Reality Construction Through Info-Computation
Gordana Dodig-Crnkovic 1
Abstract. Some intriguing questions such as: What is reality for
an agent? How does reality of a bacterium differ from a reality
of a human brain? Do we need representation in order to
understand reality? are still widely debated. Starting with the
presentation of the computing nature as an info-computational
framework, where information is defined as a structure, and
computation as information processing, I will address questions
of evolution of increasingly complex living agents through
interactions with the environment. In this context, the concept of
computation will be discussed and the sense in which
computation is observer-relative. Using the results on
morphological/morphogenetic computation as information self-
organization I argue that reality for an agent is a result of
networked agent-based computation. Consciousness is a
(computational) process of information integration that evolved
in organisms with nervous system. I present an argument why
pancomputationalism (computing nature) is a sound scientific
strategy and why panpsychism is not.1
1 INTRODUCTION: WHAT IS REALITY (FOR
This paper addresses the question of reality for different
classes of cognitive agents. When discussing cognition as a
bioinformatic process of special interest, we use the notion of
agent, i.e. a system able to act on its own behalf . Agency in
biological systems has been explored in . The world as it
appears to an agent depends on the type of interaction through
which the agent acquires information .
Agents communicate by exchanging messages (information)
that help them coordinate their actions based on the (partial)
information they possess and share as a part of social cognition.
It starts from the definition of agency and cognition as a
property of all living organisms. The subsequent question will be
how artifactual agents should be built in order to possess
different degrees of cognition and eventually even
consciousness. Is it possible at all, given that cognition in living
organisms is a deeply biologically rooted process? Recent
advances in natural language processing, present examples of
developments towards machines capable of both “understanding
natural language” and “speaking” in a human way. Along with
reasoning, language is considered high-level cognitive activity
that only humans are capable of. Increasing levels of cognition
developed in living organisms evolutionary, starting from basic
automatic behaviours such as found in bacteria and even insects
(even though they have nervous system and brain, they lack the
limbic system that controls our emotional response to physical
1 School of Innovation, Design and Engineering, Mälardalen University,
Sweden. Email: email@example.com
stimuli, suggesting they don't process physical stimuli
emotionally) to increasingly complex behaviour in higher
organisms such as mammals. Can AI “jump over” evolutionary
steps in the development of cognition?
The framework for the discussion in this article is the
computing nature in the form of info-computationalism. It takes
reality to be information for an agent with a dynamics of
information understood as computation. Information is a
structure and computation its dynamics. Information is observer
relative and so is computation. 
Cognition is studied as information processing in such simple
organisms as bacteria ,  as well as cognitive processes in
other, more complex multicellular life forms. We discuss
computational mind and consciousness that have recently been
widely debated in the work of Giulio Tononi  and Christoph
Koch.  While the idea that cognition is a biological process in
all living organisms, as argued by Humberto Maturana and
Francisco Varela , , it is not at all clear that all cognitive
processes in different kinds of organisms are accompanied by
anything akin to (human) consciousness. The suggestion is made
that cognitive agents with nervous systems are the step in
evolution that first enabled consciousness of the kind that
humans possess. Argument is advanced that ascribing
consciousness to the whole of the universe is not justified.
So defining reality as information leaves us with the question:
what is it in the world that corresponds to information and its
dynamics, computation? How do we model information/
computation? Answers are many and they are not unambiguous.
We can compare the present situation with the history of the
development of other basic scientific concepts. Ideas about
matter, energy, space and time in physics have their history. The
same is true of the idea of number in mathematics or the idea of
life in biology. So, we should not be surprised to notice the
development in the theory of computation that goes along with
the development of information science, robotics, cognitive
science, computability, new computational devices and new
domains of the real world that can be understood info-
2 THE COMPUTING NATURE.
COMPUTATIONAL NATURALISM AND
For Naturalism, nature is the only reality, in other words: no
miracles,  p. 73. It describes nature through its structures,
processes and relationships using a scientific approach.
Naturalism studies the evolution of the entire natural world,
including the life and development of humanity as a part of
nature. Social and cultural phenomena are studied in its physical
manifestations. An example of currently very active naturalist
research field is social cognition with its network-based studies
of social behaviors.
Computational naturalism (pancomputationalism, naturalist
computationalism, computing nature) is the view that the entire
nature is a huge network of computational processes, which,
according to physical laws, computes (dynamically develops) its
own next state from the current one. Representatives of this
approach are Zuse, Fredkin, Wolfram, Chaitin and Lloyd, who
proposed different varieties of computational naturalism.
According to the idea of computing nature, one can view the
time development (dynamics) of physical states in nature as
information processing (natural computation). Such processes
include self-assembly, self-organization, developmental
processes, gene regulation networks, gene assembly, protein-
protein interaction networks, biological transport networks,
social computing, evolution and similar processes of
morphogenesis (creation of form). The idea of computing nature
and the relationships between two basic concepts of information
and computation are explored in detail in .
In the computing nature, cognition is studied as a natural
process. If cognition is seen as a result of natural bio-chemical
processes, the important question is what is the minimal
cognition? Recently, empirical studies have revealed an
unexpected richness of cognitive behaviors (perception,
information processing, memory, decision making) in organisms
as simple as bacteria. ,  Single bacteria are too small to
be able to sense anything but their immediate environment, and
they live too briefly to be able to memorize a significant amount
of data. On the other hand bacterial colonies, swarms and films
exhibit an unanticipated complexity of behaviors that can
undoubtedly be characterized as cognition.
Apart from bacteria and similar organisms without nervous
system (such as e.g. slime mold, multinucleate or multicellular
Amoebozoa, which recently has been used to compute shortest
paths), even plants are typically thought of as living systems
without cognitive capacities. However, plants too have been
found to possess memory (in their bodily structures that change
as a result of past events), the ability to learn (plasticity, ability
to adapt through morphodynamics), and the capacity to
anticipate and direct their behavior accordingly. Plants are
argued to possess rudimentary forms of knowledge, according to
 p. 121,  p. 7 and  p. 61.
In this article we take primitive cognition to be the totality of
processes of self-generation, self-regulation and self-
maintenance that enables organisms to survive using information
from the environment. The understanding of cognition as it
appears in degrees of complexity in living nature can help us
better understand the step between inanimate and animate matter
from the first autocatalytic chemical reactions to the first
3 INFORMATIONAL STRUCTURE OF
REALITY FOR A COGNITIVE AGENT
Talking about computing nature, we can ask: what is the
hardware for this computation? The surprising answer is: the
hardware on one level of organization of information is the
software of the next level in the sense of Georg Kampis’ self-
modifying systems . And on the basic level, the “hardware”
is potential information, the structure of the world that one
usually describes as matter-energy.  As cognizing agents
interacting with nature through information exchange, we
experience the world cognitively as information. Informational
structural realism of Luciano Floridi  and Kennet Sayre 
is a framework that takes information as the fabric of the
universe (for an agent). Even the physicists Zeilinger  and
Vedral  suggest that information and reality are one
epistemologically. For a cognizing agent in the informational
universe, the dynamical changes of its structures make it a huge
computational network . The substrate, the “hardware”, is
information that defines data-structures on which computation
Info-computationalism is a synthesis of informational
structural realism and natural computationalism
(pancomputationalism, computing nature) - the view that the
universe computes its own next state from the previous one.
It builds on two basic complementary concepts: information
(structure) and computation (the dynamics of informational
structure) as described in .
The physical world for a cognizing agent exists as potential
information, corresponding to Kant’s das Ding an sich. Through
interactions, this potential information becomes actual
information, “a difference that makes a difference” .
Shannon describes the process as the conversion of latent
information into manifest information . Even though
Bateson’s definition of information as a difference that makes a
difference (for an agent) is a widely cited one, there is a more
general definition that includes the fact that information is
relational and subsumes Bateson’s definition:
“Information expresses the fact that a system is in a certain
configuration that is correlated to the configuration of another
system. Any physical system may contain information about
another physical system.”  p. 293
Combining the Bateson and Hewitt insights, at the basic level,
information is a difference in one physical system that makes a
difference in another physical system.
4 COMPUTATION IN NETWORKS OF
Informational structures constituting the fabric of physical
nature for an agent can be seen as networks of networks, which
represent semantic relations between data.  Information is
organized in layers, from quantum level to atomic, molecular,
and so on. Computation in general can be understood as
information processing, or more specifically as data structure
exchanges within informational networks, represented by Carl
Hewitt’s actor model . Different types of computation
emerge at different levels of organization in nature. 
According to the Handbook of Natural Computing ,
natural computing is “the field of research that investigates both
human-designed computing inspired by nature and computing
taking place in nature.” It includes among others areas of cellular
automata and neural computation, evolutionary computation,
molecular computation, quantum computation, nature-inspired
algorithms and alternative models of computation.
An important characteristic of the research in natural
computing is that knowledge is generated bi-directionally,
through the interaction between computer science and natural
sciences. While natural sciences are adopting tools,
methodologies and ideas of information processing, computer
science is broadening the notion of computation, recognizing
information processing found in nature as computation. 
Based on that, Denning argues that computing today is a natural
science.  Computation found in nature is understood as a
physical process, where nature computes with physical bodies as
objects. Physical laws govern processes of computation, which
necessarily appears on many different levels of organization of
With its layered computational architecture, natural
computation provides a basis for a unified understanding of
phenomena of embodied cognition, intelligence and knowledge
generation.  Natural computation can be modelled as a
process of exchange of information in a network of informational
agents . As mentioned before, an agent is defined as an
entity capable of acting on its own behalf.
One sort of computation is found on the quantum-mechanical
level where agents are elementary particles, and messages
(information carriers) are exchanged by force carriers, while
different types of computation can be found on other levels of
organization. In biology, information processing is going on in
cells, tissues, organs, organisms and eco-systems, with
corresponding agents and message types. In biological
computing or social computing the message carriers are complex
chunks of information such as molecules, or sentences and the
computational nodes (agents) can be molecules, cells, organisms
or groups/societies. 
As a result of a synthesis of the informational structural
realism  (the view of nature as a complex informational
structure for a cognizing agent) with the idea of computing
nature (pancomputationalism, or natural computationalism) 
, info-computationalism is construed .
The notion of computation in this framework refers to the
most general concept of intrinsic computation that is a
spontaneous computation processes in computing nature, and
which is used as a basis of specific kinds of designed
computation found in computing machinery . Intrinsic
(natural) computation includes quantum computation ,
processes of self-organization, self-assembly, developmental
processes, gene regulation networks, gene assembly, protein-
protein interaction networks, biological transport networks, and
similar. It is both analog (such as found in dynamic systems) and
digital. The majority of info-computational processes are sub-
symbolic and some of them are symbolic (like languages).
Within info-computational framework, computation on a
given level of organization of information presents a
realization/actualization of the laws that govern interactions
between constituent parts. On the basic level, computation is
manifestation of causation in the physical substrate. In every
next layer of organization a set of rules governing the system
switch to the new emergent regime. It remains yet to be
established how this process exactly goes on in nature, and how
emergent properties occur. Research in natural computing is
expected to uncover those mechanisms.
In words of Rozenberg and Kari: “(O)ur task is nothing less
than to discover a new, broader, notion of computation, and to
understand the world around us in terms of information
processing.”  From the research in complex dynamical
systems, biology, neuroscience, cognitive science, networks,
concurrency and more, new insights essential for the info-
computational universe may be expected in the years to come.
Back in 1952 Turing wrote a paper that may be considered as
a predecessor of natural computing. It addressed the process of
morphogenesis proposing a chemical model as the explanation
of the development of biological patterns such as the spots and
stripes on animal skin.  Turing did not claim that physical
system producing patterns actually performed computation.
Nevertheless, from the perspective of computing nature we can
argue that morphogenesis is a process of morphological
computing. Physical process – though not computational in the
traditional sense, presents natural morphological computation.
Essential element in this process is the interplay between the
informational structure and the computational process -
information self-structuring and information integration, both
synchronic and diachronic, going on in different time and space
scales in physical bodies. Informational structure presents a
program that governs computational process  which in its
turn changes that original informational structure obeying/
implementing/ realizing physical laws.
Morphology is the central idea in understanding of the
connection between computation (morphological/
morphogenetic) and information. What is observed as material
on one level of analysis represents morphology on the lower
level, recursively. So water as material presents arrangements of
[molecular [atomic [elementary particle  ]]] structures.
Info-computationalism describes nature as informational
structure – a succession of levels of organization of (natural)
information. Morphological/morphogenetic computing on that
informational structure leads to new informational structures via
processes of self-organization of information. Evolution itself is
a process of morphological computation on a long-term scale. It
is also possible to study morphogenesis of morphogenesis
(Meta-morphogenesis) as done by Aaron Sloman in .
Leslie Valiant  studies evolution by ecorithms – learning
algorithms that perform probably approximately correct PAC
computation. Unlike present paradigm of computing, the results
are not perfect but just good enough.
Intrinsic/natural/ physical computation can be used for
physical computing which, broadly construed, means building
interactive physical systems by the use of software and hardware
consisting of interactive system connected with the real world
via sensors and actuators.
6 GENERATION OF REALITY FROM RAW
Cognition can be seen as a result of processes of
morphological computation on informational structures of a
cognitive agent in the interaction with the physical world, with
processes going on at both sub-symbolic and symbolic levels.
This morphological computation establishes connections
between an agent’s body, its nervous (control) system and its
environment. Through the embodied interaction with the
informational structures of the environment, via sensory-motor
coordination, information structures are induced in the sensory
data of a cognitive agent, thus establishing perception,
categorization and learning. Those processes result in constant
updates of memory and other structures that support behaviour,
particularly anticipation. Embodied and corresponding induced
in the Sloman’s sense of virtual machine) informational
structures are the basis of all cognitive activities, including
consciousness and language as a means of maintenance of
Essential element in this process is the interplay between the
informational structures and the computational processes -
information self-structuring and information integration, both
synchronic and diachronic, going on in different time and space
From the simplest cognizing agents such as bacteria to the
complex biological organisms with nervous systems and brains,
the basic informational structures undergo transformations
through morphological computation (developmental and
evolutionary form generation).
Here an explanation is in order regarding cognition that is
defined in general way of Maturana and Varela who take it to be
synonymous with life. ,  All living organisms possess
some degree of cognition and for the simplest ones like bacteria
cognition consists in metabolism and (my addition) locomotion.
 This “degree” is not meant as continuous function but as a
qualitative characterisation that cognitive capacities increase
from simplest to the most complex organisms. The process of
interaction with the environment causes changes in the
informational structures that correspond to the body of an agent
and its control mechanisms, which define its future interactions
with the world and its inner information processing.
Informational structures of an agent become semantic
information first in the case of highly intelligent agents.
7 INFO-COMPUTATION, AGENCY AND
Even though we are far from having a consensus on the
concept of information, the most general view is that information
is a structure consisting of data. Floridi  has the following
definition of datum: “In its simplest form, a datum can be
reduced to just a lack of uniformity, that is, a binary difference.”
Bateson’s “the difference that makes the difference”  is a
datum in that sense. Information is both the result of observed
differences (differentiation of data) and the result of synthesis of
those data into a common informational structure (integration of
data), as argued by Schroeder in  In the process of
knowledge generation an intelligent agent moves between those
two processes – differentiation and integration of data, see 
p. 38. It is central to keep in mind that for something to be actual
information there must exist an agent from which perspective
this structure is established. Thus information is a network of
data points related from an agent’s perspective.
There is a distinction between the world as it exists
autonomously, independent of any agent, Kantian ”Ding an
sich”, (thing in itself, noumenon) and the world for an agent,
things as they appear through interactions (phenomena).
Informational realists (like Floridi, Sayre, Zeilinger, Vedral)
 take the reality/world/universe to be
information. In  I added by analogy ”information an sich”
representative of the ”Ding an sich” as potential information.
When does this potential information become actual information
for an agent?
The world in itself is (proto)information that gets actualized
through interactions with agents. Huge parts of the universe are
potential information for different kinds of agents – from
elementary particles, to molecules, etc. all the way up to humans
Living organisms as complex agents inherit bodily structures
as a result of a long evolutionary development of species. Those
structures are embodied memory of the evolutionary past. They
present the means for agents to interact with the world, get new
information that induces memories, learn new patterns of
behaviour and construct knowledge. World via Hebbian learning
forms a human’s (or an animal’s) informational structures. As an
example neural networks that “self-organize stable pattern
recognition codes in real-time in response to arbitrary sequences
of input patterns” can be used .
If we say that for something to be information there must
exist an agent from whose perspective this structure is
established, and we argue that the fabric of the world is
informational, the question can be asked: who/what is the agent?
An agent (an entity capable of acting on its own behalf) can be
seen as interacting with the points of inhomogeneity (data),
establishing the connections between those data and the data that
constitute the agent itself (a particle, a system). There are
myriads of agents for which information of the world makes
differences – from elementary particles to molecules, cells,
organisms, societies… - all of them interact and exchange
information on different levels of scale and this information
dynamics is natural computation.
On the fundamental level of quantum mechanical substrate,
information processes represent laws of physics. Physicists are
already working on reformulating physics in terms of
information. This development can be related to the Wheeler’s
idea “it from bit”.  For more details on current research, see
the special issue of the journal Information dedicated to
matter/energy and information , and a special issue of the
journal Entropy addressing natural/unconventional computing
 that explores the space of natural computation and
relationships between the physical (matter/energy), information
When it comes to agents, our habitual way of understanding
is in terms of energy and work. 
All living beings possess cognition (understood as all
processes necessary for an organism to survive, both as an
individual and as a part of a social group – social cognition), in
different forms and degrees, from bacteria to humans. Cognition
is based on agency; it would not exist without agency. The
building block of life, the living cell, is a network of networks of
processes and those processes may be understood as
computation. Of course it is not any computation whatsoever,
but exactly that biological process itself, understood as
Now one might ask what would be the point in seeing
metabolic processes or growth (morphogenesis) as computation?
The answer is that we try to connect cell processes to the
conceptual apparatus of concurrent computational models and
information exchange that has been developed within the field of
computation and not within biology – we talk about “executable
cell biology”.  Info-computational approach gives something
substantial that no other approach gives, and that is the
possibility of studying real-time dynamics of a system.
Processes of life and thus mind are critically time-dependent.
Concurrent computational models are the field that can help
us understand real-time interactive concurrent networked
behaviour in complex systems of biology and its physical
That is the pragmatic reason why it is well justified to use
conceptual and practical tools of info-computation in order to
study living being. Of course, in nature there are no labels
saying: this process is computation. We can see as computation,
conceptualize in terms of computation, model as computation
and call computation any process in the physical world. Doing
so we expand our understanding of natural processes (physical,
chemical, biological and cognitive) and computation.
8 COMPUTATIONAL MIND. COMPUTATION
ALL THE WAY DOWN TO QUANTUM
In his new book, Explaining the Computational Mind 
Marcin Miłkowski portrays current state of the ideas about
computational mind. The author presents and systematically
dissects number of misconceptions about what is computation,
clearly placing both neural networks and dynamical systems into
the domain of computational. This is something that some
philosophers would deny, while practitioners would agree with.
 Miłkowski also proposes his own view of computational
models in the following:
“(O)n my mechanistic account, only one level of the
mechanism – the so-called isolated level – is explained in
computational terms. The rest of the mechanism is not
computational, and, indeed, according to the norms of this kind
of explanation, it cannot be computational through and
In this article I argue that this one-level-approach is not adequate
for natural (intrinsic) computation which appear in hierarchy of
levels. The reason why Miłkowski tries to avoid multiplicity of
computational levels is a fear of computationalism being trivial:
“Obviously, pancomputationalists, who claim that all
physical reality is computational, would immediately deny the
latter claim. However, the bottoming-out principle of
mechanistic explanation does not render pancomputationalism
false a priori. It simply says that a phenomenon has to be
explained as constituted by some other phenomenon than itself.
For a pancomputationalist, this means that there must be a
distinction between lower-level, or basic, computations and the
higher level ones. Should pancomputationalism be unable to
mark this distinction, it will be explanatorily vacuous.” 
Miłkowski’s proposal is that “the physical implementation of
a computational system – and its interaction with the
environment – lies outside the scope of computational
From the above I infer that the model of computation, which
Miłkowski assumes in his book, is a top-down, designed
computation. Even though he rightly argues that neural networks
are computational models and even dynamical systems can be
understood as computational, Miłkowski does not think of
intrinsic computation as grounded in physical process driven by
causal mechanism, characteristics of computing nature.
The fundamental question that worries Miłkowski is the
grounding problem that can lead to the conclusion about
triviality. I will argue that this really is a non-problem.
To start with, grounding is always anchored in an agent who
is the narrator of the explanation. The narrator choses the
granularity of the account. No picture has infinite granularity and
nothing hinders to imagine even lower levels of existence (such
as more and more elementary particles). This means that
grounding is done over and over again in all sciences.
When constructing computational models, Miłkowski’s focus
on only one layer is pragmatically justified, but not a matter of
principle. Even though one can reconstruct many intrinsic
computational layers in the human brain (depending on the
granularity of the account), for an observer/narrator often one
layer is in focus at a time. In such simplified models the layers
above and below, even though computational, are sketchy and
used to represent constraints and not mechanisms. That is at least
the case in designed computation as found in conventional
computers. But e.g. looking at the experimental work of Subrata
Ghosh et al. building a functional model of brain, we find
twelve-layer computational architecture applied. 
“Computational descriptions of physical systems need not be
vacuous. We have seen that there is a well-motivated formalism,
that of combinatorial state automata, and an associated account
of implementation, such that the automata in question are
implemented approximately when we would expect them to be:
when the causal organization of a physical system mirrors the
formal organization of an automaton. In this way, we establish a
bridge between the formal automata of computation theory and
the physical systems of everyday life. We also open the way to a
computational foundation for the theory of mind.” David
Causation is transfer of information  and computation is
causation at work. What are the implications of the above view
for the AI? Miłkowski mentions that currently, computers are
beating humans in chess and Jeopardy, they are capable of
theorem proving, speech recognition and generation, natural
language translation etc. 
“However, AI systems are capable of all this and more, so
we ought to be more careful: if there is no mathematical proof
that something cannot be done, any verdicts are mere
speculation.” p. 204.
Regarding mathematical proof, it is not that simple.
Mathematics is an intelligent adaptive system that develops
continuously. If we lack mathematical tools within present state
mathematics, we can construct them in the next step.
Possibility of human level AI will most likely be
demonstrated constructively – by development of human level
artifactual intelligent devices and not via mathematical proof that
such devices are possible. That conclusion is based on the
observation that human learning is an open-ended inductive and
What is at stake in a theory of implementation? The problem
seems to me exactly the opposite. It is not so instructive to study
how brain implements computation (how do we know 1+1=2
top-down) but how intrinsic information processing, that is
evidently going on in the brain can be interpreted as
computation. What are the characteristics of that new kind of
computation that information processes in the brain constitute?
In that sense of bottom-up intrinsic computation Chalmers
characterization holds,  p. 326:
“A physical system implements a given computation when the
causal structure of the physical system mirrors the formal
structure of the computation.”
This position is called the Standard Position (SP) by Sprevak.
 p. 112. It is applicable to intrinsic computation (bottom up,
natural/intrinsic), but not to designed conventional computation
(top-down) as this “mirroring” would be a very complex process
of interpretation, coding, decoding and interpretation again.
Thus, not only neurons and whole brains compute (in the
framework of computing nature) but also the rest of nature
computes at variety of levels of organization.
“As to information, there is also a precise and powerful
mathematical theory that defines information as the reduction of
uncertainty about the state of a system. The same theory can be
used to quantify the amount of information that can be
transmitted over a communication channel. Again, the
mathematical theory of information does not tell us whether and
how the brain processes information, and in what sense. So
establishing the foundations of computational neuroscience
requires more work.” 
9 COMPUTATIONAL MODELS OF MIND
Historically, computationalism as a theory of mind has been
accused of many sins. In what follows I would like to answer
three Sprevak’s  p. 108 concerns about computationalism:
(R1) Clarity: “Ultimately, the foundations of our sciences
should be clear.” Computationalism is suspected to lack clarity.
(R2) Response to triviality arguments: “(O)ur conventional
understanding of the notion of computational implementation is
threatened by triviality arguments.” Computationalism is
accused of triviality.
Searle’s  informal triviality argument (“that a brick wall
contains some pattern of physical transitions with the same
structure as Microsoft Word”) and Putnam’s triviality argument
(“The physical transitions in the rock mirror the formal
transitions: A ! B ! A ! B. Therefore, according to SP, the
rock implements FSA M.”)
(R3) Naturalistic foundations: “The ultimate aim of cognitive
science is to offer, not just any explanation of mental
phenomena, but a naturalistic explanation of the mind.”
Computationalism is questioned for being formal and unnatural.
Sprevak concludes that meeting all three above expectations
of computational implementation is hard, and that “Chalmers’
account provides the best attempt to do so, but even his proposal
falls short.” Chalmers account, I will argue should be seen from
the perspective of intrinsic, natural computation.
Let me summarize the distinction between intrinsic /natural/
spontaneous computation and designed computation used in our
In the info-computationalism, that is a variety of
pancomputationalism, physical nature spontaneously performs
different kinds of computations (information dynamics) at
different levels of organization. This is intrinsic, natural
computation and is specific for a given physical system. Intrinsic
computation(s) of a physical system can be used for designed
computation, such as one found in computational machinery, but
it is far from all computation that can be found in nature.
Why is natural computationalism not vacuous? For the same
reason that physics is not vacuous which makes the claim that
the entire physical universe is material. Now we will not enter
the topic of ordinary matter-energy vs. dark matter-energy.
Those are all considered to be the same kind of phenomena –
natural phenomena that must be studied with methods of
If we would apply the same logic as critics of natural
computationalism, we would demand from physicists to explain
where matter comes from. Where does elementary particle come
from? They are simply empirical facts, for which we have
enough evidence to believe that they exist. We might not know
all of their properties and relationships, we might not know all of
them, but we can be pretty sure that they exist.
When physical entities exist in nature, unobserved, they are
part of Ding an sich. How do we know that they exist? We find
out through interactions. What are interactions? They are
information exchanges. Epistemologically, constraints or
boundary conditions are also information for a system.
So the bottom layer for computational universe is the bottom
layer of its material substrate and it is not different from the
question of physical models and the status of matter-energy in
the physical world. They are considered empirically justified.
10 WHY PANCOMPUTATIONALISM IS
USEFUL AND PANPSYCHISM IS NOT
Some computational models of consciousness , , ,
 seem to lead to panpsychism - a phenomenon defined as
“Panpsychism is the doctrine that mind is a fundamental
feature of the world which exists throughout the universe.” 
Pancomputationalism (natural computationalism, computing
nature) is the doctrine that whole of the universe, every physical
system, computes. In the words of :
“Which physical systems perform computations? According
to pancomputationalism, they all do. Even rocks, hurricanes,
and planetary systems — contrary to appearances — are
computing systems. Pancomputationalism is quite popular
among some philosophers and physicists.”
Info-computationalism starts bottom-up, from natural
processes understood as computation. It means that computation
appears as quantum, chemical, biological, …etc. Only those
transformations of informational structure that correspond to
intrinsic processes in natural systems qualify as computation.
‘Studying biological systems at different levels of organization
as layered computational architectures give us powerful
conceptual and technological tools for studying of real world
systems. Even though we can fancy any sort of imaginary
mappings those will not work on the hardware of the universe.
We can simulate virtual worlds, but computation behind this
visualisation relies on physical substrate with causal processes.
Given the argument for info-computational modelling of
nature, and the argument that every living organism possess
some extent of cognition one can ask: why should we not do
similar move and ascribe consciousness to the whole of the
universe (hypothesis called panpsychism)? Searle describes
consciousness as follows:
“Consciousness consists of states of awareness or sentience
or feeling. These typically begin in the morning when you wake
up from a dreamless sleep and go on all day until you go to sleep
or otherwise become 'unconscious.' ” 
The simple answer why panpsychism is not a good idea is: in
the case of panpsychism we have no good model. Unlike
computational models of physical processes we have no good
psychical models. In fact only naturalists accounts of
consciousness provide models, others prefer to see
consciousness as totally inexplicable in rational terms, a
“mystery”. From the naturalist, knowledge generation point of
view, trying to understand everything as psyche got it backwards
– we do not know what to do after the very first move, other than
to say that it is “mysterious”.
On the contrary, if we try to understand psyche or better to
say mind and consciousness as manifestations of physical info-
computational processes in the nervous system of a cognizing
agent, we immediately have an arsenal of modelling tools to
address the problem with and successively and systematically
learn more about it, even construct artefacts (such as cognitive
robots) and test it.
That is the main reason why panpsychism is not a good
scientific hypothesis. Instead of opening all doors for
investigation, it declares consciousness permeating the entire
universe and that's it. One can always generalize concepts if they
lead to better understanding and enable further modelling. But
generalizations of the idea of psyche is akin to homeopathic
procedure diluting it to concentrations close to zero, and that will
not give us anything in terms of understanding of mechanisms of
Moreover, as a theory panpsychism belongs to medieval
tradition – that which is to be explained is postulated. I wonder
how would anyone ever get unconscious in a conscious
universe? What would be the difference between human
consciousness and the “consciousness” of a bacterium or even a
consciousness of vacuum?
Up to now I explicated my info-computationalist position
relative to natural computationalism, pancomputationalism,
computing nature and computationalism (with respect to human
mind, as presented by Miłkowski) as well as why I do not see
panpsychism as a fruitful approach and coherent theoretical
11 CONCLUSIONS AND FUTURE WORK
Questions that we posed in the beginning of the article: What is
reality for an agent? How does reality of a bacterium differ from
a reality of a human brain? Do we need representation in order
to understand reality? led us to the discussion of info-
computational models of cognition and consciousness. When
talking about models of cognition, the very mention of
“computationalism” typically evokes reactions against Turing
machine model of the brain and perceived determinism of
computation. Neither of those two problems affects natural
computation or computing nature where model of computation is
broader than deterministic symbol manipulation. Computing
nature consists of physical structures that form levels of
organization, on which computation processes differ. It has been
argued that on the lower levels of organization finite automata or
Turing machines might be adequate, while on the level of the
whole-brain non-Turing computation is necessary, according to
Andre Ehresmann  and Subrata Ghosh et al. 
Within info-computational framework, cognition is understood
as synonymous with process of life. Following Maturana and
Varela’s argument from 1980 , we understand the entire
living word as possessing cognition of various degrees of
complexity. In that sense bacteria possess rudimentary cognition
expressed in quorum sensing and other collective phenomena
based on information communication and information
processing. Brain of a complex organism consists of neurons that
are networked communication computational units. Signalling
and information processing modes of a brain are much more
complex and consist of more layers than bacterial colony. Even
though Maturana and Varela did not think of cognition as
computation, given the broader view of computation as found in
info-computationalism, capable of representing processes of life
as studied in bioinformatics and biocomputation. Reality for an
agent is an informational structure that is established as a result
of as well the interactions of the agent with the environment as
the information processes in agents own intrinsic structures –
reasoning, anticipation, etc.
Finally, an argument is advanced that the idea of panpsychism as
a consequence of computational models by no means should be
understood as necessary. It rather seems to be an artefact of the
model and there is a variety of ways to correct the model so that
non-physical properties do not follow.
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connections between the low level cognitive processes and the
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