It is fairly well-known that certain hard computational problems (that is, 'difficult' problems for a digital processor to solve) can in fact be solved much more easily with an analog machine. This raises questions about the true nature of the distinction between analog and digital computation (if such a distinction exists). I try to analyze the source of the observed difference in terms of (1) expanding parallelism and (2) more generally, infinite-state Turing machines. The issue (...) of discreteness vs continuity will also be touched upon, although it is not so important for analyzing these particular problems. (shrink)

The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it (...) is with computing, which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine. (shrink)

The ‘received view’ of the analog–digital distinction holds that analog representations are continuous while digital representations are discrete. In this paper I first provide support for the received view by showing how it (1) emerges from the theory of computation, and (2) explains engineering practices. Second, I critically assess several recently offered alternatives, arguing that to the degree they are justified they demonstrate not that the received view is incorrect, but rather that distinct senses of (...) the terms have become entrenched specific fields, perhaps most notably in the cognitive psychology of mental imagery. (shrink)

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)

We examine the interrelationships between analog computational modelling and analogue (physical) modelling. To this end, we attempt a regimentation of the informal distinction between analog and digital, which turns on the consideration of computing in a broader context. We argue that in doing so one comes to see that (scientific) computation is better conceptualised as an epistemic process relative to agents, wherein representations play a key role. We distinguish between two, conceptually distinct, kinds of representation that, (...) we argue, are both involved in each case of computing. Based on the semantic and syntactic properties of each of these representations, we put forward a new account of the distinction between analog and digital computing. We discuss how the developed account is able to explain various properties of different models of computation, and we finish by conceptually comparing analog computational modelling to analogue (scale) modelling. It is concluded that, contrary to the standard view, the two practices are orthogonal, differing both in their foundations and in the epistemic functions they fulfil. (shrink)

Church's thesis, that all reasonable definitions of “computability” are equivalent, is not usually thought of in terms of computability by acontinuouscomputer, of which the general-purpose analog computer (GPAC) is a prototype. Here we prove, under a hypothesis of determinism, that the analytic outputs of aC∞GPAC are computable by a digital computer.In [POE, Theorems 5, 6, 7, and 8], Pour-El obtained some related results. (The proof there of Theorem 7 depends on her Theorem 2, for which the proof in (...) [POE] is incorrect, but for which a correct proof is given in [LIR]. Also, the proof in [POE] of Theorem 8 depends on the unproved assertion that a solution of an algebraic differential equation must be analytic on an open subset of its domain. However, this assertion was later proved in [BRR].) As in [POE], we reduce the problem to a problem about solutions of certain systems of algebraic differential equations (ADE's). If such a system is nonsingular (i.e. if the “separant” does not vanish along the given solution), then the argument is very easy (see [VSD] for an even simpler situation), so that the essential difficulties arise fromsingularsystems. Our main tools in handling these difficulties are drawn from the excellent (and difficult) paper [DEL] by Denef and Lipshitz. The author especially wants to thank Leonard Lipshitz for his kind help in the preparation of the present paper.We emphasize here that our proof of the simulation result applies only to the GPAC as described below. The GPAC's form a natural subclass of the class of all analog computers, and are based on certain idealized components (“black boxes”), mostly associated with the technology of past decades. One can easily envisage other kinds of black boxes of an input-output character that would lead to different kinds of analog computers. (For example, one could incorporate delays, or spatial integrators in addition to the present temporal integrators, etc.) Whether digital simulation is possible for these “extended” analog computers poses a rich and challenging set of research questions. (shrink)

This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to (...) formalise algorithmic computation and to offer foundations for scientific computing. The dissertation consists of three parts. In the first part, we examine what notion of 'algorithmic computation' underlies each approach and how it is respectively formalised. It is argued that the very existence of the two rival frameworks indicates that 'algorithm' is not one unique concept in mathematics, but it is used in more than one way. We test this hypothesis for consistency with mathematical practice as well as with key foundational works that aim to define the term. As a result, new connections between certain subfields of mathematics and computer science are drawn, and a distinction between 'algorithms' and 'effective procedures' is proposed. In the second part, we focus on the second goal of the two rival approaches to real computation; namely, to provide foundations for scientific computing. We examine both frameworks in detail, what idealisations they employ, and how they relate to floating-point arithmetic systems used in real computers. We explore limitations and advantages of both frameworks, and answer questions about which one is preferable for computational modelling and which one for addressing general computability issues. In the third part, analog computing and its relation to analogue (physical) modelling in science are investigated. Based on some paradigmatic cases of the former, a certain view about the nature of computation is defended, and the indispensable role of representation in it is emphasized and accounted for. We also propose a novel account of the distinction between analog and digital computation and, based on it, we compare analog computational modelling to physical modelling. It is concluded that the two practices, despite their apparent similarities, are orthogonal. (shrink)

This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to (...) formalise algorithmic computation and to offer foundations for scientific computing. -/- The dissertation consists of three parts. In the first part, we examine what notion of 'algorithmic computation' underlies each approach and how it is respectively formalised. It is argued that the very existence of the two rival frameworks indicates that 'algorithm' is not one unique concept in mathematics, but it is used in more than one way. We test this hypothesis for consistency with mathematical practice as well as with key foundational works that aim to define the term. As a result, new connections between certain subfields of mathematics and computer science are drawn, and a distinction between 'algorithms' and 'effective procedures' is proposed. -/- In the second part, we focus on the second goal of the two rival approaches to real computation; namely, to provide foundations for scientific computing. We examine both frameworks in detail, what idealisations they employ, and how they relate to floating-point arithmetic systems used in real computers. We explore limitations and advantages of both frameworks, and answer questions about which one is preferable for computational modelling and which one for addressing general computability issues. -/- In the third part, analog computing and its relation to analogue (physical) modelling in science are investigated. Based on some paradigmatic cases of the former, a certain view about the nature of computation is defended, and the indispensable role of representation in it is emphasized and accounted for. We also propose a novel account of the distinction between analog and digital computation and, based on it, we compare analog computational modelling to physical modelling. It is concluded that the two practices, despite their apparent similarities, are orthogonal. (shrink)

The issue of symbol grounding is not essentially different in analog and digital computation. The principal difference between the two is that in analog computers continuous variables change continuously, whereas in digital computers discrete variables change in discrete steps (at the relevant level of analysis). Interpretations are imposed on analog computations just as on digital computations: by attaching meanings to the variables and the processes defined over them. As Harnad (2001) claims, states acquire (...) intrinsic meaning through their relation to the real (physical) environment, for example, through transduction. However, this is independent of the question of the continuity or discreteness of the variables or the transduction processes. (shrink)

This paper considers the particular ways in which the familiar letter and twenty-first century technologies like the Internet differingly shaped and shape our experience of distance and presence. It follows Heidegger, Dreyfus, and Borgmann in critiquing the kinds of experience and action the Internet makes possible, and—by way of Benjamin’s concept of “aura”—argues that while mediated communication over distance might have never been easier, faster, or cheaper, this increase in our effective power comes at the cost of a diminution of (...) the affective power of the messages carried. (shrink)

Computationalism about the brain is the view that the brain literally performs computations. For the view to be interesting, we need an account of computation. The most well-developed account of computation is Turing Machine computation, the account provided by theoretical computer science which provides the basis for contemporary digital computers. Some have thought that, given the seemingly-close analogy between the all-or-nothing nature of neural spikes in brains and the binary nature of digital logic, neural (...) class='Hi'>computation could be a species of digital computation. A few recent authors have offered arguments against this idea; here, I review recent findings in neuroscience that further cement the implausibility of this view. However, I argue that we can retain the view that the brain is a computer if we expand what we mean by “computation” to include analog computation. I articulate an account of analog computation as the manipulation of analog representations based on previous work on the difference between analog and non-analog representations, extending a view originally articulated in Shagrir :271–279, 2010). Given that analog computation constitutes a significant chapter in the history of computation, this revision of computationalism to include analog computation is not an ad hoc addition. Brains may well be computers, but of the analog kind, rather than the digital kind. (shrink)

Representation is central to contemporary theorizing about the mind/brain. But the nature of representation--both in the mind/brain and more generally--is a source of ongoing controversy. One way of categorizing representational types is to distinguish between the analog and the digital: the received view is that analog representations vary smoothly, while digital representations vary in a step-wise manner. I argue that this characterization is inadequate to account for the ways in which representation is used in cognitive science; (...) in its place, I suggest an alternative taxonomy. I will defend and extend David Lewis's account of analog and digital representation, distinguishing analog from continuous representation, as well as digital from discrete representation. I will argue that the distinctions available in this four-fold account accord with representational features of theoretical interest in cognitive science more usefully than the received analog/digital dichotomy. (shrink)

Computational neuroscientists not only employ computer models and simulations in studying brain functions. They also view the modeled nervous system itself as computing. What does it mean to say that the brain computes? And what is the utility of the ‘brain-as-computer’ assumption in studying brain functions? In previous work, I have argued that a structural conception of computation is not adequate to address these questions. Here I outline an alternative conception of computation, which I call the analog-model. (...) The term ‘analog-model’ does not mean continuous, non-discrete or non-digital. It means that the functional performance of the system simulates mathematical relations in some other system, between what is being represented. The brain-as-computer view is invoked to demonstrate that the internal cellular activity is appropriate for the pertinent information-processing task.Keywords: Computation; Computational neuroscience; Analog computers; Representation; Simulation. (shrink)

In this paper, I argue for three claims. The first is that the difference between analog and digital representation lies in the format and not the medium of representation. The second is that whether a given system is analog or digital will sometimes depend on facts about the user of that system. The third is that the first two claims are implicit in Haugeland's (1998) account of the distinction.

``Neural computing'' is a research field based on perceiving the human brain as an information system. This system reads its input continuously via the different senses, encodes data into various biophysical variables such as membrane potentials or neural firing rates, stores information using different kinds of memories (e.g., short-term memory, long-term memory, associative memory), performs some operations called ``computation'', and outputs onto various channels, including motor control commands, decisions, thoughts, and feelings. We show a natural model of neural computing (...) that gives rise to hyper-computation. Rigorous mathematical analysis is applied, explicating our model's exact computational power and how it changes with the change of parameters. Our analog neural network allows for supra-Turing power while keeping track of computational constraints, and thus embeds a possible answer to the superiority of the biological intelligence within the framework of classical computer science. We further propose it as standard in the field of analog computation, functioning in a role similar to that of the universal Turing machine in digital computation. In particular an analog of the Church-Turing thesis of digital computation is stated where the neural network takes place of the Turing machine. (shrink)

Pervasive digitality reveals us as analogue creatures that are unprepared for a world and a logic generated increasingly through automation. Promulgated by capitalism, digitality has created a new form of alienation, one far more powerful and comprehensive than that envisaged by either Marx or Lukács in the analogue-industrial age. Digital alienation-through-automation is the central process in our digital post-modernity. The effects reach increasing registers and spheres of culture, economy and politics. This essay considers the effects within the production (...) of knowledge within higher education. Through a critique of Clayton Christensen’s concept of ‘disruptive technologies’, it develops the argument that knowledge produced in our higher institutions of learning suffer from two forms of ‘knowledge capture’. The first and most comprehensive is knowledge increasingly oriented toward instrumental outcomes, generated in vocationally oriented and market-pressured contexts, and produced by the general-sector institutions; second is knowledge, much of it profound and potentially world-changing—in economics and climate science in the cases discussed here. These are generated in both prestigious general-sector universities, and in the elite Ivy League and Russell Group universities but are unable to gain traction with an ascendant and alien post-Westphalian, post-modern and post-Enlightenment political context—created by a globalising digitality. I argue that unless we recognise our analogue essence, and that this has been alienated by digital automation, then the aporias of digitality, with knowledge production an important element, then our institution of higher learning will continue to produce much valuable knowledge that has diminishing practical (political) effect. (shrink)

Pervasive digitality reveals us as analogue creatures that are unprepared for a world and a logic generated increasingly through automation. Promulgated by capitalism, digitality has created a new form of alienation, one far more powerful and comprehensive than that envisaged by either Marx or Lukács in the analogue-industrial age. Digital alienation-through-automation is the central process in our digital post-modernity. The effects reach increasing registers and spheres of culture, economy and politics. This essay considers the effects within the production (...) of knowledge within higher education. Through a critique of Clayton Christensen’s concept of ‘disruptive technologies’, it develops the argument that knowledge produced in our higher institutions of learning suffer from two forms of ‘knowledge capture’. The first and most comprehensive is knowledge increasingly oriented toward instrumental outcomes, generated in vocationally oriented and market-pressured contexts, and produced by the general-sector institutions; second is knowledge, much of it profound and potentially world-changing—in economics and climate science in the cases discussed here. These are generated in both prestigious general-sector universities, and in the elite Ivy League and Russell Group universities but are unable to gain traction with an ascendant and alien post-Westphalian, post-modern and post-Enlightenment political context—created by a globalising digitality. I argue that unless we recognise our analogue essence, and that this has been alienated by digital automation, then the aporias of digitality, with knowledge production an important element, then our institution of higher learning will continue to produce much valuable knowledge that has diminishing practical (political) effect. (shrink)

Gualtiero Piccinini articulates and defends a mechanistic account of concrete, or physical, computation. A physical system is a computing system just in case it is a mechanism one of whose functions is to manipulate vehicles based solely on differences between different portions of the vehicles according to a rule defined over the vehicles. Physical Computation discusses previous accounts of computation and argues that the mechanistic account is better. Many kinds of computation are explicated, such as (...) class='Hi'>digital vs. analog, serial vs. parallel, neural network computation, program-controlled computation, and more. Piccinini argues that computation does not entail representation or information processing although information processing entails computation. Pancomputationalism, according to which every physical system is computational, is rejected. A modest version of the physical Church-Turing thesis, according to which any function that is physically computable is computable by Turing machines, is defended. (shrink)

Any analysis of the concept of computation as it occurs in the context of a discussion of the computational model of the mind must be consonant with the philosophic burden traditionally carried by that concept as providing a bridge between a physical and a psychological description of an agent. With this analysis in hand, one may ask the question: are connectionist-based systems consistent with the computational model of the mind? The answer depends upon which of several versions of connectionism (...) one presupposes: non-learning connectionist-based systems as simulated on digital computers are consistent with the computational model of the mind, whereas connectionist-based systems (/dynamical systems) qua analog systems are not. (shrink)

Digital and analog: What do these terms mean today? The use and meaning of such terms change through time. The analog, in particular, seems to go through various phases of popularity and disuse, its appeal pegged most frequently to nostalgic longings for nontechnical or romantic modes of art and culture. The definition of the digital vacillates as well, its precise definition often eclipsed by a kind of fever-pitched industrial bonanza around the latest technologies and the latest (...) commercial ventures. One common response to the question of the digital is to make reference to things like Twitter, Playstation, or computers in general. Here one might be correct, but only coincidentally, for the basic order of digitality (the digitality of digitality) has not yet been demonstrated through mere denotation. And the second question—the question of the analog—is harder still, with responses often also consisting of mere denotations of things: sound waves, the phonograph needle, magnetic tape, a sundial. At least denotation itself is analogical. This article will aim to define the analog explicitly and argue, perhaps counterintuitively, that the golden age of analog thinking was not a few decades past, prior to the wide-spread adoption of the computer. In fact, the fields of structuralism and poststructuralism that emerged in the middle to late twentieth century, concurrent with the deployment of the digital computer, were characteristically digital, what with their focus on the symbolic order and logical economies. If anything, the golden age of analog is happening today, all around us, as evidenced by the proliferation of characteristically analog concerns: sensation, materiality, experience, affect, ethics, and aesthetics. (shrink)

Typical accounts of imagistic content have focused on the apparent analog character or continuous variability of images. In contrast, I consider the distinctive features of digital images, those composed of finite sets of discrete pixels. A rich source of evidence on digital imagistic content is found in the content-preserving algorithms that resize and reproduce digital images on computer screens and printers. I argue that these algorithms reveal a distinctive structural feature: digital images are always compositional (...) (their parts contribute systematically to overall content), but never inverse compositional (atomic parts may be replaced nonsynonymously without changing content). This indicates a sharp contrast with linguistic representations, which may or may not be compositional, and may or may not be inverse compositional. I argue this result sheds new light on the claim that imagistic content is inherently perspectival. (shrink)

As two fundamental modes of communication, analog and digital communication are not only ways of information transmission but also two mental habits in our perception and representation of the perception in the creation of communication sign systems. In a broader sense, analog and digital communication are not only for electronic communication or high technology computer networking communication. Language is featured by both analog and digital communication,especially in the development of the writing system. The development (...) of the writing system from images or icons to alphabets is the development of an analog communication to a digital communication. The creation and development of Chinese pictographs illustrate the trend from low digitization to high digitization. From this perspective, we can say that the creation and development of the writing system is a process of digitization. (shrink)

Cognition is commonly taken to be computational manipulation of representations. These representations are assumed to be digital, but it is not usually specified what that means and what relevance it has for the theory. I propose a specification for being a digital state in a digital system, especially a digital computational system. The specification shows that identification of digital states requires functional directedness, either for someone or for the system of which it is a part. (...) In the case or digital representations, to be a token of a representational type, where the function of the type is to represent. [An earlier version of this paper was discussed in the web-conference "Interdisciplines" https://web.archive.org/web/20100221125700/http://www.interdisciplines.org/adaptation/papers/7 ]. (shrink)

A radically empirical exploration of movement and technology and the transformations of choreography in a digital realm. Digital technologies offer the possibility of capturing, storing, and manipulating movement, abstracting it from the body and transforming it into numerical information. In Moving without a Body, Stamatia Portanova considers what really happens when the physicality of movement is translated into a numerical code by a technological system. Drawing on the radical empiricism of Gilles Deleuze and Alfred North Whitehead, she argues (...) that this does not amount to a technical assessment of software's capacity to record motion but requires a philosophical rethinking of what movement itself is, or can become. Discussing the development of different audiovisual tools and the shift from analog to digital, she focuses on some choreographic realizations of this evolution, including works by Loie Fuller and Merce Cunningham. Throughout, Portanova considers these technologies and dances as ways to think—rather than just perform or perceive—movement. She distinguishes the choreographic thought from the performance: a body performs a movement, and a mind thinks or choreographs a dance. Similarly, she sees the move from analog to digital as a shift in conception rather than simply in technical realization. Analyzing choreographic technologies for their capacity to redesign the way movement is thought, Moving without a Body offers an ambitiously conceived reflection on the ontological implications of the encounter between movement and technological systems. (shrink)

As two fundamental modes of communication, analog and digital communication are not only ways of information transmission but also two mental habits in our perception and representation of the perception in the creation of communication sign systems. In a broader sense, analog and digital communication are not only for electronic communication or high technology computer networking communication. Language is featured by both analog and digital communication,especially in the development of the writing system. The development (...) of the writing system from images or icons to alphabets is the development of an analog communication to a digital communication. The creation and development of Chinese pictographs illustrate the trend from low digitization to high digitization. From this perspective, we can say that the creation and development of the writing system is a process of digitization. (shrink)

The central claim of computationalism is generally taken to be that the brain is a computer, and that any computer implementing the appropriate program would ipso facto have a mind. In this paper I argue for the following propositions: (1) The central claim of computationalism is not about computers, a concept too imprecise for a scienti c claim of this sort, but is about physical calculi (instantiated discrete formal systems). (2) In matters of formality, interpretability, and so forth, analog (...) computation and digital computation are not essentially di erent, and so arguments such as Searle's hold or not as well for one as for the other. (3) Whether or not a biological system (such as the brain) is computational is a scienti c matter of fact. (4) A substantive scienti c question for cognitive science is whether cognition is better modeled by discrete representations or by continuous representations. (5) Cognitive science and AI need a theoretical construct that is the continuous analog of a calculus. The discussion of these propositions will illuminate several terminology traps, in which it's all too easy to become ensnared. (shrink)

I offer an explication of the notion of computer, grounded in the practices of computability theorists and computer scientists. I begin by explaining what distinguishes computers from calculators. Then, I offer a systematic taxonomy of kinds of computer, including hard-wired versus programmable, general-purpose versus special-purpose, analog versus digital, and serial versus parallel, giving explicit criteria for each kind. My account is mechanistic: which class a system belongs in, and which functions are computable by which system, depends on the (...) system's mechanistic properties. Finally, I briefly illustrate how my account sheds light on some issues in the history and philosophy of computing as well as the philosophy of mind. (shrink)

Information Theory, Evolution and The Origin ofLife: The Origin and Evolution of Life as a Digital Message: How Life Resembles a Computer, Second Edition. Hu- bert P. Yockey, 2005, Cambridge University Press, Cambridge: 400 pages, index; hardcover, US $60.00; ISBN: 0-521-80293-8. The reason that there are principles of biology that cannot be derived from the laws of physics and chemistry lies simply in the fact that the genetic information content of the genome for constructing even the simplest organisms is (...) much larger than the information content of these laws. Yockey in his previous book (1992, 335) In this new book, Information Theory, Evolution and The Origin ofLife, Hubert Yockey points out that the digital, segregated, and linear character of the genetic information system has a fundamental significance. If inheritance would blend and not segregate, Darwinian evolution would not occur. If inheritance would be analog, instead of digital, evolution would be also impossible, because it would be impossible to remove the effect of noise. In this way, life is guided by information, and so information is a central concept in molecular biology. The author presents a picture of how the main concepts of the genetic code were developed. He was able to show that despite Francis Crick's belief that the Central Dogma is only a hypothesis, the Central Dogma of Francis Crick is a mathematical consequence of the redundant nature of the genetic code. The redundancy arises from the fact that the DNA and mRNA alphabet is formed by triplets of 4 nucleotides, and so the number of letters (triplets) is 64, whereas the proteome alphabet has only 20 letters (20 amino acids), and so the translation from the larger alphabet to the smaller one is necessarily redundant. Except for Tryptohan and Methionine, all amino acids are coded by more than one triplet, therefore, it is undecidable which source code letter was actually sent from mRNA. This proof has a corollary telling that there are no such mathematical constraints for protein-protein communication. With this clarification, Yockey contributes to diminishing the widespread confusion related to such a central concept like the Central Dogma. Thus the Central Dogma prohibits the origin of life "proteins first." Proteins can not be generated by "self-organization." Understanding this property of the Central Dogma will have a serious impact on research on the origin of life. (shrink)

This article argues that if panpsychism is true, then there are grounds for thinking that digitally-based artificial intelligence may be incapable of having coherent macrophenomenal conscious experiences. Section 1 briefly surveys research indicating that neural function and phenomenal consciousness may be both analog in nature. We show that physical and phenomenal magnitudes—such as rates of neural firing and the phenomenally experienced loudness of sounds—appear to covary monotonically with the physical stimuli they represent, forming the basis for an analog (...) relationship between the three. Section 2 then argues that if this is true and micropsychism—the panpsychist view that phenomenal consciousness or its precursors exist at a microphysical level of reality—is also true, then human brains must somehow manipulate fundamental microphysical-phenomenal magnitudes in an analog manner that renders them phenomenally coherent at a macro level. However, Sect. 3 argues that because digital computation abstracts away from microphysical-phenomenal magnitudes—representing cognitive functions non-monotonically in terms of digits—digital computation may be inherently incapable of realizing coherent macroconscious experience. Thus, if panpsychism is true, digital AI may be incapable of achieving phenomenal coherence. Finally, Sect. 4 briefly examines our argument’s implications for Tononi’s Integrated Information Theory theory of consciousness, which we contend may need to be supplanted by a theory of macroconsciousness as analog microphysical-phenomenal information integration. (shrink)

I do not wish at this time to dispute either or. I do not believe, however, that the intermediate step can be adequately justified, and hence remain unconvinced by the purported conclusion. The most recent presentation of this argument is in Professor Dreyfus' article "Why Computers must have Bodies in order to be Intelligent," a discussion of which will serve to explain my lack of confidence in any argument of this general form.

The purpose of this paper is to provide an account of the epistemology and metaphysics of universe creation on a computer. The paper begins with F.J.Tipler's argument that our experience is indistinguishable from the experience of someone embedded in a perfect computer simulation of our own universe, hence we cannot know whether or not we are part of such a computer program ourselves. Tipler's argument is treated as a special case of epistemological scepticism, in a similar vein to `brain-in-a-vat' arguments. (...) It is argued that the hypothesis that our universe is a program running on a digital computer in another universe generates empirical predictions, and is therefore a falsifiable hypothesis. The computer program hypothesis is also treated as a hypothesis about what exists beyond the physical world, and is compared with Kant's metaphysics of noumena. It is proposed that a theory about what exists beyond the physical world should be formulated with the precise concepts of mathematics, and should generate physical predictions. It is argued that if our universe is a program running on a digital computer, then our universe must have compact spatial topology, and the possibilities of observationally testing this prediction are considered. The possibility of testing the computer program hypothesis with the value of the density parameter Omega_0 is also analysed. The informational requirements for a computer to represent a universeexactly and completely are considered. Consequent doubt is thrown upon Tipler's claim that if a hierarchy of computer universes exists, we would not be able to know which `level of implementation' our universe exists at. It is then argued that a digital computer simulation of a universe cannot exist as a universe. However, the paper concludes with the acknowledgement that an analog computer simulation can be objectively related to the thing it represents, hence an analog computer simulation of a universe could, in principle, exist as a universe. (shrink)

There are currently considerable confusion and disarray about just how we should view computationalism, connectionism and dynamicism as explanatory frameworks in cognitive science. A key source of this ongoing conflict among the central paradigms in cognitive science is an equivocation on the notion of computation simpliciter. ‘Computation’ is construed differently by computationalism, connectionism, dynamicism and computational neuroscience. I claim that these central paradigms, properly understood, can contribute to an integrated cognitive science. Yet, before this claim can be defended, (...) a better understanding of ‘computation’ is required. ‘Digital computation’ is an ambiguous concept. It is not just the classical dichotomy between analogue and digital computation that is the basis for the equivocation on ‘computation’ simpliciter in cognitive science, but also the diversity of extant accounts of digital computation. There are many answers on what it takes for a system to perform digital computation. Answers to this problem range from Turing machine computation, through the formal manipulation of symbols, the execution of algorithms and others, to the strong-pancomputational thesis, according to which every physical system computes every Turing-computable function. Despite some overlap among them, extant accounts of concrete digital computation are non-equivalent, thus, rendering ‘digital computation’ ambiguous. The objective of this dissertation is twofold. First, it is to promote a clearer understanding of concrete digital computation. Accordingly, my main thesis is that not only are extant accounts of concrete digital computation non-equivalent, but most of them are inadequate. I show that these accounts are not just intensionally different (this is quite trivially the case), but also extensionally distinct. In the course of examining several key accounts of concrete digital computation, I propose the instructional information processing account, according to which digital computation is the processing of discrete data in accordance with finite instructional information. The second objective is to establish the foundational role of computation in cognitive science whilst rejecting the purported representational nature of computation. (shrink)

We analyzed the association between the analog and the digital home learning environment in toddlers’ and preschoolers’ homes, and whether both aspects are associated with children’s social and academic competencies. Here, we used data of the national representative sample of Growing up in Germany II, which includes 4,914 children aged 0–5 years. The HLE was assessed via parental survey that included items on the analog HLE and items on the digital HLE. Children’s socio-emotional, practical life skills, (...) and academic competencies were assessed via standardized parental ratings. Our results indicate that there are two dimensions of the HLE, an analog and a digital, that are slightly positively associated, especially in the toddler age group. For toddlers, only analog HLE activities were associated with better socio-emotional outcomes and practical life skills. However, interaction effects indicate that toddlers with less frequent analog HLE activities showed better socio-emotional skills in households with more frequent digital activities. For preschoolers, digital HLE activities were associated with weaker socio-emotional skills but higher academic skills, although the analog HLE shows higher effect sizes for the academic outcomes. Our study points out that analog and digital HLE activities seem to be partly associated, but not interchangeable. Further, they seem to be important variables that can explain individual differences in young children’s socio-emotional, practical life, and academic competencies. However, digital media usage at home may also have negative effects on children’s social–emotional competencies. This association needs to be investigated further. (shrink)

Cognitive representations are typically analysed in terms of content, vehicle and format. While current work on formats appeals to intuitions about external representations, such as words and maps, in this paper we develop a computational view of formats that does not rely on intuitions. In our view, formats are individuated by the computational profiles of vehicles, i.e., the set of constraints that fix the computational transformations vehicles can undergo. The resulting picture is strongly pluralistic, it makes space for a variety (...) of different formats, and is intimately tied to the computational approach to cognition in cognitive science and artificial intelligence. (shrink)

Hume's attempt to show that deduction is the only legitimate form of inference presupposes that enumerative induction is the only non-deductive form of inference. In actuality, enumerative induction is not even a form of inference: all supposed cases of enumerative induction are disguised cases of Inference to the Best Explanation (IBE), so far as they aren't simply cases of mentation of a purely associative kind and, consequently, of a kind that is non-inductive and otherwise non-inferential. The justification for IBE lies (...) in two truths, each analytic, viz. that good explanations eliminate anomalies and, second, that anomaly-elimination is identical with discontinuity-elimination. Given these truisms, along with the empirical data to which even skeptics must grant that we have access, it follows that induction is truth-conducive, a corrollary being that skepticism with regard to the veridicality of our senses is indefensible. Hume's erroneous beliefs about non-deductive inference are consequences of his belief that the world has a digital, as opposed to an analogue, structure--of, in other words, his belief that the spatiotemporal manifold decomposes into minimal units. This belief of his is not only false, but analytically so, and its negation has the consequence--for reasons additional to the one already given---that many forms of skepticism are non-starters. It also has the consequence, later given empirical confirmation by the (apparent) truth of Relativity Theory, that spatiotemporal relations are causal relations. Finally, it has the consequence that, contrary to what Hume argues, there are instances of causation. Additional reinforcemet for this claim is shown to lie in a fact first identified by Kant, viz. that we couldn't even ask whether or not there were instances of causation unless there were in fact such instances. Other, similarly 'transcendental' arguments of Kant's are put forth, sometimes in a modified form, so as to buttress the anti-skeptical positions advocated in thsi paper. (shrink)

The central claim of computationalism is generally taken to be that the brain is a computer, and that any computer implementing the appropriate program would ipso facto have a mind. In this paper I argue for the following propositions: (1) The central claim of computationalism is not about computers, a concept too imprecise for a scientific claim of this sort, but is about physical calculi (instantiated discrete formal systems). (2) In matters of formality, interpretability, and so forth, analog (...) class='Hi'>computation and digital computation are not essentially different, and so arguments such as Searle''s hold or not as well for one as for the other. (3) Whether or not a biological system (such as the brain) is computational is a scientific matter of fact. (4) A substantive scientific question for cognitive science is whether cognition is better modeled by discrete representations or by continuous representations. (5) Cognitive science and AI need a theoretical construct that is the continuous analog of a calculus. The discussion of these propositions will illuminate several terminology traps, in which it''s all too easy to become ensnared. (shrink)

Advancements in computing, instrumentation, robotics, digital imaging, and simulation modeling have changed science into a technology-driven institution. Government, industry, and society increasingly exert their influence over science, raising questions of values and objectivity. These and other profound changes have led many to speculate that we are in the midst of an epochal break in scientific history. -/- This edited volume presents an in-depth examination of these issues from philosophical, historical, social, and cultural perspectives. It offers arguments both for and (...) against the epochal break thesis in light of historical antecedents. Contributors discuss topics such as: science as a continuing epistemological enterprise; the decline of the individual scientist and the rise of communities; the intertwining of scientific and technological needs; links to prior practices and ways of thinking; the alleged divide between mode-1 and mode-2 research methods; the commodification of university science; and the shift from the scientific to a technological enterprise. Additionally, they examine the epochal break thesis using specific examples, including the transition from laboratory to real world experiments; the increased reliance on computer imaging; how analog and digital technologies condition behaviors that shape the object and beholder; the cultural significance of humanoid robots; the erosion of scientific quality in experimentation; and the effect of computers on prediction at the expense of explanation. -/- Whether these events represent a historic break in scientific theory, practice, and methodology is disputed. What they do offer is an important occasion for philosophical analysis of the epistemic, institutional and moral questions affecting current and future scientific pursuits. (shrink)

There is much discussion about whether the human mind is a computer, whether the human brain could be emulated on a computer, and whether at all physical entities are computers (pancomputationalism). These discussions, and others, require criteria for what is digital. I propose that a state is digital if and only if it is a token of a type that serves a particular function - typically a representational function for the system. This proposal is made on a syntactic (...) level, assuming three levels of description (physical, syntactic, semantic). It suggests that being digital is a matter of discovery or rather a matter of how we wish to describe the world, if a functional description can be assumed. Given the criterion provided and the necessary empirical research, we should be in a position to decide on a given system (e.g. the human brain) whether it is a digital system and can thus be reproduced in a different digital system (since digital systems allow multiple realization). (shrink)

Phenomenology presents itself not as an explanation or interpretation of phenomena but as a description of them. Describing experience means making its internal structure explicit, which, in phenomenology, is an eidetic structure. The method of phenomenological explication or clarification is, however, by no means univocal. This paper aims to isolate the two fundamental ways in which phenomenological description is achieved. The first refers to a phenomenology of manifestation, based on the concept of determination or datum, which is realized in the (...) phenomenological-static approach and, in particular, on the concept of extensive quality. The second refers to a phenomenology of disposition, based on the concept of tension or force – which is realized in the genetic approach as well as in Merleau-Ponty’s phenomenology of perception – and, in particular on the concept of intensive or forceful quality. The analysis of the difference between the two approaches allows us to introduce the crucial distinction between digital and analogue dimensions within phenomenology. (shrink)

One of the most important objections to information-based semantic theories is that they are incapable of explaining Frege cases. The worry is that if a concept’s intentional content is a function of its informational content, as such theories propose, then it would appear that coreferring expressions have to be synonymous, and if this is true, it’s difficult to see how an agent could believe that a is F without believing that b is F whenever a and b are identical. I (...) argue that this appearance is deceptive. If we heed the distinction between the analog and digital contents of a signal, it is actually possible to reconstruct something akin to Frege’s sense/reference distinction in purely information-theoretic terms. This allows informational semanticists to treat coreferring expressions as semantically distinct and to solve Frege cases in the same way that Frege did—namely, by appealing to the different contents of coreferring expressions. (shrink)

The “active image” refers to the operative nature of images, thus capturing the vast array of “actions” that images perform. This volume features essays that present a new approach to image theory. It explores the many ways images become active in architecture and engineering design processes and how, in the age of computer-based modeling, images play an indispensable role. The contributors examine different types of images, be they pictures, sketches, renderings, maps, plans, and photographs; be they analog or (...) class='Hi'>digital, planar or three-dimensional, ephemeral, realistic or imaginary. Their essays investigate how images serve as means of representing, as tools for thinking and reasoning, as ways of imagining the inexistent, as means of communicating and conveying information and how images may also perform functions and have an agency in their own. The essays discuss the role of images from the perspective of philosophy, theory and history of architecture, history of science, media theory, cognitive sciences, design studies, and visual studies, offering a multidisciplinary approach to imagery and showing the various methodologies and interpretations in current research. In addition, they offer valuable insight to better understand how images operate and function in the arts and sciences in general. (shrink)