MIND AS ALAYERED NETWORK OF COMPUTATIONAL PROCESSESALL THE WAY DOWN TO QUANTUM
Abstract.Talking about models of cognition, the very mention of “computationalism” oftenincites reactions against Turing machine model of the brain and perceived determinism of computational models of mind. Neither of those two objections affects models based on natural computation or computing nature where model of computation is broader than deterministic symbol manipulation of conventional models of computation. Computing nature consists of physical structures that form levels of organization, on which computation processes differ, from quantum up to macroscopic levels. It has been argued by(Ehresmann, 2012) and (Ghosh et al., 2014)that on the lower levels of information processing in the brain finite automata or Turing machines might be adequate models, while on the level of the whole-brain information processing computational models beyond-Turing computation is necessary. Such a layered computational architecture based on intrinsic computing of physical systems avoids objections against early versions of computationalism such as triviality, lack of clarity and lack of naturalistic foundations.
Critique of Classical ComputationalismandNew Understanding of Computation
Historically, computationalism has been accused of many sins(Miłkowski, 2013)(Scheutz, 2002). In what follows I would like to answer Mark Sprevak’s three concerns about computationalism, (Sprevak, 2012) p. 108:
(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 formal and unnatural.
Sprevak concludes that meeting all three above expectations of computational implementation is hard. As an illustration of the problems with computationalist approaches to mind, he presents David Chalmers computational formalism of combinatorial state automata and concludes that “Chalmers’ account provides the best attempt to do so, but even his proposal falls short.” In order to be fully understood, Chalmers account, I will argue, should be seen from the perspective of intrinsic, natural computation instead of a conventional designed computer.Chalmers argues:
“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.” (Chalmers, 1996)
In the above it is important to highlight the distinction between intrinsic /natural/ spontaneous computation that describes natural processes at different levels of organization and is always present in physical system and designed/conventional computation which is used in our technological devices and which uses intrinsic computation as its basis.Intrinsic computation appears naturally on different levels from processes on quantum level to molecular/chemical computation, computation (information processing) in neural networks, social computing etc.
Already in 2002 Matthias Scheutz (Scheutz, 2002) proposed new computationalism capable of accounting for embodiment and embeddedness of mind. In this article we will present the recent developments that show how this new computationalism looks like at present and in what directions it is developing.
Natural/Intrinsic Computationand Physical Implementation of ComputationalSystem
The way to avoid the criticisms against computational models of mind is to naturalize computation.
The idea of computing nature (Dodig-Crnkovic & Giovagnoli, 2013)(Zenil, 2012) builds on the notionthat the universe as a whole can be seen as a computational system which computes its own next state. This approach is called pancomputationalism or natural computationalism and dates back to Konrad Zuse with his Calculating Space - Rechnender Raum(Zuse, 1969, 1970), with many prominent representatives such as Edward Fredkin(Fredkin, 1992), Stephen Wolfram(Wolfram, 2002)and Greg Chaitin(Chaitin, 2007) among others, see (Dodig-Crnkovic, 2011). Computation as found in nature is physical computation, described in (Piccinini, 2012) and also termed “computation in materio” by Stepney (Stepney, 2008, 2012).[Here introduce info-computationalism Floridi Burgin Marcin]
In his Open Problems in the Philosophy of Information (Floridi, 2004)(Floridi, 2004) lists the five most interesting areas of research for the nascent field of Philosophy of Information (and Computation), containing eighteen fundamental questions that contain the following:
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?
In his own work Floridi as well as Sayre argue for the informational universe (Floridi, 2003)(Sayre, 1976) claiming that the fabric of the universe is information. I would add that informational fabric of the universe is always relative to an agent, as information is relational. Thus “the universe” for a spider is vastly different from “the universe” for a human or for some artifactual cognitive system. That is constructivist view of knowledge where the process of knowledge generation in a cognizing agent starts with the interactions with the environment as a potential information that actualizes and proceeds embodied and embedded in the agent’s Umwelt (or the universe accessible from the given agent’s cognitive/computational architecture).
In the framework of info-computationalism, which is a variety of natural computationalism, information presents the fabric of the universe while its dynamics is computation. Physical nature thus spontaneously performs different kinds of computations that present information processing at different levels of organization(Dodig-Crnkovic, 2012). This intrinsic computation of a physical system can be used for designed computation, which would not appear spontaneously in nature, but with constant energy supply and designed architecture performs computations such as found in conventional designed computational machinery.
It should be noted that varieties of natural computationalism/ pancomputationalism differ among themselves: some of them would insist on discreteness of computation, and the idea that on the deepest levels of description, nature should be seen as discrete. Others find the origin of the continuous/discrete distinction in the human cognitive apparatus that relies on both continuous and discrete information processing (computation), thus arguing that both discrete and continuous models are necessary (Dodig-Crnkovic & Mueller, 2009) So Lloyd argues that the dual nature of quantum mechanical objects as wave/particles implies necessity of both kinds of models(Lloyd, 2006).
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.(Kampis, 1995)
Why is natural computationalism not vacuous in spite of the underlying assumption of the whole of the universe being computational? It is not vacuous for the same reason for which physics is not vacuous even though itmakes the claim that the entire physical universe consists of matter-energy and builds on the same elementary building blocks – elementary particles. *(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 are assumed to be universal in nature.) The principle of universal validity of physical laws does not make them vacuous. Thinking of computation as implementation of physical laws on the fundamental level makes it more obvious that computation can be seen as the basisof all dynamics in nature.
Thinking of intrinsic computation on the most fundamental level of natural process “in materio” (Stepney, 2008).The field of intrinsic natural computation is presented in depth in the (Rozenberg, Bäck, & Kok, 2012). For a specific study of intrinsic computation performed by neurons in the brain, see (Crutchfield, Ditto, & Sinha, 2010; Crutchfield & Wiesner, 2008).
Intrinsic Computationand Causation
Causation is transfer of information (Collier, 1999)and computation, as information processing, is causation at work, as argued in (Dodig-Crnkovic, …). What are the implications of this view for computing nature in general and computational models of mind in particular?
Collier studies the relationship between information, computation and causation in (Collier, 2011)
Craver and Bechtel present top-down causation without top-down causes (Craver & Bechtel, 2007).
Walker Sara Imari’s article on Top-Down Causation presents the Rise of Information in the Emergence of Life(Walker, 2014)
CARL F. CRAVER,and WILLIAM BECHTEL, Top-down causation without top-down causes, Biology and Philosophy (2006)(Craver & Bechtel, 2007)
KAMPIS: CAUSAL DEPTH
Network of causal connectedness
Andre Ehresmann talks about synonymity
Memory Evolutive Systems(Ehresmann, 2012)(Ehresmann, 2014)
However, MarcinMiłkowskisuggests “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.
The problem of physical computation is related to the problem of grounding of the concept of computation. Where the following question comes from?
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, whichwith constant progress of physics is becoming more and more fine-grained.
Levels of Organization and Agent-based Model ofComputation
Mind as a Process and Computational Models of Mind
Of all computational approaches, the most controversial are the computational models of mind. There exists historically a huge variety of models, some of them taking mind to be a kind of immaterial substance opposed to material body, the most famous being Platonic andCartesian dualist models. Through hylomorphism, in contrast to reductive materialism, which identifies body and mind, and Platonic dualism, which takes body and mind to be separate substances,Aristotle proposes a unifying approach where mind represents the form of a material body.
It is more natural for computational approaches to consider mind as a process of changing form, a complex process of computation on many different levels of organization of matter.
“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.”(Blanshard, 1941)
Within info-computational framework, cognition is understood as synonymous with process of life, a view that even Brand Blanshardadopted. 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-computationalismis capable of representing processes of life as studied in bioinformatics and biocomputation.
Relation between mind and cognition [Marcin’s book]
cog·ni·tion The mental process of knowing, including aspects such as awareness, perception, reasoning, and judgment.
Starting with mind as life itself, a single cell, and studying increasingly more complex organisms such as rotifers(whichhave around a thousand cells, of which a quarter constitutetheir nervous system with brain) or the tiny Megaphragma mymaripenne wasps(that are smaller than a single-celled amoebasand yet havenervous systemand 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 levelsof computational architecture, based on computing beyond Turing model.
Conclusions
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