1
The process by which knowledge is acquired (or generated, produced) is called cognition 1 [latin co + gnoscere, to know] and it includes perception, awareness, intuition, reasoning and judgment.
Currently we lack a common understanding of the process of cognition.
Explanation here why simplest processes are cognition.
In this paper we start by adopting the view of the cognitive scientists Maturana and Varela, that
cognition is synonymous with life (Maturana & Varela, 1980). Even the simplest living organisms possess some degree of cognition such as metabolism or locomotion. This means that not all cognition is conscious but all of it is meaningful and purposeful for the cognitive agent. Apart from the importance of understanding cognition in order to be able to help people with cognitive impairments, we are interested in understanding cognition in living agents among others in order to construct artificial cognitive agents based on similar principles.
These cognitive agents can be programs or robots capable of assisting us in different tasks – from intelligently cleaning e-mails or systematising data to holding a conversation, controlling and optimizing social infrastructures or executing space missions.
Knowledge is a result of cognition and as a natural phenomenon can be seen as emerging from the biological structure of a cognitive agent. Knowledge provides evolutionary advantage and ensures the agent’s ability to cope with the real world, thus improving its cognitive capacities. In such a way a loop of interdependence between cognitive apparatus of an agent and its knowledge is established.
This generalisation of cognition to include all living organisms (also plants and unicellular organisms) and even cognitive computational artefacts is far from generally accepted. The majority view is still that only humans possess cognition, even though some people would allow that other primates do cognise to some degree , but not more than that. Our adoption of the general definition of
Maturana and Varela is motivated by the wish to provide a theory that would include all living organisms and artificial cognitive agents within the same framework.
In order to address knowledge as a natural phenomenon, the info-computational approach (Dodig-
Crnkovic, 2006) is used for the study of mechanisms of knowledge generation, both in an individual cognitive agent and in networks of agents (social cognition), both in real time and in an evolutionary perspective, on a variety of levels of organisation.
The info-computational framework builds on two basic concepts: information (structure) and computation (information dynamics). Cognitive processes unfold in a layered structure of nested information network hierarchies with corresponding computational dynamics from molecular, to cellular, organismic and social levels.
EXPLANATION OF (NATURAL) COMPUTING
Present account is a short presentation of a more extensive work. For further reading on the details of the framework and its different aspects, interested reader is referred to the original articles as follows.
1 Interestingly, Kant’s notion of Erkenntniß is translated both as knowledge and as cognition.

2
The description of the conceptual framework of info-computationalism can be found in (Dodig-
Crnkovic & Müller, 2011) (Dodig-Crnkovic, 2009) (Dodig-Crnkovic, 2006). The relationship between natural computing (such as biocomputing, DNA-computing, chemical computing, quantum computing, social computing, etc) and the traditional Turing machine model of computation is elaborated in (Dodig-Crnkovic, 2012a)(Dodig-Crnkovic, 2011a) (Dodig-Crnkovic, 2011b) (Dodig-
Crnkovic, 2010a). The constructing/generation/production of knowledge within an infocomputational framework is discussed in (Dodig-Crnkovic, 2007) (Dodig-Crnkovic, 2010b) (Dodig-
Crnkovic, 2010c) (Dodig-Crnkovic, 2008).
Cognition as a process of life is characterised by the interaction of a cognising agent with its environment, which presupposes that living systems are necessarily open systems – they exchange mater-energy and information with the environment. The problem of the relationship between closed and open systems is addressed in (Burgin & Dodig-Crnkovic, 2013) which shows the need for replacement of the notion of truth by the notion of correctness within the reasoning system and relates to the controversies about the relationship between knowledge and truth as it appears in epistemology.
Finally the idea of computing nature and the relationships between two basic concepts of information and computation are explored in (Dodig-Crnkovic & Giovagnoli, 2013) (Dodig-Crnkovic
& Burgin, 2011) and morphological computing as the underlying mechanism of all information selfstructuring (self-organisation) is addressed in (Dodig-Crnkovic, 2012b) (Dodig-Crnkovic, 2012c) .
The universe has been conceptualised in various ways in different cultures and during different epochs. At one time, it was a living organism (Tree of Life, World Turtle, Mother Earth), at yet another time, mechanical machinery - the Cartesian-Newtonian clockwork. Today the universe is understood as a gigantic computer - a network of networks of computational processes on many different levels of resolution – from quantum mechanical to the molecular (chemical), biological, sociological and ecosystemic levels.
The computer pioneer Zuse was the first to suggest (in 1967) that the physical behaviour of the entire universe is being computed on a basic level, modelled by cellular automata, by the universe itself that he referred to as “Rechnender Raum” or Computing Space/Cosmos. Consequently, Zuse was the first pancomputationalist (naturalist computationalist), followed by many others such as
Fredkin, Wolfram, Chaitin and Lloyd – to name but a few. According to the idea of computing nature
(naturalist computationalism or pancomputationalism) one can view the time development
(dynamics) of physical states in nature as information processing (natural computation). Such processes include self-assembly, developmental processes, gene regulation networks, gene assembly in unicellular organisms, protein-protein interaction networks, biological transport networks, and the like. (Dodig-Crnkovic & Giovagnoli, 2013)
What is the “hardware” that the computing universe relies on? We, as cognitive agents interacting with the universe through information exchange, experience cognitively the universe as information.
The informational structural realism (Floridi, 2003) (Floridi, 2009) (Floridi, 2008) (Sayre, 1976) (Stonier,
1997) (Zins et al., 2007) is a framework that takes information as the fabric of the universe (for an agent). The physicists Zeilinger (Zeilinger, 2005) and Vedral (Vedral, 2010) suggest that information and reality are one.
For the informational universe, the dynamical changes of its informational structures make it a huge computational network where computation is understood as information dynamics (information processing) 2 – for cognitive agents.
Info-computationalism is a synthesis of informational structural realism and natural computationalism (pancomputationalism) - the view that the universe computes its own next state
2 Computations corresponding to dynamic processes in the universe are necessarily of both discrete and continuous type, on both the symbolic and sub-symbolic level. Information and computation as two fundamental and inseparable elements are used for naturalising cognition and knowledge in
(Dodig-Crnkovic, 2009).

3 from the previous one 3 (Chaitin, 2007). It builds on two basic complementary concepts: information
(structure) and computation (the dynamics of informational structure) as described in (Dodig-
Crnkovic, 2011a) This is the basis of info-computational epistemology (Dodig-Crnkovic, 2009).
In the computing nature, the generation of knowledge should be studied as a natural process. That is the main idea of naturalised epistemology (Harms, 2006), in which the subject matter is not our concept of knowledge, but the knowledge itself as it appears in the world 4 through specific informational structures of an agent. The origin of knowledge in the first living agents is not well researched, since the idea still prevails that knowledge is possessed only by humans.
However, there are different types of knowledge and we have good reasons to ascribe “knowledge how” (procedural knowledge) and even simpler kinds of “knowledge that” (knowledge by acquaintance) to other living beings. Plants can be said to possess memory (in their bodily structures that change as a result of past events) and the ability to learn (plasticity, ability to adapt through morphodynamics) and can be argued to possess rudimentary forms of knowledge. On the topic of plant cognition see Garzón in (Pombo, O., Torres J.M., Symons J., 2012) p. 121. In his Anticipatory
systems (Rosen 1985) claims:
I cast about for possible biological instances of control of behaviour through the utilization of predictive models. To my astonishment I found them everywhere […] the tree possesses a model, which anticipates low temperature on the basis of shortening days.
Even Popper (Popper, 1999) p. 61 ascribes the ability to know to all living:
Obviously, in the biological and evolutionary sense in which I speak of knowledge, not only animals and men have expectations and therefore (unconscious) knowledge, but also plants; and, indeed, all organisms.
It is important to notice that cognition in this model does not presuppose or imply consciousness.
There is no reason to ascribe consciousness to the elementary forms of cognition, because we have no idea what that would be. On the contrary, primitive cognition as a process that helps organisms survive and adequately use information from the world is a productive scientific concept.
In sum, in the proposed framework, information is the structure, the fabric of reality for a cognitive agent. The world exists independently from us (realist position of structural realism) as potential
information, corresponding to Kant’s das Ding an sich. This potential information becomes actual information (“a difference that makes a difference” according to (Bateson, 1972)) for a cognising agent in a process of interaction through which specific aspects of the world become uncovered.
5
3 This amounts to computation being equivalent to causality. Note the difference between causality and determinism. Computation is not always deterministic but it is necessarily causal. See Collier J.,
Information, Causation And Computation Chapter 4 in (Dodig-Crnkovic & Burgin, 2011)
4 Maturana was the first to suggest that knowledge is a biological phenomenon. He and Varela argued that life should be understood as a process of cognition which enables an organism to adapt and survive in the changing environment.
5 Compare this with Kant: “To cognize, percipere, is to represent something in comparison with others and to have insight into its identity or diversity from them." - the Vienna Logic at 24:846.

4
Even though Bateson’s definition of information as a difference is the widely cited one 6 , 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. (Hewitt, 2007) (Italics added)
This has profound consequences for epistemology and relates to the ideas of participatory universe
(Wheeler, 1990), endophysics (Rössler, 1998) and observer-dependent knowledge production as understood in second-order cybernetics. Combining Bateson and Hewitt insights, on the basic level,
information is a difference in one physical system that makes a difference in another physical system.
Of special interest with respect to knowledge generation are agents, i.e. systems able to act on their
own behalf.
7
The world as it appears to an agent depends on the type of interaction through which the agent acquires information 8 . Potential information in the world is obviously much richer than what we observe, containing invisible worlds of molecules, atoms and sub-atomic phenomena, distant cosmological objects and the like. Our knowledge about this potential information which is revealed with the aid of scientific instruments continuously increases with the development of new devices and the new ways of interaction with the world, with new theoretical and material constructs (Dodig-
Crnkovic & Mueller, 2009).
As a consequence of the adoption of Hewitt’s definition of information as a relational concept, the novelty in the info-computational approach compared to other types of structuralism is that the reality
consisting of structural informational objects for an agent is agent-dependent (observer-dependent). These subjectively experienced individual agent realities are adapted to the shared reality of community in a form of inter-subjectively agreed, negotiated common world-view.
A cognitive system is a system whose organization defines a domain of interactions in which it can act with relevance to the maintenance of itself, and the process of cognition is the actual (inductive) acting or behaving in this domain. Living systems are
cognitive systems, and living as a process is a process of cognition. This statement is valid for all organisms, with and without a nervous system. (Maturana, 1970) p.13
The central role of cognition for every cognitive agent, from bacteria to humans is its efficiency in
dealing with complexity of the world (Gell-Mann, 1994) helping an agent to survive and thrive. The world is practically inexhaustible and extremely complex and exceeds by all accounts what cognizing agent can take in and cope with. Cognition is then the mechanism that enables cognizing agents to makes sense of the world and uses it as a resource for survival at the same time avoiding its threats and dangers for life.
With the development of electronic computing we are improving the ability to adequately model living systems and their cognitive functions including intelligent behaviour. From the computationalist point of view intelligence may be seen as capacity based on several levels of data
6 In the same vein, Schroeder in (Dodig-Crnkovic & Giovagnoli, 2013) distinguishes two aspects of information – selective and structural, while (Dodig-Crnkovic, 2006) defines processes of differentiation and integration of information as basic for all our information processing.
7 Agency has been explored in biological systems by Stuart Kauffman, see (Kauffman,
2000)(Kauffman, 1995)(Kauffman, 1993)
8 For example, results of observations of the same physical object (celestial body) in different wavelengths (radio, microwave, infrared, visible, ultraviolet and X-ray) give profoundly different pictures.

5 processing in a cognising agent (Minsky, 1986). HERE ADD DEEP LEARNING.
Data, information, perceptual images and knowledge are organised in a multiscale model, up to the emergent level of consciousness (Minsky, 2011). Multiresolutional models have proven to be a good way of studying complexity in biological systems, and they are also being implemented in artificial intelligence
(Goertzel, 1993). COGNITION IS NOT CONNECTED WITH CONSCIOUSNES EVEN HERE.
Consciousness is a process that results from integration of multiple cognitive processes like vision, auditory and other sensory-motor perception.
“Time longer than one second is needed to synthesize
conscious experience” (Ballard, 2002)
The advantage of computational approaches to modelling of knowledge generation and learning compared to pure conceptual analysis typical of traditional epistemology is their testability. Daniel
Dennett declared in a talk at the International Computers and Philosophy Conference, Laval, France in 2006: “AI makes philosophy honest.” Paraphrasing Dennett we can say that info-computational models make theories of knowledge and cognition more transparent and suitable for critical investigation and experimentation. Cognitive robotics research, for example, presents us with a sort of laboratory where our understanding of cognition can be tested in a rigorous manner.
From cognitive robotics it is becoming evident that cognition and intelligence are inseparable from
agency. “All cognitive systems are dynamical systems” argues Giunti in (van Gelder, T. and Port, 1995) p. 549. Anticipation, planning and control are essential features of intelligent agency. A similarity has been found between the generation of behaviour in living organisms and the formation of control sequences in artificial systems. (Pfeifer & Bongard, 2006)(Pfeifer, Lungarella, & Iida, 2007)
An agent perceives the world through information produced from sensory data. From the point of view of data processing, perception can be seen as an interface between the data (the world) and an agent’s <representation> of the world. FOR EXAMPLE IN DEEP LEARNING (Hoffman, 2009) criticises the traditional view of perception as a perfectly mirroring, true picture of the world:
Instead, our perceptions constitute a species-specific user interface that guides behaviour in a
niche. Just as the icons of a PC's interface hide the complexity of the computer, so our perceptions usefully hide the complexity of the world, and guide adaptive behaviour.
This interface theory of perception offers a framework, motivated by evolution, to guide research in object categorization.
Thus, perception cannot function cut off on one side of the interface, inside an agent and its brain.
Patterns of information are both in the world and in the functions and structures of the agent.
Information is a difference in the world that makes a difference in an agent. This is true in general even for simplest physical non-cognitive agents.
With perception as an interface, sensorimotor activities play a central role in realising the function of connecting the inside with the outside worlds of an agent. Perception has co-evolved with sensorimotor skills of living organisms. Noë, in an enactive approach to perception, emphasises the role of evolution of sensorimotor abilities in living systems that can be connected with the changing informational interface between an agent and the world, and thus increasing information exchange and the complexity of an organism’s information processing structures. (Noë, 2004)
The step from perception to higher cognitive processes is not trivial, and detailed multiresolutional computational accounts are yet to be developed. They can be expected along the lines similar to Brier’s
Cybersemiotics (Brier, 2013) with the difference that within the info-computational framework the connections between different types of scientific knowledge (in the sense of “Wissenschaft”) are construed computationally. Important connection goes via Terrence Deacons and Steven Pinkers ideas about language.
Traditionally, analyses of knowledge, cognition and intelligence are done on the level of (human) language, thus assumed to be symbolic. Not unexpectedly, the first attempts at AI were modelling cognition and intelligence as symbol manipulation. However "Good Old-Fashioned Artificial
Intelligence" (GOFAI) turned out to be insufficient as a model of human intelligence (Clark, 1989). We have experience of knowledge accessible without verbal intervention and symbol manipulation, such

6 as procedural knowledge (how to do something) that differs from propositional knowledge
(knowledge of facts, that is of prime interest for epistemology). Moreover, symbols must be grounded in something more basic which from biology and neuroscience turns out to be signal processing.
Smolensky proposed the mechanism of an intuitive processor (which is not accessible to the symbolic level of information processing) with a conscious rule interpreter: (THIS SHOULD BE BETTER
EXPLAINED)
What kinds of programs are responsible for behaviour that is not conscious rule application? I will refer to the virtual machine that runs these programs as the
*intuitive processor*. It is presumably responsible for all of animal behaviour and a huge proportion of human behaviour: Perception, practiced motor behaviour, fluent linguistic behaviour, intuition in problem solving and game-playing--in short, practically all skilled performance. (Smolensky, 1988)
It follows from the above that ascribing degrees of knowledge to simple organisms implies accepting non-symbolic knowledge as well.
Symbols can be expected for organisms that at least have nervous systems.
Smolensky’s ideas about virtual machines running intuitive information processes were developed by Sloman, who characterises the human mind as a virtual machine running on the brain hardware,
(Sloman, 2002). He also addresses the symbol grounding problem, that is the question of how symbols acquire meaning through sub-symbolic signal processing.
We humans have an impression that we interact directly with the “real world as it is”. However, that is far from an accurate characterisation of what is going on, as already mentioned in connection to perception as an interface.
Of all information processing going on in our bodies, perception is only a tiny fraction. Our perception of the world depends on the relative slowness of conscious perception. Time longer than one second is needed to synthesize conscious experience. At time scales shorter than one second, the fragmentary nature of perception is revealed.
The brain creates a picture of reality that we experience as (and mistake for) 'the actual thing'. (Ballard, 2002) (italics added)
Kant, in the Critique of Pure Reason, had already argued that “phenomena”, or things as they appear and which constitute the world of common experience, are an illusion. Kaneko and Tsuda explain why:
“(T)he brain does not directly map the external world. From this proposition follows the notion of the “interpreting brain”, i.e. the notion that the brain must interpret
symbols generated by itself even at the lowest level of information processing. It seems that many problems related to information processing and meaning in the brain are rooted in the problems of the mechanisms of symbol generation and meaning.” (Kaneko &
Tsuda, 2001) (italics added)
Consciousness provides only a rough sense of what is going on in and around us; in the first place it relates to what we take to be essential. The world as it appears for our consciousness is a sketchy simulation which is a computational construction. The belief that we can ever experience the world
'directly as it is' is an illusion (Nørretranders, 1999).
What would that mean anyway to experience the world 'directly as it is', without ourselves being part of the process? Who would experience that world without us? It is important to understand that, as (Kaneko & Tsuda, 2001) emphasise, the brain maps the information about the (part of the) world into itself, but the mapped information is always formed by the activity of the brain itself. This seems to be the view of (Maturana, 2007) as well.

7
The positivist belief in observations independent of the observer proved problematic in many fields of physics such as quantum mechanics (wave function collapse after interaction), relativity
(velocity-dependent length contraction and time dilatation) and chaos (a minor perturbation caused by measurement sufficient to switch the system to a different attractor).
In general, the observer and the systems observed are related and by understanding their relationship we can
gain insights into the limitations and power of models and simulations as knowledge generators, as argued in
(Foerster, 2003).
If what we perceive of the world is a simulation that our brain plays for us in order to manage complexity and enable us to act efficiently in the world, then our knowledge of the world must also be mediated by this computational modelling nature of cognition. Not even the most reliable knowledge about the physical world as it appears in sciences is independent of the modelling frameworks which indirectly impact what can be expressed and thus known. It does not mean that scientific knowledge is arbitrary; it only means that it is reproducible under given conditions within a given domain.
Models are always simplifications made for a purpose and they ignore aspects of the system which are irrelevant to that purpose. The properties of a system itself must be clearly distinguished from the properties of its models. All our knowledge is mediated by models. We often become so familiar with a model and its functions that we frequently act as if the model was the actual reality itself (Heylighen
& Joslyn, 2001), which of course is unjustified but can work pretty well in practice.
Awareness of the modelling character of knowledge and the active role of the cognising agent in the process
of generation of knowledge is specifically addressed by second order cybernetics. Cybernetic epistemology is constructive in recognising that knowledge cannot be passively transferred from the environment, but must be actively constructed by the cognising agent based on the elements found in the environment in combination with information stored in the agent (its morphology). The interaction with the environment eliminates inadequate models. Model construction thus proceeds through variation, information self-organisation, and selection. This agrees with Glasersfeld’s two basic principles (Glasersfeld, 1995):
Knowledge is not passively received either through the senses or by way of communication, but is actively built up by the cognizing subject.
The function of cognition is adaptive and serves the subject's organization of the experiential world, not the discovery of an ‘objective ontological reality’.
This understanding coincides with the info-computational view of knowledge generation (Dodig-
Crnkovic, 2007) (Dodig-Crnkovic & Müller, 2011). “The subject” in the above can be any living organism or indeed an artificial cognitive agent too.
When talking about computational models of biological phenomena, it is important to emphasize that within the info-computational framework computation is defined in a general way as any information
processing. This differs from the traditional theoretical model of computation, the Turing machine model, which is a special case corresponding to algorithms/effective procedures (equivalent to recursive functions or formal languages). The Turing machine is a logical device, a model for the execution of an algorithm. However, if we want to model computing nature adequately, including biological structures and embodied physical information processing, a new understanding of computation is needed such as highly interactive and networked concurrent computing models beyond Turing machines, as argued in (Dodig-Crnkovic & Giovagnoli, 2013) and (Dodig-Crnkovic,
2011b) with reference to (Hewitt, 2012) and (Abramsky, 2008). In development of a general theory of networked physical information processing, we must also generalise the ideas of what computation is and what it might be developed into. For new computing paradigms, see for example (Rozenberg,
Bäck, & Kok, 2012) (Burgin, 2005) (MacLennan, 2004) (Wegner, 1998) (Hewitt, 2012) (Abramsky, 2008).
Computation as information processing should not be identified with the notion of computation in classical cognitive science based on notions of input–output and representations in the sense of the
Turing machine model. It is important to recognise that connectionist models (e.g. neural networks) as well as dynamic systems models are equally computational as they are also based on information processing
(Scheutz, 2002) (Dodig-Crnkovic, 2009).

8
The basis for the capacity to acquire knowledge (I SHOULD EXPLAIN IN MORE DETAIL IN
WHAT WAY SOME STRUCTURE PRESENTS KNOWLEDGE PROGRAMME) is in the specific
morphology of organisms that enables perception, memory and adequate information processing that can lead to production of new knowledge out of the old one. As argued in (Dodig-Crnkovic, 2012b), morphology is the central idea in the understanding of the connection between computation and information. It should be noted that material also represents morphology, but on a more basic level of organisation – the arrangements of molecular and atomic structures. What appears as a form on a more fundamental level of organisation (e.g. an arrangement of atoms), represents 'matter' as a higherorder phenomenon (e.g. a molecule).
In morphological computing, the modelling of an agent’s behaviour (such as locomotion and sensory-motor coordination) proceeds by abstracting the principles via information self-structuring and sensory-motor coordination, (Matsushita et al. 2005), (Lungarella et al. 2005) (Lungarella and
Sporns 2005) (Pfeifer, Lungarella and Iida 2007). Brain control is decentralised based on sensory-motor coordination through interaction with the environment. Through embodied interaction with the environment, in particular through sensory-motor coordination, an information structure is induced in the sensory data, thus facilitating perception, learning and categorisation. The same principles of morphological computing (physical computing) and data self-organisation apply to biology and robotics. HERE DEEP LEARNING SHOULD BE ADDED AND NEURONS THAT GROW
CONNECTIONS WITH LEARNING WHILE CONNECTIONS GET PRUNED IF NOT USED.
From an evolutionary perspective it is crucial that the environment provides the physical source of the biological body of an organism as well as a source of energy and matter to enable its metabolism.
The nervous system and brain of an organism evolve gradually through the interaction of a living agent with its environment. This process of mutual evolutionary shaping between an organism and its environment is a result of information self-structuring. Here, both the physical environment and the physical body of an agent can at all times be described by their informational structure 9 . Fundamental computational processes, which express changes of informational structures, are implementation of physical laws. (Dodig Crnkovic 2008)
The environment provides an organism with a variety of inputs in the form of both information and matter-energy, where the difference between information and matter-energy is not in the kind, but in the type of use the organism makes of it. As there is no information without representation 10 , all
information is carried by some physical carrier (light, sound, radio-waves, chemical molecules, etc.). The same physical object can be used by an organism as a source of information and as a source of nourishment/matter/energy. A single type of signal, such as light, may be used by an organism both as information necessary for orientation in the environment, and for the photosynthetic production of energy. Thus, the question of what will be used 'only' as information and what will be used as a source of food/ energy depends on the nature of the organism. In general, the simpler the organism, the simpler the information structures of its body, the simpler the information carriers it relies on, and the simpler its interactions with the environment.
The environment is a resource, but at the same time it also imposes constraints which limit an agent’s possibilities. In an agent that can be described as a complex informational structure, constraints imposed by the environment drive the time development (computation) of its structures, and thus even its shape and behaviour, to specific trajectories.
This relationship between an agent and its environment is called structural coupling by (Maturana &
Varela 1980) and is described by (Quick and Dautenhahn 1999) as “non-destructive perturbations
9 Here is the definition by John Daintith, A Dictionary of Computing (2004) http://www.encyclopedia.com/doc/1O11-datastructure.html
Data structure (information structure) - an aspect of data type expressing the nature of values that are composite, i.e. not atoms. The non-atomic values have constituent parts (which need not themselves be atoms), and the data structure expresses how constituents may be combined to form a compound value or selected from a compound value.
10 Landauer, R. 1991, “Information is Physical'”, Physics Today 44, 23 - 29.
Landauer, R. 1996, “The Physical Nature of Information” Physics Letter (A 217), 188

9 between a system and its environment, each having an effect on the dynamical trajectory of the other, and this in turn affecting the generation of and responses to subsequent perturbations.”
Harms proved a theorem showing that natural selection will always lead a population to accumulate information, and so to 'learn' about its environment (Harms, 2006). Okasha (Okasha, 2005) points out that any evolving population 'learns' about its environment, in Harms' sense, even if the population is composed of organisms that lack minds entirely, hence lack the ability to have representations of the external world at all.
In anticipation of some possible criticisms of the approach, which requires generalization of the concept of cognition to all living beings, I would like to say the following.
Ascribing some rudimentary cognition and thus capacity for knowledge to all living organisms, no matter how primitive, should be seen not as a drawback of the theory but as its strength because of the generality of its naturalistic approach. It shows how cognitive capacities are a matter of degree and how they slowly and successively develop with evolution. The proposed approach suggests mechanisms that can be tested by simulations and they agree with our best current scientific knowledge about evoliution, learning and cognition (LESLIE VALIANT PAC) From bio-computing we learn that in living organisms the biological structure (hardware) is at the same time a program (software) which controls the behaviour of that hardware. (Kampis, 1991)
However, this understanding of the basic evolutionary mechanisms of accumulating information, at the same time increasing the information-processing capacities of organisms (such as memory, anticipation, computational efficiency), is only the first step towards a fully-fledged evolutionary epistemology, but the most difficult and significant one, as it requires a radical change in our understanding of fundamental concepts of knowledge, cognition, intelligence, computation and information, among others. It should be combined with the insights on the nature of language such as presented by Terry Deacon and behaviors of social cognition.
From the point of view of info-computationalism, a mechanism behind the aforementioned
Sloman’s virtual machine hierarchy (Sloman, 2002) is the computational self-organisation of information, i.e. morphological computing, see (Dodig-Crnkovic, 2012b) and references therein. In his new research programme, Sloman goes a step further studying meta-morphogenesis, which is the morphogenesis of morphogenesis, (Sloman, 2013) – a way of thinking in the spirit of second order cybernetics.
HERE IT IS NOT COMPLETELY CLEAR WHAT IS THE CONNECTION BETWEEN
DIFFERENT KINDS OF KNOWLEDGE AND MORPHOLOGICAL COMPUTATION .
In order to understand knowledge as a natural phenomenon, the process of re-construction of the origins, development and present forms and existence of life, the processes of evolution and development based on self-organisation are central. The work of Maturana and Varela on the constructivist understanding of life is fundamental. They define the process of autopoiesis of a living system as follows:
An autopoietic machine is a machine organized (defined as a unity) as a network of
processes of production (transformation and destruction) of components which:
(i) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and
(ii) constitute it (the machine) as a concrete unity in space in which they (the components) exist by specifying the topological domain of its realization as such a network. (Maturana & Varela, 1980) p. 78 (emphasis added)
What does it mean that an autopoetic system is organisationally closed? It means that it conserves its organisation. That is true of a momentaneous picture of the world in which an organism lives
(functions, operates). Obviously evolution shows that organisms change their organisation through

10 interactions with the environment. In a sense organisms preserve their organisation, but that organisation is evolving. Living beings constantly metabolise, communicate and exchange information with the world. We can say that there are different processes going on in an organism – on a short time scale they retain their (dynamical) organisation, while exchanging information with the world.
On the longer time scale they evolve and thus slowly change their organisation.
Immanuel Kant, in his Critique of Judgment, was the first to use the attribute "self-organising" arguing that teleology (goal-directed behaviour) is possible only for entities that exist through self-organisation.
Such a system is capable of acting on its own behalf (agency) and governing itself.
In such a natural product as this, every part is thought as owing its presence to the agency of all the remaining parts, and also as existing for the sake of the others and of the whole, that is as an instrument, or organ... The part must be an organ producing the other parts—each, consequently, reciprocally producing the others... Only under these conditions and upon these terms can such a product be an organized and selforganized being, and, as such, be called a physical end. http://oll.libertyfund.org/index.php?option=com_staticxt&staticfile=show.php%3Fti tle=1217&layout=html Immanuel Kant, The Critique of Judgement [1892]
Today we may ascribe purposeful (autonomous, goal-directed) behaviour to robots but even though they appear to act autonomously, they are essentially dependent on humans for production, maintenance and energy supply. After Kant, cyberneticians (Ashby, von Foerster, Pask, and Wiener) returned to the ability of self-organisation in different systems, both natural and artificial.
The idea of self-organisation was introduced in general systems theory in the 1960s, and later during the 1970s and 1980s in complex systems. Prigogine (Prigogine & Stengers, 1984) contributed by insights in the self-organisation in thermodynamic systems far from equilibrium, which showed an
ability of non-living matter to self-organise on the condition that energy is provided from the environment that is used for self-organisation. SELF-ORGANIZATION IS MORPHOLOGICAL COMPUTING.
This ability of inanimate matter (chemicals) to self organise has been studied in detail by Kauffman
(Kauffman, 1993, 1995). It has inspired research into the origins of life connecting the self-organisation of chemical molecules with the self-organisation and autopoiesis of living beings.
The importance of Maturana and Varela’s idea of autopoietic systems can hardly be overestimated, and especially the idea of life as cognition is of vital importance. However, it might need some reinterpretations when incorporated into the framework of info-computationalism. Similarly, when
Luhmann applied the ideas of Maturana and Varela to social autopoetic systems, he developed an adapted triple autopoietic model of the biological, psychic and socio-communicative systems. (Brier,
2013)
In short, the information processing model of organisms incorporates basic ideas of autopoiesis and life, from the sub-cellular to the multi-cellular, organismic and societal levels. Being cognition, life processes are different sorts of morphological computing which on evolutionary time scales affect the organisation (structures) of living beings even in a sense of meta-morphogenesis (i.e. morphogenesis of morphogenesis), (Sloman, 2013).
Through autopoietic processes with structural coupling (interactions with the environment) a biological system changes its structures and thereby the information processing patterns in a selfreflective, recursive manner (Maturana & Varela, 1992) (Maturana & Varela, 1980). Self-organisation with natural selection of organisms, responsible for nearly all information that living systems have built up in their genotypes and phenotypes, is a simple but costly method to develop knowledge capacities. Higher organisms (which are “more expensive” to evolve in terms of resources) have developed a capability of learning and reasoning as a more efficient way to accumulate knowledge.
The step from “genetic learning” (typical of more primitive forms of life) to the acquisition of cognitive skills on higher levels of organisation of the nervous system (such as found in vertebrata) will be the next step to explore in the project of naturalised epistemology.
In the info-computational formulation, the “life as cognition” process (Maturana & Varela, 1980,
1992; Maturana, 1970, 2002) corresponds to information processing in the hierarchy of levels of organisation, from molecular networks, to cells and their organisations, to organisms and their networks/societies (Dodig-Crnkovic, 2008). Thus the fundamental level proto-information (structural

11 information) corresponds to the physical structure, the fabric of reality for an agent, while cognition is a process that both unfolds in real time as information self-structuring through interactions
(morphological computing), and develops on a long-time scale (meta-morphogenesis) as a product of evolution in complex biological systems, as argued in (Dodig-Crnkovic & Hofkirchner, 2011).
Concurrently with the development of methodological and philosophical and information- and computation- theoretical arguments for the development of a new scientific paradigm motivated by the need of better understanding of biological systems, such as the info-computational approach, the number of new practical applications and implementations steadily increases. For a review of the contemporary work on Biomathics 11 , see the forthcoming article (Simeonov, 2013).
Two volumes covering topics of information and computation (Dodig-Crnkovic & Burgin, 2011) and the idea of computing nature (Dodig-Crnkovic & Giovagnoli, 2013) comprise a range of topics in which the basic ideas presented in this article have been developed and applied.
The work of Wolff is another interesting practical application (Wolff, 2003, 2006). In the book
Unifying Computing and Cognition, Wolff presents his SP theory with its applications. In the words of author:
The SP theory of intelligence aims to simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme. It is conceived as a brain-like system that receives 'New' information and stores some or all of it in compressed form as
'Old' information. It is realised in the form of a computer model -- a first version of the
SP machine. The concept of 'multiple alignment' is a powerful central idea. Using heuristic techniques, the system builds multiple alignments that are 'good' in terms of information compression.
Even though SP theory may find many interesting applications and provides new conceptual tools to unpack the complex issue of cognition, it is only the beginning of a research in this direction that has many open questions to address. For example Kauffman argues that living organisms exhibit high
resilience exactly because of redundant information they embody. Thus in living systems information compression should not be expected to be maximal but the trade-offs with redundancy is necessary
(Kauffman, 1995).
Promises of info-computational research program rely on learning from nature using definability, simulability and (where applicable) predictability of its physical processes and structures as a means to improve our understanding of complex phenomena such as life (cognition) based on (constantly improved) concepts of computation and information. (Dodig-Crnkovic, 2011b)
Based on the info-computational framework, the following topics are of particular interest for future research.
-
Structures and functioning of the human brain, at present the subject of the huge European FET
Flagship Human brain project http://www.humanbrainproject.eu
. What can be learned about cognition, intelligence, and our epistemological and ontological premises within the framework of info-computational naturalism? Given that our brains and nervous systems are info-computational networks, what can we say about the mind? How do we develop artifactually intelligent autonomous
11 A new phase of scientific development in which mathematicians turn to biological processes for inspiration in creating novel formalisms in mathematics appropriate to describe biological phenomena.

12 systems based on insights from natural (organic) computing? Embodiedness of all natural phenomena including the mind: (JEFF HAWKINS, LEARNING FROM NEUROSCIENCE)
-
Biology – mechanisms and origins of life: What computational problems can our understanding of natural self-organisation and management of complexity help to solve? The origins of life and connectedness between the living and the non-living world.
-
Physics – information physics as a project of re-formulating physics in terms of information and its dynamics (computation). We lack understanding of physics at very small and very large dimensions, and do not understand the incompatibility between quantum mechanics and general relativity. Matter and energy constitute only 4% of what we see in the universe – the remaining 96% contains 21% dark matter and 75% dark energy. Can informational reconceptualisation of physics help to explain this discrepancy? Do we need to take into account observer dependence of information generation, including scientific knowledge? Theories of emergent phenomena on different scales defined informationally:
-
Complexity. In a complex system, what we see is dependent on where we are and what sort of interaction is used to study the system. Generative Models – how does the complexity arise? Evolution is the most well-known generative mechanism, with complexity arising from simplicity by the selforganisation of informational structures. Complex behaviour can emerge from simple generators! Selfstar properties in organic systems: self-organisation, self-configuration (auto-configuration), self-
optimisation (automated optimisation), self-repair (self-healing), self-protection (automated computer security), self-explaining, and self-awareness (context-awareness) – all are part of autopoiesis. Complex adaptive artificial systems are studied inspired by biological systems.
Modelling and simulation understood as info-computation. We are used to studying linear systems which possess decomposibility - Modelled by Analysis – Top-down – Global (Reductionism)
However, non-linear systems behave as a whole and are appropriately modelled by synthesis
(integration) - bottom-up, distributed, networked). Here, instead of analytical methods, Holism and
System approaches apply.
Agent-based Models. An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous individuals in a network, with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi agent systems, and evolutionary programming. Semiotics distinguishes between first person – second person – third person accounts, and agent-based models correspond to first-person accounts (Simeonov, 2013)
Computing nature. Along with the study of biological and other complex phenomena within the info-computationalist framework, a lot of work remains on the modelling of natural phenomena based on understanding of the universe as a network of info-computational processes. Continuous and discrete, analogue and digital computing are all parts of the computing universe and should be studied, understood and modelled. Understanding of evolution as an info-computational, morphodynamic process based on self-structuring of information through morphological computation:
-
Information (for an agent) – From the difference that makes the difference for an agent - unification as synthesis (integration of information) and search as differentiation (Dodig-Crnkovic,
2006). The meaning of the concept “information” is the resolution of categorical opposition of one-and-
many. (Schroeder, 2013a) (Schroeder, 2013b)
Computation as (natural) information processing – a Computing Nature project such as defined in
(Zenil, 2012) (Dodig-Crnkovic & Giovagnoli, 2013) (Dodig-Crnkovic, 2011b) (Stepney, 2008; Stepney et al., 2005, 2006) and (MacLennan, 2004).
This article presents a new understanding of knowledge as a natural phenomenon, based on an infocomputational approach. The idea is to provide stable methodological and practical grounds for the

13 existing approaches to knowledge and to complement them by new insights into the phenomenon of knowledge. It may help to resolve old epistemological problems such as:
 The extent of knowledge (how much is possible to know) by pointing to info-computational and evolutionary origins of (agent-dependent) knowledge.
 The sources of knowledge (empirical experience vs. reasoning or other local information post-processing within an agent ), which are informational structures with computational dynamics, both in the world understood as potential information, and in the agent itself
(embodiment, embeddedness), which for an agent is actualised through interactions.
 The nature of knowledge, traditionally the question about how the concept of knowledge should be defined, in the info-computational framework becomes transformed into the
As we have seen from its applications, the info-computational approach to knowledge generation can contribute both to epistemology and to knowledge management and the understanding of learning. question: what in the physical world is knowledge? Knowledge is seen as a result of
learning that produces in informational structure on which it is possible to act.
Finally, the info-computational approach can contribute to rethinking cognition as a self-organising bio-chemical life process in humans and other living beings. Thus we can start to learn how to adequately model living systems which have traditionally been impossible to effectively frame theoretically, simulate and study in their full complexity. (Dodig-Crnkovic & Müller, 2011)
To conclude, let me quote Feynman from The Character of Physical Law: Our imagination is stretched to the utmost, not, as in fiction, to imagine things which are not really there, but just to comprehend those things which are there. (Feynman, 1965)
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