MIND AS A LAYERED NETWORK OF COMPUTATIONAL
PROCESSES ALL THE WAY DOWN TO QUANTUM
Abstract. Talking about models of cognition, the very mention of “computationalism” often incites reactions
against Turing machine model of the brain and perceived determinism of the computational model. Neither of
those two objections affects models based on 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, from quantum level up. 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
(Ehresmann, 2012) and Subrata Ghosh et al. (Ghosh et al., 2014) .
Critique of Computationalism Answered
Causation is transfer of information (Collier, 1999) and computation, as information processing, is causation at
work, as I argued in my previous articles (references). What are the implications of this view for computing
nature and computational models of mind?
Historically, computationalism has been accused of many sins. In what follows I would like to answer three
Sprevak’s concerns about computationalism, (Sprevak, 2012) p. 108:
(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.
(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.
Mark 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. He argues:
“Computational descriptions of physical systems need not be vacuous. We have seen that there is a wellmotivated 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 Chalmers (Chalmers, 1996)
In the above it is important to highlight the distinction between intrinsic /natural/ spontaneous computation and
designed computation which is used in our technological devices.
In the framework of info-computationalism, which is a variety of pancomputationalism, physical nature
spontaneously performs different kinds of computations that present information dynamics at different levels of
organization of nature. This intrinsic computation of a physical system can be used for designed computation,
such as one found in conventional computational machinery.
Why is natural computationalism not vacuous in spite of the whole of the universe being computational? It is not
vacuous for the same reason for which physics is not vacuous which makes the claim that the entire physical
universe consists of matter-energy. Here we will not enter the discussion 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 by methods of physics.
However, Marcin Miłkowski suggests “the physical implementation of a computational system – and its
interaction with the environment – lies outside the scope of computational explanation”.
”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, 2007)
From the above I infer that the model of computation, which Miłkowski assumes is a top-down, designed
computation. Even though he rightly argues that neural networks 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.
If we would apply the above logic, we would demand from physicists to explain where matter comes from.
Where do the elementary particles come from? They are simply empirical facts for which we have enough
evidence. We might not know all of their properties and relationships, we might not know all of them, but we can
be sure at least that they exist. The bottom layer for the computational universe is the bottom layer of its
material substrate, which with constant progress of physics is becoming more and more fine-grained.
Computational Models of Mind
“To sum up: mind 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. It has achieved
consciousness, but this consciousness is restlessly transforming itself under the spell of a secret end. What is this
end?” (Blanshard, 1941)
Within info-computational framework, cognition is understood as synonymous with process of life, a view that
even Brand Blanshard adopted. Following Maturana and Varela’s argument from 1980 (Maturana & Varela,
1980), we can 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 computational layers than bacterial colony. Even though
Maturana and Varela did not think of cognition as computation, the broader view of computation as found in
info-computationalism is capable of representing processes of life as studied in bioinformatics and
biocomputation.
Starting with mind as life itself, a single cell, and studying increasingly more complex organisms such as rotifers
(which have around a thousand cells, of which a quarter constitute their nervous system with brain) or the tiny
Megaphragma mymaripenne wasps (that are smaller than a single-celled amoebas and yet have nervous system
and brains) – with more and more layers of cognitive information-processing architectures we can follow the
evolution of mind as a capacity of a living organism to act on their own behalf:
“(W)herever mind is present, there the pursuit of ends is present”. (…) ”Mental activity is the sort of activity
everywhere whose reach exceeds its grasp.” (…) “Now mind, at all of its levels and in all of its manifestations, is a
process of this kind” [i.e. a drive toward a special end]. (Blanshard, 1941)
And the process powering this goal-directed behavior on a variety of levels of organization in living organisms is
information self-organization. Andre Ehresmann (Ehresmann, 2012) proposes the model of brain where lower
levels are made of Turing machines while the higher levels of cognitive activity are non-Turing, based on the fact
that the same symbol has several possible interpretations. In contrast, Subrata Ghosh et al. (Ghosh et al., 2014)
remarkable brain model demonstrates how mind can be modelled from the level of quantum field theory up to
the macroscopic whole-brain, in twelve levels of computational architecture, based on computing beyond Turing
model.
REFERENCES
Blanshard, B. (1941). The Nature of Mind. The Journal of Philosophy, 38(8), 207–216.
Chalmers, D. J. (1996). Does a Rock Implement Every Finite-State Automaton? Synthese, 108, 309–33.
Collier, J. (1999). Causation is the transfer of information. In H. Sankey (Ed.), Causation, natural laws and
explanation (pp. 279–331). Dordrecht: Kluwer.
Ehresmann, A. C. (2012). MENS, an Info-Computational Model for (Neuro-)cognitive Systems Capable of
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Ghosh, S., Aswani, K., Singh, S., Sahu, S., Fujita, D., & Bandyopadhyay, A. (2014). Design and Construction of a
Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a
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Maturana, H., & Varela, F. (1980). Autopoiesis and cognition: the realization of the living. Dordrecht Holland: D.
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Miłkowski, M. (2007). Is computationalism trivial? In G. Dodig-Crnkovic & S. Stuart (Eds.), Computation,
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Press.
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Science, 13(2), 107–143.