Is the brain really a computer? In particular, is our intelligence a computational achievement: is it because our brains are computers that we get on in the world as well as we do? In this paper I will evaluate an ambitious new argument to the contrary, developed in Landgrebe and Smith (2021a, 2022). Landgrebe and Smith begin with the fact that many dynamical systems in the world are difficult or impossible to model accurately (inter alia, because it is intractable to (...) find exact solutions to the differential equations that describe them—meaning we have to approximate—but at the same time they are such that small differences in starting conditions lead to big differences in final conditions, thwarting accurate approximation). Yet we manage to survive and thrive in a world full of such systems. Landgrebe and Smith argue from these premises that it is not because our brains are computers that we get on as well as we do: instead it is because of the various ways that we dynamically couple with such systems, these couplings themselves impossible to model well enough to emulate in silico. Landgrebe and Smith thus defend a dynamical systems model in the lineage of Gibson (1979), Van Gelder (1995), and Thompson (2007), though their focus is on decisively refuting the computationalist alternatives rather than developing the positive account. Here I will defend the claim that human intelligence is genuinely computational (and that whole brain emulation and others forms of AGI may be possible) against this argument. (shrink)

A function is diagonally non-computable if it diagonalizes against the universal partial computable function. D.n.c. functions play a central role in algorithmic randomness and reverse mathematics. Flood and Towsner asked for which functions h, the principle stating the existence of an h-bounded d.n.c. function implies Ramsey-type weak König’s lemma. In this paper, we prove that for every computable order h, there exists an ω\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\omega}$$\end{document} -model of h-DNR which is not a not (...) model of the Ramsey-type graph coloring principle for two colors and therefore not a model of RWKL. The proof combines bushy tree forcing and a technique introduced by Lerman, Solomon and Towsner to transform a computable non-reducibility into a separation over ω\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\omega}$$\end{document} -models. (shrink)

Background: how mind functions is subject to continuing scientific discussion. A simplistic approach says that, since no convincing way has been found to model subjective experience, mind cannot exist. A second holds that, since mind cannot be described by classical physics, it must be described by quantum physics. Another perspective concerns mind's hypothesized ability to interact with the world of quanta: it should be responsible for reduction of quantum wave packets; physics producing 'Objective Reduction' is postulated to form the basis (...) for mind-matter interactions. This presentation describes results derived from a new approach to these problems. It is based on well-established biology involving physics not previously applied to the fields of mind, or consciousness studies, that of critical feedback instability. -/- Methods: 'self-organized criticality' in complexity biology places system loci of control at critical instabilities, physical properties of which, including information properties, are presented. Their elucidation shows that they can model hitherto unexplained properties of experience. -/- Results: All results depend on physical properties of critical instabilities. First, at least one feed-back or feed-forward loop must have feedback gain, g = 1: information flows round the loop impress perfect images of system states back on themselves: they represent processes of perfect self-observation. This annihilates system quanta: system excitations are instability fluctuations, which cannot be quantized. Major results follow: -/- 1. Information vectors representing criticality states must include at least one attached information loop denoting self-observation. -/- 2. Such loop structures are attributed a function, 'registering the state's own existence', explaining -/- a. Subjective 'awareness of one's own presence' -/- b. How content-free states of awareness can be remembered (Jon Shear) -/- c. Subjective experience of time duration (Immanuel Kant) -/- d. The 'witness' property of experience – often mentioned by athletes 'in the zone' -/- e. The natural association between consciousness and intelligence -/- This novel, physically and biologically sound approach seems to satisfactorily model subjectivity. -/- Further significant results follow: -/- 1. Registration of external information in excited states of systems at criticality reduces external wave-packets: the new model exhibits 'Objective Reduction' of wave packets. -/- 2. High internal coherence (postulated by Domash & Penrose) leading to a. Non-separable information vector bundles. b. Non-reductive states (Chalmers's criterion for experience). -/- 3. Information that is: a. encoded in coherence negentropy; b. non-digitizable, and therefore c. computationally without digital equivalent (posited by Penrose). -/- Discussion and Conclusions: instability physics implies anharmonic motion, preventing excitation quantization, and totally different from the quantum physics of simple harmonic motion at stability. Instability excitations are different from anything hitherto conceived in information science. They can model aspects of mind never previously treated, including genuine subjectivity, objective reduction of wave-packets, and inter alia all properties given above. (shrink)

The premise of this paper is that an adequate model of consciousness will be able to account for the fundamental duality in experience typified by thought and feeling, objectivity and subjectivity, science and art, and that it will do so without any of these terms assimilating its counterpart. The paper argues that such an account is possible using existing models of the cell assembly, but only if consciousness is conceived in structural rather than information processing terms. To this end, the (...) paper contests the viability of information processing models that identify consciousness with a substrate-independent flow of information, and instead identifies consciousness with the physical structure of the cell assembly itself. This allows a fuller and more parsimonious account of consciousness than existing information processing models, as well as the integration of a range of key related matters from the fields of neuroanatomy, psychology, philosophy, and the physical sciences. (shrink)

A true Turing machine (TM) requires an infinitely long paper tape. Thus a TM can be housed in the infinite world of Newtonian spacetime (the spacetime of common sense), but not necessarily in our world, because our world-at least according to our best spacetime theory, general relativity-may be finite. All the same, one can argue for the "existence" of a TM on the basis that there is no such housing problem in some other relativistic worlds that are similar ("close") to (...) our world. But curiously enough-and this is the main point of this paper-some of these close worlds have a special spacetime structure that allows TMs to perform certain Turing unsolvable tasks. For example, in one kind of spacetime a TM can be used to solve first-order predicate logic and the halting problem. And in a more complicated spacetime, TMs can be used to decide arithmetic. These new computers serve to show that Church's thesis is a thoroughly contingent claim. Moreover, since these new computers share the fundamental properties of a TM in ordinary operation (e.g. intuitive, finitely programmed, limited in computational capability), a computability theory based on these non-Turing computers is no less worthy of investigation than orthodox computability theory. Some ideas about this new mathematical theory are given. (shrink)

This article outlines a new framework of computational fairness for the analysis of data-driven statistical discrimination. It begins by identifying the limitations of the still dominant paradigm of fairness through unawareness. Building on this critique, the article proposes an approach that distinguishes concerns of procedural fairness from questions of distributive justice when evaluating potentially discriminatory procedures. In contrast to a widespread view, it argues that procedural computational fairness rests on a single formal criterion. A computational procedure is non-discriminatory, it is (...) claimed, if it achieves equal predictive values across demographic groups. In addition, two non-procedural requirements of distributive justice are advanced for selective procedures: the proportionality of individual burdens and their compatibility with a just overall social distribution of benefits and burdens. (shrink)

The Church-Turing Thesis is widely regarded as true, because of evidence that there is only one genuine notion of computation. By contrast, there are nowadays many different formal logics, and different corresponding foundational frameworks. Which ones can deliver a theory of computability? This question sets up a difficult challenge: the meanings of basic mathematical terms are not stable across frameworks. While it is easy to compare what different frameworks say, it is not so easy to compare what they mean. (...) We argue for some minimal conditions that must be met if two frameworks are to be compared; if frameworks are radical enough, comparison becomes hopeless. Our aim is to clarify the dialectical situation in this bourgeoning area of research, shedding light on the nature of non-classical logic and the notion of computation alike. (shrink)

What can computers do in principle? What are their inherent theoretical limitations? These are questions to which computer scientists must address themselves. The theoretical framework which enables such questions to be answered has been developed over the last fifty years from the idea of a computable function: intuitively a function whose values can be calculated in an effective or automatic way. This book is an introduction to computability theory (or recursion theory as it is traditionally known to mathematicians). Dr (...) Cutland begins with a mathematical characterisation of computable functions using a simple idealised computer (a register machine); after some comparison with other characterisations, he develops the mathematical theory, including a full discussion of non-computability and undecidability, and the theory of recursive and recursively enumerable sets. The later chapters provide an introduction to more advanced topics such as Gildel's incompleteness theorem, degrees of unsolvability, the Recursion theorems and the theory of complexity of computation. Computability is thus a branch of mathematics which is of relevance also to computer scientists and philosophers. Mathematics students with no prior knowledge of the subject and computer science students who wish to supplement their practical expertise with some theoretical background will find this book of use and interest. (shrink)

ABSTRACT Michael Rescorla has argued that it makes sense to compute directly with numbers, and he faulted Turing for not giving an analysis of number-theoretic computability. However, in line with a later paper of his, it only makes sense to compute directly with syntactic entities, such as strings on a given alphabet. Computing with numbers goes via notation. This raises broader issues involving de re propositional attitudes towards numbers and other non-syntactic abstract entities.

In this article, I focus on the role of computer simulations as exploratory strategies. I begin by establishing the non-theory-driven nature of simulations. This refers to their ability to characterize phenomena without relying on a predefined conceptual framework that is provided by an implemented mathematical model. Drawing on Steinle’s notion of exploratory experimentation and Gelfert’s work on exploratory models, I present three exploratory strategies for computer simulations: (1) starting points and continuation of scientific inquiry, (2) varying the parameters, and (3) (...) scientific prototyping. (shrink)

We present a simple example that disproves the universality principle. Unlike previous counter-examples to computational universality, it does not rely on extraneous phenomena, such as the availability of input variables that are time varying, computational complexity that changes with time or order of execution, physical variables that interact with each other, uncertain deadlines, or mathematical conditions among the variables that must be obeyed throughout the computation. In the most basic case of the new example, all that is used is a (...) single pre-existing global variable whose value is modified by the computation itself. In addition, our example offers a new dimension for separating the computable from the uncomputable, while illustrating the power of parallelism in computation. (shrink)

Currently, there is widespread skepticism that higher cognitive processes, given their apparent flexibility and globality, could be carried out by specialized computational devices, or modules. This skepticism is largely due to Fodor’s influential definition of modularity. From the rather flexible catalogue of possible modular features that Fodor originally proposed has emerged a widely held notion of modules as rigid, informationally encapsulated devices that accept highly local inputs and whose opera- tions are insensitive to context. It is a mistake, however, to (...) equate such features with computational devices in general and therefore to assume, as Fodor does, that higher cognitive processes must be non-computational. Of the many possible non-Fodorean architectures, one is explored here that offers possible solutions to computational problems faced by conventional modular systems: an ‘enzymatic’ architecture. Enzymes are computational devices that use lock-and-key template matching to iden- tify relevant information (substrates), which is then operated upon and returned to a common pool for possible processing by other devices. Highly specialized enzymes can operate together in a common pool of information that is not pre-sorted by information type. Moreover, enzymes can use molecular ‘tags’ to regulate the operations of other devices and to change how particular substrates are construed and operated upon, allowing for highly interactive, context-specific processing. This model shows how specialized, modular processing can occur in an open system, and suggests that skepti- cism about modularity may largely be due to failure to consider alternatives to the standard model. (shrink)

Computable Wavefunction Realism (CWFR) is an ontological framework for quantum theory derived from the semantic commitments of explanatory realism. Explanatory realism requires that physical magnitudes denote under admissible representations and that lawful evolution be closed on the intended ontic domain. Read literally, continuum ontology challenges these requirements through three distinct forms of semantic instability: domain instability arising from unbounded aggregation of fine-scale structure, range instability arising from non-open predicate structure, and closure failure under lawful evolution. CWFR does not begin by (...) postulating computability or finite information; both arise as consequences of the semantic conditions required for physically meaningful ontology. The resulting answer is given by four postulates: Lorentz-invariant spectral band-limitation, identification of the ontic domain with states admitting Type-2 computable names under the chosen representation, admissible successor dynamics given by total Type-2 computable maps on the admissible domain, and restriction of physically meaningful predicates to those whose truth sets are open in the representation topology. The paper develops the structural consequences of these constraints and establishes non-vacuity through explicit admissible successor constructions. CWFR is not proposed as a replacement for continuum quantum mechanics at the level of mathematical formalism, but as a restriction on ontological interpretation imposed by semantic stability conditions. Macroscopic structure arises from refinement geometry induced by admissible stability predicates, classical worlds emerge as maximal refinement threads, and probability assignments operate on locally finite refinement partitions. Extension to realistic interacting quantum field theories is identified as a programmatic direction rather than a completed result. (shrink)

This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman, Woodward, and Lange. By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. I also use this framework to (...) shed light on the dispute over the interpretation of dynamical models of the brain. _1_ Introduction _1.1_ Efficient coding explanation in computational neuroscience _1.2_ Defining non-causal explanation _2_ Case I: Hybrid Computation _3_ Case II: The Gabor Model Revisited _4_ Case III: A Dynamical Model of Prefrontal Cortex _4.1_ A new explanation of context-dependent computation _4.2_ Causal or non-causal? _5_ Causal and Non-causal: Does the Difference Matter? (shrink)

Computer simulations and experiments share many important features. One way of explaining the similarities is to say that computer simulations just are experiments. This claim is quite popular in the literature. The aim of this paper is to argue against the claim and to develop an alternative explanation of why computer simulations resemble experiments. To this purpose, experiment is characterized in terms of an intervention on a system and of the observation of the reaction. Thus, if computer simulations are experiments, (...) either the computer hardware or the target system must be intervened on and observed. I argue against the first option using the non-observation argument, among others. The second option is excluded by e.g. the over-control argument, which stresses epistemological differences between experiments and simulations. To account for the similarities between experiments and computer simulations, I propose to say that computer simulations can model possible experiments and do in fact often do so. (shrink)

Provability, Computability and Reflection.

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous (...) signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation. (shrink)

The Computational Theory of Mind (CTM) holds that cognitive processes are essentially computational, and hence computation provides the scientific key to explaining mentality. The Representational Theory of Mind (RTM) holds that representational content is the key feature in distinguishing mental from non-mental systems. I argue that there is a deep incompatibility between these two theoretical frameworks, and that the acceptance of CTM provides strong grounds for rejecting RTM. The focal point of the incompatibility is the fact that representational content is (...) extrinsic to formal procedures as such, and the intended interpretation of syntax makes no difference to the execution of an algorithm. So the unique 'content' postulated by RTM is superfluous to the formal procedures of CTM. And once these procedures are implemented in a physical mechanism, it is exclusively the causal properties of the physical mechanism that are responsible for all aspects of the system's behaviour. So once again, postulated content is rendered superfluous. To the extent that semantic content may appear to play a role in behaviour, it must be syntactically encoded within the system, and just as in a standard computational artefact, so too with the human mind/brain - it's pure syntax all the way down to the level of physical implementation. Hence 'content' is at most a convenient meta-level gloss, projected from the outside by human theorists, which itself can play no role in cognitive processing. (shrink)

Can machines ever become conscious? A central debate in this context concerns the question whether consciousness requires biological states. Within this debate, there exists a fundamental dispute between two widely endorsed views: biological naturalism and computational functionalism about consciousness. Specifically, whereas biological naturalists hold that consciousness requires biological states, computational functionalists fiercly deny this. This fundamental dispute has hitherto remained unresolved, and we discuss several reasons why it is unlikely that it will be resolved anytime soon. Accordingly, we suggest that (...) both sides will likely remain in a dialectical stalemate regarding the fundamental dispute and, by extension, are unlikely to reach a consensus on positive verdicts about consciousness in AI. However, we suggest that progress can be made by investigating potential non-biological constraints on implementation. Such constraints can help reach a consensus between both sides to the effect that certain types of artificial systems are not conscious, if they fail to satisfy relevant constraints. If correct, a consensus on negative verdicts about consciousness in certain challenging types of AI is possible, even if both sides remain in conflict about positive verdicts about AI consciousness. (shrink)

Computational explanations focus on information processing required in specific cognitive capacities, such as perception, reasoning or decision-making. These explanations specify the nature of the information processing task, what information needs to be represented, and why it should be operated on in a particular manner. In this article, the focus is on three questions concerning the nature of computational explanations: What type of explanations they are, in what sense computational explanations are explanatory and to what extent they involve a special, “independent” (...) or “autonomous” level of explanation. In this paper, we defend the view computational explanations are genuine explanations, which track non-causal/formal dependencies. Specifically, we argue that they do not provide mere sketches for explanation, in contrast to what for example Piccinini and Craver :283–311, 2011) suggest. This view of computational explanations implies some degree of “autonomy” for the computational level. However, as we will demonstrate that does not make this view “computationally chauvinistic” in a way that Piccinini or Kaplan :339–373, 2011) have charged it to be. (shrink)

A Computable Universe is a collection of papers discussing computation in nature and the nature of computation, a compilation of the views of the pioneers in the contemporary area of intellectual inquiry focused on computational and informational theories of the world. This volume is the definitive source of informational/computational views of the world, and of cutting-edge models of the universe, both digital and quantum, discussed from a philosophical perspective as well as in the greatest technical detail. The book discusses the (...) foundations of computation in relation to nature. It focuses on two main questions: What is computation? How does nature compute? The contributors are world-renowned experts who have helped shape a cutting-edge computational understanding of the universe. They discuss computation in the world from a variety of perspectives, ranging from foundational concepts to pragmatic models to ontological conceptions and their philosophical implications. The volume provides a state-of-the-art collection of technical papers and non-technical essays representing a field that takes information and computation to be key to understanding and explaining the basic structure underpinning physical reality. It also includes a new edition of Konrad Zuse's "Calculating Space", and a panel discussion transcription on the topic, featuring worldwide experts (including a Nobel prize) in quantum mechanics, physics, cognition, computation and algorithmic complexity.A Computable Universe is a collection of papers discussing computation in nature and the nature of computation, a compilation of the views of the pioneers in the contemporary area of intellectual inquiry focused on computational and informational theories of the world. This volume is the definitive source of informational/computational views of the world, and of cutting-edge models of the universe, both digital and quantum, discussed from a philosophical perspective as well as in the greatest technical detail. The book discusses the foundations of computation in relation to nature. It focuses on two main questions: What is computation? How does nature compute? The contributors are world-renowned experts who have helped shape a cutting-edge computational understanding of the universe. They discuss computation in the world from a variety of perspectives, ranging from foundational concepts to pragmatic models to ontological conceptions and their philosophical implications. The volume provides a state-of-the-art collection of technical papers and non-technical essays representing a field that takes information and computation to be key to understanding and explaining the basic structure underpinning physical reality. It also includes a new edition of Konrad Zuse's "Calculating Space", and a panel discussion transcription on the topic, featuring worldwide experts (including a Nobel prize) in quantum mechanics, physics, cognition, computation and algorithmic complexity. (shrink)

Critical in the computationalist account of the mind is the phenomenon called computational or computer simulation of human thinking, which is used to establish the theses that human thinking is a computational process and that computing machines are thinking systems. Accordingly, if human thinking can be simulated computationally then human thinking is a computational process; and if human thinking is a computational process then its computational simulation is itself a thinking process. This paper shows that the said phenomenon—the computational simulation (...) of human thinking—is ill-conceived, and that, as a consequence, the theses that it intends to establish are problematic. It is argued that what is simulated computationally is not human thinking as such but merely its behavioral manifestations; and that a computational simulation of these behavioral manifestations does not necessarily establish that human thinking is computational, as it is logically possible for a non-computational system to exhibit behaviors that lend themselves to a computational simulation. (shrink)

Does Nature permit the implementation of behaviours that cannot be simulated computationally? We consider the meaning of physical computation in some detail, and present arguments in favour of physical hypercomputation: for example, modern scientific method does not allow the specification of any experiment capable of refuting hypercomputation. We consider the implications of relativistic algorithms capable of solving the (Turing) Halting Problem. We also reject as a fallacy the argument that hypercomputation has no relevance because non-computable values are indistinguishable from sufficiently (...) close computable approximations. In addition to considering the nature of computability relative to any given physical theory, we can consider the relationship between versions of computability corresponding to different models of physics. Deutsch and Penrose have argued on mathematical grounds that quantum computation and Turing computation have equivalent formal power. We suggest this equivalence is invalid when considered from the physical point of view, by highlighting a quantum computational behaviour that cannot meaningfully be considered feasible in the classical universe. (shrink)

Computational thinking (CT) is currently being introduced into non-computer science disciplines at all educational levels. Many who have written on or implemented CT in educational contexts claim that CT is a universally applicable set of general problem-solving skills. I challenge this claim by discussing how CT is used in practice within everyday contexts and in learning within different disciplines. I propose an alternative conceptualization of CT as being socially and disciplinarily situated and transforming across these contexts. I discuss the significance (...) of a situated conceptualization of CT for educational and research practices, highlighting through example cases the risks that choosing an inadequate conceptualization of CT has for disciplinary learning. These risks include concentrating student learning time on neither meeting discipline-specific learning objectives nor on developing computer science skills, but on an artificial intermediary. Key questions for future research are identified, centring around the realization of CT in different disciplinary settings, its transformation across contexts, and how to develop and test assessment formats that take CT’s situated nature into account. (shrink)

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. (shrink)

We examine a case in which non-computable behavior in a model is revealed by computer simulation. This is possible due to differing notions of computability for sets in a continuous space. The argument originally given for the validity of the simulation involves a simpler simulation of the simulation, still further simulations thereof, and a universality conjecture. There are difficulties with that argument, but there are other, heuristic arguments supporting the qualitative results. It is urged, using this example, that absolute (...) validation, while highly desirable, is overvalued. Simulations also provide valuable insights that we cannot yet (if ever) prove. (shrink)

Any computer can create a model of reality. The hypothesis that quantum computer can generate such a model designated as quantum, which coincides with the modeled reality, is discussed. Its reasons are the theorems about the absence of “hidden variables” in quantum mechanics. The quantum modeling requires the axiom of choice. The following conclusions are deduced from the hypothesis. A quantum model unlike a classical model can coincide with reality. Reality can be interpreted as a quantum computer. The physical processes (...) represent computations of the quantum computer. Quantum information is the real fundament of the world. The conception of quantum computer unifies physics and mathematics and thus the material and the ideal world. Quantum computer is a non-Turing machine in principle. Any quantum computing can be interpreted as an infinite classical computational process of a Turing machine. Quantum computer introduces the notion of “actually infinite computational process”. The discussed hypothesis is consistent with all quantum mechanics. The conclusions address a form of neo-Pythagoreanism: Unifying the mathematical and physical, quantum computer is situated in an intermediate domain of their mutual transformation. (shrink)

Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility of models (...) to non-programming researchers is essentially non-existent, and even for other modelers, the profusion of source code in a multitude of programming languages, written without programming guidelines, makes it almost impossible to access, check, explore, re-use, or continue to develop. It is high time to change this situation, especially since the tools are now readily available to do so. We propose that the modeling community adopt three simple guidelines that would ensure that computational models would be accessible to the broad range of researchers in cognitive science. We further emphasize the pivotal role that journal editors must play in making computational models accessible to readers of their journals. (shrink)

Computational modeling plays an increasingly important explanatory role in cases where we investigate systems or problems that exceed our native epistemic capacities. One clear case where technological enhancement is indispensable involves the study of complex systems.1 However, even in contexts where the number of parameters and interactions that define a problem is small, simple systems sometimes exhibit non-linear features which computational models can illustrate and track. In recent decades, computational models have been proposed as a way to assist us in (...) understanding emergent phenomena. (shrink)

A brain-computer interface designed to restore motor function detects neural activity related to intended movement and thereby enables a person to control an external device, for example, a robotic limb, or even their own body. It would seem legitimate, therefore, to describe a BCI as a system that translates thought into action. This paper argues that present BCI-mediated behavior fails to meet the conditions of intentional physical action as proposed by causal and non-causal theories of action. First, according to the (...) causal theory of action physical actions are bodily movements that are causally related to a person’s intentions. It can be argued, however, that the proximate cause of action in present BCI-mediated behavior is not the person’s intention, and that the behavior fails to meet the conditions of reliability, sensitivity and difference-making. Second, BCI-mediated behavior can be accommodated by a Volitionist account of action if we can equate imagining movement with trying to move. The argument presented is that the novelty, and limited functionality and sensory feedback of present BCIs challenges this equation. (shrink)

Brain-Computer Interface is a set of technologies that are of increasing interest to researchers. BCI has been proposed as assistive technology for individuals who are non-communicative or paralyzed, such as those with amyotrophic lateral sclerosis or spinal cord injury. The technology has also been suggested for enhancement and entertainment uses, and there are companies currently marketing BCI devices for those purposes as well as health-related purposes. The unprecedented direct connection created by BCI between human brains and computer hardware raises various (...) ethical, social, and legal challenges that merit further examination and discussion. To identify and characterize the key issues associated with BCI use, we performed a scoping review of biomedical ethics literature, analyzing the ethics concerns cited across multiple disciplines, including philosophy and medicine. Based on this investigation, we report that BCI research and its potential translation to therapeutic intervention generate significant ethical, legal, and social concerns, notably with regards to personhood, stigma, autonomy, privacy, research ethics, safety, responsibility, and justice. Our review of the literature determined, furthermore, that while these issues have been enumerated extensively, few concrete recommendations have been expressed. We conclude that future research should focus on remedying a lack of practical solutions to the ethical challenges of BCI, alongside the collection of empirical data on the perspectives of the public, BCI users, and BCI researchers. (shrink)

Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The extreme (...) dynamicist answer: computation is not required for explaining cognition. The first four answers are only accurate to a first approximation. But the “devil” is in the details. The last answer cashes in on the parenthetical “if any” in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation. (shrink)

In this paper, we argue for the centrality of prediction in the use of computational models in science. We focus on the consequences of the irreversibility of computational models and on the conditional or ceteris paribus, nature of the kinds of their predictions. By irreversibility, we mean the fact that computational models can generally arrive at the same state via many possible sequences of previous states. Thus, while in the natural world, it is generally assumed that physical states have a (...) unique history, representations of those states in a computational model will usually be compatible with more than one possible history in the model. We describe some of the challenges involved in prediction and retrodiction in computational models while arguing that prediction is an essential feature of non-arbitrary decision making. Furthermore, we contend that the non-predictive virtues of computational models are dependent to a significant degree on the predictive success of the models in question. (shrink)

The relation between computational and intentional psychology has always been a vexing issue. The worry is that if mental processes are computational, then these processes, which are defined over symbols, are sensitive solely to the non-semantic properties of symbols. If so, perhaps psychology could dispense with adverting in its laws to intentional/semantic properties of symbols. Stich, as is well-known, has made a great deal out of this tension and argued for a purely "syntactic" psychology by driving a wedge between a (...) semantic individuation of symbol tokens and their narrow functional individuation. If the latter can be carried out, he claimed, we do not need semantic typing. I argue that since a narrow functional individuation cannot type-identify symbol tokens across organisms, a semantic account of typing must be the only option given that interpersonal physical individuation of tokens is not to be taken seriously. (shrink)

A computable topological presentation of a space is given by an effective list of a countable basis of non-empty open sets so that the intersection of the basic sets is uniformly effectively enumerable. We show that every countably-based $T_0$ -space has a computable topological presentation, and that, conversely, every (formal) computable topological presentation represents some Polish space. In the compact case, we give a computable uniform list of computable topological presentations such that every compact Polish space is represented by exactly (...) one presentation from the list. Note that none of these results assume that the Polish (or $T_0$ ) spaces are effective. Quite surprisingly, the effectively compact topological presentations turn out to be rather well behaved. Not only do such presentations allow one to construct a $\Delta ^0_2$ (complete) metric compatible with the topology, but also, under a mild extra condition, they can be turned into a computably compact Polish presentation of the space. (shrink)

Computational perspectivalism has been recently proposed as an alternative to mainstream accounts of physical computation, and especially to the teleologically-based mechanistic view. It takes physical computation to be partly dependent on explanatory perspectives and eschews appeal to teleology in helping individuate computational systems. I assess several varieties of computational perspectivalism, showing that they either collapse into existing non-perspectival views or end up with unsatisfactory or implausible accounts of physical computation. Computational perspectivalism fails, therefore, to be a compelling alternative to perspective-independent (...) theories of computation in physical systems. I conclude that a teleologically-based, non-perspectival mechanistic account of physical computation is to be preferred. 1Introduction 2The Mechanistic View of Computation 2.1Teleological functions and meeting the desiderata 3Varieties of Perspectivalism 4Computational Perspectivalism 4.1Computational perspectivalism and pancomputationalism 4.2Computational perspectivalism and miscomputation 5The Varieties of Computational Perspectivalism Assessed 5.1Innocuous computational perspectivalism 5.2Non-innocuous computational perspectivalism 5.2.1Computational instrumentalism 5.2.2Ontic perspectivalism 6Concluding Remarks. (shrink)

The hallmark of the deductive systems known as ‘conceptivist’ or ‘containment’ logics is that for all theorems of the form , all atomic formulae appearing in also appear in . Significantly, as a consequence, the principle of Addition fails. While often billed as a formalisation of Kantian analytic judgements, once semantics were discovered for these systems, the approach was largely discounted as merely the imposition of a syntactic filter on unrelated systems. In this paper, we examine a number of prima (...) facie unrelated deductive contexts in which Addition fails and attempt to harmonise them by developing a computational interpretation of conceptivist logics. (shrink)

In this paper, we pursue three general aims: (I) We will define a notion of fundamental opacity and ask whether it can be found in High Energy Physics (HEP), given the involvement of machine learning (ML) and computer simulations (CS) therein. (II) We identify two kinds of non-fundamental, contingent opacity associated with CS and ML in HEP respectively, and ask whether, and if so how, they may be overcome. (III) We address the question of whether any kind of opacity, contingent (...) or fundamental, is unique to ML or CS, or whether they stand in continuity to kinds of opacity associated with other scientific research. (shrink)

Recently, Horsman et al. have proposed a new framework, Abstraction/Representation theory, for understanding and evaluating claims about unconventional or non-standard computation. Among its attractive features, the theory in particular implies a novel account of what is means to be a computer. After expounding on this account, I compare it with other accounts of concrete computation, finding that it does not quite fit in the standard categorization: while it is most similar to some semantic accounts, it is not itself a semantic (...) account. Then I evaluate it according to the six desiderata for accounts of concrete computation proposed by Piccinini. Finding that it does not clearly satisfy some of them, I propose a modification, which I call Agential AR theory, that does, yielding an account that could be a serious competitor to other leading account of concrete computation. (shrink)

The rise of various unique, or uniquely transformed, ethical issues supports the claim that computer ethics deserves to be regarded as an academic field in its own right. Some of these issues are unique because they inherit the unique properties of the technology that generates or transforms them. When we are unable to resolve these issues through non-computer moral analogies, we are forced to discover new moral values, formulate new moral principles and develop new policies.

What does it mean to say that an object or system computes? What is it about laptops, smartphones, and nervous systems that they are considered to compute, and why does it seldom occur to us to describe stomachs, hurricanes, rocks, or chairs that way? Though computing systems are everywhere today, it is very difficult to answer these questions. The book aims to shed light on the subject by arguing for the semantic view of computation, which states that computingsystems are always (...) accompanied by representations. This view is presented as an alternative to non-semantic views such as the mechanistic account of computation. (shrink)

We formalize a general semantic constraint on physically admissible procedures: discrete outcomes must exhibit bounded input dependence, meaning that determinate outcomes are supported by finite stability margins in the underlying state space. We prove that any such procedure can discriminate only properties corresponding to open regions of state space. Properties whose truth sets are topologically thin, having empty interior with dense complement, are operationally unresolvable under admissible physical semantics. This limitation is structural rather than computational. It arises from topological constraints (...) induced by stability at the semantic interface and applies uniformly across continuum ontologies. Stability is shown to be preserved under lawful continuous dynamics, implying that thin distinctions cannot be amplified into operational differences by finite physical evolution followed by admissible readout. Computability provides a canonical representative example. Computable and noncomputable elements form interwoven dense subsets of continuum domains, so computability is not a topological property of states but a property of representation structure. As such, it cannot be operationally extracted by bounded-input-dependent procedures. Restrictions motivated by bounded input dependence thus express ontological parsimony: they eliminate distinctions incapable of supporting stable physical discrimination. The result provides a semantic foundation for aligning ontological commitments with structurally admissible distinctions in realist physical theories. -/- . (shrink)

2.4 The Example: Infants and object-(im)permanence : : : : : : : : : : : : : : 17 2.4.1 Why a contentful account is warranted: Perspectival sensitivity : : : 17 2.4.2 The \searching under a cloth" and \AB" data : : : : : : : : : : : : 24 2.4.3 Two constraints on objectuality : : : : : : : : : : : : : : : : : : : : (...) : 25.. (shrink)

We prove that if $A$, $B$ are noncomputable c.e. sets, $A<_{sQ_{1}}B$ and [($B$ is not simple and $A\oplus B\leq _{sQ_{1}}B$) or $B\equiv _{sQ_{1}}B\times \omega $], then there exist infinitely many pairwise $sQ_{1}$-incomparable c.e. sets $\{C_{i}\}_{i\in \omega }$ such that $A<_{sQ_{1}}C_{i}<_{sQ_{1}}B$, for all $i\in \omega $. We also show that there exist infinite collections of $sQ_{1}$-degrees $\{\boldsymbol {a_{i}}\}_{i\in \omega }$ and $\{\boldsymbol {b_{i}}\}_{i\in \omega }$ such that for every $i, j,$ (1) $\boldsymbol {a_{i}}<_{sQ_{1}}\boldsymbol {a_{i+1}}$, $\boldsymbol {b_{j+1}}<_{sQ_{1}}\boldsymbol {b_{j}}$ and $\boldsymbol {a_{i}}<_{sQ_{1}}\boldsymbol {b_{j}}$; (...) (2) every c.e set in $\boldsymbol {a_{i}}$ is a maximal set; and (3) every c.e. set in $\boldsymbol {b_{j}}$ is a non-maximal hyperhypersimple set. We prove that a noncomputable c.e. $sQ$-degree contains either only one or infinitely many c.e. $sQ_{1}$-degrees. The $sQ_{1}$-degrees of c.e. cylinders are dense upper semilattice. (shrink)