# 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

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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