# The Architecture of Mind as a Network of Networks of Natural Computational Processes

## Abstract

**:**

## 1. Critique of Classical Computationalism and a New Understanding of Computation

- (R1)
- A lack of clarity: “Ultimately, the foundations of our sciences should be clear”. Computationalism is suspected of lacking clarity.
- (R2)
- Triviality: “[O]ur conventional understanding of the notion of computational implementation is threatened by trivial arguments”. Computationalism is accused of triviality.
- (R3)
- A lack of 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 [2] (p. 108).

“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”.[4]

“Today it seems clear, for example, that classical notions of computation alone cannot serve as foundations for a viable theory of the mind, especially in light of the real-world, real-time, embedded, embodied, situated, and interactive nature of minds, although they may well be adequate for a limited subset of mental processes (e.g., processes that participate in solving mathematical problems). Reservations about the classical conception of computation, however, do not automatically transfer and apply to real-world computing systems. This fact is often ignored by opponents of computationalism, who construe the underlying notion of computation as that of Turing-machine computation”.[1] (p. 176) (emphasis added)

## 2. Natural/Intrinsic Computation as Information Processing in Nature: Why Natural Computationalism is Not Trivial

“17. The “It from Bit” hypothesis: Is the universe essentially made of informational stuff, with natural processes, including causation, as special cases of information dynamics?”[24]

“If now somebody writes a tricky language that goes beyond the capabilities of LISP and changes its own interpreter as well, and then perhaps it changes the operating system, and so on, finally we find ourselves at the level of the processor chip of the computer that carries out the machine code instructions. Now, unlike the earlier levels, this level belongs to a piece of physical hardware, where things will be done the way the machine was once built, and this can no more be a matter of negotiations. Ultimately, this is what serves as that Archimedean starting point (similar to the initial translation that opens up self-reference) that defines a constant framework for the programs. The importance of this is that we understand: self-modification, and self-reference, are not really just issues of programming (that is, of using the right software), but of designing a whole machine in some sense. Therefore, the impossibility of achieving complete self-modification depends, ultimately, on the separability of machine from program (and the way around): the separability of software from hardware.”[37] (p. 95) (emphasis added)

“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”.[38]

“For any sufficiently complex physical object O (i.e., an object with a sufficiently large number of distinguishable parts) and for any arbitrary program P, there exists an isomorphic mapping M from some subset S of the physical states of O to the formal structure of P”.[40] (p. 27)

## 3. Levels of Organization, Dynamics, Causal Relations and Deacon’s Framework

- Syntactic information: Shannon theory; describes data/patterns/signals as used in data communication;
- Semantic information: Shannon + Boltzmann theories; describes intentionality, aboutness, reference, representation, used to define the relation to object or referent;
- Pragmatic information (behavior): Shannon + Boltzmann + Darwin theories; describes function, interpretation, used to define pragmatics of agency.

“A system with greater dynamical depth than another consists of a greater number of such nested dynamical levels. Thus, a mechanical or linear thermodynamic system has less dynamical depth than an inorganic self-organized system, which has less dynamical depth than a living system. Including an assessment of dynamical depth can provide a more precise and systematic account of the fundamental difference between inorganic systems (low dynamical depth) and living systems (high dynamical depth), irrespective of the number of their parts and the causal relations between them”.[47] (p. 404)

## 4. Hewitt’s Model of Computation of Actors/Agents

“In the Actor Model [49,50], computation is conceived as distributed in space, where computational devices communicate asynchronously and the entire computation is not in any well-defined state. (An Actor can have information about other Actors that it has received in a message about what it was like when the message was sent.) Turing’s Model is a special case of the Actor Model”.[39]

“I have already endorsed the importance of recognizing neurons as ‘complex self-modifying’ agents, but the (ultra-)plasticity of such units can and should be seen as the human brain’s way of having something like the competence of a silicon computer to take on an unlimited variety of temporary cognitive roles, ‘implementing’ the long-division virtual machine, the French-speaking virtual machine, the flying-a-plane virtual machine, the sightreading-Mozart virtual machine and many more. These talents get ‘installed’ by various learning processes that have to deal with the neurons’ semi-autonomous native talents, but once installed, they can structure the dispositions of the whole brain so strongly that they create higher levels of explanation that are both predictive and explanatory”.[52]

## 5. Mind as a Process and Computational Architecture of Mind

“Aristotle describes mind (nous, often also rendered as ‘intellect’ or ‘reason’) as ‘the part of the soul by which it knows and understands’ (De Anima iii 4, 429a9–10; cf. iii 3, 428a5; iii 9, 432b26; iii 12, 434b3), thus characterizing it in broadly functional terms”.[54]

“Because there are no material entities that are not also processes, and because processes are defined by their organization, we must acknowledge the possibility that organization itself is a fundamental determinant of physical causality. At different levels of scale and compositionality, different organizational possibilities exist. And although there are material properties that are directly inherited from lower-order component properties, it is clear that the production of some forms of process organization is only expressed by dynamical regularities at that level. So the emergence of such level-specific forms of dynamical regularity creates the foundation for level-specific forms of physical influence”.[46] (p. 177)

“(M)ind is a set of processes distinguished from others through their control by an immanent end. (…) At one extreme it dwindles into mere life, which is incipient mind. At the other extreme it vanishes in the clouds; it does not yet appear what we shall be. Mind as it exists in ourselves is on an intermediate level”.[59]

“Any system, cognitive or biological, which is able to relate internally, self-organized, stable structures (eigenvalues) to constant aspects of its own interaction with an environment can be said to observe eigenbehavior. Such systems are defined as organizationally closed because their stable internal states can only be defined in terms of the overall dynamic structure that supports them”.[60] (p. 342)

Information | Mechanism | Dynamics | Aristotle cause |
---|---|---|---|

Syntactic | Mass-energetic | Thermodynamics | Efficient |

Semantic | Self-organization | Morphodynamics | Formal |

Pragmatic | Self-preservation (autopoiesis) | Teleodynamics | Final |

## 6. Conclusions

(R1) Lack of Clarity: “Ultimately, the foundations of our sciences should be clear”. Computationalism is suspected to lack clarity.

(R2) Triviality: “(O)ur conventional understanding of the notion of computational implementation is threatened by triviality arguments”. Computationalism is accused of triviality.

(R3) Lack of 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 abstract, formal and unnatural.

## Acknowledgments

## Conflicts of Interest

## References

- Scheutz, M. Computationalism New Directions; MIT Press: Cambridge, MA, USA, 2002. [Google Scholar]
- Sprevak, M. Three challenges to Chalmers on computational implementation. J. Cogn. Sci. (Seoul)
**2012**, 13, 107–143. [Google Scholar] [CrossRef] - Miłkowski, M. Explaining the Computational Mind; MIT Press: Cambridge, MA, USA, 2013. [Google Scholar]
- Chalmers, D.J. Does a Rock Implement Every Finite-State Automaton? Synthese
**1996**, 108, 309–333. [Google Scholar] [CrossRef] - Crutchfield, J.; Ditto, W.; Sinha, S. Introduction to Focus Issue: Intrinsic and Designed Computation: Information Processing in Dynamical Systems—Beyond the Digital Hegemony. Chaos
**2010**, 20. [Google Scholar] [CrossRef] [PubMed] - Dodig-Crnkovic, G. Information, Computation, Cognition. Agency-based Hierarchies of Levels. arXiv:1311.0413. 2013. Available online: http://arxiv.org/abs/1311.0413 (accessed on 8 December 2015).
- Dodig-Crnkovic, G.; Giovagnoli, R. Computing Nature; Springer: Berlin, Germany; Heidelberg, Germany, 2013. [Google Scholar]
- Zenil, H. A Computable Universe. Understanding Computation & Exploring Nature as Computation; Zenil, H., Ed.; World Scientific Publishing Company/Imperial College Press: Singapore, Singapore, 2012. [Google Scholar]
- Zuse, K. Rechnender Raum; Friedrich Vieweg & Sohn: Braunschweig, Germany, 1969. [Google Scholar]
- Fredkin, E. Finite Nature. In Proceedings of the XXVIIth Rencotre de Moriond, Les Arcs, Savoie, France, 15–22 March 1992.
- Wolfram, S. A New Kind of Science; Wolfram Media: Champaign, IL, USA, 2002. [Google Scholar]
- Chaitin, G. Epistemology as Information Theory: From Leibniz to Ω. In Computation, Information, Cognition—The Nexus and The Liminal; Dodig Crnkovic, G., Stuart, S., Eds.; Cambridge Scholars Pub.: Newcastle, UK, 2007; pp. 2–17. [Google Scholar]
- Dodig-Crnkovic, G. Significance of Models of Computation from Turing Model to Natural Computation. Minds Mach.
**2011**, 21, 301–322. [Google Scholar] [CrossRef] - Piccinini, G. Computation in Physical Systems. In Stanford Encyclopedia of Philosophy; Stanford University: Stanford, CA, USA, 2012. [Google Scholar]
- Putnam, H. Representation and Reality; The MIT press: Cambridge, MA, USA, 1988. [Google Scholar]
- Searle, J.R. The Rediscovery of the Mind; The MIT Press: Cambridge, MA, USA, 1992. [Google Scholar]
- Fresco, N. Physical Computation and Cognitive Science; Springer Berlin Heidelberg: Berlin, Germany; Heidelberg, Germany, 2014. [Google Scholar]
- Stepney, S. The neglected pillar of material computation. Phys. D Nonlinear Phenom.
**2008**, 237, 1157–1164. [Google Scholar] [CrossRef] - Stepney, S. Programming Unconventional Computers: Dynamics, Development, Self-Reference. Entropy
**2012**, 14, 1939–1952. [Google Scholar] [CrossRef] - Rozenberg, G.; Bäck, T.; Kok, J.N. (Eds.) Handbook of Natural Computing; Springer: Berlin, Germany; Heidelberg, Germany, 2012.
- Dodig-Crnkovic, G.; Müller, V. A Dialogue Concerning Two World Systems: Info-Computational vs. Mechanistic. In Information and Computation; Dodig Crnkovic, G., Burgin, M., Eds.; World Scientific Publishing Company/Imperial College Press: Singapore, Singapore, 2011; pp. 149–184. [Google Scholar]
- Lloyd, S. Programming the Universe: A Quantum Computer Scientist Takes on the Cosmos; Knopf: New York, NY, USA, 2006. [Google Scholar]
- Wheeler, J.A. Information, physics, quantum: The search for links. In Complexity, Entropy, and the Physics of Information; Zurek, W., Ed.; Addison-Wesley: Redwood City, CA, USA, 1990. [Google Scholar]
- Floridi, L. Open Problems in the Philosophy of Information. Metaphilosophy
**2004**, 35, 554–582. [Google Scholar] [CrossRef] - Floridi, L. Informational realism. In Selected Papers from Conference on Computers and Philosophy—Volume 37 (CRPIT’03); Weckert, J., Al-Saggaf, Y., Eds.; Australian Computer Society, Inc.: Darlinghurst, Australia, 2003; pp. 7–12. [Google Scholar]
- Sayre, K.M. Cybernetics and the Philosophy of Mind; Routledge & Kegan Paul: London, UK, 1976. [Google Scholar]
- Floridi, L. Against digital ontology. Synthese
**2009**, 168, 151–178. [Google Scholar] [CrossRef][Green Version] - Nir, F.; Staines, P.J. A revised attack on computational ontology. Minds Mach.
**2014**, 24, 101–122. [Google Scholar] [CrossRef] - Dodig-Crnkovic, G. Info-computational Constructivism and Cognition. Constr. Found.
**2014**, 9, 223–231. [Google Scholar] - Kull, K. Umwelt. In The Routledge Companion to Semiotics; Cobley, P., Ed.; Routledge: London, UK, 2010; pp. 348–349. [Google Scholar]
- Vedral, V. Decoding Reality: The Universe as Quantum Information; Oxford University Press: Oxford, UK, 2010. [Google Scholar]
- Chiribella, G.; D’Ariano, G.M.; Perinotti, P. Quantum Theory, Namely the Pure and Reversible Theory of Information. Entropy
**2012**, 14, 1877–1893. [Google Scholar] [CrossRef] - Goyal, P. Information Physics—Towards a New Conception of Physical Reality. Information
**2012**, 3, 567–594. [Google Scholar] [CrossRef] - Dodig-Crnkovic, G. Knowledge Generation as Natural Computation. J. Syst. Cybern. Inform.
**2008**, 6, 12–16. [Google Scholar] - Dodig-Crnkovic, G. Physical Computation as Dynamics of Form that Glues Everything Together. Information
**2012**, 3, 204–218. [Google Scholar] [CrossRef][Green Version] - Kampis, G. Self-Modifying Systems in Biology and Cognitive Science: A New Framework for Dynamics, Information, and Complexity; Pergamon Press: Amsterdam, The Netherlands, 1991. [Google Scholar]
- Kampis, G. Computability, Self-Reference, and Self-Amendment. Commun. Cogn. Artif. Intell.
**1995**, 12, 91–109. [Google Scholar] - Miłkowski, M. Is computationalism trivial? In Computation, Information, Cognition—The Nexus and the Liminal; Dodig-Crnkovic, G., Stuart, S., Eds.; Cambridge Scholars Press: Newcastle, UK, 2007; pp. 236–246. [Google Scholar]
- Hewitt, C. What is computation? Actor Model versus Turing’s Model. In A Computable Universe, Understanding Computation & Exploring Nature as Computation; Zenil, H., Ed.; World Scientific Publishing Company/Imperial College Press: Singapore, Singapore, 2012. [Google Scholar]
- Searle, J.R. Is the brain a digital computer? Proc. Addresses Am. Philos. Assoc.
**1990**, 64, 21–37. [Google Scholar] [CrossRef] - Chrisley, R. Why everything doesn’t realize every computation. Minds Mach.
**1994**, 4, 403–420. [Google Scholar] [CrossRef] - Dodig-Crnkovic, G. Epistemology Naturalized: The Info-Computationalist Approach. APA Newsl. Philos. Comput.
**2007**, 6, 9–13. [Google Scholar] - Crutchfield, J.; Wiesner, K. Intrinsic Quantum Computation. Phys. Lett. A
**2008**, 374, 375–380. [Google Scholar] [CrossRef] - Maldonado, C.E.; Gómez Cruz, A.N. Biological hypercomputation: A new research problem in complexity theory. Complexity
**2014**, 20, 8–18. [Google Scholar] [CrossRef] - Fisher, J.; Henzinger, T.A. Executable cell biology. Nat. Biotechnol.
**2007**, 25, 1239–1249. [Google Scholar] [CrossRef] [PubMed] - Deacon, T. Incomplete Nature. How Mind Emerged from Matter; W.W. Norton & Company: New York, NY, USA; London, UK, 2011. [Google Scholar]
- Deacon, T.; Koutroufinis, S. Complexity and Dynamical Depth. Information
**2014**, 5, 404–423. [Google Scholar] [CrossRef] - Friston, K. Hierarchical models in the brain. PLoS Comput. Biol.
**2008**, 4, e1000211. [Google Scholar] [CrossRef] [PubMed] - Hewitt, C.; Bishop, P.; Steiger, P. A Universal Modular ACTOR Formalism for Artificial Intelligence. In IJCAI-Proceedings of the 3rd International Joint Conference on Artificial Intelligence, Standford, CA, USA, August 1973; Nilsson, N.J., Ed.; William Kaufmann: Stanford, CA, USA; pp. 235–245.
- Hewitt, C. Actor Model for Discretionary, Adaptive Concurrency. CoRR. 2010. abs/1008.1. Available online: http://arxiv.org/abs/1008.1459 (accessed on 8 December 2015).
- Fitch, W.T. Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition. Phys. Life Rev.
**2014**, 11, 329–364. [Google Scholar] [CrossRef] [PubMed] - Dennett, D. The Software/Wetware Distinction: Comment on “Unifying approaches from cognitive neuroscience and comparative cognition” by W Tecumseh Fitch. Phys. Life Rev.
**2014**, 11, 367–368. [Google Scholar] [CrossRef] [PubMed] - Burgin, M.; Dodig-Crnkovic, G. A Taxonomy of Computation and Information Architecture. In Proceedings of the 2015 European Conference on Software Architecture Workshops (ECSAW’15), Dubrovnik/Cavtat, Croatia, 7–11 September 2015; Galster, N., Ed.; ACM Press: New York, NY, USA, 2015. [Google Scholar]
- Shields, C. Aristotle’s Psychology. In The Stanford Encyclopedia of Philosophy; Zalta, E.N., Ed.; 2015; Available online: http://plato.stanford.edu/archives/spr2015/entries/aristotle-psychology/ (accessed on 8 December 2015).
- Aristotle. On the Soul. (De Anima.). Available online: http://classics.mit.edu/Aristotle/soul.html (accessed on 8 December 2015).
- Maturana, H.; Varela, F. Autopoiesis and Cognition: The Realization of the Living; D. Reidel Pub. Co.: Dordrecht Holland, The Netherlands, 1980. [Google Scholar]
- Dodig-Crnkovic, G. Modeling Life as Cognitive Info-Computation. In Computability in Europe 2014; LNCS; Beckmann, A., Csuhaj-Varjú, E., Meer, K., Eds.; Springer: Berlin, Germany; Heidelberg, Germany, 2014; pp. 153–162. [Google Scholar]
- Sloman, A.; Chrisley, R. Virtual machines and consciousness. J. Conscious. Stud.
**2003**, 10, 113–172. [Google Scholar] - Blanshard, B. The Nature of Mind. J. Philos.
**1941**, 38, 207–216. [Google Scholar] [CrossRef] - Rocha, L.M. Selected Self-Organization And the Semiotics of Evolutionary Systems. In Evolutionary Systems: Biological and Epistemological Perspectives on Selection and Self-Organization; Salthe, S., van de Vijver, G., Delpos, M., Eds.; Kluwer Academic Publishers: Dodrecht, The Netherlands, 1998; pp. 341–358. [Google Scholar]
- Basti, G.; Perrone, A. On the cognitive function of deterministic chaos in neural networks. In Proceedings of the IEEE International Conference on Neural Networks, Washington, DC, USA, 18–22 June 1989; Volume I, pp. 657–663.
- Bateson, G. Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and Epistemology; Adriaans, P., van Benthem, J., Eds.; University of Chicago Press: Amsterdam, The Netherlands, 1972. [Google Scholar]
- Ghosh, S.; Aswani, K.; Singh, S.; Sahu, S.; Fujita, D.; Bandyopadhyay, A. Design and Construction of a Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System. Information
**2014**, 5, 28–100. [Google Scholar] [CrossRef] - Ehresmann, A.C. MENS, an Info-Computational Model for (Neuro-)cognitive Systems Capable of Creativity. Entropy
**2012**, 14, 1703–1716. [Google Scholar] [CrossRef] - Ehresmann, A.C. A Mathematical Model for Info-computationalism. Constr. Found.
**2014**, 9, 235–237. [Google Scholar]

© 2015 by the author; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Dodig-Crnkovic, G.
The Architecture of Mind as a Network of Networks of Natural Computational Processes. *Philosophies* **2016**, *1*, 111-125.
https://doi.org/10.3390/philosophies1010111

**AMA Style**

Dodig-Crnkovic G.
The Architecture of Mind as a Network of Networks of Natural Computational Processes. *Philosophies*. 2016; 1(1):111-125.
https://doi.org/10.3390/philosophies1010111

**Chicago/Turabian Style**

Dodig-Crnkovic, Gordana.
2016. "The Architecture of Mind as a Network of Networks of Natural Computational Processes" *Philosophies* 1, no. 1: 111-125.
https://doi.org/10.3390/philosophies1010111