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

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  1. Jeffrey White (forthcoming). Manufacturing Morality A General Theory of Moral Agency Grounding Computational Implementations: The ACTWith Model. In Computational Intelligence. Nova Publications.score: 27.0
    The ultimate goal of research into computational intelligence is the construction of a fully embodied and fully autonomous artificial agent. This ultimate artificial agent must not only be able to act, but it must be able to act morally. In order to realize this goal, a number of challenges must be met, and a number of questions must be answered, the upshot being that, in doing so, the form of agency to which we must aim in developing artificial agents (...)
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  2. Jakub Szymanik (2009). The Computational Complexity of Quantified Reciprocals. In Peter Bosch, David Gabelaia & Jérôme Lang (eds.), Lecture Notes on Artificial Intelligence 5422, Logic, Language, and Computation 7th International Tbilisi Symposium on Logic, Language, and Computation. Springer.score: 25.0
    We study the computational complexity of reciprocal sentences with quantified antecedents. We observe a computational dichotomy between different interpretations of reciprocity, and shed some light on the status of the so-called Strong Meaning Hypothesis.
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  3. Susan Schneider, The Central System as a Computational Engine.score: 24.0
    The Language of Thought program has a suicidal edge. Jerry Fodor, of all people, has argued that although LOT will likely succeed in explaining modular processes, it will fail to explain the central system, a subsystem in the brain in which information from the different sense modalities is integrated, conscious deliberation occurs, and behavior is planned. A fundamental characteristic of the central system is that it is “informationally unencapsulated” -- its operations can draw from information from any cognitive domain. The (...)
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  4. Clément Vidal (2010). Computational and Biological Analogies for Understanding Fine-Tuned Parameters in Physics. Foundations of Science 15 (4):375 - 393.score: 24.0
    In this philosophical paper, we explore computational and biological analogies to address the fine-tuning problem in cosmology. We first clarify what it means for physical constants or initial conditions to be fine-tuned. We review important distinctions such as the dimensionless and dimensional physical constants, and the classification of constants proposed by Lévy-Leblond. Then we explore how two great analogies, computational and biological, can give new insights into our problem. This paper includes a preliminary study to examine the two (...)
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  5. Bartlomiej Swiatczak (2011). Conscious Representations: An Intractable Problem for the Computational Theory of Mind. [REVIEW] Minds and Machines 21 (1):19-32.score: 24.0
    Advocates of the computational theory of mind claim that the mind is a computer whose operations can be implemented by various computational systems. According to these philosophers, the mind is multiply realisable because—as they claim—thinking involves the manipulation of syntactically structured mental representations. Since syntactically structured representations can be made of different kinds of material while performing the same calculation, mental processes can also be implemented by different kinds of material. From this perspective, consciousness plays a minor role (...)
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  6. Gualtiero Piccinini (2006). Computational Explanation in Neuroscience. Synthese 153 (3):343-353.score: 24.0
    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 (...)
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  7. Jon Cogburn & Jason Megill (2010). Are Turing Machines Platonists? Inferentialism and the Computational Theory of Mind. Minds and Machines 20 (3):423-439.score: 24.0
    We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.
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  8. David Michael Kaplan (2011). Explanation and Description in Computational Neuroscience. Synthese 183 (3):339-373.score: 24.0
    The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or (...)
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  9. Stefan Wintein (2012). Assertoric Semantics and the Computational Power of Self-Referential Truth. Journal of Philosophical Logic 41 (2):317-345.score: 24.0
    There is no consensus as to whether a Liar sentence is meaningful or not. Still, a widespread conviction with respect to Liar sentences (and other ungrounded sentences) is that, whether or not they are meaningful, they are useless . The philosophical contribution of this paper is to put this conviction into question. Using the framework of assertoric semantics , which is a semantic valuation method for languages of self-referential truth that has been developed by the author, we show that certain (...)
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  10. Louis C. Charland (1995). Feeling and Representing: Computational Theory and the Modularity of Affect. Synthese 105 (3):273-301.score: 24.0
    In this paper I review some leading developments in the empirical theory of affect. I argue that (1) affect is a distinct perceptual representation governed system, and (2) that there are significant modular factors in affect. The paper concludes with the observation thatfeeler (affective perceptual system) may be a natural kind within cognitive science. The main purpose of the paper is to explore some hitherto unappreciated connections between the theory of affect and the computational theory of mind.
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  11. John Symons (2008). Computational Models of Emergent Properties. Minds and Machines 18 (4):475-491.score: 24.0
    Computational modeling plays an increasingly important explanatory role in cases where we investigate systems or problems that exceed our native epistemic capacities. One clear case where technological enhancement is indispensable involves the study of complex systems.1 However, even in contexts where the number of parameters and interactions that define a problem is small, simple systems sometimes exhibit non-linear features which computational models can illustrate and track. In recent decades, computational models have been proposed as a way to (...)
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  12. Gert-Jan Lokhorst (2011). Computational Meta-Ethics. Minds and Machines 21 (2):261-274.score: 24.0
    It has been argued that ethically correct robots should be able to reason about right and wrong. In order to do so, they must have a set of do’s and don’ts at their disposal. However, such a list may be inconsistent, incomplete or otherwise unsatisfactory, depending on the reasoning principles that one employs. For this reason, it might be desirable if robots were to some extent able to reason about their own reasoning—in other words, if they had some meta-ethical capacities. (...)
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  13. Gualtiero Piccinini (2004). The First Computational Theory of Mind and Brain: A Close Look at McCulloch and Pitts' Logical Calculus of Ideas Immanent in Nervous Activity. Synthese 141 (2):175-215.score: 24.0
    Despite its significance in neuroscience and computation, McCulloch and Pitts's celebrated 1943 paper has received little historical and philosophical attention. In 1943 there already existed a lively community of biophysicists doing mathematical work on neural networks. What was novel in McCulloch and Pitts's paper was their use of logic and computation to understand neural, and thus mental, activity. McCulloch and Pitts's contributions included (i) a formalism whose refinement and generalization led to the notion of finite automata (an important formalism in (...)
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  14. Gualtiero Piccinini & Sonya Bahar (2013). Neural Computation and the Computational Theory of Cognition. Cognitive Science 37 (3):453-488.score: 24.0
    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 is sui generis. Analog computation requires continuous (...)
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  15. Jakub Szymanik (2009). Quantifiers in TIME and SPACE. Computational Complexity of Generalized Quantifiers in Natural Language. Dissertation, University of Amsterdamscore: 24.0
    In the dissertation we study the complexity of generalized quantifiers in natural language. Our perspective is interdisciplinary: we combine philosophical insights with theoretical computer science, experimental cognitive science and linguistic theories. -/- In Chapter 1 we argue for identifying a part of meaning, the so-called referential meaning (model-checking), with algorithms. Moreover, we discuss the influence of computational complexity theory on cognitive tasks. We give some arguments to treat as cognitively tractable only those problems which can be computed in polynomial (...)
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  16. Branden Fitelson & Edward N. Zalta (2007). Steps Toward a Computational Metaphysics. Journal of Philosophical Logic 36 (2):227-247.score: 24.0
    In this paper, the authors describe their initial investigations in computational metaphysics. Our method is to implement axiomatic metaphysics in an automated reasoning system. In this paper, we describe what we have discovered when the theory of abstract objects is implemented in PROVER9 (a first-order automated reasoning system which is the successor to OTTER). After reviewing the second-order, axiomatic theory of abstract objects, we show (1) how to represent a fragment of that theory in PROVER9's first-order syntax, and (2) (...)
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  17. Theodor Leiber (1999). Deterministic Chaos and Computational Complexity: The Case of Methodological Complexity Reductions. [REVIEW] Journal for General Philosophy of Science 30 (1):87-101.score: 24.0
    Some problems rarely discussed in traditional philosophy of science are mentioned: The empirical sciences using mathematico-quantitative theoretical models are frequently confronted with several types of computational problems posing primarily methodological limitations on explanatory and prognostic matters. Such limitations may arise from the appearances of deterministic chaos and (too) high computational complexity in general. In many cases, however, scientists circumvent such limitations by utilizing reductional approximations or complexity reductions for intractable problem formulations, thus constructing new models which are (...)
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  18. Jakub Szymanik (2010). Computational Complexity of Polyadic Lifts of Generalized Quantifiers in Natural Language. Linguistics and Philosophy 33 (3):215-250.score: 24.0
    We study the computational complexity of polyadic quantifiers in natural language. This type of quantification is widely used in formal semantics to model the meaning of multi-quantifier sentences. First, we show that the standard constructions that turn simple determiners into complex quantifiers, namely Boolean operations, iteration, cumulation, and resumption, are tractable. Then, we provide an insight into branching operation yielding intractable natural language multi-quantifier expressions. Next, we focus on a linguistic case study. We use computational complexity results to (...)
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  19. Jakub Szymanik & Marcin Zajenkowski (2009). Comprehension of Simple Quantifiers. Empirical Evaluation of a Computational Model. Cognitive Science: A Multidisciplinary Journal 34 (3):521-532.score: 24.0
    We examine the verification of simple quantifiers in natural language from a computational model perspective. We refer to previous neuropsychological investigations of the same problem and suggest extending their experimental setting. Moreover, we give some direct empirical evidence linking computational complexity predictions with cognitive reality.
    In the empirical study we compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and push-down automata is psychologically relevant. Our research improves (...)
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  20. Danielle S. McNamara (2011). Computational Methods to Extract Meaning From Text and Advance Theories of Human Cognition. Topics in Cognitive Science 3 (1):3-17.score: 24.0
    Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This (...)
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  21. Paul Humphreys (1995). Computational Empiricism. Foundations of Science 1 (1):119-130.score: 24.0
    I argue here for a number of ways that modern computational science requires a change in the way we represent the relationship between theory and applications. It requires a switch away from logical reconstruction of theories in order to take surface mathematical syntax seriously. In addition, syntactically different versions of the same theory have important differences for applications, and this shows that the semantic account of theories is inappropriate for some purposes. I also argue against formalist approaches in the (...)
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  22. Lorenzo Magnani (2004). Conjectures and Manipulations. Computational Modeling and the Extra- Theoretical Dimension of Scientific Discovery. Minds and Machines 14 (4):507-538.score: 24.0
    Computational philosophy (CP) aims at investigating many important concepts and problems of the philosophical and epistemological tradition in a new way by taking advantage of information-theoretic, cognitive, and artificial intelligence methodologies. I maintain that the results of computational philosophy meet the classical requirements of some Peircian pragmatic ambitions. Indeed, more than a 100 years ago, the American philosopher C.S. Peirce, when working on logical and philosophical problems, suggested the concept of pragmatism(pragmaticism, in his own words) as a logical (...)
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  23. Marcin Mostowski & Jakub Szymanik (2007). Computational Complexity of Some Ramsey Quantifiers in Finite Models. Bulletin of Symbolic Logic 13:281--282.score: 24.0
    The problem of computational complexity of semantics for some natural language constructions – considered in [M. Mostowski, D. Wojtyniak 2004] – motivates an interest in complexity of Ramsey quantifiers in finite models. In general a sentence with a Ramsey quantifier R of the following form Rx, yH(x, y) is interpreted as ∃A(A is big relatively to the universe ∧A2 ⊆ H). In the paper cited the problem of the complexity of the Hintikka sentence is reduced to the problem of (...)
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  24. Patrick Blackburn & Edith Spaan (1993). A Modal Perspective on the Computational Complexity of Attribute Value Grammar. Journal of Logic, Language and Information 2 (2):129-169.score: 24.0
    Many of the formalisms used in Attribute Value grammar are notational variants of languages of propositional modal logic, and testing whether two Attribute Value Structures unify amounts to testing for modal satisfiability. In this paper we put this observation to work. We study the complexity of the satisfiability problem for nine modal languages which mirror different aspects of AVS description formalisms, including the ability to express re-entrancy, the ability to express generalisations, and the ability to express recursive constraints. Two main (...)
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  25. Caspar Addyman & Robert M. French (2012). Computational Modeling in Cognitive Science: A Manifesto for Change. Topics in Cognitive Science 4 (3):332-341.score: 24.0
    Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility of (...)
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  26. Inti A. Brazil, Laurence T. Hunt, Berend H. Bulten, Roy Pc Kessels, Ellen Ra de Bruijn & Rogier B. Mars (2013). Psychopathy-Related Traits and the Use of Reward and Social Information: A Computational Approach. Frontiers in Psychology 4:952.score: 24.0
    Psychopathy is often linked to disturbed reinforcement-guided adaptation of behaviour in both clinical and non-clinical populations. Recent work suggests that these disturbances might be due to a deficit in actively using information to guide changes in behaviour. However, how much information is actually used to guide behaviour is difficult to observe directly. Therefore, we used a computational model to estimate the use of information during learning. Thirty-six female subjects were recruited based on their total scores on the Psychopathic Personality (...)
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  27. Mauricio Salgado & Nigel Gilbert (2013). Emergence and Communication in Computational Sociology. Journal for the Theory of Social Behaviour 43 (1):87-110.score: 24.0
    Computational sociology models social phenomena using the concepts of emergence and downward causation. However, the theoretical status of these concepts is ambiguous; they suppose too much ontology and are invoked by two opposed sociological interpretations of social reality: the individualistic and the holistic. This paper aims to clarify those concepts and argue in favour of their heuristic value for social simulation. It does so by proposing a link between the concept of emergence and Luhmann's theory of communication. For Luhmann, (...)
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  28. Marcin Zajenkowski, Rafał Styła & Jakub Szymanik (2011). A Computational Approach to Quantifiers as an Explanation for Some Language Impairments in Schizophrenia. Journal of Communication Disorder 44:2011.score: 24.0
    We compared the processing of natural language quantifiers in a group of patients with schizophrenia and a healthy control group. In both groups, the difficulty of the quantifiers was consistent with computational predictions, and patients with schizophrenia took more time to solve the problems. However, they were significantly less accurate only with proportional quantifiers, like more than half. This can be explained by noting that, according to the complexity perspective, only proportional quantifiers require working memory engagement.
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  29. David J. Kijowski, Harry Dankowicz & Michael C. Loui (2013). Observations on the Responsible Development and Use of Computational Models and Simulations. Science and Engineering Ethics 19 (1):63-81.score: 24.0
    Most previous works on responsible conduct of research have focused on good practices in laboratory experiments. Because computation now rivals experimentation as a mode of scientific research, we sought to identify the responsibilities of researchers who develop or use computational modeling and simulation. We interviewed nineteen experts to collect examples of ethical issues from their experiences in conducting research with computational models. We gathered their recommendations for guidelines for computational research. Informed by these interviews, we describe the (...)
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  30. Michael Rescorla (2014). A Theory of Computational Implementation. Synthese 191 (6):1277-1307.score: 24.0
    I articulate and defend a new theory of what it is for a physical system to implement an abstract computational model. According to my descriptivist theory, a physical system implements a computational model just in case the model accurately describes the system. Specifically, the system must reliably transit between computational states in accord with mechanical instructions encoded by the model. I contrast my theory with an influential approach to computational implementation espoused by Chalmers, Putnam, and others. (...)
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  31. Kees van Deemter, Albert Gatt, Roger P. G. van Gompel & Emiel Krahmer (2012). Toward a Computational Psycholinguistics of Reference Production. Topics in Cognitive Science 4 (2):166-183.score: 24.0
    This article introduces the topic ‘‘Production of Referring Expressions: Bridging the Gap between Computational and Empirical Approaches to Reference’’ of the journal Topics in Cognitive Science. We argue that computational and psycholinguistic approaches to reference production can benefit from closer interaction, and that this is likely to result in the construction of algorithms that differ markedly from the ones currently known in the computational literature. We focus particularly on determinism, the feature of existing algorithms that is perhaps (...)
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  32. Afsaneh Fazly, Afra Alishahi & Suzanne Stevenson (2010). A Probabilistic Computational Model of Cross-Situational Word Learning. Cognitive Science 34 (6):1017-1063.score: 24.0
    Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement (...)
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  33. Robert L. Causey (2003). Computational Dialogic Defeasible Reasoning. Argumentation 17 (4):421-450.score: 24.0
    This article begins with an introduction to defeasible (nonmonotonic) reasoning and a brief description of a computer program, EVID, which can perform such reasoning. I then explain, and illustrate with examples, how this program can be applied in computational representations of ordinary dialogic argumentation. The program represents the beliefs and doubts of the dialoguers, and uses these propositional attitudes, which can include commonsense defeasible inference rules, to infer various changing conclusions as a dialogue progresses. It is proposed that (...) representations of this kind are a useful tool in the analysis of dialogic argumentation, and, in particular, demonstrate the important role of defeasible reasoning in everyday arguments using commonsense reasoning. (shrink)
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  34. Marcin Miłkowski (2012). Limits of Computational Explanation of Cognition. In Vincent Muller (ed.), Philosophy and Theory of Artificial Intelligence. Springer.score: 24.0
    In this chapter, I argue that some aspects of cognitive phenomena cannot be explained computationally. In the first part, I sketch a mechanistic account of computational explanation that spans multiple levels of organization of cognitive systems. In the second part, I turn my attention to what cannot be explained about cognitive systems in this way. I argue that information-processing mechanisms are indispensable in explanations of cognitive phenomena, and this vindicates the computational explanation of cognition. At the same time, (...)
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  35. Hector Zenil, Fernando Soler-Toscano & Joost J. Joosten (2012). Empirical Encounters with Computational Irreducibility and Unpredictability. Minds and Machines 22 (3):149-165.score: 24.0
    The paper presents an exploration of conceptual issues that have arisen in the course of investigating speed-up and slowdown phenomena in small Turing machines, in particular results of a test that may spur experimental approaches to the notion of computational irreducibility. The test involves a systematic attempt to outrun the computation of a large number of small Turing machines (3 and 4 state, 2 symbol) by means of integer sequence prediction using a specialized function for that purpose. The experiment (...)
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  36. John Symons & Fabio Boschetti (2013). How Computational Models Predict the Behavior of Complex Systems. Foundations of Science 18 (4):809-821.score: 24.0
    In this paper, we argue for the centrality of prediction in the use of computational models in science. We focus on the consequences of the irreversibility of computational models and on the conditional or ceteris paribus, nature of the kinds of their predictions. By irreversibility, we mean the fact that computational models can generally arrive at the same state via many possible sequences of previous states. Thus, while in the natural world, it is generally assumed that physical (...)
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  37. Tyler D. Bancroft (2013). Ethical Aspects of Computational Neuroscience. Neuroethics 6 (2):415-418.score: 24.0
    Recent research in computational neuroscience has demonstrated that we now possess the ability to simulate neural systems in significant detail and on a large scale. Simulations on the scale of a human brain have recently been reported. The ability to simulate entire brains (or significant portions thereof) would be a revolutionary scientific advance, with substantial benefits for brain science. However, the prospect of whole-brain simulation comes with a set of new and unique ethical questions. In the present paper, we (...)
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  38. Stefan Huber, Korbinian Moeller, Hans-Christoph Nuerk & Klaus Willmes (2013). A Computational Modeling Approach on Three‐Digit Number Processing. Topics in Cognitive Science 5 (2):317-334.score: 24.0
    Recent findings indicate that the constituting digits of multi-digit numbers are processed, decomposed into units, tens, and so on, rather than integrated into one entity. This is suggested by interfering effects of unit digit processing on two-digit number comparison. In the present study, we extended the computational model for two-digit number magnitude comparison of Moeller, Huber, Nuerk, and Willmes (2011a) to the case of three-digit number comparison (e.g., 371_826). In a second step, we evaluated how hundred-decade and hundred-unit compatibility (...)
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  39. Giovanni Pezzulo, Lawrence W. Barsalou, Angelo Cangelosi, Martin H. Fischer, Michael Spivey & Ken McRae (2011). The Mechanics of Embodiment: A Dialog on Embodiment and Computational Modeling. Frontiers in Psychology 2.score: 24.0
    Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit (...)
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  40. Tangming Yuan, David Moore & Alec Grierson (2003). Computational Agents as a Test-Bed to Study the Philosophical Dialogue Model "DE": A Development of Mackenzie's DC. Informal Logic 23 (3).score: 24.0
    This paper reports research concerning a suitable dialogue model for human computer debate. In particular, we consider the adoption of Moore's (1993) utilization of Mackenzie's (1979) game DC, means of using computational agents as the test-bed to facilitate evaluation of the proposed model, and means of using the evaluation results as motivation to further develop a dialogue model, which can prevent fallacious argument and common errors. It is anticipated that this work will contribute toward the development of human computer (...)
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  41. Nicolas Fillion & Robert M. Corless (2014). On the Epistemological Analysis of Modeling and Computational Error in the Mathematical Sciences. Synthese 191 (7):1451-1467.score: 24.0
    Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler’s recipe. We then describe a (...)
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  42. Conrad Perry, Johannes C. Ziegler & Marco Zorzi (2013). A Computational and Empirical Investigation of Graphemes in Reading. Cognitive Science 37 (5):800-828.score: 24.0
    It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for (...)
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  43. Patrick Saint-Dizier & Evelyne Viegas (eds.) (1995). Computational Lexical Semantics. Cambridge University Press.score: 24.0
    Lexical semantics has become a major research area within computational linguistics, drawing from psycholinguistics, knowledge representation, computer algorithms and architecture. Research programmes whose goal is the definition of large lexicons are asking what the appropriate representation structure is for different facets of lexical information. Among these facets, semantic information is probably the most complex and the least explored.Computational Lexical Semantics is one of the first volumes to provide models for the creation of various kinds of computerised lexicons for (...)
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  44. Klaus Mainzer (1999). Computational Models and Virtual Reality. New Perspectives of Research in Chemistry. Hyle 5 (2):135 - 144.score: 24.0
    Molecular models are typical topics of chemical research depending on the technical standards of observation, computation, and representation. Mathematically, molecular structures have been represented by means of graph theory, topology, differential equations, and numerical procedures. With the increasing capabilities of computer networks, computational models and computer-assisted visualization become an essential part of chemical research. Object-oriented programming languages create a virtual reality of chemical structures opening new avenues of exploration and collaboration in chemistry. From an epistemic point of view, virtual (...)
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  45. Matthew M. Botvinick & Jonathan D. Cohen (2014). The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers. Cognitive Science 38 (6):1249-1285.score: 24.0
    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 (...)
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  46. David J. Buller (1993). Confirmation and the Computational Paradigm (Or: Why Do You Think They Call Itartificial Intelligence?). [REVIEW] Minds and Machines 3 (2):155-181.score: 24.0
    The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition (...)
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  47. Judith Gaspers & Philipp Cimiano (2014). A Computational Model for the Item‐Based Induction of Construction Networks. Cognitive Science 38 (2):439-488.score: 24.0
    According to usage-based approaches to language acquisition, linguistic knowledge is represented in the form of constructions—form-meaning pairings—at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item-based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous (...)
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  48. Floriana Grasso (2002). Towards Computational Rhetoric. Informal Logic 22 (3).score: 24.0
    The notions of argument and argumentation have become increasingly ubiquitous in Artificial Intelligence research, with various application and interpretations. Less attention has been, however, specifically devoted to rhetorical argument The work presented in this paper aims at bridging this gap, by proposing a framework for characterising rhetorical argumentation, based on Perelman and Olbrechts-Tyteca's New Rhetoric. The paper provides an overview of the state of the art of computational work based on, or dealing with, rhetorical aspects of argumentation, before presenting (...)
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  49. Witold Marciszewski (1997). Rational Beliefs as Produced by Computational Processes. Foundations of Science 2 (1):87-106.score: 24.0
    Intelligent problem-solving depends on consciously applied methods of thinking as well as inborn or trained skills. The latter are like resident programs which control processes of the kind called (in Unix) daemons. Such a computational process is a fitting reaction to situations (defined in the program in question) which is executed without any command of a computer user (or without any intention of the conscious subject). The study of intelligence should involve methods of recognizing those beliefs whose existence is (...)
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  50. Fabian Schlotterbeck & Oliver Bott (2013). Easy Solutions for a Hard Problem? The Computational Complexity of Reciprocals with Quantificational Antecedents. Journal of Logic, Language and Information 22 (4):363-390.score: 24.0
    We report two experiments which tested whether cognitive capacities are limited to those functions that are computationally tractable (PTIME-Cognition Hypothesis). In particular, we investigated the semantic processing of reciprocal sentences with generalized quantifiers, i.e., sentences of the form Q dots are directly connected to each other, where Q stands for a generalized quantifier, e.g. all or most. Sentences of this type are notoriously ambiguous and it has been claimed in the semantic literature that the logically strongest reading is preferred (Strongest (...)
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