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Special Issue: Consciousness science and its theories
Minimal physicalism as a scale-free substrate for
cognition and consciousness
Chris Fields
1,
*, James F. Glazebrook
2,3
and Michael Levin
4
1
23 Rue des Lavandie`res, 11160 Caunes Minervois, France;
2
Department of Mathematics and Computer
Science, Eastern Illinois University, 600 Lincoln Ave, Charleston, IL 61920 USA;
3
Department of Mathematics,
Adjunct Faculty, University of Illinois at Urbana–Champaign, 1409 W. Green Street, Urbana, IL 61801, USA;
4
Allen Discovery Center, Tufts University, 200 College Avenue, Medford, MA 02155, USA
*Correspondence address. 23 Rue des Lavandie`res, 11160 Caunes Minervois, France. Tel: þ33 06 44 20 68 69; E-mail: fieldsres@gmail.com
Abstract
Theories of consciousness and cognition that assume a neural substrate automatically regard phylogenetically basal, non-
neural systems as nonconscious and noncognitive. Here, we advance a scale-free characterization of consciousness and
cognition that regards basal systems, including synthetic constructs, as not only informative about the structure and func-
tion of experience in more complex systems but also as offering distinct advantages for experimental manipulation. Our
“minimal physicalist” approach makes no assumptions beyond those of quantum information theory, and hence is applica-
ble from the molecular scale upwards. We show that standard concepts including integrated information, state broadcast-
ing via small-world networks, and hierarchical Bayesian inference emerge naturally in this setting, and that common phe-
nomena including stigmergic memory, perceptual coarse-graining, and attention switching follow directly from the
thermodynamic requirements of classical computation. We show that the self-representation that lies at the heart of hu-
man autonoetic awareness can be traced as far back as, and serves the same basic functions as, the stress response in bac-
teria and other basal systems.
Keywords: active inference; aneural systems; basal cognition; classical computation; integrated information; memory;
quantum computation; self-representation; state broadcasting
Introduction
The starting point for sciences of consciousness and cognition
has traditionally been human consciousness and cognition. The
Cartesian presumption against nonhuman consciousness and
cognition remained strong enough, even just two decades ago,
that prominent researchers found it necessary to argue in print
that nonhuman animals and even human infants experienced
perception and interoception and engaged in intentional
actions (e.g. Panksepp 2005;Trevarthen 2010;Rochat 2012).
Those days are thankfully behind us. While researchers in
comparative cognition may debate the levels of complexity,
compositionality, or stimulus-independence an information-
processing system must have to be regarded as “cognitive” (see
Bayne et al. 2019 for a recent snapshot), big-brained creatures
that learn readily and display flexible behavior in complex envi-
ronments are now regularly regarded as cognitive systems that
are aware of both their environments and their own bodily
states, even if they are birds (e.g. Gu¨ ntu¨ rku¨ n and Bugnyar 2016;
Nieder et al. 2020) or cephalopods (e.g. Mather 2019;Schnell et al.
2020). To insist that such creatures altogether lack phenomenal
consciousness, that they are incapable of experiencing pain and
Received: 4 January 2021; Revised: 4 April 2021. Accepted: 5 April 2021
V
CThe Author(s) 2021. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
1
Neuroscience of Consciousness, 2021, 7(2): niab013
doi: 10.1093/nc/niab013
Research Article
likewise experience nothing whatsoever while hunting, impro-
vising tools, or engaging in mating displays as a doctrinaire
Cartesian presumably would, seems by current standards chau-
vinistic or even perverse.
Characterizing birds and cephalopods, and for that matter
other mammals and even other people, as phenomenally con-
scious begs, of course, the philosophical questions of what phe-
nomenal consciousness is ontologically, and of how it relates to
observable structures, functions or behaviors. These questions
constitute the “hard problem” (HP) of consciousness (Chalmers
1996;cf.Dietrich and Hardcastle 2005;Dietrich et al. 2021). We
agree with Klein and Barron (2020) that the HP is not a problem
to be solved, but rather a set of intuitions to be overcome. The
questions of interest regarding birds, cephalopods, other mam-
mals, and other people are not, as far as we are concerned,
whether or why, in some philosophical sense, they are capable
of experience, but rather: (1) what any particular organism or
other system of interest is aware of, and (2) how its awareness
drives its cognition. Our interest here is not, moreover, in what
any particular experience is like for any given system, but in
whether it is like something or other; to employ a common ex-
ample, it is not the particular characters of red or green qualia
that are of interest (see e.g. Hacker 2002 for an argument that no
such particular characters even exist), but whether some differ-
ence between what we, as 3rd party observers, call “red” and
“green” is experienced by the system of interest, and whether
this difference has some effect on what the system does. Hence
we regard consciousness as a (potential) property that a system
may have, independently of any details about what it experien-
ces: it is enough, in our view, that it experiences something,
that some experience occurs (see Lamme 2018 for a discussion
of some of the subtleties that this “binary” view elides). We will
use the terms “awareness” and “consciousness” to mean the ca-
pacity for or capability of having phenomenal experiences,
however basic or minimally structured, and will focus exclu-
sively in what follows on the “what” and “how” questions
above.
A well-established literature extends the concepts of con-
sciousness—the capability of having phenomenal experiences,
however basic or minimally structured—and cognition to phylo-
genetically basal systems, including free-living or facultatively
communal unicells, whether pro- or eukaryotic (Maturana and
Varela 1980;Pattee 1982;Stewart 1996;di Primio et al. 2000;Lyon
2015;Balu
ska and Levin 2016; Balu
ska and Reber 2019,2021;
Levin 2019,2020;Fields and Levin 2020a;Lyon 2020;Balu
ska
et al. 2021), plants (Calvo and Kiejzer 2009;Gagliano and
Grimonprez 2015;Balu
ska et al. 2018;Debono and Souza 2019;
Calvo et al. 2020), and aneural or lower (than mammals, birds, or
cephalopods) complexity neural metazoa, particularly flat-
worms (Shomrat and Levin 2013;Inoue et al. 2015;Levin 2020)
and insects (Menzel 2012;Perry et al. 2017;Pfeffer and Wolf
2020). Like the extension of these concepts from humans to
nonhuman mammals and then to big-brained nonmammals,
this extension to more basal organisms was initially motivated
by observations of communication, learning, and behavioral
flexibility, and by functional similarities between the mecha-
nisms supporting information processing and learning in basal
systems and in more complex systems such as mammals. Both
molecular and bioelectric mechanisms of cellular information
processing, memory, communication, and error correction are,
in particular, evolutionarily ancient and conserved across phy-
logeny (Fields and Levin 2018,2020b;Fields et al. 2020;Levin
2020). Neither the specialization of some epithelial cells for
communication, i.e. as neurons (Nickel 2010;Arendt et al. 2015;
Burkhardt and Sprecher 2017) nor the gradual elaboration of
brains (Holland et al. 2013;Liebeskind et al. 2017) introduce fun-
damentally novel molecular or bioelectric mechanisms. Hence
while claims of specific kinds of experience, e.g. pain in plants
(Draguhn et al. 2020), are controversial, there is no evident bio-
logical discontinuity below which organisms can clearly be
viewed as completely unaware of their bodily states or their
environments, i.e. as utterly lacking phenomenal conscious-
ness. Nor is it obvious that supra-organismal systems utterly
lack phenomenal consciousness (Friedman and Søvik 2021).
Indeed, the criteria for “having experiences” may be as vague,
general, and extensible to non-Terrestrial or even artificial sys-
tems as plausible criteria for “life” are (e.g. Bartlett and Wong
2020; but see also Mariscal and Doolittle 2020 for an argument
that such “definitionism” is scientifically pointless).
With the development of more sophisticated, biologically
motivated theoretical models of consciousness and cognition,
the functional similarities between information processing in
basal and in more elaborated systems have become even more
evident. It is now well-known that signal transduction and gene
regulatory networks in single cells share the scale-free, small-
world topology (Agrawal 2002;Baraba´ si and Oltvai 2004;Schlitt
and Brazma 2007) found ubiquitously in nervous systems
(Sporns and Honey 2006;Hagmann et al. 2008;Shanahan 2012;
Sporns 2013) and even in social and technological networks
(Watts 1999;Baraba´ si 2016; but see Broido and Clauset 2019 for
evidence that some biological and social networks are small-
world but not strictly scale-free). Networks with high fan-in/
high fan-out “bow-tie” topology (Wang et al. 2016;Niss et al.
2020), in particular, implement state-broadcasting functions
analogous, at the cellular level, to the broadcasting functions of
long-distance connections between network hubs in global neu-
ronal workspace (GNW) models (Dehaene and Naccache 2001;
Baars and Franklin 2003;Wallace 2005;Baars et al. 2013;
Dehaene et al. 2014;Mashour et al. 2020). Signal transduction
and gene regulatory networks are, even when they contain
bow-tie nodes, characterized by multiple, typically cross-modu-
lating feedback loops that enable both behavioral plasticity and
learning (e.g. Brun-Usan et al. 2020); hence they have positive in-
tegrated information Uand render their minimal containing
systems conscious by the criteria of Integrated Information
Theory (IIT; Tononi 2008;Oizumi et al. 2014;Tononi and Koch
2015). As the locus of molecular, thermodynamic, and bioelec-
tric exchange with the environment, the cell membrane imple-
ments a Markov Blanket (MB) that renders its interior
conditionally independent of its exterior (Pearl 1988;Clark
2017); this allows the cell to be described as a Bayesian active in-
ference system (Friston 2010,2013; see also Cooke 2020 for a var-
iation on this approach). The utility of this Bayesian approach
has been demonstrated in simulation models of cell–cell com-
munication driving morphoghenesis (Friston et al. 2015;
Kuchling et al. 2020). These cross-scale similarities motivate a
hypothesis that consciousness and cognition are scale-free phe-
nomena that characterize all living systems (Levin 2019,2020;
Fields and Levin 2020a).
If consciousness and cognition are scale-free phenomena,
we can expect them to be supported by common, scalable
mechanisms that can be investigated in whatever systems per-
mit the most straightforward theoretical and experimental
approaches. Phylogenetically basal organisms, in vitro prepara-
tions, and synthetic constructs (e.g. Kriegman et al. 2020) pro-
vide obvious advantages of manipulability and environmental
control. Studies of basal systems are, moreover, especially effec-
tive in overcoming the intuitions that give rise to the HP, as
2|Fields et al.
they allow the mechanisms via which single cells and relatively
simple multicellular organisms navigate their environments—
mechanisms that they share with most of our cells, and with us
as organisms—to be investigated in detail. A theoretical frame-
work suitable for such systems should be scalable, provide a full
suite of formal capabilities, and make as few assumptions as
possible. It should, in particular, make no scale-dependent ar-
chitectural assumptions.
Here, we provide a straightforward construction of funda-
mental, scale-free features of consciousness and cognition
within a generic description of system-environment informa-
tion exchange as bipartite physical interaction (Fields and
Glazebrook 2020a;Fields and Marciano` 2020a,b). We term this
description “minimal physicalism” (MP) as it makes no assump-
tions about classical computational architecture, in particular,
no assumptions about network architecture (Broido and Clauset
2019), and no physical assumptions beyond those of quantum
information theory (Nielsen and Chuang 2000 provide a stan-
dard introduction). At the level of molecular interactions at the
Angstrom, femtosecond (A
˚, fs) scale of molecular dynamics cal-
culations (Zwier and Chong 2010;Vlachakis et al. 2014), biologi-
cal systems are quantum systems, and biological information
processing is quantum computation: cellular energy budgets of
both prokaryotes and eukaryotes fall orders of magnitude short
of the power required to maintain classical states of just protein
conformation and localization at this scale (Fields and Levin
2021), despite the massive consumption of ATP by big-brained
eukaryotes such as humans (Ueno et al. 2005;Okuno et al. 2011).
Hence cellular information processing cannot be entirely, or
even primarily classical, the experimental difficulties (Cao et al.
2020) of unambiguously observing quantum coherence in bio-
logical systems notwithstanding.
As we show below, the thermodynamic requirements of
classical computation by quantum systems have profound con-
sequences for cellular, and hence for organismal, awareness
and information processing. In general, basing MP on the physi-
cal assumptions of quantum information theory has significant
empirical consequences that extend far beyond a mere rejection
of dualism. These include:
1. MP is completely scale-free, applying in the same form to
interactions between molecules, cells, tissues, organisms,
social groups, or ecosystems and their respective environ-
ments. Hence it predicts common mechanisms that can be
probed empirically at any scale. It makes no assumptions
about the structure or dynamics of the environment, at any
scale, beyond its being a physical system.
2. MP requires every property of either itself or the environ-
ment to which an organism or other living system is differ-
entially sensitive to be specified explicitly in terms of the
information processing employed by the organism or sys-
tem to detect and respond to that property. This includes
properties often taken for granted, such as the existence of
external objects or their embedding in three-dimensional
(3d) space, and applies regardless of whether detection and/
or response is recorded to memory or “reportable” via any
specific assay.
3. MP treats information and energy as formally equivalent,
explicitly enforces conservation of energy, and hence
requires the thermodynamics of classical computation
(Landauer 1961;Bennett 1982) to be represented explicitly. It
requires the thermodynamic cost of memory to be
accounted for at every scale, and automatically enforces a
metabolism—cognition tradeoff.
Information processing functions, including writing to or
reading from a classical memory, are specified in this frame-
work using the scale- and implementation-independent, cate-
gory-theoretic language of Barwise-Seligman (“token” – “type”)
classifiers and their associated infomorphisms (Barwise and
Seligman 1997; see Fields and Glazebrook 2019a for review
and example applications). This generic specification language
has been previously employed to characterize human object
and event categorization using both abstraction and mereo-
logical hierarchies (Fields and Glazebrook 2019b) and human
problem solving within a GNW context (Fields and Glazebrook
2020b). As it requires the reference frames employed to give
internal, e.g. perceptual or motor, representations operational
meaning to be explicitly specified, this language requires all
semantic hypotheses to be formulated in explicit, experimen-
tally accessible molecular or network-theoretic terms.
We outline the main components of MP in the next sec-
tion, keeping formalism to the minimum and relying on previ-
ous work for details. We show that MBs and a notion of
actionable or “meaningful” (Bateson 1972;Roederer 2005) in-
formation emerge naturally in an MP framework. We then dis-
cuss predictions of MP regarding perception, memory, and
attention, providing examples primarily from basal systems
for illustration. We show that requirements for coarse-grain-
ing and attention switching emerge naturally from the ther-
modynamics of classical computation. We then consider
resource-usage monitoring as a requirement of homeo- or
allostasis, and the emergence of a “self” as a representation of
resource usage.
Components of MP: Information Exchange,
Markov Blankets, and Reference Frames
Physical interaction is information exchange
The re-interpretation of physics as an information theory was
initiated by Feynman (1982) and Wheeler (1990), but has its
roots in the work of Boltzmann (1995), who first recognized that
reducing uncertainty has an energetic cost, and Shannon (1948),
who linked uncertainty reduction with communication. It is
deeply rooted in Wheeler’s (1983) idea of “observer-partic-
ipancy”: that information can only be obtained by intervention,
i.e. by actively posing a question (see Mermin 2018;Mu¨ ller 2020
for recent, somewhat philosophical discussions). Physics is in-
trinsically quantum due to the quantization of obtainable infor-
mation into bits: answers to yes/no questions (Wheeler 1983).
The basic formalism of this quantum information theory is sur-
prisingly simple, and rests on only the two assumptions speci-
fied below.
The simplest physical interactions are bipartite: some
physical, i.e. quantum system A(conventionally called “Alice”)
interacts with some other system B(“Bob”). We can therefore
write:
U¼AB (1)
or more explicitly, in terms of Hilbert spaces:
HU¼H
AH
B(2)
where Uis the joint system (“universe”) comprising Aand Band
is the Hilbert-space tensor product. The dynamics of the joint
system is completely described by a Hamiltonian operator:
Minimal physicalism as a scale-free substrate |3
HU¼HAþHBþHAB (3)
where H
A
and H
B
are the internal or “self” interactions of Aand
Brespectively and H
AB
is their mutual interaction. The operator
H
U
has units of energy and drives state change via the
Schro¨ dinger equation:
ihðo=otÞjUðtÞ>¼HUjUðtÞ>(4)
where i¼冑-1, h¼h/2p,his Planck’s constant, and jU(t)>is the
time-dependent state of U. The mutual interaction H
AB
describes Alice’s observations of and actions on Bob, and Bob’s
observations of and actions on Alice. The internal interactions
H
A
and H
B
describe Alice’s and Bob’s internal state changes.
The above descriptions are completely generic and charac-
terize any bipartite interaction between quantum systems
(Nielsen and Chuang 2000). As a quantum system is simply a
collection of physical degrees of freedom, this formalism
makes, in particular, no assumptions about implementation: it
applies equally to matter (e.g. molecules) or fields (e.g. electro-
magnetic fields as in McFadden 2020). We now impose two
assumptions:
1. We require the dimension of the joint system Uto be finite.
This limits both Aand Bto finite energy resources and H
AB
to
finite resolution.
2. We require the joint state jU>¼jAB>to be separable, i.e.
jU>¼jAB>¼jA>jB>. Equivalently, Aand Bare not
entangled. In this case, H
AB
specifies the entire dependence
of Aon Band vice-versa, and we can talk about the state jA>
of Aand the state jB>of Bindependently.
With these two assumptions, the interaction H
AB
can be
written, without loss of generality, as:
HABðtÞ¼bkkBTkRiak
iðtÞMk
i(5)
where k¼Aor B,i¼1... Nfor finite N,k
B
is Boltzmann’s con-
stant, T
k
is k’s temperature, b
k
ln 2 is an inverse measure of k’s
average thermodynamic efficiency that depends on the internal
dynamics H
k
, the a
ki
(t) are Nreal functions such that, for any
macroscopic time interval Dt:
RiðDt
ak
iðtÞdt¼Dt(6)
and the M
ki
are NHermitian operators with binary eigenvalues
þ1 or -1 that can be regarded as “questions to Nature” with yes/
no answers [Wheeler 1983; see Fields and Glazebrook 2020a;
Fields and Marciano` 2019,2020b for details on Equations (5) and
(6) and their interpretation]. The requirement that b
k
ln 2 in
Equation (5) enforces Landauer’s principle of the finite per-bit
cost of irreversibly acquiring an observational outcome
(Landauer 1961;Bennett 1982), while Equation (6) imposes a
normalization condition, equivalent to the requirement that the
probability of acquiring some outcome whenever a measure-
ment is made is unity. As we discuss below, Equations (5) and
(6) are the only physical requirements needed to describe both
free-energy exchange and information transfer in a generic bi-
partite interaction between finite quantum systems in a separa-
ble joint state. We can write Equation (5) in English as:
Physical interaction ¼ðthermodynamicsÞðyes=no questionsÞ
This is the conceptual heart of quantum information theory. It,
like Equations (5) and (6), applies at any scale.
Interactions induce MBs
The complete generality of Equation (5) allows us to view any
interaction between two finite quantum systems in a separable
joint state as classical communication: the exchange of some fi-
nite number of finite bit strings during any finite time interval
Dt. The interaction can be depicted as in Fig. 1:Aand Bcan be
regarded as alternately preparing, and then measuring Ninde-
pendent, mutually noninteracting quantum bits (qubits), e.g.
photon polarizations, electron spins, or any other systems hav-
ing two clearly distinguishable values of a manipulable state
variable. In each cycle of interaction, Aprepares the Nqubits
and then Bmeasures them; the roles then reverse, with Bpre-
paring and Ameasuring. As Nbecomes large, the interaction
may appear “continuous” at macroscopic scales, but remains
constrained to finite resolution, and hence a finite bit rate, by
the restriction to finite thermodynamic resources built into
Equation (5). The time required for Ato prepare, and then Bto
measure (or vice versa), the Nsystems can be regarded as the
smallest possible “macroscopic” and hence effectively classical
time interval (see Fields and Glazebrook 2020 for an explicit con-
struction of this interval). The collection of all Nqubits encodes,
at each such minimal macroscopic time, one N-bit string speci-
fying one of the 2
N
eigenvalues of H
AB
; each cycle of interaction
is thus an exchange of N-bit strings, each one encoding an (in
general different) eigenvalue.
The encoding of eigenvalues by qubits shown in Fig. 1 satis-
fies the requirements for a holographic encoding: the only infor-
mation about Bavailable to A, or vice versa, is the sequence of
Figure 1. The interaction H
AB
specified by Equation (5) can, without
loss of generality, be implemented by alternating preparation and
measurement of Nmutually noninteracting qubits. Each cycle of in-
teraction has two phases: first, Aprepares the Nqubits and then B
measures them, then Bprepares the Nqubits and Ameasures them.
The qubit array defines a holographic screen Bseparating Afrom B.
This screen enforces conditional independence between Aand B,
and hence functions as a MB. There is no source of classical noise in
the interaction; however, there is quantum noise, and hence poten-
tial classical communication error, whenever Aand Bemploy differ-
ent reference frames (e.g. different z-axes) to prepare and measure
the qubits. Adapted from Fields and Marciano` (2020b); CC BY license.
4|Fields et al.
eigenvalues of H
AB
encoded by the qubit array. Hence we can
consider the qubit array as a holographic screen Bseparating
Afrom B. Any such holographic screen defines a classical com-
munication channel, i.e. a channel via which Aand Bex-
change finite bit strings (Fields and Marciano` 2020b;Fields
et al. 2021). As no other system interferes with this channel, it
is free of classical noise. However, it is characterized by quan-
tum noise whenever Aand Bemploy different reference
frames (e.g. different z-axis reference frames in the case of
qubits implemented by electron spins) to prepare and measure
the qubits. The effect of such quantum noise is to impose a
classical probability distribution Prob(measuredjprepared) on
each qubit; i.e. its observable effect is indistinguishable from
classical noise.
If we now consider the internal (quantum) states jA>and jB>
and the states jB>of the qubit array forming the holographic
screen between them, we see that jA>affects jB>only via its ef-
fect on jB>, i.e. only via the action of H
AB
. This is, as noted above,
a consequence of separability: separability implies conditional
independence. Conditional independence of jA>from jB>and
vice-versa is, however, also the MB condition (Pearl 1988;Friston
2013;Clark 2017); hence we can view Bas an MB. Indeed, the gen-
eralization of the holographic principle stated by Equation (5)
and illustrated in Fig. 1 implies that any holographic screen
defines a classical information channel that functions as an MB,
and vice versa (Fields and Marciano` 2020a;Fields et al. 2021).
As discussed in Friston (2013), MBs are ubiquitous features of
living systems, and are indeed what distinguish living systems
from their environments (see also Friston et al. 2015;Kuchling
et al. 2020;Fields and Levin 2020c). Indeed MBs are ubiquitous
features of all physical systems with ergodic behavior (Friston
2019). The MB concept is scale-free; Rubin et al. (2020) show, e.g.
that it applies to Earth’s biosphere as a whole. The cell mem-
brane, and in eukaryotic cells, organelle membranes, imple-
ment the most fundamental biological MBs. The blanket states
of these MBs comprise the states of the embedded receptors,
channels, and other exchange molecules, together with the per-
meability, elasticity, and other properties of the membrane
itself. The existence of the MB enables the cell to have a well-de-
fined state, conditionally independent of its environment;
hence it enables homeostatic/allostatic processes. The informa-
tion about the environment available to the cell, and vice-versa,
is limited to that encoded by the state of the MB. A similarly
strong conditional independence result has been obtained via
evolutionary game theory by considering generic systems sub-
jected to competition for environmental resources (Hoffman
et al. 2015;Prakash et al. 2020a,b).
The central importance to the cell of the MB implemented by
its membrane strongly suggests that enclosure by a membrane
was the fundamental requirement for the origin of life (Friston
2013;Fields and Levin 2020c). What was enclosed by the MB/
membrane of the last universal common ancestor (LUCA) repre-
sents the initial condition and oldest memory (Fields and Levin
2018) of all subsequent life. Viewed from an informational per-
spective, the state space of LUCA’s membrane-enclosed cyto-
plasm, including its nucleic-acid, protein, and small-molecule
components, specifies the initial information about the envi-
ronment available prior to membrane enclosure and hence
physical separation from the environment. While the cytoplas-
mic conditions of later cells have diverged from these initial
conditions, sometimes radically, they remain a fixed constraint
with which all paths of divergence must be consistent. Here, the
classical meaning of “separation” from the environment as the
presence of a physical barrier accords with its quantum-
theoretic meaning of state distinguishability. We can, indeed,
see the former as implied by the latter.
Actionable information is encoded by quantum
reference frames
The information that transits the cell membrane, and is thereby
encoded on the MB implemented by the membrane, is action-
able or meaningful to the cell: it “makes a difference” to what
the cell does (Bateson 1972;Roederer 2005). When the cell’s in-
teraction with its environment is represented as measurement
as in Equation (5) or Fig. 1, what renders the information mean-
ingful becomes clear: meaning requires measurement with re-
spect to some reference frame (Fields and Levin 2020a;Fields
et al. 2021). Viewed abstractly, a reference frame is a value, or
more generally a vector, from which deviation is detectable.
Consider, e.g. measuring the states of an array of qubits imple-
mented by electron spins. Determining whether the spin of a
qubit is up (þ1) or down (–1) requires a reference frame that
defines a particular z-axis, a vector in 3d space. Such a vector
can be defined by, e.g. a Stern-Gerlach apparatus oriented in
some particular way relative to the Earth’s gravitational field.
Only the presence of a common reference frame allows multiple
measurements to be compared, and hence makes any differen-
ces between them meaningful. Sequential measurements of
electron spin with respect to a randomly varying z-axis merely
yield noise.
While in classical physics a reference frame is an abstraction
that can be completely described to arbitrary precision, this is
not true in quantum theory (Bartlett et al. 2007). Quantum the-
ory requires all reference frames to be implemented by physical
systems, but forbids the description of any physical system to
arbitrary precision. Hence quantum reference frames (QRFs)
cannot be completely described to arbitrary precision; they en-
code, in virtue of their physical implementation, “unspeakable”
(Bell 1987) or “nonfungible” (Bartlett et al. 2007) information.
This is even true of meter sticks, the lengths of which become
uncertain as the energies required to probe them become com-
parable to the binding energies of nucleii. It is much more obvi-
ously true of complex systems such as Stern-Gerlach apparatus
or atomic clocks.
The intrinsically nonclassical nature of QRFs has some surpris-
ing consequences. Transferring a QRF from one observer to an-
other requires transferring the physical implementation;
transferring a finite bit string is provably insufficient (see Bartlett
et al. 2007 for proof and examples). Any such transfer requires,
moreover, that sender and receiver already share a QRF sufficient
to identify the transferred system (Fields and Marciano` 2019). We
can, therefore, without loss of generality regard all QRFs available
to any observer, including any organism, as physically imple-
mented by the dynamical structure of that system, i.e. by its inter-
nal Hamiltonian. All QRFs are, therefore, quantum informational
processes, i.e. quantum computations, implemented by physical
systems that act as observers. The computations that implement
QRFs are, as we will see below, key to understanding both what
organisms are aware of and how their awareness drives their cog-
nition. They are, therefore, the central components of any scien-
tific theory of consciousness and cognition that is mathematically
consistent with quantum information theory.
The Che-Y system employed by bacterial chemotaxis, e.g. in
Escherichia coli, provides a simple example of a biological QRF. If
the concentration [Che-Y-P] of phosphorylated Che-Y is high,
the flagellar motor spins counter-clockwise and the bacterium
swims in a straight line; if [Che-Y-P] is low, the motor spins
Minimal physicalism as a scale-free substrate |5
clockwise and the bacterium tumbles (Wadhams and Armitage
2004;Micali and Endres 2016). “High” and “low” concentrations
of phosphorylated Che-Y are defined with respect to some de-
fault concentration ratio [Che-Y-P]/[Che-Y] of phosphorylated
to unphosphorylated forms, which in turn depends on the bal-
ance of relevant kinases and phosphorylases. The default ratio
[Che-Y-P]/[Che-Y] is the QRF for the Che-Y system; it fixes a par-
ticular value at which behavior switches from straight-line
swimming to tumbling. Whether the measured [Che-Y-
P]/[Che-Y] at any given time is above or below this fixed value
effectively determines whether a stimulus is approached, i.e. is
considered “good” by E. coli or avoided, i.e. considered “bad.”
In any electrically excitable cell, the resting membrane po-
tential V
0mem
is a QRF for polarization: values of V
mem
>V
0mem
are depolarized, while values of V
mem
<V
0mem
are hyperpolar-
ized. The setting of this reference frame is critical to cell behav-
ior in both neurons and nonneural cells. In the planarian
Dugesia japonica, altering V
0mem
of blastomere cells by blocking
cell communication via gap junctions during regeneration can
effect a homeotic transformation of the posterior from tail mor-
phology to head morphology, including a complete functional
brain (Oviedo et al. 2010;Durant et al. 2017). In both neurons and
electrically excitable plant cells, blocking gap junctions via
anesthetics induces cell quiescence (Balu
ska et al. 2016;
Gremiaux et al. 2014; see also Kelz and Mashour 2019 for a gen-
eral discussion of anesthetic effects across phylogeny).
The quantum theory of complex, macroscopic systems is
generally intractable; hence investigating the general properties
of biological QRFs requires an abstract, scale-free specification
language. The category-theoretic formalism of Channel Theory,
developed by Barwise and Seligman (1997) to describe networks
of communicating information processors, provides a suitably
general language for specifying the functions of QRFs without
any specific assumptions about their implementation. The in-
formation processing elements in this representation are logical
constraints termed “classifiers” that can be thought of as quan-
tum logic gates; they are connected by “infomorphisms” that
preserve the imposed constraints (numerous application exam-
ples, mainly in computer science, are reviewed in Fields and
Glazebrook 2019a). Combinations of these elements are able to
implement “models” in the sense of good-regulator theory
(Conant and Ashby 1970). Suitable networks of classifiers and
their connecting infomorphisms, e.g. provide a generalized rep-
resentation of artificial neural networks (ANNs) and support
standard learning algorithms such as back-propagation.
Networks satisfying the commutativity requirements that de-
fine “cones” and “cocones” (“limits” and “colimits,” respectively,
when these are defined) in category theory (Goguen 1991) pro-
vide a natural representation of both abstraction and mereologi-
cal hierarchies (Fields and Glazebrook 2019b) and of
expectation-driven, hierarchical problem solving, e.g. hierarchi-
cal Bayesian inference and active inference (Fields and
Glazebrook 2020b,c). Commutativity within a cone—cocone
structure, in particular, enforces Bayesian coherence on infer-
ences made by the structure; failures of commutativity indicate
“quantum” context switches (Fields and Glazebrook 2020c).
Predictions of MP: Awareness, Memory, and
Attention
Awareness of X requires a QRF for X
Theories of consciousness typically assume that the agent of in-
terest is embedded in an environment that has observer-
independent features and contains observer-independent
objects. The visual system, e.g., is often described as using
“inverse optics” to compute an “observer-independent” 3d lay-
out from a 2d image (e.g. Pizlo 2001). Assuming observer-inde-
pendent, ontological “givens” both restricts the agent of interest
to some subset of our (typically assumed a priori) ontology and
risks under-predicting the agent’s computational requirements.
It makes von Uexku¨ ll’s (1957) question of defining the organ-
ism’s umwelt, and hence Nagel’s (1974) “what is it like?” ques-
tion harder, and makes the Cartesian response that most
organisms are aware of nothing, and hence have no umwelt
easier.
Quantum theory itself forces MP to reject environmental
“givens” on purely formal grounds. The Hilbert space H
B
in
Equation (2) and internal Hamiltonian H
B
in Equation (3) can be
decomposed in any arbitrary way without affecting the interac-
tion H
AB
at all (Fields 2012,2016); hence H
AB
can communicate
no information to Aabout any such decompositions. This is the
case for any bipartite interaction, and hence any interaction de-
scribed by Equation (5), regardless of the physical makeup of the
interacting components. It is the conditional independence be-
tween the inside and outside of an MB expressed in more fun-
damental physical terms.
By rejecting any a priori ontology for the environment, MP
requires any perceived ontology to be fully supported by the in-
formation-processing capabilities of the perceiver. In particular,
any perceived feature of, or perceived object embedded in, the
environment Bof any agent of interest Amust be rendered both
detectable and meaningful by a QRF implemented by A. This is
illustrated in cartoon form in Fig. 2: detecting an environmental
feature (a box) with a property (a color) requires a QRF for “box-
ness” and a specific detector for the color. The box-detecting
QRF encodes those properties (reference state observables) that
boxes must have to qualify as boxes; hence it picks out boxes,
and only boxes, against the general background of B. Informally,
the QRF can be thought of as an attractor in an “interpretation
space” associated with the system implementing the QRF. The
property detector determines the value of some “pointer” ob-
servable that characterizes all systems that qualify as boxes;
here, the box’s color. In quantum-theoretic language, identify-
ing a bounded system (feature or object) having some specific
property “decoheres” Brelative to A(see Fields and Glazebrook
2020a;Fields and Marciano` 2020b for formal analysis). It is im-
portant to emphasize that A’s perceptual and cognitive capabili-
ties have no effect on the physical state jB>, and in particular do
not render it separable. The features or objects that Adetects in
Bare strictly relative to A, not “objective” in any observer-inde-
pendent sense (again see Mermin 2018;Mu¨ ller 2020 for discus-
sion from a purely quantum-theoretic perspective).
Specifying “objects of awareness” entirely in terms of QRFs,
with no claims of observer-independent ontology, renders typi-
cal formulations of the “combination problem” ill-posed.
Combination is well-defined for QRFs: two QRFs implemented
by a single system can be combined if, but only if, all of the
operators (i.e. the Barwise-Seligman infomorphisms) involved
mutually commute (Fields and Glazebrook 2020c). This is the
“combination” implemented by standard binding processes, e.g.
feature or feature-motion binding during object categorization
(Fields and Glazebrook 2019b). “Combination” of QRFs imple-
mented by different systems is, on the hand, strictly undefined:
the measurement operators implemented by different systems
act on different environments, each of which by definition con-
tains (at least partially) the other system. Hence what Chalmers
(2017) calls the “quality” and “structure” combination problems
6|Fields et al.
cannot be coherently posed in MP. What Chalmers calls the
“subject” combination problem is solved, for living systems, by
their environments; systems that cannot maintain homeosta-
sis/allostasis do not survive, and are unlikely to assemble at all.
Systems that do survive are described by MP if they satisfy
Equations (5) and (6), but they may implement only trivial QRFs.
Hence we agree with the deflationary position advocated by
Montero (2017), though for more technical reasons.
This universal requirement that awareness be supported by
QRFs yields somewhat counter-intuitive predictions of mini-
mality in basal organisms:
Prediction 1: Moving in 3d space does not require a QRF for 3d
space, and hence does not require experiencing 3d space. E. coli
chemotaxis provides an example. E. coli has a 1d spatial QRF: its
body axis, with (mainly) anterior chemoreceptors and (mainly)
posterior flagella. Directed “approach” motion is along this axis;
undirected “tumbling” re-orients this axis randomly in the 3d
“lab” frame of an observer equipped with a 3d QRF. E. coli has no
known means of computing the relative angle between its pre-
and post-tumbling linear motion, i.e. it has no known 3d QRF;
hence tumbling appears to implement a 3d random walk
(Wadhams and Armitage 2004). Colonial microbes living in pla-
nar mats, on the other hand, can potentially use differential
cell–cell or cell–substrate interactions to distinguish left from
right (Jauffred et al. 2017) and hence establish a 2d QRF;
microbes inhabiting multi-species 3d mats with vertical divi-
sion of labor may have 3d QRFs (Prieto-Barajas et al. 2018).
Differentiated cells of multicellular eukaryotes clearly employ
such QRFs (e.g. Bajpai et al. 2021); interestingly, multi-axis mor-
phology correlates with the presence of neurons and appears to
be directly instructed by neural signaling (Fields et al. 2020).
How the representation of 3d space in migrating cells is coupled
to the representations of cell- or extra-cellular surface charac-
teristics, bioelectric and morphogen gradients, and other mor-
phogenetic signals remains a central question in
developmental biology (Grossberg 1978;Pezzulo and Levin
2015).
Prediction 2: Successful interaction with an object does not re-
quire a QRF that identifies that object, and hence does not re-
quire experiencing that object. E. coli mating provides an
example: the mating pilus extends randomly in 3d space, and is
tipped by an adhesin of unknown specificity (Cabezo´n et al.
2015). Commonplace lateral gene transfer (LGT) between mem-
bers of distant microbial lineages (Robbins et al. 2016) suggests
that mating without mate detection is routine in the microbial
world; viral-mediated gene transfer and direct uptake of nucleic
acids from the environment provide even more extreme exam-
ples. Communal microbes such as Myxococcus xanthus that dif-
ferentiate kin from nonkin even below the species level,
however, appear to have sophisticated, though yet uncharacter-
ized, QRFs for other organisms (Mu~
noz-Dorado et al. 2016;
Thiery and Kaimer 2020). Fungi that engage in differential anas-
tomosis appear similarly equipped (de la Providencia et al. 2005).
Cell-type identification QRFs appear to be implemented in part
bioelectrically in multi-species microbial mats (Humphries et al.
2017;Yang et al. 2020); the ubiquitous use of bioelectric signaling
in fungi suggests that this may be the case for fungal cell-type
QRFs as well.
Prediction 3: Successful causation does not require a mecha-
nism for detecting causation, and hence does not require
experiencing causation. Hunting swarms of M. xanthus kill and
eat other microbes, but appear to have separate, noncommuni-
cating detection systems for prey species and edible prey com-
ponents (Thiery and Kaimer 2020). Hence they have no way of
causally associating the killing of prey with the subsequent
availability of edible prey components.
These can be summarized by the following, which recog-
nizes the key role of memory in enabling the experiences of
space, objects, and causation:
Prediction 4: Having a memory does not require a QRF for lin-
ear time, and hence does not require experiencing time or re-
trievable memory. All organisms have genomes that record
their phylogenetic history, but they have no mechanism for
reading their previous genomic states. The genome does not,
therefore, function as an internal QRF for linear time. This
applies, in particular, to us, although we can employ the
genomes of other organisms as external linear time QRFs
(Kumar 2005).
If this last prediction is correct, microbes and perhaps other
organisms lacking linear time QRFs may live in a “continuous
present” characterized mainly by chemoreception (“taste”) and
stress (see below). Their sensory capabilities may change radi-
cally via LGT or other mechanisms, but they cannot “notice”
such changes.
Biological memories exist at multiple scales (Fields and
Levin 2018). Characterizing the scales at which memories are
encoded, and the QRFs that enable them, emerges as a central
task for any theory of consciousness that assumes MP.
Bioenergetic studies may provide a content-independent means
of approaching these questions, as discussed below.
Experienced memories are encoded on boundaries
Actions by an organism on its environment are, in the language
of Fig. 1, preparations of an MB state jB>that its environment
Figure 2. Simplified cartoon of feature or object perception in MP. The
depicted relationship between Aand Bis topological: they are sepa-
rated by the boundary B. There is no implied geometry, and the in-
teraction is bipartite: there is no third system “outside” U¼AB with
which Aor Binteract. Features or objects “embedded” in the envi-
ronment Bare perceptible only by systems Aequipped with QRFs
and property detectors that render the features/objects both detect-
able and meaningful, and are defined only relative to such systems;
this lack of observer-independent ontology is indicated here by
dashed boundaries. Triangles within Asuggest the form of classifier
cocones when drawn as diagrams (Fields and Glazebrook 2019a,b;
2020a,b,c); arrows indicate binding operations. The analogy with
mammalian visual feature detection is obvious; see Fields and
Glazebrook (2019b) for a detailed formal construction.
Minimal physicalism as a scale-free substrate |7
observes. A swimming bacterium, e.g. prepares a new environ-
mental state by expending energy to change its location. In
Friston’s (2013) terms, this is active inference: acting on the en-
vironment to change its state alters, in consequence, the subse-
quent environmental states observed by the active system.
Actions are, by definition, thermodynamically irreversible:
they transfer classical information, via B, from the organism to
its environment. Transfers of classical information across
boundaries such as Bare, moreover, the only thermodynami-
cally irreversible events in MP. As with the requirement for
QRFs to enable awareness, this is a consequence of MP’s quan-
tum theoretic foundation. The state jA>is not, in general, as-
sumed to be separable; hence its Schro¨ dinger evolution is time-
reversible and incurs no energetic cost (Bennett 1982).
Information can only be retrievable at some later time if it is ir-
reversibly and hence classically encoded. Hence retrievable
memories can, in MP, only be encoded on boundaries such as B.
As experiencing a memory as such requires an ability to com-
pare two experiences—either two memories or a memory and a
current state—it requires retrieval. Any experienced memory
must, therefore, be both encoded on, and retrievable via an ap-
propriate QRF from B.
This fundamental thermodynamic constraint has immedi-
ate consequences for biological architecture and for the struc-
ture of QRFs for linear time:
Prediction 5: All retrievable biological memories are stigmer-
gic. Beginning with bacteria (Gloag et al. 2015), biological sys-
tems ubiquitously employ stigmergic memories (Heylighen
2016). This is not a surprising observation to be explained, but
rather an empirical confirmation in MP. The idea that the
experiencing mind “extends” (Clark 1998) into the environment
via stigmergic memory is a direct consequence of quantum
theory.
The stigmergic nature of memory can be reconciled with the
experience of memory as an internal, private phenomenon only
if the agent Ais compartmentalized by internalizing part of Bto
provide an internal boundary Con which classical information
can be encoded. This internal boundary imposes a separability
condition jA>¼jA
1
>jA
2
>as shown in Fig. 3;A
1
becomes part of
the “environment” of A
2
and vice-versa. The interaction be-
tween A
1
and A
2
can be written in the form of Equation (5);
hence information flow across Cis bidirectional classical com-
munication between the components A
1
and A
2
. If we view A
2
as implementing perception and A
1
as implementing action,
this communication, together with the environment’s response,
forms a closed loop. Hence we have:
Prediction 6: Internal awareness requires internal boundaries.
Any system Acapable of experiencing internal memories, in
particular, has a separable internal state and positive integrated
information U. Internal memories are built into all systems that
qualify as conscious in IIT (Oizumi et al. 2014, see especially Fig.
19) and are the basis for such systems having U>0. Here, we
see this as a consequence of quantum information theory.
As discussed above in connection with MBs, this prediction
links separability in its quantum-theoretic sense of state distin-
guishability with separability in its classical sense of separation
by a boundary: the internal boundary Cfunctions as an MB that
encodes classical information and imposes conditional inde-
pendence. It provides for a general expectation that living sys-
tems will be compartmentalized by internal boundaries on
which classical information can be encoded. Because MBs limit,
as well as enabling, information transfer, it also predicts a sys-
tematic inability to determine the source of a memory. In
particular:
Prediction 7: Compartmentalized systems cannot determine
the sources of their encoded memories. Systems can be
expected to behave as if memories they encode reflect their
own past experience, whether they do or not. Studies of mem-
ory transplantation (Pietsch and Schneider 1969) and false-
memory induction (Ramirez et al. 2013) in nonhuman animals
provide mechanistic support for this prediction (see Levin 2020
for additional examples and discussion), as does the psychology
(Henkel and Carbuto 2008) and neuroscience (Straube 2012)of
false-memory induction in humans.
Organisms record memories as messages to their future
selves. Neither the mechanism that recorded a memory, the in-
ternal or environmental events that induced recording, nor the
context in which the recording occurred are discoverable, how-
ever, when the memory is later retrieved. This fundamental un-
certainty about the sources of memories can be seen as a
consequence of a more general uncertainty about whether QRFs
are shared, either by distinct observers at a single time, or by a
single observer across time. This question of QRF sharing is
provably finite Turing undecidable (Fields et al. 2021).
The compartmentalization in Fig. 3 can be arbitrarily
generalized:
Prediction 8: Experiential complexity scales with internal
compartmentalization. Evolution “discovered” the benefits of
internal compartmentalization ca. 3.5 billion years ago with the
development of microbial biofilms exhibiting differential expo-
sure to the open environment and division of metabolic labor
(Stal 2012). The organelles of eukaryotic cells, including internal
membrane complexes such as the Golgi apparatus, are canoni-
cal intracellular compartments. Multicellularity is the most
common form of macroscopic compartmentalization, but is not
required; examples such as Acetabularia (Schweiger 1969), glass
sponges (Leys 2016), and syncitial fungi (Roper et al. 2015) all il-
lustrate complex functional compartmentalization within sin-
gle giant cells.
This prediction is clearly in line with the expectations of IIT,
and again provides a physical basis for these expectations.
Cellular compartments and intercellular boundaries are,
from a biological perspective, dynamic structures that must be
Figure 3. Cartoon representation of a system Awith an internal
boundary Cand hence a separable state jA>¼jA
1
>jA
2
>. Again the
relationships depicted are topological, not geometric. Triangles rep-
resent QRFs; fand gare internal informational states. Information
flow across Cis bidirectional by Equation (5); information also flows
through the environment (dashed arrow). The communication loop
is closed, generating positive U(Oizumi et al. 2014).
8|Fields et al.
preserved through metabolic activity. This requirement can be
stated abstractly:
Prediction 9: Memory stability scales with the frequency of
read/write cycles. The stabilization of classical bit values by re-
peated cycles of preparation followed by observation is called
the “quantum Zeno effect” (Misra and Sudarshan 1977); the
probability that the state remains stable is proportional to the
frequency of observations. Hence memory decay—
“forgetting”—is predicted whenever memories are not routinely
accessed. This is broadly observed across phylogeny. Repair sys-
tems for nucleic acids and degradation systems for proteins
provide molecular-scale examples.
Rewriting classical information costs free energy, i.e.
requires metabolism as discussed below. This requirement for
read/write cycles suggests that compartmentalization plays a
key role in the implementation of linear time QRFs:
Prediction 10: Any ordered sequence of separate memories to-
gether with a comparison function can serve as a linear time
QRF. Trajectories, including looming, are the simplest linear
time QRFs; in the limit, they may support only sequential com-
parisons and hence only distinguish “then” from “now.” Insects
are capable of at least short-sequence linear time perception;
spiders are capable of longer sequence perception (Japyassu´
and Laland 2017). Merely executing a fixed action pattern does
not require experiencing linear time, although it clearly requires
an internal clock. Molecular cell-cycle clocks are as old as life,
and circadian clocks are as old as cyanobacteria (Johnson 2004);
both are highly conserved across phylogeny (Doherty and Kay
2010). Possessing a molecular clock does not, by itself, enable
time perception.
Prediction 11: Time and object/feature identity are duals.
Perceiving a trajectory requires perceiving an object or feature
executing that trajectory; conversely, motion perception is the
basis of object identity (Fields 2011). Whether insects or rela-
tively low-complexity vertebrates are aware of objects as such
or only features of their environments is unclear; bees at least
have robust spatial memory and feature perception, and may
recognize objects as such (Chittka 2017), while fish appear to
recognize conspecifics as distinct objects (Sovrano et al. 2018).
Spiders are capable of robust object perception and object-di-
rected planning (Japyassu´ and Laland 2017), as are cephalopods
(Mather 2019), birds and mammals.
Environmental objects and features that are experienced as
having stable identities are, to close the circle, the canonical
bearers of stigmergic memories. Without perceptible object
identity, time is not perceptible and hence memories cannot be
recognized as such. Only an organism capable of experiencing
linear time and objects or features with stable identities can ex-
perience memories. Such an organism inhabits a rich and
meaningful umwelt, whether its ontology easily relates to our
own or not.
The free-energy costs of irreversibility induce coarse-
graining and attention-switching
With the understanding of QRFs developed above, we can re-
turn to the question of bioenergetics, see why living systems
can have only limited awareness, cognitive capacity, and mem-
ory, and understand the tradeoffs between these.
The dynamics described by Equation (5) explicitly conserves
energy: b
A
T
A
¼b
B
T
B
. Hence some fraction of the bits transferred
from Bto Amust be “burned” by Ato supply the free energy re-
quired to irreversibly encode classical information (Fields and
Glazebrook 2020a). These free-energy supplying bits are not
available as input to any QRF (Fields et al. 2021). As no QRF can
read the values encoded by these bits, their values are irrelevant
and indeed “invisible” to A. They can, therefore, be considered
random for A, i.e. to constitute environmental “noise” or in ther-
modynamic terms, a heat bath. As noted above, however, the
source of this noise is quantum, not classical; there is no third
system injecting noise into the bipartite A-B interaction (see
Fields et al. 2021, for further discussion).
A central question of biology is: how can organisms main-
tain their phenomenal complexity with such small free energy
budgets? The answer is alluded to above: quantum computa-
tional complexity, implemented by unitary Schro¨ dinger evolu-
tion, is energetically free (Bennett 1982). Classical, i.e.
observable or experienceable computational complexity is ex-
pensive, ln2 k
B
Tper bit. Hence we have:
Prediction 12: Organisms only require the energy needed to
maintain their classical states. As seen above, these are encoded
on either exterior or intercompartmental boundaries. An organ-
ism’s energy budget must, therefore, supply the free energy
needed to maintain, via Zeno-effect read/write cycles, the clas-
sical states of their compartment boundaries. Nonboundary
states can remain quantum, evolve unitarily, and consume no
free energy. Only the inputs and outputs of these quantum
computations are classically encoded, all on boundaries.
Clearly not all boundary-localized processes are classical;
electron-transport processes operating in the THz range could
consume a cell’s entire energy budget if fully classical. The tech-
nical difficulty of observing nonclassical behavior in such sys-
tems (e.g. Cao et al. 2020) is not surprising. All of our
observational outcomes are classical by definition; observing
quantum coherence requires observing expectation violations
in probability distributions over recorded classical events (e.g.
Mermin 1993). This suggests that an indirect approach to quan-
titating nonclassicality in biological systems is required. From
the above considerations, stable memories provide a quantita-
tive lower limit on classicality, while the cellular energy budget
provides an upper limit. Determining what states a cell or or-
ganism commits free energy to maintain, i.e. what the set
points for homeostasis/allostasis are, may be the best approach
to estimating the net classicality of biologically encoded
information.
Biological encodings of classical information have lower lim-
its of 1–2 nm in radius, e.g. the size of a typical protein active
site (Liang et al. 1998) or a gap-junction channel (Sosinsky and
Nicholson 2005), and about 200 fs in time, e.g. the response time
of rhodopsin to photons (Wang et al. 1994). Cellular response
times, even for bioelectric responses, are orders of magnitude
larger and involve much larger areas. This loss in resolution is a
consequence of bioenergetics:
Prediction 13: Actionable classical encodings are coarse-
grained. Actionability requires irreversible encoding as in Fig. 3.
The free energy to support this encoding must come from B,
and hence must consume some of the bits encoded on Bas fuel.
The information encoded by these bits is irreversibly lost to A;
hence all of A’s irreversible encodings are coarse-grained.
Any system Athat encodes information irreversibly is,
therefore, faced with a choice that its computational architec-
ture must resolve: the tradeoff between preserving information
via memory and losing information due to coarse-graining. A
flexible solution to this tradeoff is to devote memory resources
to only some input information, i.e. to the results computed by
only some QRFs, allowing these selected results to be recorded
at higher resolution while recording others either at low resolu-
tion or not at all. Hence we have:
Minimal physicalism as a scale-free substrate |9
Prediction 14: Living systems in complex, dynamic environ-
ments will evolve attention-switching systems. Attention has
long been associated with consciousness (Engel and Singer
2001) and attention allocation is one of the main functions of
competition for access to the workspace in GNW models
(Dehaene and Naccache 2001), whether formulated in terms of
the “rich club” (Sporns 2013), “connective core” (Shanahan 2012)
or “giant component” (Wallace 2005) of the larger network.
Indeed the “self” has been described as a working model of at-
tention allocation (Graziano and Webb 2014; see also below). In
humans, attention can drive entirely illusory object perception
(Ongchoco and Scholl 2019), consistent with active-inference
models of Bayesian-precision allocation (Kanai et al. 2015). Here,
we see a requirement for active attention as a consequence of
the thermodynamic requirements of classically encoding
information.
Theapproach/avoidswitchinginsimplechemotacticsystems
such as E. coli provides a basal model of the switch between active
exploration and expectation revision at the heart of active infer-
ence theory (Friston 2010,2013). Such models suggest that every
such switch is governed by a reference value set by some QRF.
Dorsal/ventral attention system switching in humans (Vossel et al.
2014) is sensitive to a vast array of expectations, the reference val-
ues of which are unknown and in at least some cases subject to
considerable individual variation. Salience assignments driving
both exploratory and reactive behavior are, in particular, highly de-
pendent on individual experience, strongly coupled to the core
self-representation and the reward system, and subject to varia-
tion in both prosocial and pathological directions (Uddin 2015).
Much of ethology can be viewed as the comparative study of sa-
lience. Understanding how QRFs that regulate salience vary across
phylogeny will be a major step toward answering the “what is it
like?” question in a systematic way.
Resource Usage, Interoception, and the Self
The considerations above reinforce the obvious point that ac-
quiring and monitoring the usage of free-energy resources is
one of the core functions of any living system. A second core
function, equally necessary for the maintenance of homeosta-
sis/allostasis, is damage control. Within the MP framework,
these core functions are supported by a QRF that encodes the
set points that serve as the overall homeostatic attractor. This
QRF defines the “self” in MP.
Prediction 15: The “self” comprises three core monitoring
functions, for free-energy availability, physiological status,
and organismal integrity, and three core response functions,
free-energy acquisition, physiological damage control, and de-
fense against parasites and other invaders. These will be
found in every organism. Indeed they are found even in E. coli,
which has inducible metabolite acquisition and digestion sys-
tems (Jacob and Monod 1961), the generalized “heat shock”
stress response system (Burdon 1986), and restriction enzymes
that detect and destroy foreign, e.g. viral DNA (Horiuchi and
Zinder 1972). All of these responses act to restore an overall
homeostatic setpoint, i.e. an expected nonequilibrium state;
hence they can all be viewed as acting to minimize environ-
mental variational free energy or Bayesian expectation viola-
tion (Friston 2010;2013).
Specialized molecular pathways for the core functions of
the self are supplemented by specialized intercellular commu-
nication pathways in multicellular organisms and by inter-in-
dividual communication pathways in social organisms, e.g.
eusocial insects (Robinson 1992) or humans. Networks of
neurons support feeding, locomotion (a primary stress re-
sponse) and defense already in Cnidarians (Bosch et al. 2017)
and at least feeding and locomotion in Ctenophores (Moroz
2015); these functions become far more complex in bilaterian
animals, particularly in active animals including arthropods,
cephalopods, and vertebrates (Liebeskind et al. 2017), and form
the basis of “core consciousness” in Damasio’s (2000)
framework.
Considerable evidence now indicates that interoception in
humans, and so presumably in mammals generally, employs a
predictive coding mechanism (Hohwy 2013;Seth 2013) and is
strongly coupled to the core self-representation and the sa-
lience network via the insula—cingulate—orbitofrontal loop
(Craig 2010;Uddin 2015;Seth and Tsakiris 2018). This predictive
coding system manages homeostasis/allostasis across the scale
hierarchy from cells to organ systems (Corcoran and Hohwy
2019) and couples interoception to exteroception and proprio-
ception (Barrett and Simmons 2015;Seth 2013;Barrett 2017;
Seth and Tsakiris 2018). Intriguingly, emotional and stress
responses, core components of the self, are highly sensitive to
gut microbiome activity in humans and other animals (Vuong
et al. 2017;Sarkar et al. 2018). As all multicellular eukaryotes
have obligate symbiotic microbiomes (hence are “holobionts”;
Gilbert 2014), one can expect that microbial contributions to
stress detection and response are universal.
The thermodynamic costs of memory impose a particu-
lar restriction on the self, which predatory eukaryotic uni-
cells such as Paramecium, animals, and quite possibly plants
solve by weighting the less-certain future lower in resource
priority than the (by default assumed to be) more-certain
past:
Prediction 16: Organisms bias their “cognitive light cones”
(Levin 2019), their representational and computational bound-
aries of concern or goal-directedness, toward the past. Memory,
in other words, takes precedence over planning. The extent to
which food caching, cooperative hunt organization, and other
future-directed activities by nonhuman animals provides evi-
dence of deliberative, explicit planning remains controversial
(Bayne et al. 2019), with some arguing that all forms of imagina-
tive “mental time travel” are human-specific (Suddendorf and
Corballis 2007). It is, however, clear that explicit planning
requires classically encoded representations of future events
and so competes for resources with memory. As planning also
requires explicit memory, it cannot win this competition.
The free-energy costs of mental time travel in either direc-
tion constrain it to an “off-line” activity when organisms are
faced with rapid change that requires high-resolution percep-
tion and action. Such constraints also apply to the real-time
representational costs of the self. Hence we can predict:
Prediction 17: Increasing real-time response requirements will
disrupt encoding of the self-representation. This is observed in
humans in “flow” states (Csikszentmiha´ lyi 1990), in highly auto-
mated, expertise-dependent activities including most social inter-
action (Bargh and Ferguson 2000;Bargh et al. 2012), and in
experimental manipulations in which fatigue in various modalities
affects cognitive performance (Massar et al. 2018). The extent to
which nonhuman animals are able to apply “theory of mind” rea-
soning to themselves, and hence maintain a metacognitive self-
representation, remains controversial (Martin and Santos 2016).
The picture that emerges from these energetic considera-
tions is of a self-representation with critical functions and deep
evolutionary roots (Levin 2020), but with only limited and tran-
sient exposure to awareness via explicit encoding. This is
broadly consistent with the constructive views of the self
10 | Fields et al.
outlined by Blackmore (2002),Chater (2018),Graziano and Webb
(2014) among others.
Context Change Drives Evolution,
Development, and Learning
Psychology in the 20th century was consumed with controver-
sies pitting evolution (“Nature”) against learning (“Nurture”),
with developmental processes straddling the conceptual gap in
between. The development of robust machine learning algo-
rithms, the advent of evo–devo (Mu¨ ller 2007;Carroll 2008), the
recognition that evolution itself can be viewed as a learning pro-
cess (Power et al. 2015;Watson and Szathma´ ry 2016;Kouvaris
et al. 2017), and in particular, the scale-free applicability of ac-
tive inference models (Friston 2013;Friston et al. 2015;Fields
and Levin 2020b,c; Kuchling et al. 2020) have done much to de-
construct these distinctions. As a scale-free approach in which
any lineage can also be considered an individual (Fields and
Levin 2020c), MP treats evolution, development, and learning as
instances of a common mechanism. It distinguishes two nono-
verlapping cases: gradual alterations in the relative contribu-
tions of QRFs or components of QRFs to outcomes, and saltatory
changes in QRFs themselves.
Prediction 18: Context changes drive QRF changes. Here, we
define context changes strictly: For a fixed set fo
i
gof observ-
ables, a context change x!yhas occurred if the probability dis-
tributions Prob(o
i
jx) and Prob (o
i
jy) are well defined but the joint
distribution Prob(o
i
jx_y) is not (Kochen and Specker 1967;
Dzhafarov et al. 2017). Here, “observables” are by definition clas-
sifiers, each of whose components consist of event/(condition,
context)/valuation triples (Fields and Glazebrook 2020c;Fields
et al. 2021). Under these conditions, QRF change in response to
context change is required to maintain Bayesian coherence
(Fields and Glazebrook 2020c).
Context switches are hard to measure, as doing so requires
predicting what “background” variables are relevant in fact to
how an event is processed, a prediction problem that is in gen-
eral intractable (Dietrich and Fields 2020). Saltatory changes in
event representations, e.g. canonical “Aha!” moments (Kounios
and Beeman 2015), may reflect context-driven QRF changes.
Basal systems may provide uniquely manipulable windows into
such processes: e.g. Pezzulo et al. (2020) have recently suggested
that bioelectrically driven saltatory morphological changes in
planaria may employ the same underlying mechanism as salta-
tory changes in episodic memories in mammals.
Conclusions
Quantum theory has had a long and somewhat disreputable as-
sociation, dating back at least to von Neumann (1932), with the
science of consciousness. A dominant concern, beginning at
least with Wigner (1961) and extending through Penrose (1989),
Schwartz et al. (2005), and Hameroff and Penrose (2014) to
Georgiev (2020) has been to employ quantum theory to establish
an ontological foundation for a science of consciousness. Our
interest here has not been ontological, but rather empirical: to
derive as much as possible from the simple assumption that
consciousness involves information exchange subject to the
constraints of quantum information theory. We have shown
that the MP framework that follows from this assumption
allows many of the key features of consciousness to be under-
stood as simple, scale-independent consequences of
thermodynamics.
In direct contrast with strict Cartesianism, MP holds that we
can better understand our own awareness by understanding
the awareness of our more basal cousins. Our homeostatic/allo-
static drives and the mechanisms that satisfy them are phylo-
genetically continuous with those of prokaryotic unicells
including E. coli. Our concepts and categories are implemented
by QRFs playing the same roles, and satisfying the same basic
requirements, as the Che-Y system regulating bacterial chemo-
taxis. The tradeoffs that we implement, and adjust in real time,
between perception, memory, and planning are tradeoffs that
have been explored and adjusted in niche-specific ways by all
organisms throughout evolutionary history. We can take advan-
tage of these fundamental mechanistic similarities to design
theoretical and experimental paradigms that reveal and assess
scale-free properties of consciousness in both natural and engi-
neered systems.
Acknowledgments
The authors thank E. Dietrich, D. Hoffman, A Marciano`, C.
Prakash, and R. Prentner for relevant discussions, noting
that they may nonetheless reject some or all of our conclu-
sions. They also thank the two reviewers for their careful
reading and helpful comments.
Funding
M.L. gratefully acknowledges support of the Barton Family
Foundation, the Elisabeth Giauque Trust, and the Templeton
World Charity Foundation.
Conflict of interest statement. None declared.
References
Agrawal H. Extreme self-organization in networks constructed
from gene expression data. Phys Rev Lett 2002;89:268702.
Arendt D, Benito-Gutierrez E, Brunet T, et al. Gastric pouches and
the mucociliary sole: setting the stage for nervous system evo-
lution. Philos Trans R Soc Lond B Biol Sci 2015;370:20150286.
Baars BJ, Franklin S. How conscious experience and working
memory interact. Trends Cogn Sci 2003;7:166–72.
Baars BJ, Franklin S, Ramsoy TZ. Global workspace dynamics:
cortical “binding and propagation” enables conscious con-
tents. Front Psychol 2013;4:200.
Bajpai S, Prabhakar B, Chelakkot R, Chelakkot R, Inamdar M. Role
of cell polarity dynamics and motility in pattern formation due
to contact dependent signalling. bioRxiv https://doi.org/10.
1101/2020.10.09.331009, 2021.
Balu
ska F, Gagliano M, Wizany G (eds). Memory and Learning in
Plants. Cham: Springer, 2018.
Balu
ska F, Levin M. On having no head: cognition throughout bi-
ological systems. Front Psychol 2016;7:902.
Balu
ska F, Reber A. Sentience and consciousness in single cells:
how the first minds emerged in unicellular species. BioEssays
2019;41:1800229.
Balu
ska F, Reber A. Senomic and ephaptic principles of cellular
consciousness: the biomolecular basis for plant and animal
sentience. J Cons Stud 2021;28:31–49.
Balu
ska F, Miller WB, Reber A. Biomolecular basis of cellular con-
sciousness via subcellular nanobrains. Int J Mol Sci 2021; 22:
25452021.
Minimal physicalism as a scale-free substrate |11
Balu
ska F, Yokawa K, Mancuso S, et al. Understanding of anes-
thesia - Why consciousness is essential for life and not based
on genes. Commun Integr Biol 2016;9:e1238118.
Baraba´ si A-L. Network Science. Cambridge, UK: Cambridge
University Press, 2016.
Baraba´ si A-L, Oltvai ZN. Network biology: understanding the
cell’s functional organization. Nat Rev Genet 2004;5:101–14.
Bargh JA, Ferguson MJ. Beyond behaviorism: on the automaticity
of higher mental processes. Psychol Bull 2000;126:925–45.
Bargh JA, Schwader KL, Hailey SE, et al. Automaticity in social-
cognitive processes. Trends Cogn Sci 2012;16:593–605.
Barrett LF. The theory of constructed emotion: an active infer-
ence account of interoception and categorization. Soc Cogn
Affect Neurosci 2017;12:1–23.
Barrett LF, Simmons WK. Interoceptive predictions in the brain.
Nat Rev Neurosci 2015;16:419–29.
Bartlett SD, Rudolph T, Spekkens RW. Reference frames, super-
selection rules, and quantum information. Rev Mod Phys 2007;
79:555–609.
Bartlett S, Wong ML. Defining Lyfe in the Universe: from three
privileged functions to four pillars. Life 2020;10:42.
Barwise J, Seligman J, Information Flow: The Logic of Distributed
Systems (Cambridge Tracts in Theoretical Computer Science, 44).
Cambridge, UK: Cambridge University Press, 1997.
Bateson G. Steps to an Ecology of Mind: Collected Essays in
Anthropology, Psychiatry, Evolution, and Epistemology. Northvale,
NJ, USA: Jason Aronson, 1972.
Bayne T, Brainard D, Bryne RW, et al. What is cognition? Curr Biol
2019;29:R608–15.
Bell JS. Speakable and unspeakable in quantum mechanics. In:
Bell, JS (ed.), Speakable and Unspeakable in Quantum Mechanics.
Cambridge, UK: Cambridge University Press, 1987, 169–72.
Bennett CH. The thermodynamics of computation. Int J Theor
Phys 1982;21:905–40.
Blackmore S. There is no stream of consciousness. J Cons Stud
2002;9:17–28.
Boltzmann L. Lectures on Gas Theory. New York, NY: Dover,
1995.
Bosch TCG, Klimovich A, Domazet-Lo
so T, et al. Back to the
basics: Cnidarians start to fire. Trends Neurosci 2017;40:92–105.
Broido AD, Clauset A. Scale-free networks are rare. Nat Comms
2019;10:1017.
Brun-Usan M, Thies C, Watson RA. How to fit in: the learning
principles of cell differentiation. PLoS Comput Biol 2020;16:
e1006811.
Burdon RH. Heat shock and the heat shock proteins. Biochem J
1986;240:313–24.
Burkhardt P, Sprecher SG. Evolutionary origin of synapses and
neurons – Bridging the gap. BioEssays 2017;39:1700024.
Cabezo´ n E, Ripoll-Rozada J, Pe~
na A, et al. Towards an integrated
model of bacterial conjugation. FEMS Microbiol Rev 2015;39:
81–95.
Calvo P, Balu
ska F, Trewavas A, Integrated information as a pos-
sible basis for plant consciousness. Biochem Biophys Res Comm
2020. 10.1016/j.bbrc.2020.10.022
Calvo P, Kiejzer F. Plant cognition. In: Balu
ska F (ed.), Plant-
Environment Interactions, Signaling and Communication in Plants.
Berlin: Springer, 2009, 247–66.
Cao J, Cogdell RJ, Coker DF, et al. Quantum biology revisited. Sci
Adv 2020;6:eaaz4888.
Carroll SB. Evo-devo and an expanding evolutionary synthesis: a
genetic theory of morphological evolution. Cell 2008;134:25–36.
Chalmers D. The Conscious Mind: In Search of a Fundamental Theory.
Oxford: Oxford University Press, 1996.
Chalmers D. The combination problem for panpsychism. In:
Bru¨ ntrup G, Jaskolla L (eds.), Panpsychism: Contemporary
Perspectives. Oxford: Oxford University Press, 2017, 179–214.
Chater N. The Mind is Flat. London: Allen Lane, 2018.
Chittka L. Bee cognition. Curr Biol 2017;27:R1037–59.
Clark A. Where brain, body, and world collide. Daedalus 1998;127:
257–80.
Clark A. How to knit your own Markov blanket: Resisting the
Second Law with metamorphic minds. In: Metzinger T, Wiese
W (eds), Philosophy and Predictive Processing: 3. Frankfurt am
Main, MIND Group, 2017.
Conant RC, Ashby SW. Every good regulator of a system must be
a model of that system. Int J Syst Sci 1970;1:89–97.
Cooke JE. The living mirror theory of consciousness. J Cons Stud
2020;27:127–47.
Corcoran AW, Hohwy J. Allostasis, interoception, and the free
energy principle: feeling our way forward. In: Tsakiris M, De
Preester H (eds.), The Interoceptive Mind: From Homeostasis to
Awareness. Oxford: Oxford University Press, 2019, 272–92.
Craig AD. The sentient self. Brain Struct Funct 2010;214:563–77.
Csikszentmiha´ lyi M. Flow: The Psychology of Optimal Experience.
New York, NY: Harper and Row, 1990.
Damasio A. The Feeling of What Happens: Body, Emotion and the
Making of Consciousness. London: Heinemann, 2000.
de la Providencia IE, de Souza FA, Ferna´ ndez F, et al. Arbuscular
mycorrhizal fungi reveal distinct patterns of anastomosis for-
mation and hyphal healing mechanisms between different
phylogenic groups. New Phytol 2005;165:261–71.
Debono M-W, Souza GM. Plants as electromic plastic interfaces:
a mesological approach. Prog. Biophys Mol Biol 2019;146:123–33.
Dehaene S, Charles L, King J-R, et al. Toward a computational the-
ory of conscious processing. Curr Opin Neurobiol 2014;25:76–84.
Dehaene S, Naccache L. Towards a cognitive neuroscience of
consciousness: basic evidence and a workspace framework.
Cognition 2001;79:1–37.
Dietrich E, Fields C. Equivalence of the Frame and Halting prob-
lems. Algorithms 2020;13:175.
Dietrich E, Fields C, Sullins JP, et al. Great Philosophical Objections to
Artificial Intelligence: The History and Legacy of the AI Wars.
London: Bloomsbury, 2021.
Dietrich E, Hardcastle V. Sisyphus’s Boulder: Consciousness and the
Limits of the Knowable. Amsterdam: John Benjamins, 2005.
di Primio F, Mu¨ ller BF, Lengeler JW. Minimal cognition in unicel-
lular organisms. In: Meyer JA, Berthoz A, Floreano D, Roitblat
HL, Wilson SW (eds), From Animals to Animats. Honolulu, HI:
International Society for Adaptive Behavior, 2000, 3–12.
Doherty CJ, Kay SA. Circadian control of global gene expression
patterns. Annu Rev Genet 2010; 44:419–44.
Draguhn A, Mallatt JM, Robinson DG. Anesthetics and plants: no
pain, no brain, and therefore no consciousness. Protoplasma
2021; 258: 239-48.
Durant F, Morokuma J, Fields C, et al. Long-term, stochastic edit-
ing of regenerative anatomy via targeting endogenous bioelec-
tric gradients. Biophys J 2017;112:2231–43.
Dzhafarov EN, Cervantes VH, Kujala JV. Contextuality in canoni-
cal systems of random variables. Philos Trans R Soc A 2017;375:
20160389.
Engel AK, Singer W. Temporal binding and the neural correlates
of sensory awareness. Trends Cogn Sci 2001;5:16–25.
Feynman RP. Simulating physics with computers. Int J Theor Phys
1982;21:467–88.
Fields C. Trajectory recognition as the basis for object individua-
tion: a functional model of object file instantiation and object
token encoding. Front Psychol Percept Sci 2011;2:49.
12 | Fields et al.
Fields C. If physics is an information science, what is an ob-
server? Information 2012;3:92–123.
Fields C. Building the observer into the system: toward a realistic
description of human interaction with the world. Systems 2016;
4:32.
Fields C, Bischof J, Levin M. Morphological coordination: a com-
mon ancestral function unifying neural and non-neural sig-
naling. Physiology 2020; 35:16–30.
Fields C, Glazebrook JF. A mosaic of Chu spaces and Channel
Theory I: category-theoretic concepts and tools. J Expt Theor
Artif Intell 2019a;31:177–213.
Fields C, Glazebrook JF. A mosaic of Chu spaces and Channel
Theory II: applications to object identification and mereologi-
cal complexity. J Expt Theor Artif Intell 2019b;31:237–65.
Fields C, Glazebrook JF. Representing measurement as a thermo-
dynamic symmetry breaking. Symmetry 2020a;12:810.
Fields C, Glazebrook JF. Do Process-1 simulations generate the
epistemic feelings that drive Process-2 decision making? Cogn
Proc 2020b;21:533.53.
Fields C, Glazebrook JF. Information flow in context-dependent
hierarchical Bayesian inference. J Expt Theor Artif Intell 2020c.
10.1080/0952813X.2020.1836034
Fields C, Glazebrook JF, Marciano` A. Reference frame induced
symmetry breaking on holographic screens. Symmetry 2021;
13:408.
Fields C, Levin M. Multiscale memory and bioelectric error cor-
rection in the cytoplasm-cytoskeleton-membrane system.
WIRES Syst Biol Med 2018;10:e1410.
Fields C, Levin M. How do living systems create meaning?
Philosophies 2020a;5:36.
Fields C, Levin M. Integrating evolutionary and developmental
thinking into a scale-free biology. BioEssays 2020b;42:1900228.
Fields C, Levin M. Does evolution have a target morphology?
Organisms 2020c;4:57–76.
Fields C, Levin M. Metabolic limits on classical information proc-
essing by biological cells. Preprint Arxiv 2103;17061v1
[quant.ph], 2021.
Fields C, Marciano` A. Sharing nonfungible information requires
shared nonfungible information. Quant Rep 2019; 1:252–9.
Fields C, Marciano` A. Markov blankets are general physical inter-
action surfaces. Phys Life Rev 2020a;33:109–11.
Fields C, Marciano` A. Holographic screens are classical informa-
tion channels. Quant Rep 2020b;2:326.36.
Friedman DA, Søvik E. The ant colony as a test for scientific theo-
ries of consciousness. Synthese 2021;198:1457–80.
Friston KJ. The free-energy principle: a unified brain theory? Nat
Rev Neurosci 2010;11:127–38.
Friston KJ. Life as we know it. J R Soc Interface 2013;10:20130475.
Friston KJ. A free energy principle for a particular physics.
Preprint arxiv:1906.10184v1 [q-bio.NC], 2019.
Friston K, Levin M, Sengupta B, et al. Knowing one’s place: a free-
energy approach to pattern regulation. J R Soc Interface 2015;12:
20141383.
Gagliano M, Grimonprez M. Breaking the silence–Language and
the making of meaning in plants. Ecopsychol 2015;7:145. 52.
Georgiev DD. Quantum information theoretic approach to the
mind–brain problem. Prog Biophys Mol Biol 2020;158:16–32.
Gilbert SF. Symbiosis as the way of eukaryotic life: the depen-
dent co-origination of the body. J Biosci 2014;39:201–9.
Gloag ES, Turnbull L, Whitchurch CB. Bacterial stigmergy: an
organising principle of multicellular collective behaviours of
bacteria. Scientifica 2015;2015:387342.
Goguen JA. A categorical manifesto. Math Struct Comp Sci 1991;1:
49–67.
Graziano MSA, Webb TW. A mechanistic model of conscious-
ness. Int J Mach Cons 2014;6:1–14.
Gremiaux A, Yokawa K, Mancuso S, et al. Plant anesthesia sup-
ports similarities between animals and plants: Claude
Bernard’s forgotten studies. Plant Signal Behav 2014;9:e27886.
Grossberg S. Communication, memory and development. Prog
Theor Biol 1978;5:183–232.
Gu¨ ntu¨ rku¨ n O, Bugnyar T. Cognition without cortex. Trends Cogn
Sci 2016;20:291–303.
Hacker PMS. Is there anything it is like to be a bat? Philosophy
2002;77:157–74.
Hagmann P, Cammoun L, Gigandet X, et al. Mapping the
structural core of the human cerebral cortex. PLoS Biol
2008;6:e159.
Hameroff S, Penrose R. Consciousness in the universe: a review
of the ‘OrchOR’ theory. Phys Life Rev 2014;11:39–78.
Henkel LA, Carbuto M. Remembering what we did: how source
misattributions arise from verbalization, mental imagery, and
pictures. In: Kelley MR (ed.), Applied Memory. Hauppauge, NY:
Nova, 2008;213–34.
Heylighen F. Stigmergy as a universal coordination mechanism
II: varieties and evolution. Cogn Syst Res 2016;38:50–9.
Hoffman DD, Singh M, Prakash C. The interface theory of percep-
tion. Psych Bull Rev 2015;22:1480–506.
Hohwy J, The Predictive Mind. Oxford, UK: Oxford University Press,
2013.
Holland LZ, Carvalho JE, Escriva H, et al. Evolution of bilaterian
central nervous systems: a single origin? EvoDevo 2013;4:27.
Horiuchi K, Zinder ND. Cleavage of bacteriophage fl DNA by the
restriction enzyme of Escherichia coli B. Proc Natl Acad Sci USA
1972;69:3220–4.
Humphries J, Xiong L, Liu J, et al. Species-independent attraction
to biofilms through electrical signaling. Cell 2017;168:200–9.
Inoue T, Hoshino H, Yamashita T, et al. Planarian shows deci-
sion-making behavior in response to multiple stimuli by inte-
grative brain function. Zool Lett 2015;1:7.
Jacob F, Monod J. Genetic regulatory mechanisms in the synthe-
sis of proteins. J Mol Biol 1961;3:318–56.
Japyassu´ HF, Laland KN. Extended spider cognition. Anim Cogn
2017;20:375–95.
Jauffred L, Vejborg RM, Korolev KS, et al. Chirality in microbial
biofilms is mediated by close interactions between the cell sur-
face and the substratum. ISME J 2017;11:1688–701.
Johnson CH. Precise circadian clocks in prokaryotic cyanobacte-
ria. Curr Issues Mol Biol 2004;6:103–10.
Kanai R, Komura Y, Shipp S, et al. Cerebral hierarchies: predictive
processing, precision and the pulvinar. Philos Trans R Soc Lond B
Biol Sci 2015;370:20140169.
Kelz MB, Mashour GA. The biology of general anesthesia from
paramecium to primate. Curr Biol 2019;29:R1199–210.
Klein C, Barron AB. How experimental neuroscientists can fix
the hard problem of consciousness. Neurosci Cons 2020;6:
niaa009.
Kochen S, Specker EP. The problem of hidden variables in quan-
tum mechanics. J Math Mech 1967;17:59–87.
Kounios J, Beeman M. The Eureka Factor: Creative Insights and the
Brain. London: Windmill, 2015.
Kouvaris K, Clune J, Kounious L, et al. How evolution learns to
generalise: using the principles of learning theory to under-
stand the evolution of developmental organisation. PLoS Comp
Biol 2017;13:e1005358.
Kriegman S, Blackiston D, Levin M, et al. A scalable pipeline for
designing reconfigurable organisms. Proc Natl Acad Sci USA
2020;117:1853–9.
Minimal physicalism as a scale-free substrate |13
Kuchling F, Friston K, Georgiev G, et al. Morphogenesis as
Bayesian inference: a variational approach to pattern forma-
tion and control in complex biological systems. Phys Life Rev
2020;33:88–108.
Kumar S. Molecular clocks: four decades of evolution. Nat Rev
Genet 2005;6:654–62.
Lamme VAF. Challenges for theories of consciousness: seeing
or knowing, the missing ingredient and how to deal with
panpsychism. Philos Trans R Lond Soc B Biol Sci 2018;373:
20170344.
Landauer R. Irreversibility and heat generation in the computing
process. IBM J Res Devel 1961;5:183–95. [Database].
Levin M. The computational boundary of a “self”: developmental
bioelectricity drives multicellularity and scale-free cognition.
Front Psychol 2019;10:2688.
Levin M. Life, death, and self: fundamental questions of primi-
tive cognition viewed through the lens of body plasticity and
synthetic organisms. Biochem Biophys Res Comm 2020.
doi.org/10.1016/j.bbrc.2020.10.077
Leys SP. Elements of a ‘nervous system’ in sponges. J Expt Biol
2016;218:581–91.
Liang J, Edelsbrunner H, Woodward C. Anatomy of protein pock-
ets and cavities: measurement of binding site geometry and
implications for ligand design. Protein Sci 1998;7:1884–97.
Liebeskind BJ, Hofmann HA, Hillis DM, et al. Evolution of animals
neural systems. Annu Rev Ecol Evol Syst 2017;48:377–98.
Lyon P. The cognitive cell: bacterial behavior reconsidered. Front
Microbiol 2015;6:264.
Lyon P. Of what is “minimal cognition” the half-baked version?
Adapt Behav 2020;28:407–28.
Mariscal C, Doolittle WF. Life and life only: a radical alternative
to life definitionism. Synthese 2020;197:2975–89.
Martin A, Santos LR. What cognitive representations support pri-
mate Theory of Mind? Trends Cogn Sci 2016;20:375–82.
Mashour GA, Roelfsema P, Changeux J-P, et al. Conscious proc-
essing and the global neuronal workspace hypothesis. Neuron
2020;105:776–98.
Massar SAA, Csatho´ A, Van der Linden D. Quantifying the moti-
vational effects of cognitive fatigue through effort-based deci-
sion making. Front Psychol 2018;9:843.
Mather J. What is in an octopus’s mind? Animal Sent 2019;4:26.1.
Maturana HR, Varela FJ. Autopoesis and Cognition: The Realization of
the Living. D. Reidel: Dordrecht, 1980.
McFadden J. Integrating information in the brain’s EM field: the
cemi field theory of consciousness. Neurosci Cons 2020;2020:
niaa016.
Menzel R. The honeybee as a model for understanding the basis
of cognition. Nat Rev Neurosci 2012;13:758–68.
Mermin ND. Hidden variables and the two theorems of John Bell.
Rev Mod Phys 1993;65:803–15.
Mermin ND. Making better sense of quantum mechanics. Rep
Prog Phys 2018;82:012002.
Micali G, Endres RG. Bacterial chemotaxis: information process-
ing, thermodynamics, and behavior. Curr Opin Microbiol 2016;
30:8–15.
Misra B, Sudarshan ECG. The Zeno’s paradox in quantum theory.
J Math Phys 1977;18:756–63.
Montero BG. What combi nation problem? In: Bru¨ ntrup G,
Jaskolla L (eds.), Panpsychism: Contemporary Perspectives. Oxford:
Oxford University Press, 2017, 215–28.
Moroz LL. Convergent evolution of neural systems in cteno-
phores. J Expt Biol 2015;218:598–611.
Mu¨ ller GB. Evo-devo: extending the evolutionary synthesis. Nat
Rev Genet 2007;8:943–9.
Mu¨ ller MP. Law without law: from observer states to physics via
algorithmic information theory. Quantum 2020;4:301.
Mu~
noz-Dorado JM, Torres FJ, Garcı´a-Bravo E, et al. Myxobacteria:
moving, killing, feeding, and surviving together. Front Microbiol
2016;7:781.
Nagel T. What is it like to be a bat? Philos Rev 1974;83:435–50.
Nickel M. Evolutionary emergence of synaptic nervous systems:
what can we learn from the non-synaptic, nerveless Porifera?
Invert Biol 2010;129:1–16.
Nieder A, Wagener L, Rinnert P. A neural correlate of sensory
consciousness in a corvid bird. Science 2020; 369:1626–9.
Nielsen MA, Chuang IL, Quantum Computation and Quantum
Information. Cambridge, UK: Cambridge University Press, 2000.
Niss K, Gomez-Casado C, Hjaltelin JX, et al. Complete topological
mapping of a cellular protein interactome reveals bow-tie
motifs as ubiquitous connectors of protein complexes. Cell Rep
2020;31:107763.
Oizumi M, Albantakis L, Tononi G. From the phenomenology to
the mechanisms of consciousness: Integrated Information
Theory 3.0. PLoS Comp Biol 2014;10:e1003588.
Okuno D, Iino R, Noji H. Rotation and structure of F
o
F
1
-ATP syn-
thase. J Biochem 2011;149:655–64.
Ongchoco JDK, Scholl BJ. How to create objects with your mind:
from object-based attention to attention-based objects. Psychol
Sci 2019;30:1648–55.
Oviedo NJ, Morokuma J, Walentek P, et al. Long-range neural and
gap junction protein-mediated cues control polarity during
planarian regeneration. Dev Biol 2010;339:188–e99.
Panksepp J. Affective consciousness: core emotional feelings in
animals and humans. Cons Cognit 2005;14:30–80.
Pattee HH. Cell psychology. Cognit Brain Theory 1982;5:325–41.
Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of
Plausible Inference. San Mateo, CA: Morgan Kaufmann, 1988.
Penrose R. The Emperor’s New Mind: Concerning Computers, Minds,
and the Laws of Physics. Oxford: Oxford University Press, 1989.
Perry CJ, Barron AB, Chittka L. The frontiers of insect cognition.
Curr Opin Behav Sci 2017;16:111–8.
Pezzulo G, LaPalme J, Durant F, et al. Bistability of somatic pat-
tern memories: stochastic outcomes in bioelectric circuits un-
derlying regeneration. Philos Trans R Soc Lond B Biol Sci 2021;
376: 20190765.
Pezzulo G, Levin M. Re-membering the body: applications of
computational neuroscience to the top-down control of regen-
eration of limbs and other complex organs. Integr Biol 2015;7:
1487–517.
Pfeffer S, Wolf H. Arthropod spatial cognition. Anim Cogn 2020;
23:1041–9.
Pietsch P, Schneider CW. Brain transplantation in salamanders –
an approach to memory transfer. Brain Res 1969;14:707–15.
Pizlo Z. Perception viewed as an inverse problem. Vis Res 2001;
41:3145–61.
Power DA, Watson RA, Szathma´ry E, et al. What can ecosystems
learn? Expanding evolutionary ecology with learning theory.
Biol Direct 2015;10:69.
Prakash C, Fields C, Hoffman DD, et al. Fact, fiction, and fitness.
Entropy 2020;22:514.
Prakash C, Stephens KD, Hoffman DD, et al. Fitness beats truth in
the evolution of perception. Acta Biotheor 2020. doi:
10.1007/s10441-020-09400-0
Prieto-Barajas CM, Valencia-Cantero E, Santoyo G. Microbial mat
ecosystems: structure types, functional diversity, and biotech-
nological application. Electr J Biotech 2018; 31:48–56.
Ramirez S, Liu X, Lin PA, et al. Creating a false memory in the hip-
pocampus. Science 2013;341:387–e91.
14 | Fields et al.
Robbins RJ, Krishtalka L, Wooley JC. Advances in biodiversity:
metagenomics and the unveiling of biological dark matter.
Stand Genomic Sci 2016;11:69.
Robinson GE. Regulation of division of labor in insect societies.
Annu Rev Entomol 1992;37:637.65.
Rochat P. Primordial sense of embodied self-unity. In:
Slaughter V, Brownell CA (eds.), Early Development of Body
Representations. Cambridge, UK: Cambridge University Press,
2012, 3–18.
Roederer J. Information and Its Role in Nature. Berlin: Springer, 2005.
Roper M, Lee C-H, Hickey PC, et al. Life as a moving fluid: fate of
cytoplasmic macromolecules in dynamic fungal syncytia. Curr
Opin Microbiol 2015;26:116–22.
Rubin S, Parr T, Da Costa L, et al. Future climates: markov blan-
kets and active inference in the biosphere. J R Soc Interface 2020;
17:20200503.
Sarkar A, Harty S, Lehto SM, et al. The microbiome in psychol-
ogy and cognitive neuroscience. Trends Cogn. Sci 2018; 22:
611–36.
Schlitt T, Brazma A. Current approaches to gene regulatory net-
work modelling. BMC Bioinformatics 2007;8:(Suppl S9).
Schnell AK, Amodio P, Boeckle M, et al. How intelligent is a ceph-
alopod? Lessons from comparative cognition. Biol Rev 2021; 96:
162-78.
Schwartz JM, Stapp HP, Beauregard M. Quantum physics in neu-
roscience and psychology: a neurophysical model of mind-
brain interaction. Philos Trans R Soc Lond B Biol Sci 2005;360:
1309–27.
Schweiger HG. Cell biology of Acetabularia.Current Topics in
Microbiology and Immunology 1969; 50:1–36.
Seth AK. Interoceptive inference, emotion, and the embodied
self. Trends Cogn Sci 2013;17:565–73.
Seth AK, Tsakiris M. Being a beast machine: the somatic basis of
selfhood. Trends Cogn Sci 2018;22:969–81.
Shanahan M. The brain’s connective core and its role in animal
cognition. Philos Trans R Soc Lond B Biol Sci 2012;367:2704–14.
Shannon CE. A mathematical theory of communication. Bell Syst
Tech J 1948;27:379–423.
Shomrat T, Levin M. An automated training paradigm reveals
long-term memory in planarians and its persistence through
head regeneration. J Expt Biol 2013;216:3799–810.
Sosinsky GE, Nicholson BJ. Structural organization of gap junc-
tion channels. Biochem Biophys Acta 2005;1711:99–125.
Sovrano VA, Baratti G, Potrich D. A detour task in four species of
fishes. Front Psychol 2018;9:2341.
Sporns O. Network attributes for segregation and integration in
the human brain. Curr Opin Neurobiol 2013;23:162–71.
Sporns O, Honey CJ. Small worlds inside big brains. Proc Natl Acad
Sci USA 2006;111:15220–5.
Stal LJ, Cyanobacterial mats and stromatolites. In: Whitton BA
(ed.), Ecology of Cyanobacteria II: Their Diversity in Space and Time.
Berlin: Springer, 2012, 65–125.
Stewart J. Cognition ¼Life: implications for higher-level cogni-
tion. Behav Process 1996;35:311–26.
Straube B. An overview of the neuro-cognitive processes in-
volved in the encoding, consolidation, and retrieval of true and
false memories. Behav Brain Funct 2012;8:35.
Suddendorf T, Corballis MC. The evolution of foresight: what is
mental time travel and is it unique to humans? Behav Brain Sci
2007;30:299–313.
Thiery S, Kaimer C. The predation strategy of Myxococcus xanthus.
Front Microbiol 2020;11:2
Tononi G. Consciousness as integrated information: a provi-
sional manifesto. Biol Bull 2008;215:216–42.
Tononi G, Koch C. Consciousness: here, there and everywhere?
Philos Trans R Soc Lond B Biol Sci 2015;370:20140167.
Trevarthen C. What is it like to be a person who knows nothing?
Defining the active intersubjective mind of a newborn human
being. Inf Child Dev 2010;20:119–35.
Ueno H, Suzuki T, Kinosita K, Jr, et al. ATP-driven stepwise rotation
of F
o
F
1
-ATP synthase. Proc Natl Acad Sci USA 2005;102:1333–8.
Uddin LQ. Salience processing and insular cortical function and
dysfunction. Nat Rev Neurosci 2015;16:55–61.
Vlachakis D, Bencurova E, Papangelopoulos N, et al. Current
state-of-the-art molecular dynamics methods and applica-
tions. Adv Prot Chem Struct Biol 2014;94:269–313.
von Neumann J. Mathematical Foundations of Quantum Mechanics.
Princeton: Princeton University Press, 1955.
von Uexku¨ ll J, A Stroll through the wo rlds of animals an d men.
In: Schiller C (ed.), Instinctive Behavior. New York, NY:
International Universities Press, 1957, 5–80.
Vossel S, Geng JJ, Fink GR. Dorsal and ventral attention systems:
distinct neural circuits but collaborative roles. Neuroscientist
2014;20:150–9.
Vuong HE, Yano JM, Fung TC, Hsiao EY. The microbiome and
host behavior. Annu Rev Neurosci 2017;40:21–49.
Wadhams GH, Armitage JP. Making sense of it all: bacterial che-
motaxis. Nat Rev Mol Cell Biol 2004;5:1024–37.
Wallace R, Consciousness: A Mathematical Treatment of the Global
Neuronal Workspace Model. New York, NY: Springer, 2005.
Wang D, Jin S, Zou X. Crosstalk between pathways enhances the
controllability of signalling networks. [Database] IET Syst Biol
2016;10:2–9.
Wang Q, Schoenlein RW, Peteanu LA, et al. Vibrationally coher-
ent photochemisty in the femtosecond primary event of vi-
sion. Science 1994;266:422–5.
Watson RA, Szathma´ ry E. Can evolution learn? Trends Ecol Evol
2016;31:147–57.
Watts D. Small Worlds: The Dynamics of Networks Between Order and
Randomness. Princeton NJ, Princeton University Press, 1999.
Wheeler JA. Law without law. In: Wheeler JA, Zurek WH (eds.),
Quantum Theory and Measurement. Princeton: Princeton
University Press, 1983, 182–213.
Wheeler JA. Information, physics, quantum: the search for links.
In: Zurek WH (ed.), Complexity, Entropy, and the Physics of
Information. Boulder, CO: Westview, 1990, 3–28.
Wigner EP. Remarks on the mind-body question. In: Good IJ (ed.),
The Scientist Speculates. London: Heinemann, 1961, 284–302.
Yang C-Y, Bialecka-Fornal M, Weatherwax C, et al. Encoding
membrane-potential-based memory within a microbial com-
munity. Cell Syst 2020;10:417–23.
Zwier MC, Chong LT. Reaching biological timescales with all-
atom molecular dynamics simulations. Curr Opin Pharmacol
2010;10:745–52.
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