Physical Models of Computation

Cognition can be understood in at least two different ways. In the human-centered tradition, cognition is defined as the set of mental processes such as perception, memory, reasoning, and language, through which humans acquire and use knowledge. Intelligence, in this view, present the ability of cognition to solve problems, learn, anticipate, and adapt. By contrast, the life-centered approaches identify cognition with processes of life itself through which living systems maintain and adapt through valuation, sense-making and interaction with their environments. In this broader perspective, intelligence refers to the competency with which organisms solve problems under conditions of novelty and uncertainty. These two frameworks, while rarely in direct debate, have farreaching consequences for how we understand the nature of mind, life, and intelligent technologies.

The rapid advancement of brain research and neuroscience has enabled us to identify which areas of the brain are associated with specific behaviors, memories, and other cognitive functions. However, the relationship between the brain and cognition remains a subject of ongoing debate, drawing interest from scholars across various disciplines who seek to understand the nature of 'the mind' . Computationalists often claim that the human brain is a computer, emphasizing the central role of the brain in cognitive processes . Yet this view is unsatisfying to 4E cognitivists, who advocate for a more holistic and relational understanding of cognition-one that situates thought within the dynamic interplay of brain, body, environment, and social context . In this essay, I support the 4E cognition approach by arguing that, while computationalism offers useful explanatory tools, as Richmond (2022) suggests, it fails to account for the embodied, enactive, and relational nature of cognition. I will strengthen this argument by drawing on 4E perspectives such as Di Paolo's analysis of autopoiesis (2018) and Louise Barrett's critique of computationalism (2016). In doing so, I show my opposition

Computationalism has been variously defined as the idea that the human mind can be modelled by means of mechanisms broadly equivalent to Turing Machines. Computationalism's claims have been hotly debated and arguments against and for have drawn extensively from mathematics, cognitive sciences and philosophy, although the debate is hardly settled. On the other hand, in his 2002 book New Kind of Science, Stephen Wolfram advanced what he called the Principle of Computational Equivalence (PCE), whose main contention is that fairly simple systems can easily reach very complex behaviour and become as powerful as any possible system based on rules (that is, they are computationally equivalent). He also claimed that any natural (and even human) phenomenon can be explained as the interaction of very simple rules. Of course, given the universality of Turing Machine-like mechanisms, PCE could be considered simply a particular brand of computationalism, subject to the same objections as previous attempts. In this paper we analyse in depth if this view of PCE is justified or not and hence if PCE can overcome some criticisms and be a different and better model of the human mind. 12

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Following a methodology we have proposed for analysing the nature of experimental computation, we prove that there is a 3-dimensional Newtonian machine which given any point x ∈ [0, 1] can generate an infinite sequence [p n , q n ], for n = 1, 2,. . ., of rational number interval approximations that converges to x as n → ∞. The machine is a system for scattering and collecting particles. The theorem implies that every point x ∈ [0, 1] is computable by a simple Newtonian kinematic system that is bounded in space and mass and for which the calculation of the n−th approximation of x takes place in O(n) time with O(n) energy. We describe a set of variants of the scatter machine which explain why our machine is non-deterministic.

Computationalism has been variously defined as the idea that the human mind can be modelled by means of mechanisms broadly equivalent to Turing Machines. Computationalism's claims have been hotly debated and arguments against and for have drawn extensively from mathematics, cognitive sciences and philosophy, although the debate is hardly settled. On the other hand, in his 2002 book New Kind of Science, Stephen Wolfram advanced what he called the Principle of Computational Equivalence (PCE), whose main contention is that fairly simple systems can easily reach very complex behaviour and become as powerful as any possible system based on rules (that is, they are computationally equivalent). He also claimed that any natural (and even human) phenomenon can be explained as the interaction of very simple rules. Of course, given the universality of Turing Machine-like mechanisms, PCE could be considered simply a particular brand of computationalism, subject to the same objections as previous attempts. In this paper we analyse in depth if this view of PCE is justified or not and hence if PCE can overcome some criticisms and be a different and better model of the human mind. 12

Three special issues of Entropy journal have been dedicated to the topics of “Information-Processing and Embodied, Embedded, Enactive Cognition”. They addressed morphological computing, cognitive agency, and the evolution of cognition. The contributions show the diversity of views present in the research community on the topic of computation and its relation to cognition. This paper is an attempt to elucidate current debates on computation that are central to cognitive science. It is written in the form of a dialog between two authors representing two opposed positions regarding the issue of what computation is and could be, and how it can be related to cognition. Given the different backgrounds of the two researchers, which span physics, philosophy of computing and information, cognitive science, and philosophy, we found the discussions in the form of Socratic dialogue appropriate for this multidisciplinary/cross-disciplinary conceptual analysis. We proceed as follows. First, the p...

Three special issues of Entropy journal have been dedicated to the topics of “Information-Processing and Embodied, Embedded, Enactive Cognition”. They addressed morphological computing, cognitive agency, and the evolution of cognition. The contributions show the diversity of views present in the research community on the topic of computation and its relation to cognition. This paper is an attempt to elucidate current debates on computation that are central to cognitive science. It is written in the form of a dialog between two authors representing two opposed positions regarding the issue of what computation is and could be, and how it can be related to cognition. Given the different backgrounds of the two researchers, which span physics, philosophy of computing and information, cognitive science, and philosophy, we found the discussions in the form of Socratic dialogue appropriate for this multidisciplinary/cross-disciplinary conceptual analysis. We proceed as follows. First, the proponent (GDC) introduces the info-computational framework as a naturalistic model of embodied, embedded, and enacted cognition. Next, objections are raised by the critic (MM) from the point of view of the new mechanistic approach to explanation. Subsequently, the proponent and the critic provide their replies. The conclusion is that there is a fundamental role for computation, understood as information processing, in the understanding of embodied cognition.

In addition to his famous Chinese Room argument, John Searle has posed a more radical problem for views on which minds can be understood as programs. Even his wall, he claims, implements the WordStar program according to the standard definition of implementation because there is some ''pattern of molecule movements'' that is isomorphic to the formal structure of WordStar. Program implementation, Searle charges, is merely observer-relative and thus not an intrinsic feature of the world. I argue, first, that analogous charges involving other concepts (motion and meaning) lead to consequences no one accepts. Second, I show that Searle's treatment of computation is incoherent, yielding the consequence that nothing computes anything: even our standard personal computers fail to run any programs on this account. I propose an alternative account, one that accords with the way engineers, programmers, and cognitive scientists use the concept of computation in their empirical work. This alternative interpretation provides the basis of a philosophical analysis of program implementation, one that may yet be suitable for a computational theory of the mind.

In this paper, I review the objections against the claim that brains are computers, or, to be precise, information-processing mechanisms. By showing that practically all the popular objections are based on uncharitable (or simply incorrect) interpretations of the claim, I argue that the claim is likely to be true, relevant to contemporary cognitive (neuro)science, and non-trivial. The computational theory of mind, or computationalism, has been fruitful in cognitive research. The main tenet of the computational theory of mind is that the brain is a kind of information-processing mechanism, and that information-processing is necessary for cognition; it is non-trivial and is generally accepted in cognitive science. The positive view will not be developed here, in particular the account of physical computation, because it has already been elucidated in book-length accounts (Fresco, 2014; Miłkowski, 2013; Piccinini, 2015). Instead, a review of objections is offered here, as no comprehensive survey is available. The survey suggests that the majority of objections fail just because they make computationalism a straw man. Some of them, however, have shown that stronger versions of the computational theory of mind are untenable, as well. Historically, they have helped to shape the theory and methodology of computational modeling. In particular, a number of objections show that cognitive systems are not only computers, or that computation is not the sole condition of cognition; no objection, however, establishes that there might be cognition without computation.

In most accounts of realization of computational processes by physical mechanisms, it is presupposed that there is one-to-one correspondence between the causally active states of the physical process and the states of the computation. Yet such proposals either stipulate that only one model of computation is implemented, or they do not reflect upon the variety of models that could be implemented physically.

In this paper, I claim that mechanistic accounts of computation should allow for a broad variation of models of computation. In particular, some non-standard models should not be excluded a priori. The relationship between mathematical models of computation and mechanistically adequate models is studied in more detail.

The paper is a critical review of the book Gödel, Putnam, and Functionalism:
A New Reading of Representation and Reality by Jeff Buechner, which is
a defense of computational functionalism against arguments formulated by
Putnam, Searle, Fodor, Lucas and others. Buechner, after having meticulously
analyzed these arguments, concludes that all of them fail to show that
computational functionalism is not a viable strategy to model the mind in
cognitive science. As such, it is a defense of a mathematically-informed version
of computational functionalism. We discuss Beuchner’s strategy in quite a bit
of detail and make some comments.

Abstract. I set out the more fundamental of John Searle’s two 1992 arguments against cognitivism, and argue that its terms are problematic. However, I also identify what I take to be an important point underlying his argument, which is that devices are digital computers in virtue of some person or other having effected an assignment or labelling of their internal states. Consequently, although computational devices do literally compute, we shouldn’t think of their computational operations as intrinsic to them, or as machine computation as a natural-scientific property. A lesson is also drawn here for our understanding of ‘natural’ computation.

David Chalmers characterizes the central commitments of computational cognitive science in terms of two theses: computational suf ficiency, the idea that the right kind of computational structure suffices for the possession of a mind, and computational explanation, the idea that computation provides a general framework for the explanation of cognitive processes and behavior. The computational program has been challenged by Hilary Putnam and John Searle (1991), who argue that every physical system implements every computation, with the consequence that any computational 'explanation' of cognition is utterly trivial. What is needed, according to Chalmers, is an account of implementation, which would both answer the Searle/Putnam challenge and provide a foundation for computational cognitive theorizing.

Some have suggested that there is no fact to the matter as to whether or not a particular physical system relaizes a particular computational description. This suggestion has been taken to imply that computational states are not “real”, and cannot, for example, provide a foundation for the cognitive sciences.

In Darwin’s Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett’s analysis, evolutionary processes are trivially algorithmic because he assumes that all natural processes are algorithmic. I will argue that there are more robust ways to understand algorithmic processes that make the claim that evolution is algorithmic empirical and not conceptual. While laws of nature can be seen as compression algorithms of information about the world, it does not follow logically that they are implemented as algorithms by physical processes. For that to be true, the processes have to be part of computational systems. The basic difference between mere simulation and real computing is having proper causal structure. I will show what kind of requirements this poses for natural evolutionary processes if they are to be computational.

Abstract: In this paper, I want to deal with the triviality threat to computationalism. On one hand, the controversial and vague claim that cognition involves computation is still denied. On the other, contemporary physicists and philosophers alike claim that all physical processes are indeed computational or algorithmic. This claim would justify the computationalism claim by making it utterly trivial. I will show that even if these two claims were true, computationalism would not have to be trivial.
First,