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  1. Connectionism and Interlevel Relations.William Bechtel - 1988 - Behavioral and Brain Sciences 11 (1):24-25.
  • Connecting Twenty-First Century Connectionism and Wittgenstein.Charles W. Lowney, Simon D. Levy, William Meroney & Ross W. Gayler - 2020 - Philosophia 48 (2):643-671.
    By pointing to deep philosophical confusions endemic to cognitive science, Wittgenstein might seem an enemy of computational approaches. We agree that while Wittgenstein would reject the classicist’s symbols and rules approach, his observations align well with connectionist or neural network approaches. While many connectionisms that dominated the later twentieth century could fall prey to criticisms of biological, pedagogical, and linguistic implausibility, current connectionist approaches can resolve those problems in a Wittgenstein-friendly manner. We present the basics of a Vector Symbolic Architecture (...)
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  • The Unbearable Shallow Understanding of Deep Learning.Alessio Plebe & Giorgio Grasso - 2019 - Minds and Machines 29 (4):515-553.
    This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deep” learning architectures. A precise account of “success” is given, in order to sieve out (...)
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  • Connectionism and Cognitive Architecture: A Critical Analysis.Jerry A. Fodor & Zenon W. Pylyshyn - 1988 - Cognition 28 (1-2):3-71.
    This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h (...)
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  • Learning of Rules That Have High-Frequency Exceptions: New Empirical Data and a Hybrid Connectionist Model.John K. Kruschke & Michael A. Erickson - 1994 - In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. pp. 514--519.
     
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  • Précis of Semantic Cognition: A Parallel Distributed Processing Approach.Timothy T. Rogers & James L. McClelland - 2008 - Behavioral and Brain Sciences 31 (6):689-714.
    In this prcis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (...)
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  • LOT, CTM, and the Elephant in the Room.Susan Schneider - 2009 - Synthese 170 (2):235 - 250.
    According to the language of thought (LOT) approach and the related computational theory of mind (CTM), thinking is the processing of symbols in an inner mental language that is distinct from any public language. Herein, I explore a deep problem at the heart of the LOT/CTM program—it has yet to provide a plausible conception of a mental symbol.
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  • The Connectionism/Classicism Battle to Win Souls.Brian P. McLaughlin - 1993 - Philosophical Studies 71 (2):163-190.
  • Connectionist models of mind: scales and the limits of machine imitation.Pavel Baryshnikov - 2020 - Philosophical Problems of IT and Cyberspace 2 (19):42-58.
    This paper is devoted to some generalizations of explanatory potential of connectionist approaches to theoretical problems of the philosophy of mind. Are considered both strong, and weaknesses of neural network models. Connectionism has close methodological ties with modern neurosciences and neurophilosophy. And this fact strengthens its positions, in terms of empirical naturalistic approaches. However, at the same time this direction inherits weaknesses of computational approach, and in this case all system of anticomputational critical arguments becomes applicable to the connectionst models (...)
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  • What Does It Mean to Claim That Something Is 'Innate'? Response to Clark, Harris, Lightfoot and Samuels.Annette Karmiloff-Smith, Kim Plunkett & Mark H. Johnson - 1998 - Mind and Language 13 (4):588-597.
  • Evolutionary Theory Meets Cognitive Psychology: A More Selective Perspective.Lawrence Shapiro & William Epstein - 1998 - Mind and Language 13 (2):171-94.
    Quite unexpectedly, cognitive psychologists find their field intimately connected to a whole new intellectual landscape that had previously seemed remote, unfamiliar, and all but irrelevant. Yet the proliferating connections tying together the cognitive and evolutionary communities promise to transform both fields, with each supplying necessary principles, methods, and a species of rigor that the other lacks. (Cosmides and Tooby, 1994, p. 85).
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  • Minimally Innate Ideas.Michele Merritt - unknown
    This project provides a detailed examination and critique of current philosophical, linguistic, and cognitive accounts of first language acquisition. In particular, I focus on the concept of "innate" and how it is embraced, marginally utilized, or abandoned altogether in efforts to describe the way that a child comes to be a competent user of a language. A central question that naturally falls out of this general inquiry is therefore what exactly is supposed to be "innate," according to various theories? Philosophically, (...)
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  • Acquisition of English Relative Clauses by German L1 and Turkish L1 Speakers.Emin Yas - 2016 - Dissertation, Freie Universität Berlin
    The dissertation is a contrastive analysis. It deals with the acquisition of English relative clause (RC) by German and Turkish students(in Germany and Turkey) learning English as a second and third language and attending the 11th grades of a German school. The main question of the study is to find out whether the acquisition of English RCs is more difficult for German or for Turkish learners. The other study is the corpus analysis of the English relative clauses. For this research (...)
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  • Book: Cognitive Design for Artificial Minds.Antonio Lieto - 2021 - London, UK: Routledge, Taylor & Francis Ltd.
    Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...)
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  • Systematicity and Natural Language Syntax.Barbara C. Scholz - 2007 - Croatian Journal of Philosophy 7 (3):375-402.
    A lengthy debate in the philosophy of the cognitive sciences has turned on whether the phenomenon known as ‘systematicity’ of language and thought shows that connectionist explanatory aspirations are misguided. We investigate the issue of just which phenomenon ‘systematicity’ is supposed to be. The much-rehearsed examples always suggest that being systematic has something to do with ways in which some parts of expressions in natural languages (and, more conjecturally, some parts of thoughts) can be substituted for others without altering well-formedness. (...)
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  • Beyond the Icon: Core Cognition and the Bounds of Perception.Sam Clarke - forthcoming - Mind and Language:1-20.
    This paper refines a controversial proposal: that core systems belong to a perceptual kind, marked out by the format of its representational outputs. Following Susan Carey, this proposal has been understood in terms of core representations having an iconic format, like certain paradigmatically perceptual outputs. I argue that they don’t, but suggest that the proposal may be better formulated in terms of a broader analogue format type. Formulated in this way, the proposal accommodates the existence of genuine icons in perception, (...)
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  • The physics of representation.Russell A. Poldrack - forthcoming - Synthese:1-19.
    The concept of “representation” is used broadly and uncontroversially throughout neuroscience, in contrast to its highly controversial status within the philosophy of mind and cognitive science. In this paper I first discuss the way that the term is used within neuroscience, in particular describing the strategies by which representations are characterized empirically. I then relate the concept of representation within neuroscience to one that has developed within the field of machine learning. I argue that the recent success of artificial neural (...)
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  • Rules Versus Statistics: Insights From a Highly Inflected Language.Jelena Mirković, Mark S. Seidenberg & Marc F. Joanisse - 2011 - Cognitive Science 35 (4):638-681.
    Inflectional morphology has been taken as a paradigmatic example of rule-governed grammatical knowledge (Pinker, 1999). The plausibility of this claim may be related to the fact that it is mainly based on studies of English, which has a very simple inflectional system. We examined the representation of inflectional morphology in Serbian, which encodes number, gender, and case for nouns. Linguists standardly characterize this system as a complex set of rules, with disagreements about their exact form. We present analyses of a (...)
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  • Weighted Constraints in Generative Linguistics.Joe Pater - 2009 - Cognitive Science 33 (6):999-1035.
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  • Similarity and Rules United: Similarity‐ and Rule‐Based Processing in a Single Neural Network.Tom Verguts & Wim Fias - 2009 - Cognitive Science 33 (2):243-259.
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  • Language and Thought in Normal and Handicapped Children.Heather K. J. Lely - 1993 - Mind and Language 8 (3):450-458.
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  • The Cognizer's Innards: A Psychological and Philosophical Perspective on the Development of Thought.Andy Clark & Annette Karmiloff-Smith - 1993 - Mind and Language 8 (4):487-519.
  • Making Nets Work Hard.Kim Plunkett - 1993 - Mind and Language 8 (4):549-558.
  • Is Logicist Cognitive Science Possible?Alan Garnham - 1993 - Mind and Language 8 (1):49-71.
    This paper argues against Oaksford and Chater's claim that logicist cognitive science is not possible. It suggests that there arguments against logicist cognitive science are too closely tied to the account of Pylyshyn and of Fodor, and that the correct way of thinking about logicist cognitive science is in a mental models framework.
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  • Beyond Eliminativism.Andy Clark - 1989 - Mind and Language 4 (4):251-79.
    There is a school of thought which links connectionist models of cognition to eliminativism-the thesis that the constructs of commonsense psychology do not exist. This way of construing the impact of connectionist modelling is, I argue, deeply mistaken and depends crucially on a shallow analysis of the notion of explanation. I argue that good, higher level descriptions may group together physically heterogenous mechanisms, and that the constructs of folk psychology may fulfil such a grouping function even if they fail to (...)
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  • A Bound on Synchronically Interpretable Structure.Jon M. Slack - 2004 - Mind and Language 19 (3):305–333.
    Multiple explanatory frameworks may be required to provide an adequate account of human cognition. This paper embeds the classical account within a neural network framework, exploring the encoding of syntacticallystructured objects over the synchronicdiachronic characteristics of networks. Synchronic structure is defined in terms of temporal binding and the superposition of states. To accommodate asymmetric relations, synchronic structure is subject to the type uniqueness constraint. The nature of synchronic structure is shown to underlie Xbar theory that characterizes the phrasal structure of (...)
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  • Epistemic and Non-Epistemic Theories of Remembering.Steven James - 2017 - Pacific Philosophical Quarterly:109-127.
    Contemporary memory sciences describe processes that are dynamic and constructive. This has led some philosophers to weaken the relationship between memory and epistemology; though remembering can give rise to epistemic success, it is not itself an epistemic success state. I argue that non-epistemic theories will not do; they provide neither necessary nor sufficient conditions for remembering that p. I also argue that the shortcomings of the causal theory are epistemic in nature. Consequently, a theory of remembering must account for both (...)
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  • Performance-Similarity Reasoning as a Source for Mechanism Schema Evaluation.Raoul Gervais - 2020 - Topoi 39 (1):69-79.
    In this paper, I explicate and discuss performance-similarity reasoning as a strategy for mechanism schema evaluation, understood in Lindley Darden’s sense. This strategy involves inferring hypotheses about the mechanism responsible for cognitive capacities from premises describing the performance of those capacities; performance-similarity reasoning is a type of Inference to the Best Explanation, or IBE. Two types of such inferences are distinguished: one in which the performance of two systems is compared, and another when the performance of two systems under intervention (...)
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  • Active Symbols and Internal Models: Towards a Cognitive Connectionism. [REVIEW]Stephen Kaplan, Mark Weaver & Robert French - 1990 - AI and Society 4 (1):51-71.
    In the first section of the article, we examine some recent criticisms of the connectionist enterprise: first, that connectionist models are fundamentally behaviorist in nature (and, therefore, non-cognitive), and second that connectionist models are fundamentally associationist in nature (and, therefore, cognitively weak). We argue that, for a limited class of connectionist models (feed-forward, pattern-associator models), the first criticism is unavoidable. With respect to the second criticism, we propose that connectionist modelsare fundamentally associationist but that this is appropriate for building models (...)
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  • Connectionism, Explicit Rules, and Symbolic Manipulation.Robert F. Hadley - 1993 - Minds and Machines 3 (2):183-200.
    At present, the prevailing Connectionist methodology forrepresenting rules is toimplicitly embody rules in neurally-wired networks. That is, the methodology adopts the stance that rules must either be hard-wired or trained into neural structures, rather than represented via explicit symbolic structures. Even recent attempts to implementproduction systems within connectionist networks have assumed that condition-action rules (or rule schema) are to be embodied in thestructure of individual networks. Such networks must be grown or trained over a significant span of time. However, arguments (...)
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  • Connectionism and Artificial Intelligence as Cognitive Models.Daniel Memmi - 1990 - AI and Society 4 (2):115-136.
    The current renewal of connectionist techniques using networks of neuron-like units has started to have an influence on cognitive modelling. However, compared with classical artificial intelligence methods, the position of connectionism is still not clear. In this article artificial intelligence and connectionism are systematically compared as cognitive models so as to bring out the advantages and shortcomings of each. The problem of structured representations appears to be particularly important, suggesting likely research directions.
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  • Connectionism and Three Levels of Nativism.William Ramsey & Stephen P. Stich - 1990 - Synthese 82 (2):177-205.
    Along with the increasing popularity of connectionist language models has come a number of provocative suggestions about the challenge these models present to Chomsky's arguments for nativism. The aim of this paper is to assess these claims. We begin by reconstructing Chomsky's argument from the poverty of the stimulus and arguing that it is best understood as three related arguments, with increasingly strong conclusions. Next, we provide a brief introduction to connectionism and give a quick survey of recent efforts to (...)
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  • The Case for Connectionism.William Bechtel - 1993 - Philosophical Studies 71 (2):119-54.
  • Superpositional Connectionism: A Reply to Marinov. [REVIEW]Andy Clark - 1993 - Minds and Machines 3 (3):271-81.
    Marinov''s critique I argue, is vitiated by its failure to recognize the distinctive role of superposition within the distributed connectionist paradigm. The use of so-called subsymbolic distributed encodings alone is not, I agree, enough to justify treating distributed connectionism as a distinctive approach. It has always been clear that microfeatural decomposition is both possible and actual within the confines of recognizably classical approaches. When such approaches also involve statistically-driven learning algorithms — as in the case of ID3 — the fundamental (...)
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  • Doing Without Representing?Andy Clark & Josefa Toribio - 1994 - Synthese 101 (3):401-31.
    Connectionism and classicism, it generally appears, have at least this much in common: both place some notion of internal representation at the heart of a scientific study of mind. In recent years, however, a much more radical view has gained increasing popularity. This view calls into question the commitment to internal representation itself. More strikingly still, this new wave of anti-representationalism is rooted not in armchair theorizing but in practical attempts to model and understand intelligent, adaptive behavior. In this paper (...)
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  • Currents in Connectionism.William Bechtel - 1993 - Minds and Machines 3 (2):125-153.
    This paper reviews four significant advances on the feedforward architecture that has dominated discussions of connectionism. The first involves introducing modularity into networks by employing procedures whereby different networks learn to perform different components of a task, and a Gating Network determines which network is best equiped to respond to a given input. The second consists in the use of recurrent inputs whereby information from a previous cycle of processing is made available on later cycles. The third development involves developing (...)
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  • Connectionism Isn't Magic.Hugh Clapin - 1991 - Minds and Machines 1 (2):167-84.
    Ramsey, Stich and Garon's recent paper Connectionism, Eliminativism, and the Future of Folk Psychology claims a certain style of connectionism to be the final nail in the coffin of folk psychology. I argue that their paper fails to show this, and that the style of connectionism they illustrate can in fact supplement, rather than compete with, the claims of a theory of cognition based in folk psychology's ontology. Ramsey, Stich and Garon's argument relies on the lack of easily identifiable symbols (...)
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  • The Language of Thought Hypothesis.Murat Aydede - 2010 - Stanford Encyclopedia of Philosophy.
    A comprehensive introduction to the Language of Though Hypothesis (LOTH) accessible to general audiences. LOTH is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in (...)
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  • Optimization in Neural Networks and in Universal Grammar.Paul Smolensky - unknown
     
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  • Theories of Early Language Acquisition.Kim Plunkett - 1997 - Trends in Cognitive Sciences 1 (4):146-153.
  • Evaluating Artificial Models of Cognition.Marcin Miłkowski - 2015 - Studies in Logic, Grammar and Rhetoric 40 (1):43-62.
    Artificial models of cognition serve different purposes, and their use determines the way they should be evaluated. There are also models that do not represent any particular biological agents, and there is controversy as to how they should be assessed. At the same time, modelers do evaluate such models as better or worse. There is also a widespread tendency to call for publicly available standards of replicability and benchmarking for such models. In this paper, I argue that proper evaluation ofmodels (...)
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  • Connectionist Models and Linguistic Theory: Investigations of Stress Systems in Language.Prahlad Gupta & David S. Touretzky - 1994 - Cognitive Science 18 (1):1-50.
  • Levels of Description and Explanation in Cognitive Science.William Bechtel - 1994 - Minds and Machines 4 (1):1-25.
    The notion of levels has been widely used in discussions of cognitive science, especially in discussions of the relation of connectionism to symbolic modeling of cognition. I argue that many of the notions of levels employed are problematic for this purpose, and develop an alternative notion grounded in the framework of mechanistic explanation. By considering the source of the analogies underlying both symbolic modeling and connectionist modeling, I argue that neither is likely to provide an adequate analysis of processes at (...)
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  • On the Biological Plausibility of Grandmother Cells: Implications for Neural Network Theories in Psychology and Neuroscience.Jeffrey S. Bowers - 2009 - Psychological Review 116 (1):220-251.
    A fundamental claim associated with parallel distributed processing theories of cognition is that knowledge is coded in a distributed manner in mind and brain. This approach rejects the claim that knowledge is coded in a localist fashion, with words, objects, and simple concepts, that is, coded with their own dedicated representations. One of the putative advantages of this approach is that the theories are biologically plausible. Indeed, advocates of the PDP approach often highlight the close parallels between distributed representations learned (...)
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  • Connectionism, Modularity, and Tacit Knowledge.Martin Davies - 1989 - British Journal for the Philosophy of Science 40 (December):541-55.
    In this paper, I define tacit knowledge as a kind of causal-explanatory structure, mirroring the derivational structure in the theory that is tacitly known. On this definition, tacit knowledge does not have to be explicitly represented. I then take the notion of a modular theory, and project the idea of modularity to several different levels of description: in particular, to the processing level and the neurophysiological level. The fundamental description of a connectionist network lies at a level between the processing (...)
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  • Natural Language and Natural Selection.Steven Pinker & Paul Bloom - 1990 - Behavioral and Brain Sciences 13 (4):707-27.
    Many people have argued that the evolution of the human language faculty cannot be explained by Darwinian natural selection. Chomsky and Gould have suggested that language may have evolved as the by-product of selection for other abilities or as a consequence of as-yet unknown laws of growth and form. Others have argued that a biological specialization for grammar is incompatible with every tenet of Darwinian theory – that it shows no genetic variation, could not exist in any intermediate forms, confers (...)
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  • Past Tense Learning.Amit Almor - 2002 - In M. Arbib (ed.), The Handbook of Brain Theory and Neural Networks. MIT Press. pp. 848--851.
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  • Against Neuroclassicism: On the Perils of Armchair Neuroscience.Alex Morgan - forthcoming - Mind and Language.
    Neuroclassicism is the view that cognition is explained by “classical” computing mechanisms in the nervous system that exhibit a clear demarcation between processing machinery and read–write memory. The psychologist C. R. Gallistel has mounted a sophisticated defense of neuroclassicism by drawing from ethology and computability theory to argue that animal brains necessarily contain read–write memory mechanisms. This argument threatens to undermine the “connectionist” orthodoxy in contemporary neuroscience, which does not seem to recognize any such mechanisms. In this paper I argue (...)
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  • A Developmental Approach to Machine Learning?Linda B. Smith & Lauren K. Slone - 2017 - Frontiers in Psychology 8.
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  • Structured Semantic Knowledge Can Emerge Automatically From Predicting Word Sequences in Child-Directed Speech.Philip A. Huebner & Jon A. Willits - 2018 - Frontiers in Psychology 9.
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