Results for 'Computation'

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  1.  98
    Explaining the Computational Mind.Marcin Miłkowski - 2013 - MIT Press.
    In the book, I argue that the mind can be explained computationally because it is itself computational—whether it engages in mental arithmetic, parses natural language, or processes the auditory signals that allow us to experience music. All these capacities arise from complex information-processing operations of the mind. By analyzing the state of the art in cognitive science, I develop an account of computational explanation used to explain the capacities in question.
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  2.  90
    Computation and Cognition: Toward a Foundation for Cognitive Science.Zenon W. Pylyshyn - 1984 - Cambridge: MIT Press.
    This systematic investigation of computation and mental phenomena by a noted psychologist and computer scientist argues that cognition is a form of computation, that the semantic contents of mental states are encoded in the same general way as computer representations are encoded. It is a rich and sustained investigation of the assumptions underlying the directions cognitive science research is taking. 1 The Explanatory Vocabulary of Cognition 2 The Explanatory Role of Representations 3 The Relevance of Computation 4 (...)
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  3. Computation and Cognition: Issues in the Foundation of Cognitive Science.Zenon W. Pylyshyn - 1980 - Behavioral and Brain Sciences 3 (1):111-32.
    The computational view of mind rests on certain intuitions regarding the fundamental similarity between computation and cognition. We examine some of these intuitions and suggest that they derive from the fact that computers and human organisms are both physical systems whose behavior is correctly described as being governed by rules acting on symbolic representations. Some of the implications of this view are discussed. It is suggested that a fundamental hypothesis of this approach is that there is a natural domain (...)
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  4. Computer Science Logic 11th International Workshop, Csl '97 : Annual Conference of the Eacsl, Aarhus, Denmark, August 23-29, 1997 : Procedings. [REVIEW]M. Nielsen, Wolfgang Thomas & European Association for Computer Science Logic - 1998
     
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  5. Computer Science Logic: 10th International Workshop, Csl '96, Annual Conference of the Eacsl, Utrecht, the Netherlands, September 21 - 27, 1996, Selected Papers. [REVIEW]D. van Dalen, M. Bezem & European Association for Computer Science Logic - 1997 - Springer Verlag.
    The related fields of fractal image encoding and fractal image analysis have blossomed in recent years. This book, originating from a NATO Advanced Study Institute held in 1995, presents work by leading researchers. It is developing the subjects at an introductory level, but it also has some recent and exciting results in both fields. The book contains a thorough discussion of fractal image compression and decompression, including both continuous and discrete formulations, vector space and hierarchical methods, and algorithmic optimizations. The (...)
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  6. Computing Machinery and Intelligence.Alan M. Turing - 1950 - Mind 59 (October):433-60.
    I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to (...)
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  7.  10
    Quantum Computation and Quantum Information.Michael A. Nielsen, Isaac L. Chuang & Isaac L. Chuang - 2000 - Cambridge University Press.
    First-ever comprehensive introduction to the major new subject of quantum computing and quantum information.
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  8. Computability and Logic.George Boolos, John Burgess, Richard P. & C. Jeffrey - 2002 - Cambridge University Press.
    Computability and Logic has become a classic because of its accessibility to students without a mathematical background and because it covers not simply the staple topics of an intermediate logic course, such as Godel’s incompleteness theorems, but also a large number of optional topics, from Turing’s theory of computability to Ramsey’s theorem. Including a selection of exercises, adjusted for this edition, at the end of each chapter, it offers a new and simpler treatment of the representability of recursive functions, a (...)
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  9. A Computational Foundation for the Study of Cognition.David Chalmers - 2011 - Journal of Cognitive Science 12 (4):323-357.
    Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these (...)
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  10.  21
    Physical Computation: A Mechanistic Account.Gualtiero Piccinini - 2015 - Oxford University Press UK.
    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 digital (...)
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  11. Computability & Unsolvability.Martin Davis - 1958 - Dover Publications.
    Classic text considersgeneral theory of computability, computable functions, operations on computable functions, Turing machines self-applied, unsolvable decision problems, applications of general theory, mathematical logic, Kleene hierarchy, computable functionals, classification of unsolvable decision problems and more.
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  12.  82
    What Computers Can’T Do: The Limits of Artificial Intelligence.Hubert L. Dreyfus - 1972 - Harper & Row.
  13.  98
    Understanding Computers and Cognition: A New Foundation for Design.Terry Winograd & Fernando Flores - 1987 - Addison-Wesley.
    Understanding Computers and Cognition presents an important and controversial new approach to understanding what computers do and how their functioning is related to human language, thought, and action. While it is a book about computers, Understanding Computers and Cognition goes beyond the specific issues of what computers can or can't do. It is a broad-ranging discussion exploring the background of understanding in which the discourse about computers and technology takes place. Understanding Computers and Cognition is written for a wide audience, (...)
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  14. What Computers Still Can’T Do: A Critique of Artificial Reason.Hubert L. Dreyfus - 1992 - MIT Press.
    A Critique of Artificial Reason Hubert L. Dreyfus . HUBERT L. DREYFUS What Computers Still Can't Do Thi s One XZKQ-GSY-8KDG What. WHAT COMPUTERS STILL CAN'T DO Front Cover.
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  15.  5
    Computational Philosophy of Science.Paul Thagard - 1988 - MIT Press.
    By applying research in artificial intelligence to problems in the philosophy of science, Paul Thagard develops an exciting new approach to the study of scientific reasoning. This approach uses computational ideas to shed light on how scientific theories are discovered, evaluated, and used in explanations. Thagard describes a detailed computational model of problem solving and discovery that provides a conceptually rich yet rigorous alternative to accounts of scientific knowledge based on formal logic, and he uses it to illuminate such topics (...)
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  16. Neural Computation and the Computational Theory of Cognition.Gualtiero Piccinini & Sonya Bahar - 2013 - Cognitive Science 37 (3):453-488.
    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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  17.  6
    Computability and Logic.George S. Boolos, John P. Burgess & Richard C. Jeffrey - 1974 - Cambridge, England: Cambridge University Press.
    Computability and Logic has become a classic because of its accessibility to students without a mathematical background and because it covers not simply the staple topics of an intermediate logic course, such as Godel's incompleteness theorems, but also a large number of optional topics, from Turing's theory of computability to Ramsey's theorem. This 2007 fifth edition has been thoroughly revised by John Burgess. Including a selection of exercises, adjusted for this edition, at the end of each chapter, it offers a (...)
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  18.  33
    Computability Theory.S. Barry Cooper - 2003 - Chapman & Hall.
    Computability theory originated with the seminal work of Gödel, Church, Turing, Kleene and Post in the 1930s. This theory includes a wide spectrum of topics, such as the theory of reducibilities and their degree structures, computably enumerable sets and their automorphisms, and subrecursive hierarchy classifications. Recent work in computability theory has focused on Turing definability and promises to have far-reaching mathematical, scientific, and philosophical consequences. Written by a leading researcher, Computability Theory provides a concise, comprehensive, and authoritative introduction to contemporary (...)
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  19. Computation in Physical Systems: A Normative Mapping Account.Paul Schweizer - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 27-47.
    The relationship between abstract formal procedures and the activities of actual physical systems has proved to be surprisingly subtle and controversial, and there are a number of competing accounts of when a physical system can be properly said to implement a mathematical formalism and hence perform a computation. I defend an account wherein computational descriptions of physical systems are high-level normative interpretations motivated by our pragmatic concerns. Furthermore, the criteria of utility and success vary according to our diverse purposes (...)
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  20.  82
    Brain–Computer Interfaces and Disability: Extending Embodiment, Reducing Stigma?Sean Aas & David Wasserman - 2016 - Journal of Medical Ethics 42 (1):37-40.
  21. Computer Models On Mind: Computational Approaches In Theoretical Psychology.Margaret A. Boden - 1988 - Cambridge University Press.
    What is the mind? How does it work? How does it influence behavior? Some psychologists hope to answer such questions in terms of concepts drawn from computer science and artificial intelligence. They test their theories by modeling mental processes in computers. This book shows how computer models are used to study many psychological phenomena--including vision, language, reasoning, and learning. It also shows that computer modeling involves differing theoretical approaches. Computational psychologists disagree about some basic questions. For instance, should the mind (...)
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  22. Mechanistic Computational Individuation Without Biting the Bullet.Nir Fresco & Marcin Miłkowski - 2019 - British Journal for the Philosophy of Science:axz005.
    Is the mathematical function being computed by a given physical system determined by the system’s dynamics? This question is at the heart of the indeterminacy of computation phenomenon (Fresco et al. [unpublished]). A paradigmatic example is a conventional electrical AND-gate that is often said to compute conjunction, but it can just as well be used to compute disjunction. Despite the pervasiveness of this phenomenon in physical computational systems, it has been discussed in the philosophical literature only indirectly, mostly with (...)
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  23.  34
    The Computer And The Brain.John Von Neumann - 1958 - New Haven: Yale University Press.
    This book represents the views of one of the greatest mathematicians of the twentieth century on the analogies between computing machines and the living human brain.
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  24. The Computer Revolution in Philosophy: Philosophy, Science, and Models of Mind.Aaron Sloman - 1978 - Hassocks UK: Harvester Press.
    Extract from Hofstadter's revew in Bulletin of American Mathematical Society : http://www.ams.org/journals/bull/1980-02-02/S0273-0979-1980-14752-7/S0273-0979-1980-14752-7.pdf -/- "Aaron Sloman is a man who is convinced that most philosophers and many other students of mind are in dire need of being convinced that there has been a revolution in that field happening right under their noses, and that they had better quickly inform themselves. The revolution is called "Artificial Intelligence" (Al)-and Sloman attempts to impart to others the "enlighten- ment" which he clearly regrets not having (...)
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  25.  14
    Computable Structures and the Hyperarithmetical Hierarchy.C. J. Ash - 2000 - Elsevier.
    This book describes a program of research in computable structure theory. The goal is to find definability conditions corresponding to bounds on complexity which persist under isomorphism. The results apply to familiar kinds of structures (groups, fields, vector spaces, linear orderings Boolean algebras, Abelian p-groups, models of arithmetic). There are many interesting results already, but there are also many natural questions still to be answered. The book is self-contained in that it includes necessary background material from recursion theory (ordinal notations, (...)
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  26. Computation Vs. Information Processing: Why Their Difference Matters to Cognitive Science.Gualtiero Piccinini & Andrea Scarantino - 2010 - Studies in History and Philosophy of Science Part A 41 (3):237-246.
    Since the cognitive revolution, it has 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 theorists of cognition be (...)
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  27.  13
    Computability and Randomness.André Nies - 2008 - Oxford, England: Oxford University Press.
    Covering the basics as well as recent research results, this book provides a very readable introduction to the exciting interface of computability and ...
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  28.  2
    Computability and Randomness.André Nies - 2008 - Oxford, England: Oxford University Press UK.
    The interplay between computability and randomness has been an active area of research in recent years, reflected by ample funding in the USA, numerous workshops, and publications on the subject. The complexity and the randomness aspect of a set of natural numbers are closely related. Traditionally, computability theory is concerned with the complexity aspect. However, computability theoretic tools can also be used to introduce mathematical counterparts for the intuitive notion of randomness of a set. Recent research shows that, conversely, concepts (...)
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  29. Cognitive Computation Sans Representation.Paul Schweizer - 2017 - In Thomas Powers (ed.), Philosophy and Computing: Essays in epistemology, philosophy of mind, logic, and ethics,. Cham, Switzerland: Springer. pp. 65-84.
    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 (...)
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  30. Computer Simulations in Science and Engineering. Concept, Practices, Perspectives.Juan Manuel Durán - 2018 - Springer.
    This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions (...)
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  31.  15
    Computational Artifacts: Towards a Philosophy of Computer Science.Raymond Turner - 2018 - Springer Berlin Heidelberg.
    The philosophy of computer science is concerned with issues that arise from reflection upon the nature and practice of the discipline of computer science. This book presents an approach to the subject that is centered upon the notion of computational artefact. It provides an analysis of the things of computer science as technical artefacts. Seeing them in this way enables the application of the analytical tools and concepts from the philosophy of technology to the technical artefacts of computer science. With (...)
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  32. European Computing and Philosophy.Gordana Dodig-Crnkovic - 2009 - The Reasoner 3 (9):18-19.
    European Computing and Philosophy conference, 2–4 July Barcelona The Seventh ECAP (European Computing and Philosophy) conference was organized by Jordi Vallverdu at Autonomous University of Barcelona. The conference started with the IACAP (The International Association for CAP) presidential address by Luciano Floridi, focusing on mechanisms of knowledge production in informational networks. The first keynote delivered by Klaus Mainzer made a frame for the rest of the conference, by elucidating the fundamental role of complexity of informational structures that can be analyzed (...)
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  33. Content, Computation and Externalism.Oron Shagrir - 2001 - Mind 110 (438):369-400.
    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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  34.  63
    On Computational Explanations.Anna-Mari Rusanen & Otto Lappi - 2016 - Synthese 193 (12):3931-3949.
    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” (...)
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  35. Computer Simulation and the Features of Novel Empirical Data.Greg Lusk - 2016 - Studies in History and Philosophy of Science Part A 56:145-152.
    In an attempt to determine the epistemic status of computer simulation results, philosophers of science have recently explored the similarities and differences between computer simulations and experiments. One question that arises is whether and, if so, when, simulation results constitute novel empirical data. It is often supposed that computer simulation results could never be empirical or novel because simulations never interact with their targets, and cannot go beyond their programming. This paper argues against this position by examining whether, and under (...)
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  36.  74
    Computability, an Introduction to Recursive Function Theory.Nigel Cutland - 1980 - Cambridge University Press.
    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 (...)
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  37. Computational Explanation in Neuroscience.Gualtiero Piccinini - 2006 - Synthese 153 (3):343-353.
    According to some philosophers, computational explanation is proprietary
    to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that (...)
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  38. Computational Entrepreneurship: From Economic Complexities to Interdisciplinary Research.Quan-Hoang Vuong - 2019 - Problems and Perspectives in Management 17 (1):117-129.
    The development of technology is unbelievably rapid. From limited local networks to high speed Internet, from crude computing machines to powerful semi-conductors, the world had changed drastically compared to just a few decades ago. In the constantly renewing process of adapting to such an unnaturally high-entropy setting, innovations as well as entirely new concepts, were often born. In the business world, one such phenomenon was the creation of a new type of entrepreneurship. This paper proposes a new academic discipline of (...)
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  39. Computation Without Representation.Gualtiero Piccinini - 2008 - Philosophical Studies 137 (2):205-241.
    The received view is that computational states are individuated at least in part by their semantic properties. I offer an alternative, according to which computational states are individuated by their functional properties. Functional properties are specified by a mechanistic explanation without appealing to any semantic properties. The primary purpose of this paper is to formulate the alternative view of computational individuation, point out that it supports a robust notion of computational explanation, and defend it on the grounds of how computational (...)
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  40. From Computer Metaphor to Computational Modeling: The Evolution of Computationalism.Marcin Miłkowski - 2018 - Minds and Machines 28 (3):515-541.
    In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working (...)
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  41. Explaining Computation Without Semantics: Keeping It Simple.Nir Fresco - 2010 - Minds and Machines 20 (2):165-181.
    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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  42. Enzymatic Computation and Cognitive Modularity.H. Clark Barrett - 2005 - Mind and Language 20 (3):259-87.
    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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  43. Computers and Intractability. A Guide to the Theory of NP-Completeness.Michael R. Garey & David S. Johnson - 1983 - Journal of Symbolic Logic 48 (2):498-500.
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  44.  33
    The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers.Matthew M. Botvinick - 2014 - Cognitive Science 38 (6):1249-1285.
    Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on cognitive control with new energy and new ideas. On the occasion of the books' anniversary, we review computational modeling in the study of cognitive (...)
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  45. Quantum Computing.Amit Hagar & Michael Cuffaro - 2019 - Stanford Encyclopedia of Philosophy.
    Combining physics, mathematics and computer science, quantum computing and its sister discipline of quantum information have developed in the past few decades from visionary ideas to two of the most fascinating areas of quantum theory. General interest and excitement in quantum computing was initially triggered by Peter Shor (1994) who showed how a quantum algorithm could exponentially “speed-up” classical computation and factor large numbers into primes far more efficiently than any (known) classical algorithm. Shor’s algorithm was soon followed by (...)
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  46. Using Computer-Assisted Argument Mapping to Teach Reasoning to Students.Martin Davies, Ashley Barnett & Tim van Gelder - 2021 - In J. Anthony Blair (ed.), Studies in Critical Thinking (2nd Edition). Windsor, ON, Canada: Windsor Studies in Argumentation. pp. 115-152.
    Argument mapping is a way of diagramming the logical structure of an argument to explicitly and concisely represent reasoning. The use of argument mapping in critical thinking instruction has increased dramatically in recent decades. This paper overviews the innovation and provides a procedural approach for new teaches wanting to use argument mapping in the classroom. A brief history of argument mapping is provided at the end of this paper.
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  47.  67
    Objective Computation Versus Subjective Computation.Nir Fresco - 2015 - Erkenntnis 80 (5):1031-1053.
    The question ‘What is computation?’ might seem a trivial one to many, but this is far from being in consensus in philosophy of mind, cognitive science and even in physics. The lack of consensus leads to some interesting, yet contentious, claims, such as that cognition or even the universe is computational. Some have argued, though, that computation is a subjective phenomenon: whether or not a physical system is computational, and if so, which computation it performs, is entirely (...)
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  48. Computer Simulation, Measurement, and Data Assimilation.Wendy S. Parker - 2017 - British Journal for the Philosophy of Science 68 (1):273-304.
    This article explores some of the roles of computer simulation in measurement. A model-based view of measurement is adopted and three types of measurement—direct, derived, and complex—are distinguished. It is argued that while computer simulations on their own are not measurement processes, in principle they can be embedded in direct, derived, and complex measurement practices in such a way that simulation results constitute measurement outcomes. Atmospheric data assimilation is then considered as a case study. This practice, which involves combining information (...)
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  49. Computing as a Science: A Survey of Competing Viewpoints. [REVIEW]Matti Tedre - 2011 - Minds and Machines 21 (3):361-387.
    Since the birth of computing as an academic discipline, the disciplinary identity of computing has been debated fiercely. The most heated question has concerned the scientific status of computing. Some consider computing to be a natural science and some consider it to be an experimental science. Others argue that computing is bad science, whereas some say that computing is not a science at all. This survey article presents viewpoints for and against computing as a science. Those viewpoints are analyzed against (...)
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  50. Learning Computer Networks Using Intelligent Tutoring System.Mones M. Al-Hanjori, Mohammed Z. Shaath & Samy S. Abu Naser - 2017 - International Journal of Advanced Research and Development 2 (1).
    Intelligent Tutoring Systems (ITS) has a wide influence on the exchange rate, education, health, training, and educational programs. In this paper we describe an intelligent tutoring system that helps student study computer networks. The current ITS provides intelligent presentation of educational content appropriate for students, such as the degree of knowledge, the desired level of detail, assessment, student level, and familiarity with the subject. Our Intelligent tutoring system was developed using ITSB authoring tool for building ITS. A preliminary evaluation of (...)
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