Search results for 'computation' (try it on Scholar)

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  1. Marcin Miłkowski (2011). Beyond Formal Structure: A Mechanistic Perspective on Computation and Implementation. Journal of Cognitive Science 12 (4):359-379.score: 18.0
    In this article, after presenting the basic idea of causal accounts of implementation and the problems they are supposed to solve, I sketch the model of computation preferred by Chalmers and argue that it is too limited to do full justice to computational theories in cognitive science. I also argue that it does not suffice to replace Chalmers’ favorite model with a better abstract model of computation; it is necessary to acknowledge the causal structure of physical computers that (...)
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  2. B. Jack Copeland (1996). What is Computation? Synthese 108 (3):335-59.score: 18.0
    To compute is to execute an algorithm. More precisely, to say that a device or organ computes is to say that there exists a modelling relationship of a certain kind between it and a formal specification of an algorithm and supporting architecture. The key issue is to delimit the phrase of a certain kind. I call this the problem of distinguishing between standard and nonstandard models of computation. The successful drawing of this distinction guards Turing's 1936 analysis of (...) against a difficulty that has persistently been raised against it, and undercuts various objections that have been made to the computational theory of mind. (shrink)
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  3. Gualtiero Piccinini & Andrea Scarantino (2011). Information Processing, Computation, and Cognition. Journal of Biological Physics 37 (1):1-38.score: 18.0
    Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are (...)
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  4. David J. Chalmers (1994). On Implementing a Computation. Minds and Machines 4 (4):391-402.score: 18.0
    To clarify the notion of computation and its role in cognitive science, we need an account of implementation, the nexus between abstract computations and physical systems. I provide such an account, based on the idea that a physical system implements a computation if the causal structure of the system mirrors the formal structure of the computation. The account is developed for the class of combinatorial-state automata, but is sufficiently general to cover all other discrete computational formalisms. The (...)
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  5. Gordana Dodig Crnkovic & Mark Burgin (eds.) (forthcoming). INFORMATION AND COMPUTATION. World Scientific.score: 18.0
    The book focuses on relations between information and computation. Information is a basic structure of the world, while computation is a process of the dynamic change of information. In order for anything to exist for an individual, the individual must get information on it, either by means of perception or by re-organization of the existing information into new patterns and networks in the brain. With the advent of World Wide Web and a prospect of semantic web, the ways (...)
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  6. Gualtiero Piccinini & Andrea Scarantino (2010). Computation Vs. Information Processing: Why Their Difference Matters to Cognitive Science. Studies in History and Philosophy of Science Part A 41 (3):237-246.score: 18.0
    Since the cognitive revolution, it’s become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theoristError: Illegal entry in bfrange (...)
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  7. Wesley Elsberry & Jeffrey Shallit (2011). Information Theory, Evolutionary Computation, and Dembski's "Complex Specified Information". Synthese 178 (2):237 - 270.score: 18.0
    Intelligent design advocate William Dembski has introduced a measure of information called "complex specified information", or CSI. He claims that CSI is a reliable marker of design by intelligent agents. He puts forth a "Law of Conservation of Information" which states that chance and natural laws are incapable of generating CSI. In particular, CSI cannot be generated by evolutionary computation. Dembski asserts that CSI is present in intelligent causes and in the flagellum of Escherichia coli, and concludes that neither (...)
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  8. Selmer Bringsjord (1998). Cognition is Not Computation: The Argument From Irreversibility. Synthese 113 (2):285-320.score: 18.0
    The dominant scientific and philosophical view of the mind – according to which, put starkly, cognition is computation – is refuted herein, via specification and defense of the following new argument: Computation is reversible; cognition isn't; ergo, cognition isn't computation. After presenting a sustained dialectic arising from this defense, we conclude with a brief preview of the view we would put in place of the cognition-is-computation doctrine.
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  9. B. Jack Copeland & Oron Shagrir (2007). Physical Computation: How General Are Gandy's Principles for Mechanisms? [REVIEW] Minds and Machines 17 (2):217-231.score: 18.0
    What are the limits of physical computation? In his ‘Church’s Thesis and Principles for Mechanisms’, Turing’s student Robin Gandy proved that any machine satisfying four idealised physical ‘principles’ is equivalent to some Turing machine. Gandy’s four principles in effect define a class of computing machines (‘Gandy machines’). Our question is: What is the relationship of this class to the class of all (ideal) physical computing machines? Gandy himself suggests that the relationship is identity. We do not share this view. (...)
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  10. Steven Horst (1999). Symbols and Computation: A Critique of the Computational Theory of Mind. Minds and Machines 9 (3):347-381.score: 18.0
    Over the past several decades, the philosophical community has witnessed the emergence of an important new paradigm for understanding the mind.1 The paradigm is that of machine computation, and its influence has been felt not only in philosophy, but also in all of the empirical disciplines devoted to the study of cognition. Of the several strategies for applying the resources provided by computer and cognitive science to the philosophy of mind, the one that has gained the most attention from (...)
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  11. Nir Fresco (2010). Explaining Computation Without Semantics: Keeping It Simple. [REVIEW] Minds and Machines 20 (2):165-181.score: 18.0
    This paper deals with the question: how is computation best individuated? -/- 1. The semantic view of computation: computation is best individuated by its semantic properties. 2. The causal view of computation: computation is best individuated by its causal properties. 3. The functional view of computation: computation is best individuated by its functional properties. -/- Some scientific theories explain the capacities of brains by appealing to computations that they supposedly perform. The reason for (...)
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  12. Selmer Bringsjord (1994). Computation, Among Other Things, is Beneath Us. Minds and Machines 4 (4):469-88.score: 18.0
    What''s computation? The received answer is that computation is a computer at work, and a computer at work is that which can be modelled as a Turing machine at work. Unfortunately, as John Searle has recently argued, and as others have agreed, the received answer appears to imply that AI and Cog Sci are a royal waste of time. The argument here is alarmingly simple: AI and Cog Sci (of the Strong sort, anyway) are committed to the view (...)
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  13. Nir Fresco (2012). The Explanatory Role of Computation in Cognitive Science. Minds and Machines 22 (4):353-380.score: 18.0
    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 (...)
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  14. Oron Shagrir (2002). Effective Computation by Humans and Machines. Minds and Machines 12 (2):221-240.score: 18.0
    There is an intensive discussion nowadays about the meaning of effective computability, with implications to the status and provability of the Church–Turing Thesis (CTT). I begin by reviewing what has become the dominant account of the way Turing and Church viewed, in 1936, effective computability. According to this account, to which I refer as the Gandy–Sieg account, Turing and Church aimed to characterize the functions that can be computed by a human computer. In addition, Turing provided a highly convincing argument (...)
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  15. Stevan Harnad (1994). Computation is Just Interpretable Symbol Manipulation; Cognition Isn't. Minds and Machines 4 (4):379-90.score: 18.0
    Computation is interpretable symbol manipulation. Symbols are objects that are manipulated on the basis of rules operating only on theirshapes, which are arbitrary in relation to what they can be interpreted as meaning. Even if one accepts the Church/Turing Thesis that computation is unique, universal and very near omnipotent, not everything is a computer, because not everything can be given a systematic interpretation; and certainly everything can''t be givenevery systematic interpretation. But even after computers and computation have (...)
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  16. Marcin Miłkowski (2012). Is Computation Based on Interpretation? Semiotica 188 (1):219-228.score: 18.0
    I argue that influential purely syntactic views of computation, shared by such philosophers as John Searle and Hilary Putnam, are mistaken. First, I discuss common objections, and during the discussion I mention additional necessary conditions of implementation of computations in physical processes that are neglected in classical philosophical accounts of computation. Then I try to show why realism in regards of physical computations is more plausible, and more coherent with any realistic attitude towards natural science than the received (...)
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  17. Gualtiero Piccinini & Sonya Bahar (2013). Neural Computation and the Computational Theory of Cognition. Cognitive Science 37 (3):453-488.score: 18.0
    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 (...)
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  18. Paul R. Thagard (2002). How Molecules Matter to Mental Computation. Philosophy of Science 69 (3):497-518.score: 18.0
    Almost all computational models of the mind and brain ignore details about neurotransmitters, hormones, and other molecules. The neglect of neurochemistry in cognitive science would be appropriate if the computational properties of brains relevant to explaining mental functioning were in fact electrical rather than chemical. But there is considerable evidence that chemical complexity really does matter to brain computation, including the role of proteins in intracellular computation, the operations of synapses and neurotransmitters, and the effects of neuromodulators such (...)
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  19. Ronald L. Chrisley (1994). Why Everything Doesn't Realize Every Computation. Minds and Machines 4 (4):403-20.score: 18.0
    Some have suggested that there is no fact to the matter as to whether or not a particular physical system relaizes a particular computational description. This suggestion has been taken to imply that computational states are not real, and cannot, for example, provide a foundation for the cognitive sciences. In particular, Putnam has argued that every ordinary open physical system realizes every abstract finite automaton, implying that the fact that a particular computational characterization applies to a physical system does not (...)
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  20. Gordana Dodig-Crnkovic (2011). Significance of Models of Computation, From Turing Model to Natural Computation. Minds and Machines 21 (2):301-322.score: 18.0
    The increased interactivity and connectivity of computational devices along with the spreading of computational tools and computational thinking across the fields, has changed our understanding of the nature of computing. In the course of this development computing models have been extended from the initial abstract symbol manipulating mechanisms of stand-alone, discrete sequential machines, to the models of natural computing in the physical world, generally concurrent asynchronous processes capable of modelling living systems, their informational structures and dynamics on both symbolic and (...)
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  21. Bart D.’Hooghe & Jaroslaw Pykacz (2004). Quantum Mechanics and Computation. Foundations of Science 9 (4):387-404.score: 18.0
    In quantum computation non classical features such as superposition states and entanglement are used to solve problems in new ways, impossible on classical digital computers.We illustrate by Deutsch algorithm how a quantum computer can use superposition states to outperform any classical computer. We comment on the view of a quantum computer as a massive parallel computer and recall Amdahls law for a classical parallel computer. We argue that the view on quantum computation as a massive parallel computation (...)
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  22. Nir Fresco (2011). Concrete Digital Computation: What Does It Take for a Physical System to Compute? [REVIEW] Journal of Logic, Language and Information 20 (4):513-537.score: 18.0
    This paper deals with the question: what are the key requirements for a physical system to perform digital computation? Time and again cognitive scientists are quick to employ the notion of computation simpliciter when asserting basically that cognitive activities are computational. They employ this notion as if there was or is a consensus on just what it takes for a physical system to perform computation, and in particular digital computation. Some cognitive scientists in referring to digital (...)
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  23. Fabio Boschetti (2012). Causality, Emergence, Computation and Unreasonable Expectations. Synthese 185 (2):187-194.score: 18.0
    I argue that much of current concern with the role of causality and strong emergence in natural processes is based upon an unreasonable expectation placed on our ability to formalize scientific knowledge. In most disciplines our formalization ability is an expectation rather than a scientific result. This calls for an empirical approach to the study of causation and emergence. Finally, I suggest that for advances in complexity research to occur, attention needs to be paid to understanding what role computation (...)
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  24. Robert W. Kentridge (1995). Symbols, Neurons, Soap-Bubbles and the Neural Computation Underlying Cognition. Minds and Machines 4 (4):439-449.score: 18.0
    A wide range of systems appear to perform computation: what common features do they share? I consider three examples, a digital computer, a neural network and an analogue route finding system based on soap-bubbles. The common feature of these systems is that they have autonomous dynamics — their states will change over time without additional external influence. We can take advantage of these dynamics if we understand them well enough to map a problem we want to solve onto them. (...)
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  25. Mary Litch (1997). Computation, Connectionism and Modelling the Mind. Philosophical Psychology 10 (3):357-364.score: 18.0
    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 (...)
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  26. Gordana Dodig Crnkovic & Susan Stuart (eds.) (2007). Computation, Information, Cognition: The Nexus and the Liminal. Cambridge Scholars Press.score: 18.0
    Written by world-leading experts, this book draws together a number of important strands in contemporary approaches to the philosophical and scientific questions that emerge when dealing with the issues of computing, information, cognition and the conceptual issues that arise at their intersections. It discovers and develops the connections at the borders and in the interstices of disciplines and debates. This volume presents a range of essays that deal with the currently vigorous concerns of the philosophy of information, ontology creation and (...)
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  27. C. F. Boyle (1994). Computation as an Intrinsic Property. Minds and Machines 4 (4):451-67.score: 18.0
    In an effort to uncover fundamental differences between computers and brains, this paper identifies computation with a particular kind of physical process, in contrast to interpreting the behaviors of physical systems as one or more abstract computations. That is, whether or not a system is computing depends on how those aspects of the system we consider to be informational physically cause change rather than on our capacity to describe its behaviors in computational terms. A physical framework based on the (...)
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  28. Janet Anders, Michal Hajdušek, Damian Markham & Vlatko Vedral (2008). How Much of One-Way Computation Is Just Thermodynamics? Foundations of Physics 38 (6):506-522.score: 18.0
    In this paper we argue that one-way quantum computation can be seen as a form of phase transition with the available information about the solution of the computation being the order parameter. We draw a number of striking analogies between standard thermodynamical quantities such as energy, temperature, work, and corresponding computational quantities such as the amount of entanglement, time, potential capacity for computation, respectively. Aside from being intuitively pleasing, this picture allows us to make novel conjectures, such (...)
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  29. Selmer Bringsjord (2001). In Computation, Parallel is Nothing, Physical Everything. Minds and Machines 11 (1):95-99.score: 18.0
    Andrew Boucher (1997) argues that ``parallel computation is fundamentally different from sequential computation'' (p. 543), and that this fact provides reason to be skeptical about whether AI can produce a genuinely intelligent machine. But parallelism, as I prove herein, is irrelevant. What Boucher has inadvertently glimpsed is one small part of a mathematical tapestry portraying the simple but undeniable fact that physical computation can be fundamentally different from ordinary, ``textbook'' computation (whether parallel or sequential). This tapestry (...)
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  30. Nir Fresco, Concrete Digital Computation: Competing Accounts and its Role in Cognitive Science.score: 18.0
    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, (...)
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  31. Nir Fresco (2013). Information Processing as an Account of Concrete Digital Computation. Philosophy and Technology 26 (1):31-60.score: 18.0
    It is common in cognitive science to equate computation (and in particular digital computation) with information processing. Yet, it is hard to find a comprehensive explicit account of concrete digital computation in information processing terms. An information processing account seems like a natural candidate to explain digital computation. But when ‘information’ comes under scrutiny, this account becomes a less obvious candidate. Four interpretations of information are examined here as the basis for an information processing account of (...)
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  32. William A. Phillips & Wolf Singer (1997). In Search of Common Foundations for Cortical Computation. Behavioral and Brain Sciences 20 (4):657-683.score: 18.0
    It is worthwhile to search for forms of coding, processing, and learning common to various cortical regions and cognitive functions. Local cortical processors may coordinate their activity by maximizing the transmission of information coherently related to the context in which it occurs, thus forming synchronized population codes. This coordination involves contextual field (CF) connections that link processors within and between cortical regions. The effects of CF connections are distinguished from those mediating receptive field (RF) input; it is shown how CFs (...)
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  33. Gordana Dodig-Crnkovic (2009). Information and Computation Nets. Investigations Into Info-Computational World. VDM.score: 18.0
    The book presents investigations into the world of info-computational nature, in which information constitutes the structure, while computational process amounts to its change. Information and computation are inextricably bound: There is no computation without informational structure, and there is no information without computational process. Those two complementary ideas are used to build a conceptual net, which according to Novalis is a theoretical way of capturing reality. We apprehend the reality within a framework known as natural computationalism, the view (...)
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  34. Hector Zenil (2013). What Is Nature-Like Computation? A Behavioural Approach and a Notion of Programmability. Philosophy and Technology:1-23.score: 18.0
    The aim of this paper is to propose an alternative behavioural definition of computation (and of a computer) based simply on whether a system is capable of reacting to the environment—the input—as reflected in a measure of programmability. This definition is intended to have relevance beyond the realm of digital computers, particularly vis-à-vis natural systems. This will be done by using an extension of a phase transition coefficient previously defined in an attempt to characterise the dynamical behaviour of cellular (...)
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  35. Prakash Mondal (2014). Does Computation Reveal Machine Cognition? Biosemiotics 7 (1):97-110.score: 18.0
    This paper seeks to understand machine cognition. The nature of machine cognition has been shrouded in incomprehensibility. We have often encountered familiar arguments in cognitive science that human cognition is still faintly understood. This paper will argue that machine cognition is far less understood than even human cognition despite the fact that a lot about computer architecture and computational operations is known. Even if there have been putative claims about the transparency of the notion of machine computations, these claims do (...)
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  36. John Kadvany (2010). Indistinguishable From Magic: Computation is Cognitive Technology. [REVIEW] Minds and Machines 20 (1):119-143.score: 18.0
    This paper explains how mathematical computation can be constructed from weaker recursive patterns typical of natural languages. A thought experiment is used to describe the formalization of computational rules, or arithmetical axioms, using only orally-based natural language capabilities, and motivated by two accomplishments of ancient Indian mathematics and linguistics. One accomplishment is the expression of positional value using versified Sanskrit number words in addition to orthodox inscribed numerals. The second is Pāṇini’s invention, around the fifth century BCE, of a (...)
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  37. Nir Fresco & Marty J. Wolf (2014). The Instructional Information Processing Account of Digital Computation. Synthese 191 (7):1469-1492.score: 18.0
    What is nontrivial digital computation? It is the processing of discrete data through discrete state transitions in accordance with finite instructional information. The motivation for our account is that many previous attempts to answer this question are inadequate, and also that this account accords with the common intuition that digital computation is a type of information processing. We use the notion of reachability in a graph to defend this characterization in memory-based systems and underscore the importance of instructional (...)
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  38. Maarten Van den Nest & Hans J. Briegel (2008). Measurement-Based Quantum Computation and Undecidable Logic. Foundations of Physics 38 (5):448-457.score: 18.0
    We establish a connection between measurement-based quantum computation and the field of mathematical logic. We show that the computational power of an important class of quantum states called graph states, representing resources for measurement-based quantum computation, is reflected in the expressive power of (classical) formal logic languages defined on the underlying mathematical graphs. In particular, we show that for all graph state resources which can yield a computational speed-up with respect to classical computation, the underlying graphs—describing the (...)
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  39. Arnon Avron & Beata Konikowska (2009). Proof Systems for Reasoning About Computation Errors. Studia Logica 91 (2):273 - 293.score: 18.0
    In the paper we examine the use of non-classical truth values for dealing with computation errors in program specification and validation. In that context, 3-valued McCarthy logic is suitable for handling lazy sequential computation, while 3-valued Kleene logic can be used for reasoning about parallel computation. If we want to be able to deal with both strategies without distinguishing between them, we combine Kleene and McCarthy logics into a logic based on a non-deterministic, 3-valued matrix, (...)
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  40. Ken Herold (2014). Intuition, Computation, and Information. Minds and Machines 24 (1):85-88.score: 18.0
    Bynum (Putting information first: Luciano Floridi and the philosophy of information. NY: Wiley-Blackwell, 2010) identifies Floridi’s focus in the philosophy of information (PI) on entities both as data structures and as information objects. One suggestion for examining the association between the former and the latter stems from Floridi’s Herbert A. Simon Lecture in Computing and Philosophy given at Carnegie Mellon University in 2001, open problems in the PI: the transduction or transception, and how we gain knowledge about the world as (...)
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  41. Norihiro Kamide (2007). Extended Full Computation-Tree Logics for Paraconsistent Model Checking. Logic and Logical Philosophy 15 (3):251-276.score: 18.0
    It is known that the full computation-tree logic CTL * is an important base logic for model checking. The bisimulation theorem for CTL* is known to be useful for abstraction in model checking. In this paper, the bisimulation theorems for two paraconsistent four-valued extensions 4CTL* and 4LCTL* of CTL* are shown, and a translation from 4CTL* into CTL* is presented. By using 4CTL* and 4LCTL*, inconsistency-tolerant and spatiotemporal reasoning can be expressed as a model checking framework.
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  42. Mario Radovan (2000). Computation and the Three Worlds. Minds and Machines 10 (2):255-265.score: 18.0
    Discussions about the achievements and limitations of the various approaches to the development of intelligent systems can have an essential impact on empirically based research, and with that also on the future development of computer technologies. However, such discussions are often based on vague concepts and assumptions. In this context, we claim that the proposed `three-world ontology'' offers the most appropriate conceptual framework in which the basic problems concerned with cognition and computation can be suitably expressed and discussed, although (...)
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  43. Ricard V. Solé & Jordi Delgado (1996). Universal Computation in Fluid Neural Networks. Complexity 2 (2):49-56.score: 18.0
    Fluid neural networks can be used as a theoretical framework for a wide range of complex systems as social insects. In this article we show that collective logical gates can be built in such a way that complex computation can be possible by means of the interplay between local interactions and the collective creation of a global field. This is exemplified by a NOR gate. Some general implications for ant societies are outlined. ©.
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  44. Krzysztof Wójtowicz (2010). Theory of Quantum Computation and Philosophy of Mathematics. Part I. Logic and Logical Philosophy 18 (3-4):313-332.score: 18.0
    The aim of this paper is to present some basic notions of the theory of quantum computing and to compare them with the basic notions of the classical theory of computation. I am convinced, that the results of quantum computation theory (QCT) are not only interesting in themselves, but also should be taken into account in discussions concerning the nature of mathematical knowledge. The philosophical discussion will however be postponed to another paper. QCT seems not to be well-known (...)
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  45. John D. Norton (2013). Brownian Computation Is Thermodynamically Irreversible. Foundations of Physics 43 (11):1-27.score: 16.0
    Brownian computers are supposed to illustrate how logically reversible mathematical operations can be computed by physical processes that are thermodynamically reversible or nearly so. In fact, they are thermodynamically irreversible processes that are the analog of an uncontrolled expansion of a gas into a vacuum.
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  46. Keith Butler (1998). Content, Computation, and Individuation. Synthese 114 (2):277-92.score: 16.0
    The role of content in computational accounts of cognition is a matter of some controversy. An early prominent view held that the explanatory relevance of content consists in its supervenience on the the formal properties of computational states (see, e.g., Fodor 1980). For reasons that derive from the familiar Twin Earth thought experiments, it is usually thought that if content is to supervene on formal properties, it must be narrow; that is, it must not be the sort of content that (...)
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  47. Oron Shagrir (2001). Content, Computation and Externalism. Mind 110 (438):369-400.score: 16.0
    The paper presents an extended argument for the claim that mental content impacts the computational individuation of a cognitive system (section 2). The argument starts with the observation that a cognitive system may simultaneously implement a variety of different syntactic structures, but that the computational identity of a cognitive system is given by only one of these implemented syntactic structures. It is then asked what are the features that determine which of implemented syntactic structures is the computational structure of the (...)
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  48. H. Clark Barrett (2005). Enzymatic Computation and Cognitive Modularity. Mind and Language 20 (3):259-87.score: 16.0
    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 (...)
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  49. Oron Shagrir (1998). Multiple Realization, Computation and the Taxonomy of Psychological States. Synthese 114 (3):445-461.score: 16.0
    The paper criticizes standard functionalist arguments for multiple realization. It focuses on arguments in which psychological states are conceived as computational, which is precisely where the multiple realization doctrine has seemed the strongest. It is argued that a type-type identity thesis between computational states and physical states is no less plausible than a multiple realization thesis. The paper also presents, more tentatively, positive arguments for a picture of local reduction.
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