Results for 'cognitive modelling'

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  1. Cognitive Modeling and Representation of Knowledge in Ontological Engineering.Christine W. Chan - 2003 - Brain and Mind 4 (2):269-282.
    This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, which can (...)
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    Cognitive modeling: Of Gedanken beasts and human beings.Dan Lloyd - 1987 - Behavioral and Brain Sciences 10 (3):442-443.
  3. Cognitive modeling of action selection learning.Diana F. Gordon & Devika Subramanian - 1996 - In Garrison W. Cottrell, Proceedings of the Eighteenth Annual Conference of The Cognitive Science Society. Lawrence Erlbaum. pp. 546--551.
     
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  4.  20
    Editor's Introduction: Best of Papers From the 17th International Conference on Cognitive Modeling.Terrence C. Stewart - 2020 - Topics in Cognitive Science 12 (3):957-959.
    Cognitive modeling involves the creation of computer simulations that emulate the internal processes of the mind. This set of papers are the five best representatives of the papers presented at the 17th International Conference on Cognitive Modeling, ICCM 2019. While they represent a diversity of techniques and tasks, they all also share a striking similarity: They make strong statements about the importance of accounting for individual differences.
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  5.  24
    Cognitive modeling and intelligent tutoring.John R. Anderson, C. Franklin Boyle, Albert T. Corbett & Matthew W. Lewis - 1990 - Artificial Intelligence 42 (1):7-49.
  6.  33
    A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making.Prezenski Sabine, Brechmann André, Wolff Susann & Russwinkel Nele - 2017 - Frontiers in Psychology 8.
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  7.  16
    Cognitive Modeling of Automation Adaptation in a Time Critical Task.Junya Morita, Kazuhisa Miwa, Akihiro Maehigashi, Hitoshi Terai, Kazuaki Kojima & Frank E. Ritter - 2020 - Frontiers in Psychology 11.
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  8.  26
    Editors’ Introduction: Best Papers from the 19th International Conference on Cognitive Modeling.Terrence C. Stewart & Joost Jong - 2022 - Topics in Cognitive Science 14 (4):825-827.
    The International Conference on Cognitive Modeling brings together researchers from around the world whose main goal is to build computational systems that reflect the internal processes of the mind. In this issue, we present the five best representative papers on this work from our 19th meeting, ICCM 2021, which was held virtually from July 3 to July 9, 2021. Three of these papers provide new techniques for refining computational models, giving better methods for taking empirical data and producing accurate (...)
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  9. Cognitive modeling for cognitive engineering.Wayne D. Gray - 2008 - In Ron Sun, The Cambridge handbook of computational psychology. New York: Cambridge University Press. pp. 565--588.
  10. The role of cognitive modeling for user interface design representations: An epistemological analysis of knowledge engineering in the context of human-computer interaction. [REVIEW]Markus F. Peschl & Chris Stary - 1998 - Minds and Machines 8 (2):203-236.
    In this paper we review some problems with traditional approaches for acquiring and representing knowledge in the context of developing user interfaces. Methodological implications for knowledge engineering and for human-computer interaction are studied. It turns out that in order to achieve the goal of developing human-oriented (in contrast to technology-oriented) human-computer interfaces developers have to develop sound knowledge of the structure and the representational dynamics of the cognitive system which is interacting with the computer.We show that in a first (...)
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  11. On Cognitive Modeling and Other Minds.J. P. Gamboa - 2024 - Philosophy of Science 91 (3):615-633.
    Scientists and philosophers alike debate whether various systems such as plants and bacteria exercise cognition. One strategy for resolving such debates is to ground claims about nonhuman cognition in evidence from mathematical models of cognitive capacities. In this article, I show that proponents of this strategy face two major challenges: demarcating phenomenological models from process models and overcoming underdetermination by model fit. I argue that even if the demarcation problem is resolved, fitting a process model to behavioral data is, (...)
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  12.  83
    Cognitive Modeling of Individual Variation in Reference Production and Comprehension.Petra Hendriks - 2016 - Frontiers in Psychology 7.
  13. Cognitive modeling: research logic in cognitive science.G. Strube - 2001 - In Neil J. Smelser & Paul B. Baltes, International Encyclopedia of the Social and Behavioral Sciences. Elsevier. pp. 3--2124.
  14. Visual cognition and cognitive modeling.L. Magnani, S. Civita & G. Previde Massara - 1994 - In V. Cantoni, Human and Machine Vision: Analogies and Divergences. Plenum Publishers. pp. 229--243.
     
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  15. Introduction to computational cognitive modeling.Ron Sun - 2008 - In The Cambridge handbook of computational psychology. New York: Cambridge University Press. pp. 3--19.
  16.  27
    Editors’ Introduction: Cognitive Modeling at ICCM : Advancing the State of the Art.William G. Kennedy, Marieke K. van Vugt & Adrian P. Banks - 2018 - Topics in Cognitive Science 10 (1):140-143.
    In this issue of topiCS, we present the best papers from the ICCM meeting. These best papers represent advances in the state of the art in cognitive modeling. Since ICCM was for the first time also held jointly with the Society for Mathematical Psychology, we use this preface to also reflect on the similarities and differences between mathematical psychology and cognitive modeling.
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  17.  68
    Sleep Deprivation and Sustained Attention Performance: Integrating Mathematical and Cognitive Modeling.Glenn Gunzelmann, Joshua B. Gross, Kevin A. Gluck & David F. Dinges - 2009 - Cognitive Science 33 (5):880-910.
    A long history of research has revealed many neurophysiological changes and concomitant behavioral impacts of sleep deprivation, sleep restriction, and circadian rhythms. Little research, however, has been conducted in the area of computational cognitive modeling to understand the information processing mechanisms through which neurobehavioral factors operate to produce degradations in human performance. Our approach to understanding this relationship is to link predictions of overall cognitive functioning, or alertness, from existing biomathematical models to information processing parameters in a (...) architecture, leveraging the strengths from each to develop a more comprehensive explanation. The integration of these methodologies is used to account for changes in human performance on a sustained attention task across 88 h of total sleep deprivation. The integrated model captures changes due to time awake and circadian rhythms, and it also provides an account for underlying changes in the cognitive processes that give rise to those effects. The results show the potential for developing mechanistic accounts of how fatigue impacts cognition, and they illustrate the increased explanatory power that is possible by combining theoretical insights from multiple methodologies. (shrink)
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  18.  61
    Extending SME to Handle Large‐Scale Cognitive Modeling.Kenneth D. Forbus, Ronald W. Ferguson, Andrew Lovett & Dedre Gentner - 2017 - Cognitive Science 41 (5):1152-1201.
    Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence (...)
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  19.  34
    Where does the end begin? Problems in musico-cognitive modeling.James Kippen - 1992 - Minds and Machines 2 (4):329-344.
    Research with computer systems and musical grammars into improvisation as found in the tabla drumming system of North India has indicated that certain musical sentences comprise (a) variable prefixes, and (b) fixed suffixes (or cadences) identical with those of their original rhythmic themes. It was assumed that the cadence functioned as a kind of target in linear musical space, and yet experiments showed that defining what exactly constituted the cadence was problematic. This paper addresses the problem of the status of (...)
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  20.  14
    Introduction to neural and cognitive modeling.Sue Becker - 1993 - Artificial Intelligence 62 (1):113-116.
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  21.  24
    Hemispheric Asymmetries in Cognitive Modeling: Connectionist Modeling of Unilateral Visual Neglect.Padraic Monaghan & Richard Shillcock - 2004 - Psychological Review 111 (2):283-308.
  22.  42
    Quantum probability and cognitive modeling: Some cautions and a promising direction in modeling physics learning.Donald R. Franceschetti & Elizabeth Gire - 2013 - Behavioral and Brain Sciences 36 (3):284-285.
    Quantum probability theory offers a viable alternative to classical probability, although there are some ambiguities inherent in transferring the quantum formalism to a less determined realm. A number of physicists are now looking at the applicability of quantum ideas to the assessment of physics learning, an area particularly suited to quantum probability ideas.
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  23. Cognitive modeling repository.Jay Myung & Mark Pitt - unknown
     
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  24.  62
    Spanning seven orders of magnitude: a challenge for cognitive modeling.John R. Anderson - 2002 - Cognitive Science 26 (1):85-112.
    Much of cognitive psychology focuses on effects measured in tens of milliseconds while significant educational outcomes take tens of hours to achieve. The task of bridging this gap is analyzed in terms of Newell's (1990) bands of cognition—the Biological, Cognitive, Rational, and Social Bands. The 10 millisecond effects reside in his Biological Band while the significant learning outcomes reside in his Social Band. The paper assesses three theses: The Decomposition Thesis claims that learning occurring at the Social Band (...)
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  25. Theoretical status of computational cognitive modeling.Ron Sun - unknown
    This article explores the view that computational models of cognition may constitute valid theories of cognition, often in the full sense of the term ‘‘theory”. In this discussion, this article examines various (existent or possible) positions on this issue and argues in favor of the view above. It also connects this issue with a number of other relevant issues, such as the general relationship between theory and data, the validation of models, and the practical benefits of computational modeling. All the (...)
     
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  26. Quantum cognitive modeling of concepts: an introduction.Tomas Veloz & Pablo Razeto - 2019 - In Diederik Aerts, Dalla Chiara, Maria Luisa, Christian de Ronde & Decio Krause, Probing the meaning of quantum mechanics: information, contextuality, relationalism and entanglement: Proceedings of the II International Workshop on Quantum Mechanics and Quantum Information: Physical, Philosophical and Logical Approaches, CLEA, Brussels. New Jersey: World Scientific.
     
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  27.  37
    Uncertainty about the value of quantum probability for cognitive modeling.Christina Behme - 2013 - Behavioral and Brain Sciences 36 (3):279-280.
    I argue that the overly simplistic scenarios discussed by Pothos & Busemeyer (P&B) establish at best that quantum probability theory (QPT) is a logical possibility allowing distinct predictions from classical probability theory (CPT). The article fails, however, to provide convincing evidence for the proposal that QPT offers unique insights regarding cognition and the nature of human rationality.
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  28.  41
    The limitations of the reverse-engineering approach to cognitive modeling.Jay G. Rueckl - 2012 - Behavioral and Brain Sciences 35 (5):305.
    Frost's critique reveals the limitations of the reverse-engineering approach to cognitive modeling – the style of psychological explanation in which a stipulated internal organization explains a relatively narrow set of phenomena. An alternative is to view organization as both the explanation for some phenomena and a phenomenon to be explained. This move poses new and interesting theoretical challenges for theories of word reading.
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  29.  16
    Editors' Introduction: Best Papers From the 2018 International Conference on Cognitive Modeling.Christopher Myers, Joseph Houpt & Ion Juvina - 2019 - Topics in Cognitive Science 11 (1):220-221.
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    The implicit possibility of dualism in quantum probabilistic cognitive modeling.Donald Mender - 2013 - Behavioral and Brain Sciences 36 (3):298-299.
  31. Can quantum probability provide a new direction for cognitive modeling?Emmanuel M. Pothos & Jerome R. Busemeyer - 2013 - Behavioral and Brain Sciences 36 (3):255-274.
    Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP and QP (...)
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  32.  41
    Approaches to Cognitive Modeling in Dynamic Systems Control.Daniel V. Holt & Magda Osman - 2017 - Frontiers in Psychology 8.
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  33.  31
    Editors’ Introduction: Best Papers from the 19th International Conference on Cognitive Modeling.Terrence C. Stewart & Joost de Jong - 2022 - Topics in Cognitive Science 14 (4):825-827.
    The International Conference on Cognitive Modeling brings together researchers from around the world whose main goal is to build computational systems that reflect the internal processes of the mind. In this issue, we present the five best representative papers on this work from our 19th meeting, ICCM 2021, which was held virtually from July 3 to July 9, 2021. Three of these papers provide new techniques for refining computational models, giving better methods for taking empirical data and producing accurate (...)
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  34.  40
    Artificial Intelligence and Cognitive Modeling Have the Same Problem.Nicholas L. Cassimatis - 2012 - In Pei Wang & Ben Goertzel, Theoretical Foundations of Artificial General Intelligence. Springer. pp. 11--24.
  35.  79
    How Children Process Reduced Forms: A Computational Cognitive Modeling Approach to Pronoun Processing in Discourse.Margreet Vogelzang, Maria Teresa Guasti, Hedderik van Rijn & Petra Hendriks - 2021 - Cognitive Science 45 (4):e12951.
    Reduced forms such as the pronoun he provide little information about their intended meaning compared to more elaborate descriptions such as the lead singer of Coldplay. Listeners must therefore use contextual information to recover their meaning. Across languages, there appears to be a trade‐off between the informativity of a form and the prominence of its referent. For example, Italian adults generally interpret informationally empty null pronouns as in the sentence Corre (meaning “He/She/It runs”) as referring to the most prominent referent (...)
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  36.  24
    Implementations are not specifications: specification, replication and experimentation in computational cognitive modeling.Richard P. Cooper & Olivia Guest - 2014 - Cognitive Systems Research 27:42-49.
    Contemporary methods of computational cognitive modeling have recently been criticized by Addyman and French (2012) on the grounds that they have not kept up with developments in computer technology and human–computer interaction. They present a manifesto for change according to which, it is argued, modelers should devote more effort to making their models accessible, both to non-modelers (with an appropriate easy-to-use user interface) and modelers alike. We agree that models, like data, should be freely available according to the normal (...)
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  37.  25
    The Role of Dorsal Premotor Cortex in Resolving Abstract Motor Rules: Converging Evidence From Transcranial Magnetic Stimulation and Cognitive Modeling.Patrick Rice & Andrea Stocco - 2019 - Topics in Cognitive Science 11 (1):240-260.
    The Role of Dorsal Premotor Cortex in Resolving Abstract Motor Rules provides alternative hypotheses about the cognitive functions affected by the application of repetitive transcranial magnetic stimulation. Their model simulated the effect of stimulation of the left dorsal premotor cortex right as participants provide a Models were used to demonstrate that the increased variability in observed response times can result from interference in replanning during the process of responding to the uninstructed stimulus.
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  38.  20
    Simplicity and Cognitive Modeling: Avoiding old mistakes in new experimental contexts.Irina Mikhalevich - 2017 - In Kristin Andrews & Jacob Beck, The Routledge Handbook of Philosophy of Animal Minds. Routledge. pp. 427-437.
    In this chapter, the author examines how the simplicity heuristic adversely affects a relatively new tool in experimental comparative cognition: cognitive models. It does so, she argues, by directing intellectual resources into the development and refinement of putatively simple cognitive models at the expense of putatively more complex ones, which in turn directs experimenters to develop tests to rule out these simple models.
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  39.  43
    The vygotskian advantage in cognitive modeling: Participation precedes and thus prefigures understanding.Christine M. Johnson - 2002 - Behavioral and Brain Sciences 25 (5):628-629.
    Shanker & King's (S&K's) proposal is consistent with a Vygotskian model of development which assumes that cognition is first social and visible, and only later internalized and invisible. Rather than slipping into positing “epistemic operators” like understand or intend as generative of behavior during language learning or theory of mind tasks, this approach profits from keeping its focus on charting the ontogeny of embodied interactions.
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  40.  9
    Orality, literacy, and cognitive modeling.Eckart Scheerer - 1996 - In B. Velichkovsky & Duane M. Rumbaugh, Communicating Meaning: The Evolution and Development of Language. Hillsdale, NJ: Lawrence Erlbaum Associates. pp. 211.
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  41.  11
    Game Semantics from a Cognitive Modeling Standpoint.Emmanuel Genot & Justine Jacot - unknown
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  42.  10
    (Re)Modeling Culture in Kwara'ae: The Role of Discourse in Children's Cognitive Development.David Welchman Gegeo & Karen Ann Watson-Gegeo - 1999 - Discourse Studies 1 (2):227-246.
    We examine children's cognitive skills and cultural representations in naturally occurring discourse, integrating theoretical perspectives from psychology, cognitive anthropology, and sociolinguistics. We focus on two interactional events recorded in our 10-year study of children's language socialization in Kwara'ae involving the same child at ages 2 and 4 years interacting with an older child and an adult, respectively, around routine tasks. In both cases a potentially serious cultural anomaly that challenges the children's own constructions of cultural models tests their (...)
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  43. The Place of Modeling in Cognitive Science.James L. McClelland - 2009 - Topics in Cognitive Science 1 (1):11-38.
    I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. In these explorations, simplification is essential—through simplification, the implications of the central ideas become more transparent. This is not to say that simplification (...)
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  44. Simulations in Cyber-Security: A Review of Cognitive Modeling of Network Attackers, Defenders, and Users. [REVIEW]Vladislav D. Veksler, Norbou Buchler, Blaine E. Hoffman, Daniel N. Cassenti, Char Sample & Shridat Sugrim - 2018 - Frontiers in Psychology 9.
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  45.  32
    Multinomial modeling and the measurement of cognitive processes.David M. Riefer & William H. Batchelder - 1988 - Psychological Review 95 (3):318-339.
  46. Teleosemantic modeling of cognitive representations.Marc Artiga - 2016 - Biology and Philosophy 31 (4):483-505.
    Naturalistic theories of representation seek to specify the conditions that must be met for an entity to represent another entity. Although these approaches have been relatively successful in certain areas, such as communication theory or genetics, many doubt that they can be employed to naturalize complex cognitive representations. In this essay I identify some of the difficulties for developing a teleosemantic theory of cognitive representations and provide a strategy for accommodating them: to look into models of signaling in (...)
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  47.  40
    Cognitive Models of Choice: Comparing Decision Field Theory to the Proportional Difference Model.Benjamin Scheibehenne, Jörg Rieskamp & Claudia González-Vallejo - 2009 - Cognitive Science 33 (5):911-939.
    People often face preferential decisions under risk. To further our understanding of the cognitive processes underlying these preferential choices, two prominent cognitive models, decision field theory (DFT; Busemeyer & Townsend, 1993) and the proportional difference model (PD; González‐Vallejo, 2002), were rigorously tested against each other. In two consecutive experiments, the participants repeatedly had to choose between monetary gambles. The first experiment provided the reference to estimate the models’ free parameters. From these estimations, new gamble pairs were generated for (...)
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  48.  43
    A Cognitive Model of Dynamic Cooperation With Varied Interdependency Information.Cleotilde Gonzalez, Noam Ben-Asher, Jolie M. Martin & Varun Dutt - 2015 - Cognitive Science 39 (3):457-495.
    We analyze the dynamics of repeated interaction of two players in the Prisoner's Dilemma under various levels of interdependency information and propose an instance-based learning cognitive model to explain how cooperation emerges over time. Six hypotheses are tested regarding how a player accounts for an opponent's outcomes: the selfish hypothesis suggests ignoring information about the opponent and utilizing only the player's own outcomes; the extreme fairness hypothesis weighs the player's own and the opponent's outcomes equally; the moderate fairness hypothesis (...)
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  49.  15
    An interdisciplinary approach to cognitive modelling: a framework based on philosophy and modern science.P. Ghose - 2024 - New York, NY: Routledge. Edited by Sudip Patra.
    An Interdisciplinary Approach to Cognitive Modelling presents a new approach to cognition that challenges long-held views. It systematically develops a broad-based framework to model cognition, which is mathematically equivalent to the emerging 'quantum-like modelling' of the human mind. The book argues that a satisfactory physical and philosophical basis of such an approach is missing, a particular issue being the application of quantization to the mind for which there is no empirical evidence as yet. In response to this (...)
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  50.  70
    Cognitive Models of Science.R. Giere & H. Feigl (eds.) - 1992 - University of Minnesota Press.
    Cognitive Models of Science resulted from a workshop on the implications of the cognitive sciences for the philosophy of science held in October 1989 under the ...
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