and access consciousness.
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10 Rorot
concepts exclusive to the framework, the general picture could
differ signicantly. In a recent article, Vilas et al. (2021) review
explanations of various phenomena of consciousness specically
from the Active Inference Framework and develop a signicantly
different picture from the one proposed above, one relying heavily
on the structure of the generative model, in particular its tempo-
ral and counterfactual depth. However, as the authors accurately
point out, once we limit ourselves to Active Inference Framework
only, the emerging picture remains preliminary and needs to be
extended with mechanistic and implementational details.
A cross-section of the accounts discussed in this review pro-
vides us with a general view of consciousness that all Bayesian
cognitive science theories are likely to be congruent with. How-
ever, I believe that a more profound reading of these similarities
is justied. Namely, the postulated role of precision and complex-
ity can be regarded as Minimal Unifying Model of consciousness
(MUM; in the sense of Wiese 2020).
Can Bayesian cognitive science alone provide a
theory of consciousness?
Before turning to the discussion of the MUM, one important
objection needs to be noted.16 In a recent paper, Marvan and
Havlík (2021) analyze some of the accounts of consciousness dis-
cussed here. Their goal is to argue that PP does not by itself
provide a theory of consciousness but requires external explana-
tory machinery provided by established theories of consciousness
to do so. Given the detailed overview of accounts of consciousness
within Bayesian cognitive science provided here, their criticism
seems to be to some degree correct. Out of those theories, per-
haps, only the WH, the GE accounts, DAM, and MM do not make
explicit references to some theory of consciousness “external” to
the framework, although the last account is outside of the scope
of Marvan and Havlík interests (for reasons already discussed).
Marvan and Havlík directly criticize the main claim of the WH
approach, pointing toward incompatible evidence from blindsight
(see also Dołe
˛ga and Dewhurst 2020 criticism) and continuous
ash suppression paradigm (Tsuchiya and Koch 2005). In this way,
they reject also the GE account. DAM, however, is not mentioned
by them and seems immune to their objections. In this case, con-
scious access is implemented by only precision weighing, which
is a “proprietary” idea of Bayesian cognitive science and could
provide an explanation of continuous ash suppression. In very
informal terms, one could point out that continuous ashing even
to one eye reduces the precision assigned to all information com-
ing from visual modality. This happens since the brain starts to
classify these (unlikely) sensory inputs as noisy and unreliable
and, normally, there is a correspondence between the reliability
of both eyes, leading to distrusting both eyes. In result, it dis-
cards the correct hypothesis explaining the pattern presented to
the second eye. This kind of explanation could be experimentally
tested with participants who have worse eyesight in one eye—in
this case, ashing should cause signicantly weaker suppression
as the brain most likely would model the precision of each eye sep-
arately. Alternatively, the absence of suppression in a multimodal
version of continuous ash suppression could provide support for
this hypothesis.
16 I am grateful to an anonymous reviewer for pointing me towards this
paper and suggesting that HOSS and PGNW are somewhat immune to those
Furthermore, some of the accounts that refer to other theories
of consciousness make connections to “external” accounts with-
out depending on those theories’ explanatory tools, as in the case
of e.g. Fleming’s HOSS and the recent take on the PGNW due to
Whyte and Smith (2021). In these two cases, in fact, PP is capable
of providing an explanation of the source of ignition associated
with awareness rather than rely on this notion to explain con-
scious access. In HOSS, as discussed previously, ignition is taken
to be associated with inference and asymmetrical prediction error
appearing when the higher-order random variable tracking aware-
ness accepts hypothesis “seen. In PGNW, the phenomenon of
ignition is reproduced by the model when it is presented with
the stimulus. Whyte and Smith connect ignition in their model
with evidence accumulation and belief update at temporally deep
levels of their generative model. In this way, Bayesian cognitive
science of consciousness escapes Marvan and Havlík’s criticism
but furthermore is able to supplement more established theories
with mechanistic details.
Finally, while it is common to treat PP as a theory of cognition,
with strong unifying ambitions, such a perspective is in fact very
controversial. The unicatory ambitions have been pointed out as
failed (Litwin and Miłkowski 2020) and, among the recent shift
toward more instrumental understanding of the FEP (Colombo
and Wright 2018;van Es 2020;Andrews 2021), it has been also
suggested that PP does not hold as a “theory” in the rst place
but should rather be considered a framework or a toolbox (Litwin
and Miłkowski 2020). While this point is debatable in relation to
PP on its own, it is much less so in the context of Active Inference
Framework, the FEP, and the Bayesian cognitive science at large.
If we take such a perspective, Marvan and Havlík objections are
correct, yet misguided, since there was never a need for Bayesian
cognitive science to provide theory of consciousness all by itself.
This perspective seems also the most charitable for the interpre-
tation of precision and complexity in terms of a minimal unifying
model, as I will argue below.
Precision and complexity as a minimal
unifying model
Wiese (2020) proposes the notion of the minimal unifying model
as “a model that
1. species only necessary properties of consciousness (i.e. it
does not entail a strong sufciency claim),
2. has determinable descriptions that can be made more spe-
cic, and
3. integrates existing approaches to consciousness by high-
lighting common assumptions” (Wiese 2020, 2).
I argue that precision and complexity can be seen as ele-
ments of exactly such a model, complementary to the information
generation account that Wiese discusses. The relation between
Wiese’s account and the one proposed here is the following: since
Wiese posits only that MUM should specify necessary properties of
consciousness, information generation (Kanai et al. 2019) can be
easily supplemented by the notions of precision and complexity
in the manner discussed below (see the end of the “Conditions
for a MUM” section). Furthermore, these two proposals are log-
ically independent to a large degree. Should empirical evidence
lead us to reject either information generation or precision and
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Bayesian theories of consciousness 11
complexity as unnecessary for conscious experience, the other
element can still be considered a MUM.
To esh out my proposal, I will show that the roles that pre-
cision (optimization) and complexity (limiting) play for, respec-
tively, access and phenomenal consciousness do indeed meet
the three conditions set by Wiese. In doing so, I am abstract-
ing away from the details of particular views discussed ear-
lier in the paper and focusing on the core of the Bayesian
approach to consciousness outlined in the “Bayesian cognitive
science of consciousness” section. The key point is to show
that these two features are in fact necessary for conscious-
ness under this general Bayesian approach. Then, I will analyze
whether precision and complexity can be considered a MUM
independently of the Bayesian cognitive science that they were
developed as a part of similar to the way that Wiese disjoints
information generation from the larger proposal of Kanai and
Conditions for a MUM
The rst condition states that MUM should consist of necessary
but not sufcient features. Here, I will argue briey against the
sufciency of precision and complexity and turn to its neces-
sary character in the next section as it is closely related to the
connection between Bayesian cognitive science and the current
While all accounts discussed above assign a strong explanatory
role to precision and complexity, each of them provides further
specications and limitations with regard to relevant properties.
This in fact has been the subject of the criticisms put forward by
Marvan and Havlík (2021). The WH account is closest to estab-
lishing precision optimization as sufcient for conscious access.
However, the simulations by Parr et al. (2019a), as well as the
work of Marchi and Hohwy (2020), point out that while precision
is responsible for managing current contents of conscious expe-
rience, action and active inference are further required to specify
any particular percept. Most strongly, the insufcient character
of precision is underscored by Fleming in his HOSS, where he
explicitly points out that precision provides input into resolving
ambiguity but does not determine awareness. Complexity, on the
other hand, is strongly entangled with the processing of hypothe-
ses and updating of the generative model and, while it can be
conceptually disconnected (as shown below), it only plays a role of
a limit or a reference point for other processes and, hence, cannot
be considered a sufcient condition of consciousness.
The second condition is met quite obviously as both “precision”
and “complexity” are well-dened terms within the formalism of
Bayesian cognitive science. There are multiple competing models
arguing for specic neural or at least algorithmic interpretations
of these processes (see e.g. Spratling 2017). Regarded indepen-
dently of this framework, they still retain their strong, mathemat-
ical denitions and can be tted into multiple different theories of
consciousness as exhibited by the different connections Bayesian
cognitive science makes to established accounts of consciousness.
Finally, the third condition states that MUM should allow for
making connections between existing accounts of consciousness.
We can consider this condition on at least two levels.
First of all, if we accept the broad assumption that something
sufciently similar to the Bayesian cognitive science is a correct
theory of cognition, several of the discussed accounts make claims
(with different degree of evidence cited to support them) that the
FEP can be employed to build bridges between predominant theo-
ries of consciousness. It should sufce to refer to B-AST, PGNW,
and IWMT. Furthermore, the connections to theories such as
global neuronal workspace or IIT build upon precisely the notions
of precision and complexity.
Second of all, as mentioned above, understanding precision
and complexity as a MUM is complementary, rather than compet-
itive, to the information generation theory of Kanai et al. (2019).17
The Kanai et al. (2019) view can be summarized as a view that
the basic function of consciousness consists of generating repre-
sentations of events that are possibly spatiotemporally detached.
Information generation in this model follows from a compression–
decompression view of perception, algorithmically described by
variational autoencoders. To describe what kind of information
it is, the authors employ the notion of a generative model, in
the sense of FEP described above, and Wiese makes a connection
to the algorithmic information theory of consciousness (or sim-
ply Kolmogorov theory), advanced by Rufni (2017), which helps
clarify the compression–decompression view, and introduces the
notion of complexity into the MUM advanced by Wiese.
It is important to note that this is a different notion of complex-
ity and we should be wary of conating it with the one employed
in Bayesian cognitive science. Rufni (2017) and Wiese (2020) use
the term “complexity” in the technical sense of Kolmogorov com-
plexity (see Cover and Thomas 2006). The two notions are closely
related as they both provide quantications of the amount of
information loss (a formal treatment of those similarities is pro-
vided by e.g. Cerra and Datcu 2011). Furthermore, they are both (in
general) incomputable and can be approximated in terms of data
compression. The most important difference is that the notion
of “relative entropy” (Kullback–Leibler divergence seen in the free
energy equation) is specied within the probabilistic framework of
classical information theory and shares its inability to “describe
information content of an isolated object” (Cerra and Datcu 2011,
903). Kolmogorov complexity, on the other hand, is dened within
algorithmic information theory, which takes the notion of infor-
mation content of an isolated object as a primary concept (Cerra
and Datcu 2011, 903). Cerra and Datcu show that it is possible
to construct a parallel for the notion of relative entropy within
the algorithmic information theory, dened on the basis of Kol-
mogorov complexity, as “the compression power which is lost
when using such a representation for [a string] x instead of its
most compact one, which has length equal to its Kolmogorov
complexity” (903).
Despite these different backgrounds, both notions of com-
plexity play similar roles. For Rufni (2017), “structured con-
scious awareness is associated to information processing systems
that are efcient in describing and interacting with the external
world (information)” (5), where efciency is described as low (Kol-
mogorov) complexity of incoming data given the internal model.
In Bayesian cognitive science, complexity similarly is used to
account for “efciency” of the generative model. Hence, despite
different formal denitions employed, considering complexity as
a MUM is compatible with information generation theory. Fur-
thermore, the inclusion of precision optimization to this account
would be straightforward, as shown below.
Precision and complexity (optimization) as
necessary properties of consciousness
The question of whether precision and complexity optimization
can be considered necessary properties of a conscious system
depends to a degree on our acceptance of the Bayesian cognitive
17 I do not count Kanai et al.’s (2019) proposal as falling within the Bayesian
cognitive science as it makes reference to the predictive coding schema only in
terms of implementation of postulated mechanisms.
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12 Rorot
science story of cognition. If one agrees that this theory provides a
sufciently adequate model of cognitive systems, then the answer
to this question is obvious, as these are central terms in Bayesian
cognitive science and even more so in its account of conscious
experience. However, it is not the case once we consider a broader
picture. While Wiese (2020) argues that all theories of conscious-
ness have to contain the component of information generation,
and this claim seems uncontroversial, the same cannot be said
about precision and complexity. For example, it is an empirical
question whether a theory that would posit that the complexity of
an internal model [in a very general sense, akin to what is required
by Kanai et al. (2019) and Rufni (2017)] is set to a constant value or
that the cognitive system is incapable of estimating and managing
precision of incoming inputs would be capable of explaining con-
sciousness (the latter might be the case in IWMT, as mentioned
above). However, at the same time, the postulates of precision and
complexity optimization are not limited to an acceptance of a full
Bayesian cognitive science story about the mind and they can sup-
plement other accounts. It is especially so if we accept that these
ideas form a framework rather than a unifying theory of mind (see
Litwin and Miłkowski 2020).
Consider again the information generation theory (Kanai et al.
2019) and precision optimization. In constructing their theory,
Kanai and colleagues specically aim to address the aspect of
consciousness concerned with the broadcast of information [as
opposed to metacognition, distinction put forward by Dehaene
et al. (2017)]. They argue that what underlies functions associ-
ated with global broadcast is the “capability of generating ctional
representations using internal, sensorimotor models” (Kanai et al.
2019, 3). What their account leaves out and one more reason to
conclude that it presents necessary but not sufcient condition
for conscious experience is precisely the question of how this gen-
erated information becomes available across the brain. Kanai and
collaborators simply state that this information is maintained in
short-term memory, placing the central question of the global
workspace theory outside of the scope of their interests. However,
since information generation can be implemented in a predictive
coding scheme, it is straightforward to employ the tools of PP to
explain the short-term memory in terms of precisions (or weights)
assigned to perceived versus generated information.
We have seen, similarly, how precision and complexity can
be related to the global neuronal workspace theory and the IIT.
Hence, the claim of this paper can be read in two ways.
First, in a relatively uncontroversial but limited way:
Precision and complexity constitute a MUM for Bayesian cogni-
tive science of consciousness.
In the manner described by Wiese, casting common elements
of the Bayesian theories of consciousness in terms of a mini-
mal unifying model helps unify them [in the sense of Miłkowski
(2016) and Miłkowski and Hohol (2020)] with existing theories of
consciousness—in this case, also with formerly proposed minimal
unifying model. In this way, precision and complexity optimiza-
tion become key elements for future experiments seeking to test
Bayesian theories of consciousness. In the manner already spear-
headed in the studies of Whyte and Smith (2021) and Parr et
al. (2019a), such experiments should seek to reproduce results
of behavioral and imaging studies within standard paradigms
employed in the study of consciousness. Initial experiments
should focus especially on the role of precision and complexity to
validate the widely Bayesian approach. This could be done by e.g.
repeating the studies of Fleming (2020) with explicit modeling of
precision estimation, instead of assuming xed noise (as Fleming
Second, in a broader but fairly controversial way:
Precision and complexity constitute a MUM of consciousness in
The close relation between Bayesian approaches to conscious-
ness and the more classical accounts suggests that once we
accept the interpretation of Bayesian cognitive science in terms
of a framework or a toolbox, there is a broad group of theories
that may be extended with precision and complexity. In fact,
precision assignments can be included in almost any informa-
tion processing-based account of consciousness, which includes
variance, entropy, or another term playing a comparable role—
i.e. almost any existing account. Complexity posits additional
restrictions as it requires the account to postulate an internal
model but only in a very broad sense—again, met by a plethora of
existing accounts. This interpretation, however, might be taken to
weaken the technical sense of these two terms and in result—to
restrict the amount of the explanatory work they can do. Fur-
thermore, adding precision and complexity to existing theories
can have a post hoc, reinterpretative, and redundant charac-
ter. However, as has been shown throughout this paper, there
are some instances where these two concepts can contribute to
deliver novel, testable explanations of phenomena and properties
of consciousness for more classical accounts. One such plausible
example comes from the explanations of global ignition provided
by HOSS and PGNW. Both suggest a plausible mechanism underly-
ing ignition, often glossed over within global neuronal workspace
theory, and both could be empirically validated outside of the
setting of Bayesian cognitive science in the manner described in
a previous section. If this research program were to succeed, it
would further secure a strong foothold for the Bayesian cognitive
science as a useful toolbox for explaining mechanisms underlying
Data availability
Data availability is not applicable.
I want to thank two anonymous reviewers for providing invalu-
able feedback and suggestions that have greatly improved the
paper. I am also grateful to Adam Safron for the discussion of the
ner details of Integrated World Modeling Theory and to Paweł
Gładziejewski, Krzysztof Se
˛kowski, and the members of Student
Association for Philosophy of Mind at the University of Warsaw
for feedback on earlier versions of the manuscript. This work was
funded by the Ministry of Science and Higher Education (Poland)
research Grant DI2018 010448 as part of the “Diamentowy Grant”
This work was funded by the Ministry of Education and Science
(Poland) research Grant DI2018 010448 as part of the “Diamentowy
Grant” program.
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... This mechanism requires that sensory prediction errors have a pathway facilitated through automated (sub-personal) attention to a higher processing level where conscious cognitive processes are able to minimize an error using top-down attentional resources. Predictive processing therefore suggests a means to examine and understand cognition-not an entirely new endeavor (Hohwy, 2020;Rorot, 2021). Attempts to bridge alternate methods of examining cognition, such as the analysis of large-scale resting state network connectivity, may aid the interpretation of predictive processes in consciousness and attention. ...
... Desegregation describes the abundant deviation of connectivity from functional pathways-or decreased modularity in brain-networks and regions-and is an effect cited in reference to increased complexity-in an information theoretic senseidentified in the psychedelic state (Petri et al., 2014;Barnett et al., 2019, see Figure 1). Desegregation in cortical communities may relate the richness of psychedelic phenomenological experience which differs from other altered self-dissolving states of consciousness (Millière et al., 2018) that may relate to the complexity of a self-generative model (Rorot, 2021). ...
... Complexity is suggested as an important feature alongside precision. Interested readers are directed toRorot (2021). For review of precision and the Bayesian brain (seeYon and Frith, 2021).Frontiers in Neuroscience | ...
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Evidence suggests classic psychedelics reduce the precision of belief updating and enable access to a range of alternate hypotheses that underwrite how we make sense of the world. This process, in the higher cortices, has been postulated to explain the therapeutic efficacy of psychedelics for the treatment of internalizing disorders. We argue reduced precision also underpins change to consciousness, known as “ego dissolution,” and that alterations to consciousness and attention under psychedelics have a common mechanism of reduced precision of Bayesian belief updating. Evidence, connecting the role of serotonergic receptors to large-scale connectivity changes in the cortex, suggests the precision of Bayesian belief updating may be a mechanism to modify and investigate consciousness and attention.
... Moreover, our contribution fits in a broader and emerging debate about models of consciousness, where scholars have either proposed comparison between different models (Del Pin et al. 2020Sattin et al. 2021;Signorelli et al. 2021), convergence of elements across different features (Northoff & Lamme 2020;Sarasso et al. 2021;Rorot 2021) or comparison of different paradigms on empirical grounds (Doerig et al. 2020;Melloni et al. 2021). ...
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In this paper we take a meta-theoretical stance and compare and assess two conceptual frameworks that endeavor to explain phenomenal experience. In particular, we compare Feinberg & Mallatt’s Neurobiological Naturalism (NN) and Tononi’s and colleagues Integrated Information Theory (IIT), given that the former pointed out some similarities between the two theories (Feinberg & Mallatt 2016c-d). To probe their similarity, we first give a general introduction into both frameworks. Next, we expound a ground-plan for carrying out our analysis. We move on to articulate a philosophical profile of NN and IIT, addressing their ontological commitments and epistemological foundations. Finally, we compare the two point-by-point, also discussing how they stand on the issue of artificial consciousness.
... There is not space here to review the veritable storm of publications currently appearing around the 'principle of free energy minimization' (Friston, 2010); for recent reviews of its relationship to autopoiesis, enactivism, Bayesianism, cognition and 'predictive processing' (i.e. top-down hypothesis generation) see for example Pezzulo and Sims (2021), Ramstead et al. (2021), Rorot (2021) and Bruineberg et al. (in press). It has been claimed to account for the appearance of ontological boundaries around cognizing systems at multiple scales (e.g. ...
Enactivism is a major research programme in the philosophy of perception. Yet its metaphysical status is unclear, since it is claimed to avoid both idealism and realism yet still has aspects of both within it. One attempt to solve this conundrum is based on the fusion of enactivism with phenomenology and the mathematical concept of symmetry breaking (Moss Brender, 2013). I suggest this is not entirely successful and propose it needs the addition of a multi-level, non-reductive metaphysics (for example, Informational Structural Realism). The processes we commonly call ‘perception’ are causal transfers of information at certain levels in the hierarchy of meaningful structures that comprise physical reality. Phenomenologists could use the word ‘perception’ metaphorically across all levels, although realists need not do so.
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Recently, the mechanistic framework of active inference has been put forward as a principled foundation to develop an overarching theory of consciousness which would help address conceptual disparities in the field (Wiese 2018; Hohwy and Seth 2020). For that promise to bear out, we argue that current proposals resting on the active inference scheme need refinement to become a process theory of consciousness. One way of improving a theory in mechanistic terms is to use formalisms such as computational models that implement, attune and validate the conceptual notions put forward. Here, we examine how computational modelling approaches have been used to refine the theoretical proposals linking active inference and consciousness, with a focus on the extent and success to which they have been developed to accommodate different facets of consciousness and experimental paradigms, as well as how simulations and empirical data have been used to test and improve these computational models. While current attempts using this approach have shown promising results, we argue they remain preliminary in nature. To refine their predictive and structural validity, testing those models against empirical data is needed i.e., new and unobserved neural data. A remaining challenge for active inference to become a theory of consciousness is to generalize the model to accommodate the broad range of consciousness explananda; and in particular to account for the phenomenological aspects of experience. Notwithstanding these gaps, this approach has proven to be a valuable avenue for theory advancement and holds great potential for future research.
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Free Energy Principle underlies a unifying framework that integrates theories of origins of life, cognition, and action. Recently, FEP has been developed into a Markovian monist perspective (Friston et al. in BC 102: 227–260, 2020). The paper expresses scepticism about the validity of arguments for Markovian monism. The critique is based on the assumption that Markovian models are scientific models, and while we may defend ontological theories about the nature of scientific models, we could not read off metaphysical theses about the nature of target systems (self-organising conscious systems, in the present context) from our theories of nature of scientific models (Markov blankets). The paper draws attention to different ways of understanding Markovian models, as material entities, fictional entities, and mathematical structures. I argue that none of these interpretations contributes to the defence of a metaphysical stance (either in terms of neutral monism or reductive physicalism). This is because scientific representation is a sophisticated process, and properties of Markovian models—such as the property of being neither physical nor mental—could not be projected onto their targets to determine the ontological properties of targets easily.
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Active inference is a first principle account of how autonomous agents operate in dynamic, nonstationary environments. This problem is also considered in reinforcement learning, but limited work exists on comparing the two approaches on the same discrete-state environments. In this letter, we provide (1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in reinforcement learning, and (2) an explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline. We begin by providing a condensed overview of the active inference literature, in particular viewing the various natural behaviors of active inference agents through the lens of reinforcement learning. We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration—and account for uncertainty about their environment—in a Bayes-optimal fashion. Furthermore, we show that the reliance on an explicit reward signal in reinforcement learning is removed in active inference, where reward can simply be treated as another observation we have a preference over; even in the total absence of rewards, agent behaviors are learned through preference learning. We make these properties explicit by showing two scenarios in which active inference agents can infer behaviors in reward-free environments compared to both Q-learning and Bayesian model-based reinforcement learning agents and by placing zero prior preferences over rewards and learning the prior preferences over the observations corresponding to reward. We conclude by noting that this formalism can be applied to more complex settings (e.g., robotic arm movement, Atari games) if appropriate generative models can be formulated. In short, we aim to demystify the behavior of active inference agents by presenting an accessible discrete state-space and time formulation and demonstrate these behaviors in a OpenAI gym environment, alongside reinforcement learning agents.
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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 modelling 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 modelling 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 several 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.
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The search for the neural correlates of consciousness is in need of a systematic, principled foundation that can endow putative neural correlates with greater predictive and explanatory value. Here, we propose the predictive processing framework for brain function as a promising candidate for providing this systematic foundation. The proposal is motivated by that framework’s ability to address three general challenges to identifying the neural correlates of consciousness, and to satisfy two constraints common to many theories of consciousness. Implementing the search for neural correlates of consciousness through the lens of predictive processing delivers strong potential for predictive and explanatory value through detailed, systematic mappings between neural substrates and phenomenological structure. We conclude that the predictive processing framework, precisely because it at the outset is not itself a theory of consciousness, has significant potential for advancing the neuroscience of consciousness.
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[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.
The free energy principle, an influential framework in computational neuroscience and theoretical neurobiology, starts from the assumption that living systems ensure adaptive exchanges with their environment by minimizing the objective function of variational free energy. Following this premise, it claims to deliver a promising integration of the life sciences. In recent work, Markov Blankets, one of the central constructs of the free energy principle, have been applied to resolve debates central to philosophy (such as demarcating the boundaries of the mind). The aim of this paper is twofold. First, we trace the development of Markov blankets starting from their standard application in Bayesian networks, via variational inference, to their use in the literature on active inference. We then identify a persistent confusion in the literature between the formal use of Markov blankets as an epistemic tool for Bayesian inference, and their novel metaphysical use in the free energy framework to demarcate the physical boundary between an agent and its environment. Consequently, we propose to distinguish between ‘Pearl blankets’ to refer to the original epistemic use of Markov blankets and ‘Friston blankets’ to refer to the new metaphysical construct. Second, we use this distinction to critically assess claims resting on the application of Markov blankets to philosophical problems. We suggest that this literature would do well in differentiating between two different research programs: ‘inference with a model’ and ‘inference within a model’. Only the latter is capable of doing metaphysical work with Markov blankets, but requires additional philosophical premises and cannot be justified by an appeal to the success of the mathematical framework alone.
The meta-problem of consciousness (Chalmers, 2018) is the problem of explaining the behaviours and verbal reports that we associate with the so-called 'hard problem of consciousness'. These may include reports of puzzlement, of the attractiveness of dualism, of explanatory gaps, and the like. We present and defend a solution to the meta-problem. Our solution takes as its starting point the emerging picture of the brain as a hierarchical inference engine. We show why such a device, operating under familiar forms of adaptive pressure, may come to represent some of its mid-level inferences as especially certain. These mid-level states confidently re-code raw sensory stimulation in ways that (they are able to realize) fall short of fully determining how properties and states of affairs are arranged in the distal world. This drives a wedge between experience and the world. Advanced agents then represent these mid-level inferences as irreducibly special, becoming increasingly puzzled as a result.
The global neuronal workspace (GNW) model has inspired over two decades of hypothesis-driven research on the neural basis of consciousness. However, recent studies have reported findings that are at odds with empirical predictions of the model. Further, the macro-anatomical focus of current GNW research has limited the specificity of predictions afforded by the model. In this paper we present a neurocomputational model-based on Active Inference-that captures central architectural elements of the GNW and is able to address these limitations. The resulting 'predictive global workspace' casts neuronal dynamics as approximating Bayesian inference, allowing precise, testable predictions at both the behavioural and neural levels of description. We report simulations demonstrating the model's ability to reproduce: 1) the electrophysiological and behavioural results observed in previous studies of inattentional blindness; and 2) the previously introduced four-way taxonomy predicted by the GNW, which describes the relationship between consciousness, attention, and sensory signal strength. We then illustrate how our model can reconcile/explain (apparently) conflicting findings, extend the GNW taxonomy to include the influence of prior expectations, and inspire novel paradigms to test associated behavioural and neural predictions.