Results for 'Computational models'

998 found
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  1.  25
    How Computational Models Predict the Behavior of Complex Systems.John Symons & Fabio Boschetti - 2013 - Foundations of Science 18 (4):809-821.
    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 (...)
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  2. Computational Models of Emergent Properties.John Symons - 2008 - Minds and Machines 18 (4):475-491.
    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 (...)
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  3.  33
    Reconciling Two Computational Models of Working Memory in Aging.Violette Hoareau, Benoît Lemaire, Sophie Portrat & Gaën Plancher - 2016 - Topics in Cognitive Science 8 (1):264-278.
    It is well known that working memory performance changes with age. Two recent computational models of working memory, TBRS* and SOB-CS, developed from young adults WM performances are opposed regarding the postulated causes of forgetting, namely time-based decay and interference for TBRS* and SOB-CS, respectively. In the present study, these models are applied on a set of complex span data produced by young and older adults. As expected, these models are unable to account for the older (...)
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  4. The Nature and Function of Content in Computational Models.Frances Egan - 2018 - In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. Routledge.
    Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. Neurons are often said to represent elements of their receptive fields. Despite the ubiquity of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. The point of this chapter is to sketch an account (...)
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  5. Computational Mechanisms and Models of Computation.Marcin Miłkowski - 2014 - Philosophia Scientiæ 18:215-228.
    In most accounts of realization of computational processes by physical mechanisms, it is presupposed that there is one-to-one correspondence between the causally active states of the physical process and the states of the computation. Yet such proposals either stipulate that only one model of computation is implemented, or they do not reflect upon the variety of models that could be implemented physically. -/- In this paper, I claim that mechanistic accounts of computation should allow for a broad variation (...)
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  6.  20
    Observations on the Responsible Development and Use of Computational Models and Simulations.David J. Kijowski, Harry Dankowicz & Michael C. Loui - 2013 - Science and Engineering Ethics 19 (1):63-81.
    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 (...)
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  7.  9
    Evaluating the Theoretic Adequacy and Applied Potential of Computational Models of the Spacing Effect.Matthew M. Walsh, Kevin A. Gluck, Glenn Gunzelmann, Tiffany Jastrzembski & Michael Krusmark - 2018 - Cognitive Science 42 (S3):644-691.
    The spacing effect is among the most widely replicated empirical phenomena in the learning sciences, and its relevance to education and training is readily apparent. Yet successful applications of spacing effect research to education and training is rare. Computational modeling can provide the crucial link between a century of accumulated experimental data on the spacing effect and the emerging interest in using that research to enable adaptive instruction. In this paper, we review relevant literature and identify 10 criteria for (...)
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  8.  12
    Computational Models and Virtual Reality. New Perspectives of Research in Chemistry.Klaus Mainzer - 1999 - Hyle 5 (2):135 - 144.
    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 (...)
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  9. Computational Models: A Modest Role for Content.Frances Egan - 2010 - Studies in History and Philosophy of Science Part A 41 (3):253-259.
    The computational theory of mind construes the mind as an information-processor and cognitive capacities as essentially representational capacities. Proponents of the view claim a central role for representational content in computational models of these capacities. In this paper I argue that the standard view of the role of representational content in computational models is mistaken; I argue that representational content is to be understood as a gloss on the computational characterization of a cognitive process.Keywords: (...)
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  10.  68
    Computational Models of Performance Monitoring and Cognitive Control.William H. Alexander & Joshua W. Brown - 2010 - Topics in Cognitive Science 2 (4):658-677.
    The medial prefrontal cortex (mPFC) has been the subject of intense interest as a locus of cognitive control. Several computational models have been proposed to account for a range of effects, including error detection, conflict monitoring, error likelihood prediction, and numerous other effects observed with single-unit neurophysiology, fMRI, and lesion studies. Here, we review the state of computational models of cognitive control and offer a new theoretical synthesis of the mPFC as signaling response–outcome predictions. This new (...)
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  11. Psychological and Computational Models of Language Comprehension.David Pereplyotchik - 2011 - Croatian Journal of Philosophy 11 (1):31-72.
    In this paper, I argue for a modified version of what Devitt calls the Representational Thesis. According to RT, syntactic rules or principles are psychologically real, in the sense that they are represented in the mind/brain of every linguistically competent speaker/hearer. I present a range of behavioral and neurophysiological evidence for the claim that the human sentence processing mechanism constructs mental representations of the syntactic properties of linguistic stimuli. I then survey a range of psychologically plausible computational models (...)
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  12.  72
    Computational Models.Paul Humphreys - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S1-S11.
    A different way of thinking about how the sciences are organized is suggested by the use of cross‐disciplinary computational methods as the organizing unit of science, here called computational templates. The structure of computational models is articulated using the concepts of construction assumptions and correction sets. The existence of these features indicates that certain conventionalist views are incorrect, in particular it suggests that computational models come with an interpretation that cannot be removed as well (...)
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  13.  6
    Computational Models.Paul Humphreys - 2002 - Philosophy of Science 69 (S3):S1-S11.
    A different way of thinking about how the sciences are organized is suggested by the use of cross‐disciplinary computational methods as the organizing unit of science, here called computational templates. The structure of computational models is articulated using the concepts of construction assumptions and correction sets. The existence of these features indicates that certain conventionalist views are incorrect, in particular it suggests that computational models come with an interpretation that cannot be removed as well (...)
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  14.  34
    Computational Models in the Philosophy of Science.Paul Thagard - 1986 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:329 - 335.
    Computational models can aid in the development of philosophical views concerning the structure and growth of scientific knowledge. In cognitive psychology, computational models have proved valuable for describing the structures and processes of thought and for testing these models by writing and running computer programs using the techniques of artificial intelligence. Similarly, in the philosophy of science models can be developed that shed light on the structure, discovery, and justification of scientific theories. This paper (...)
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  15.  26
    Computational Models of the Emotions: From Models of the Emotions of the Individual to Modelling the Emerging Irrational Behaviour of Crowds. [REVIEW]Ephraim Nissan - 2009 - AI and Society 24 (4):403-414.
    Computational models of emotions have been thriving and increasingly popular since the 1990s. Such models used to be concerned with the emotions of individual agents when they interact with other agents. Out of the array of models for the emotions, we are going to devote special attention to the approach in Adamatzky’s Dynamics of Crowd-Minds. The reason it stands out, is that it considers the crowd, rather than the individual agent. It fits in computational intelligence. (...)
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  16.  11
    Computational Models of Language Meaning in Context (Dagstuhl Seminar 13462).Hans Kamp, Alessandro Lenci & James Pustejovsky - unknown
    This report documents the program and the outcomes of Dagstuhl Seminar 13462 "Computational Models of Language Meaning in Context". The seminar addresses one of the most significant issues to arise in contemporary formal and computational models of language and inference: that of the role and expressiveness of distributional models of semantics and statistically derived models of language and linguistic behavior. The availability of very large corpora has brought about a near revolution in computational (...)
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  17. Some Limitations of Behaviorist and Computational Models of Mind.John Collier - unknown
    The purpose of this paper is to describe some limitations on scientific behaviorist and computational models of the mind. These limitations stem from the inability of either model to account for the integration of experience and behavior. Behaviorism fails to give an adequate account of felt experience, whereas the computational model cannot account for the integration of our behavior with the world. Both approaches attempt to deal with their limitations by denying that the domain outside their limits (...)
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  18.  3
    Introduction to the Issue on Computational Models of Memory: Selected Papers From the International Conference on Cognitive Modeling.David Reitter & Frank E. Ritter - 2017 - Topics in Cognitive Science 9 (1):48-50.
    Computational models of memory presented in this issue reflect varied empirical data and levels of representation. From mathematical models to neural and cognitive architectures, all aim to converge on a unified theory of the mind.
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  19.  11
    Methodologies for Comparing Complex Computational Models of Eye-Movement Control in Reading: Just Fitting the Data is Not Enough.Ronan Reilly & Ralph Radach - 2003 - Behavioral and Brain Sciences 26 (4):499-500.
    As the number of computational models of eye-movement control in reading increases, so too will their coverage and complexity. This will make their comparison and testing increasingly challenging. We argue here that there is a need to develop a methodology for constructing and evaluating such models, and outline aspects of a possible methodology.
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  20.  19
    Logic and Social Cognition the Facts Matter, and so Do Computational Models.Rineke Verbrugge - 2009 - Journal of Philosophical Logic 38 (6):649-680.
    This article takes off from Johan van Benthem’s ruminations on the interface between logic and cognitive science in his position paper “Logic and reasoning: Do the facts matter?”. When trying to answer Van Benthem’s question whether logic can be fruitfully combined with psychological experiments, this article focuses on a specific domain of reasoning, namely higher-order social cognition, including attributions such as “Bob knows that Alice knows that he wrote a novel under pseudonym”. For intelligent interaction, it is important that the (...)
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  21.  4
    Computational Models of Miscommunication Phenomena.Matthew Purver, Julian Hough & Christine Howes - 2018 - Topics in Cognitive Science 10 (2):425-451.
    Miscommunication phenomena such as repair in dialogue are important indicators of the quality of communication. Automatic detection is therefore a key step toward tools that can characterize communication quality and thus help in applications from call center management to mental health monitoring. However, most existing computational linguistic approaches to these phenomena are unsuitable for general use in this way, and particularly for analyzing human–human dialogue: Although models of other-repair are common in human-computer dialogue systems, they tend to focus (...)
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  22.  13
    Modelling Empty Representations: The Case of Computational Models of Hallucination.Marcin Miłkowski - 2017 - In Gordana Dodig-Crnkovic & Raffaela Giovagnoli (eds.), Representation and Reality in Humans, Other Living Organisms and Intelligent Machines. Springer, Cham. pp. 17--32.
    I argue that there are no plausible non-representational explanations of episodes of hallucination. To make the discussion more specific, I focus on visual hallucinations in Charles Bonnet syndrome. I claim that the character of such hallucinatory experiences cannot be explained away non-representationally, for they cannot be taken as simple failures of cognizing or as failures of contact with external reality—such failures being the only genuinely non-representational explanations of hallucinations and cognitive errors in general. I briefly introduce a recent computational (...)
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  23.  47
    Agent-Based Computational Models and Generative Social Science.Joshua M. Epstein - 1999 - Complexity 4 (5):41-60.
  24.  44
    Classical Computational Models.Richard Samuels - 2018 - In Mark Sprevak & Matteo Colombo (ed.), The Routledge Handbook of the Computational Mind. Oxford, UK: pp. 103-119.
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  25. Understanding Scientists' Computational Modeling Decisions About Climate Risk Management Strategies Using Values-Informed Mental Models.Lauren Mayer, Kathleen Loa, Bryan Cwik, Nancy Tuana, Klaus Keller, Chad Gonnerman, Andrew Parker & Robert Lempert - 2017 - Global Environmental Change 42:107-116.
    When developing computational models to analyze the tradeoffs between climate risk management strategies (i.e., mitigation, adaptation, or geoengineering), scientists make explicit and implicit decisions that are influenced by their beliefs, values and preferences. Model descriptions typically include only the explicit decisions and are silent on value judgments that may explain these decisions. Eliciting scientists’ mental models, a systematic approach to determining how they think about climate risk management, can help to gain a clearer understanding of their modeling (...)
     
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  26.  55
    Computational Models of Implicit Learning.Axel Cleeremans & Zoltán Dienes - 2008 - In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 396--421.
  27.  37
    Maximizing Students' Retention Via Spaced Review: Practical Guidance From Computational Models of Memory.Mohammad M. Khajah, Robert V. Lindsey & Michael C. Mozer - 2014 - Topics in Cognitive Science 6 (1):157-169.
    During each school semester, students face an onslaught of material to be learned. Students work hard to achieve initial mastery of the material, but when they move on, the newly learned facts, concepts, and skills degrade in memory. Although both students and educators appreciate that review can help stabilize learning, time constraints result in a trade-off between acquiring new knowledge and preserving old knowledge. To use time efficiently, when should review take place? Experimental studies have shown benefits to long-term retention (...)
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  28.  88
    Computational Mechanisms and Models of Computation.Marcin Miłkowski - 2014 - Philosophia Scientae 18:215-228.
    In most accounts of realization of computational processes by physical mechanisms, it is presupposed that there is one-to-one correspondence between the causally active states of the physical process and the states of the computation. Yet such proposals either stipulate that only one model of computation is implemented, or they do not reflect upon the variety of models that could be implemented physically. In this paper, I claim that mechanistic accounts of computation should allow for a broad variation of (...)
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  29.  65
    Computational Complexity of Some Ramsey Quantifiers in Finite Models.Marcin Mostowski & Jakub Szymanik - 2007 - Bulletin of Symbolic Logic 13:281--282.
    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 (...)
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  30. Computational Models of Developmental Psychology.T. R. Shultz & S. Sirois - 2008 - In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 451--476.
  31. Computational Models of Episodic Memory.Kenneth A. Norman, G. J. Detre & Sean M. Polyn - 2008 - In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 189--224.
     
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  32. Computational Models of Skill Acquisition.Stellan Ohlsson - 2008 - In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 359--395.
  33.  13
    Computational Models of Semantic Memory.T. Rogers - 2008 - In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 226--266.
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  34.  9
    Herbert Simon's Computational Models of Scientific Discovery.Stephen Downes - 1990 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:97-108.
    In this paper I evaluate Herbert Simon 's important computational approach to scientific discovery, which can be characterized as a contribution to both the "cognitive science of science" and to naturalized philosophy of science. First, I tackle the empirical adequacy of Simon 's account of discovery, arguing that his claims about the discovery process lack evidence and, even if substantiated, they disregard the important social dimension of scientific discovery. Second, I discuss the normative dimension of Simon 's account, here (...)
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  35.  10
    Toward a Method of Selecting Among Computational Models of Cognition.Mark A. Pitt, In Jae Myung & Shaobo Zhang - 2002 - Psychological Review 109 (3):472-491.
  36.  7
    Validating Computational Models: A Critique of Anderson's Indeterminacy of Representation Claim.Zenon W. Pylyshyn - 1979 - Psychological Review 86 (4):383-394.
  37. Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience.M. Chirimuuta - 2014 - Synthese 191 (2):127-153.
    In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss (...)
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  38. Minds And Mechanisms: Philosophical Psychology And Computational Models.Margaret A. Boden - 1981 - Ithaca: Cornell University Press.
  39.  16
    Using Computational Models to Discover and Understand Mechanisms.William Bechtel - 2016 - Studies in History and Philosophy of Science Part A 56:113-121.
  40. 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 (...)
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  41.  34
    Six Principles for Biologically Based Computational Models of Cortical Cognition.Randall C. O'Reilly - 1998 - Trends in Cognitive Sciences 2 (11):455-462.
  42.  6
    Computational Models of Ethical Reasoning: Challenges, Initial Steps, and Future Directions.Bruce M. McLaren - 2011 - In M. Anderson S. Anderson (ed.), Machine Ethics. Cambridge Univ. Press. pp. 297--315.
  43.  52
    Computational Models of Collective Behavior.Robert L. Goldstone & Marco A. Janssen - 2005 - Trends in Cognitive Sciences 9 (9):424-430.
  44.  85
    Computational Models of Working Memory: Putting Long-Term Memory Into Context.Neil Burgess & Graham Hitch - 2005 - Trends in Cognitive Sciences 9 (11):535-541.
  45.  6
    Ability, Breadth, and Parsimony in Computational Models of Higher-Order Cognition.Nicholas Cassimatis, Paul Bello & Pat Langley - 2008 - Cognitive Science 32 (8):1304-1322.
  46.  30
    Computational Models for the Combination of Advice and Individual Learning.Guido Biele, Jörg Rieskamp & Richard Gonzalez - 2009 - Cognitive Science 33 (2):206-242.
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  47.  41
    Computational Models of the Hippocampal Region: Linking Incremental Learning and Episodic Memory.Mark A. Gluck, Martijn Meeter & Catherine E. Myers - 2003 - Trends in Cognitive Sciences 7 (6):269-276.
  48.  5
    Chunks, Schemata, and Retrieval Structures: Past and Current Computational Models.Fernand Gobet, Peter C. R. Lane & Martyn Lloyd-Kelly - 2015 - Frontiers in Psychology 6.
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  49.  28
    Challenges in Developing Computational Models of Emotion and Consciousness.Eva Hudlicka - 2009 - International Journal of Machine Consciousness 1 (1):131-153.
  50.  18
    Influence of Consonantal Context on the Pronunciation of Vowels: A Comparison of Human Readers and Computational Models.Rebecca Treiman, Brett Kessler & Suzanne Bick - 2003 - Cognition 88 (1):49-78.
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