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Articles de revues sur le sujet « Connectionism »

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Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 18 mai 2024

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1

MARATSOS, M. P. « Problems of Connectionism : Connections and Symbols. » Science 242, no 4883 (2 décembre 1988) : 1316–17. http://dx.doi.org/10.1126/science.242.4883.1316.

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2

Vassallo, Marta, Davide Sattin, Eugenio Parati et Mario Picozzi. « Problems of Connectionism ». Philosophies 9, no 2 (25 mars 2024) : 41. http://dx.doi.org/10.3390/philosophies9020041.

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The relationship between philosophy and science has always been complementary. Today, while science moves increasingly fast and philosophy shows some problems in catching up with it, it is not always possible to ignore such relationships, especially in some disciplines such as philosophy of mind, cognitive science, and neuroscience. However, the methodological procedures used to analyze these data are based on principles and assumptions that require a profound dialogue between philosophy and science. Following these ideas, this work aims to raise the problems that a classical connectionist theory can cause and problematize them in a cognitive framework, considering both philosophy and cognitive sciences but also the disciplines that are near to them, such as AI, computer sciences, and linguistics. For this reason, we embarked on an analysis of both the computational and theoretical problems that connectionism currently has. The second aim of this work is to advocate for collaboration between neuroscience and philosophy of mind because the promotion of deeper multidisciplinarity seems necessary in order to solve connectionism’s problems. In fact, we believe that the problems that we detected can be solved by a thorough investigation at both a theoretical and an empirical level, and they do not represent an impasse but rather a starting point from which connectionism should learn and be updated while keeping its original and profoundly convincing core.
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3

Naidu, Som. « Connectionism ». Distance Education 33, no 3 (novembre 2012) : 291–94. http://dx.doi.org/10.1080/01587919.2012.723321.

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4

Fowler, David, et David W. Brooks. « Connectionism ». Journal of Chemical Education 68, no 9 (septembre 1991) : 748. http://dx.doi.org/10.1021/ed068p748.

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5

Gasser, Michael. « Connectionism and Universals of Second Language Acquisition ». Studies in Second Language Acquisition 12, no 2 (juin 1990) : 179–99. http://dx.doi.org/10.1017/s0272263100009074.

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This article examines the implications of connectionist models of cognition for second language theory. Connectionism offers a challenge to the symbolic models which dominate cognitive science. In connectionist models all knowledge is embodied in a network of simple processing units joined by connections which are strengthened or weakened in response to regularities in input patterns. These models avoid the brittleness of symbolic approaches, and they exhibit rule-like behavior without explicit rules. A connectionist framework is proposed within which hypotheses about second language acquisition can be tested. Inputs and outputs are patterns of activation on units representing both form and meaning. Learning consists of the unsupervised association of pattern elements with one another. A network is first trained on a set of first language patterns and then exposed to a set of second language patterns with the same meanings. Several simulations of constituent-order transfer within this framework are discussed.
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6

Beaman, C. Philip. « Neurons amongst the symbols ? » Behavioral and Brain Sciences 23, no 4 (août 2000) : 468–70. http://dx.doi.org/10.1017/s0140525x00233359.

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Page's target article presents an argument for the use of localist, connectionist models in future psychological theorising. The “manifesto” marshalls a set of arguments in favour of localist connectionism and against distributed connectionism, but in doing so misses a larger argument concerning the level of psychological explanation that is appropriate to a given domain.
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Milojevic, Miljana, et Vanja Subotic. « The exploratory status of postconnectionist models ». Theoria, Beograd 63, no 2 (2020) : 135–64. http://dx.doi.org/10.2298/theo2002135m.

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This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism - from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete abandonment of its assumptions in the study of cognition. Criticisms have mostly pointed to its explanatory inadequacy as a theory of cognition or to its biological implausibility as a theory of implementation, and critics often focused on specific shortcomings of some connectionist models and argued that they apply on connectionism in general. In this paper we want to show that both types of critique are based on the assumption that the only valid explanations in cognitive science are instances of homuncular functionalism and that by removing this assumption and by adopting an alternative methodology - exploratory mechanistic strategy, we can reject most objections to connectionism as irrelevant, explain the progress of connectionist models despite their shortcomings and sketch the trajectory of their future development. By adopting mechanistic explanations and by criticizing functionalism, we will reject the objections of explanatory inadequacy, by characterizing connectionist models as generic rather than concrete mechanisms, we will reject the objections of biological implausibility, and by attributing the exploratory character to connectionist models we will show that practice of generalizing current to general failures of connectionism is unjustified.
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8

Lones, Michael A., Alexander P. Turner, Luis A. Fuente, Susan Stepney, Leo S. D. Caves et Andy M. Tyrrell. « Biochemical connectionism ». Natural Computing 12, no 4 (20 octobre 2013) : 453–72. http://dx.doi.org/10.1007/s11047-013-9400-y.

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9

Berkeley, Istvan S. N. « The Curious Case of Connectionism ». Open Philosophy 2, no 1 (12 août 2019) : 190–205. http://dx.doi.org/10.1515/opphil-2019-0018.

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AbstractConnectionist research first emerged in the 1940s. The first phase of connectionism attracted a certain amount of media attention, but scant philosophical interest. The phase came to an abrupt halt, due to the efforts of Minsky and Papert (1969), when they argued for the intrinsic limitations of the approach. In the mid-1980s connectionism saw a resurgence. This marked the beginning of the second phase of connectionist research. This phase did attract considerable philosophical attention. It was of philosophical interest, as it offered a way of counteracting the conceptual ties to the philosophical traditions of atomism, rationalism, logic, nativism, rule realism and a concern with the role symbols play in human cognitive functioning, which was prevalent as a consequence of artificial intelligence research. The surge in philosophical interest waned, possibly in part due to the efforts of some traditionalists and the so-called black box problem. Most recently, what may be thought of as a third phase of connectionist research, based on so-called deep learning methods, is beginning to show some signs of again exciting philosophical interest.
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10

Castro Moreno, Carmen Cayetana. « Secuencia y delimitación en el procesamiento textual alemán : modelo conexionista. » Futhark. Revista de Investigación y Cultura, no 7 (2012) : 85–114. http://dx.doi.org/10.12795/futhark.2012.i07.03.

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In order to simulate human speech, connectionism has developed several techniques for representation of frames and scripts. These models are able to solve some of the shortcomings of classical cognitive systems. Specifically, classical systems are unable to control when a chunk of knowledge has to be activated and what part of the chunk should be more activated. Instead, connectionist systems can use degrees in mobilizing to try this case. However, connectionism has also some limitations if considered as models of human mind, but they also are a point of interest to the application in the prosecution textual. Besides, we examinate the plausibility of any of these approaches used in computers and their implications in our own connectionist model.
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11

CHATER, NICK, et MARTIN REDINGTON. « Connectionism, theories of learning, and syntax acquisition : where do we stand ? » Journal of Child Language 26, no 1 (février 1999) : 217–60. http://dx.doi.org/10.1017/s0305000998223749.

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12

Phaf, R. Hans. « Connectionism and psychology : A psychological perspective on new connectionist research ». Acta Psychologica 85, no 2 (avril 1994) : 178–81. http://dx.doi.org/10.1016/0001-6918(94)90033-7.

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13

Seidenberg, Mark S. « Connectionist Models and Cognitive Theory ». Psychological Science 4, no 4 (juillet 1993) : 228–35. http://dx.doi.org/10.1111/j.1467-9280.1993.tb00266.x.

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Connectionist models have come to play an important role in cognitive science and in cognitive neuroscience, yet their role in explaining behavior is not necessarily obvious and has generated considerable debate. Connectionism is a body of tools and ideas that can be used in different ways. It can be treated as a form of simulation modeling in which the goal is to implement preexisting theories. In this approach, connectionist models function as a kind of statistical tool, a way of analyzing a complex set of data. Connectionism can also be seen as providing a small set of general theoretical principles that apply in a variety of domains. Construed in this way, it contributes to the development of theories that are explanatory, not merely descriptive.
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14

Dobson, Richard, Peter M. Todd, D. Gareth Loy et David Rosenboom. « Music and Connectionism ». Notes 49, no 4 (juin 1993) : 1497. http://dx.doi.org/10.2307/899412.

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15

McLaughlin, Brian. « TYE ON CONNECTIONISM ». Southern Journal of Philosophy 26, S1 (26 mars 2010) : 185–93. http://dx.doi.org/10.1111/j.2041-6962.1988.tb00472.x.

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16

SCHWARZ, GEORG. « Connectionism, Processing, Memory ». Connection Science 4, no 3-4 (janvier 1992) : 207–26. http://dx.doi.org/10.1080/09540099208946616.

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17

Kassirer, Jerome P., et Richard I. Kopelman. « Cognition and Connectionism ». Hospital Practice 25, no 2 (15 février 1990) : 15–26. http://dx.doi.org/10.1080/21548331.1990.11703906.

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18

McNaughton, Neil. « Evolution and connectionism ». Behavioral and Brain Sciences 13, no 2 (juin 1990) : 402–3. http://dx.doi.org/10.1017/s0140525x00079449.

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19

Zucker, Steven W. « Does connectionism suffice ? » Behavioral and Brain Sciences 8, no 2 (juillet 1985) : 301–2. http://dx.doi.org/10.1017/s0140525x00020823.

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20

Smolensky, Paul. « Connectionism and implementation ». Behavioral and Brain Sciences 10, no 3 (septembre 1987) : 492–93. http://dx.doi.org/10.1017/s0140525x00023712.

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21

Sterelny, Kim. « Connectionism Rules, OK ? » Dialogue 32, no 3 (1993) : 545–54. http://dx.doi.org/10.1017/s0012217300012312.

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Those familiar with Paul Churchland's earlier work will expect A Neuro-computational Perspective to be lively, provocative and interesting. They will not be disappointed. Churchland is best known for his sceptical view of belief-desire psychology. He suspects this theory is hopelessly false. This welcome collection of his essays includes this work but also his papers on the subjective aspects of the mind and his more recent adventures in philosophy of science. Three themes unify the collection: an anti-sententialist view of cognition, an emphasis on the plasticity of the human mind and a strong endorsement of connectionist models of human cognitive processes.
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22

Cordeschi, Roberto. « Early-connectionism machines ». AI & ; Society 14, no 3-4 (septembre 2000) : 314–30. http://dx.doi.org/10.1007/bf01205514.

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23

Sánchez, V. D. A. « Connectionism in perspective ». Neurocomputing 2, no 3 (juin 1991) : 135–40. http://dx.doi.org/10.1016/0925-2312(91)90059-k.

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24

Biggs, Norman. « Combinatorics and connectionism ». Discrete Mathematics 124, no 1-3 (janvier 1994) : 19–36. http://dx.doi.org/10.1016/0012-365x(92)00048-v.

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25

Clapin, Hugh. « Connectionism isn't magic ». Minds and Machines 1, no 2 (mai 1991) : 167–84. http://dx.doi.org/10.1007/bf00361035.

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26

Bechtel, William. « Currents in connectionism ». Minds and Machines 3, no 2 (mai 1993) : 125–53. http://dx.doi.org/10.1007/bf00975529.

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27

O'Brien, Gerard J. « Is connectionism commonsense ? » Philosophical Psychology 4, no 2 (janvier 1991) : 165–78. http://dx.doi.org/10.1080/09515089108573024.

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28

Plunkett, Kim. « Connectionism and development ». Infant Behavior and Development 19 (avril 1996) : 178. http://dx.doi.org/10.1016/s0163-6383(96)90233-7.

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29

Smith, Linda B. « Teleology in connectionism ». Developmental Science 5, no 2 (mai 2002) : 178–80. http://dx.doi.org/10.1111/1467-7687.00218_4.

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30

DiPalma, Louis P., et David J. Hamilton. « Music and Connectionism ». Journal of the Acoustical Society of America 96, no 2 (août 1994) : 1218–19. http://dx.doi.org/10.1121/1.410341.

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31

Garton, Brad. « Music and connectionism ». Artificial Intelligence 79, no 2 (décembre 1995) : 387–98. http://dx.doi.org/10.1016/0004-3702(95)90015-2.

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32

Marcus, Gary F. « Rethinking Eliminative Connectionism ». Cognitive Psychology 37, no 3 (décembre 1998) : 243–82. http://dx.doi.org/10.1006/cogp.1998.0694.

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33

Banan, Shahryar, Muhammad Ridwan et Abdurahman Adisaputera. « A Study of Connectionism Theory ». Budapest International Research and Critics Institute (BIRCI-Journal) : Humanities and Social Sciences 3, no 3 (13 août 2020) : 2335–42. http://dx.doi.org/10.33258/birci.v3i3.1181.

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The development of connectionism represents a paradigm shift in science. Connectionism has its root in cognitive and computational neuroscience. Likening the brain to a computer, connectionism tries to describe human mental abilities in terms of artificial neural networks. A neural network consists of a large number of nodes and units which are joined together to form an interconnection network. Within these interconnections, knowledge is distributed. Therefore learning is a processing by-product. This article is about the concept of connectionism, what it accounts for and what it doesn't take into account. Finally, different approaches to connectionism are discussed.
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34

HAHN, ULRIKE. « Language acquisition also needs non-connectionist models ». Journal of Child Language 26, no 1 (février 1999) : 217–60. http://dx.doi.org/10.1017/s0305000998283747.

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Rethinking innateness is a timely volume which forcefully demonstrates the importance of modelling in understanding development, ‘innateness’, and the nature of change. It provides an inspiring vision of what developmental psychology could one day be like, linking behaviour and biology via connectionist models. However, Rispoli's worry about the book's potential for detrimental polarization does not seem unfounded. One aspect of the book that deserves comment in this respect is the focus on connectionism to the exclusion of other types of model. It is unclear from Rethinking innateness itself whether this exclusion of other approaches merely stems from the legitimate desire to write a focused book, or whether it possibly reflects an actively held view that connectionism is the one true approach to modelling development. Regardless of the authors' intentions, the role of connectionism in relation to other computational approaches is an issue which is particularly pertinent to the study of language acquisition.To clarify straightaway, I not only concur with Elman et al. on the central role of modelling, but also strongly believe that the task of the cognitive scientist is not complete until one has an account of how a particular process is realised in a neural architecture. These two commitments give connectionist models a central role. But they do not make connectionism the exclusive modelling tool for the study of language acquisition, nor necessarily the best path to currently pursue. The reasons for this are twofold.The first is a matter of research strategy. It is possible that more rapid progress and greater success might be made if one starts with high-level models which give less immediate regard to matters of implementation.
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35

O'Loughlin, Claire F., et Annette Karmiloff-Smith. « Evaluating connectionism : A developmental perspective ». Behavioral and Brain Sciences 26, no 5 (octobre 2003) : 614–15. http://dx.doi.org/10.1017/s0140525x03310131.

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This commentary questions the applicability of the Newell Test for evaluating the utility of connectionism. Rather than being a specific theory of cognition (because connectionism can be used to model nativist, behaviorist, or constructivist theories), connectionism, we argue, offers researchers a collection of computational and conceptual tools that are particularly useful for investigating and rendering specific fundamental issues of human development. These benefits of connectionism are not well captured by evaluating it against Newell's criteria for a unified theory of cognition.
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36

Hadley, Robert F. « The Problem of Rapid Variable Creation ». Neural Computation 21, no 2 (février 2009) : 510–32. http://dx.doi.org/10.1162/neco.2008.07-07-572.

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Both Marcus ( 2001 ) and Jackendoff ( 2002 ) have emphasized the importance of finding credible explanations for the occurrence of variables within cognitive representations. Marcus, in particular, has argued that a prevailing form of connectionist modeling, eliminative connectionism, cannot adequately explain crucial forms of human generalization. Eliminative connectionism eschews the use of explicitly represented variables, and the latter, Marcus contends, play an essential role in the forms of generalization that he considers. Recently, van der Velde and de Kamps ( 2006 ) proposed a neural blackboard architecture, which they assert to have satisfied the variable representation needs that Marcus and Jackendoff identified. However, this letter argues that closely related variants of Marcus's generalization examples possess variable requirements that are incompatible with the van der Velde and de Kamps approach. Moreover, it is argued here that these newly proposed variants present a severe challenge not only for eliminative connectionism but for all network training methods that require iterative tuning of synaptic strengths. The letter focuses on generalization cases that necessitate either virtually instantaneous creation of variables or very rapid deployment of preexisting variables within highly novel contexts.
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37

Thaler, Stephen. « Lessons from connectionism in differentiating knowledge types ». e-mentor 2014, no 55 (3) (30 juin 2014) : 81–86. http://dx.doi.org/10.15219/em55.1112.

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38

Mills, Stephen. « Smolensky’s Interpretation of Connectionism ». Irish Philosophical Journal 7, no 1 (1990) : 104–18. http://dx.doi.org/10.5840/irishphil199071/25.

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Lycan, William G. « Connectionism and the Mental ». Noûs 25, no 2 (avril 1991) : 207. http://dx.doi.org/10.2307/2215585.

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Plunkett, Kim, et Chris Sinha. « Connectionism and developmental theory ». British Journal of Developmental Psychology 10, no 3 (septembre 1992) : 209–54. http://dx.doi.org/10.1111/j.2044-835x.1992.tb00575.x.

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41

Fetzer, James H. « What makes connectionism different ? » Pragmatics and Cognition 2, no 2 (1 janvier 1994) : 327–48. http://dx.doi.org/10.1075/pc.2.2.09fet.

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42

Goldstein, Laurence, et Hartley Slater. « Wittgenstein, Semantics and Connectionism ». Philosophical Investigations 21, no 4 (octobre 1998) : 293–314. http://dx.doi.org/10.1111/1467-9205.00074.

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Plunkett, Kim. « Language Acquisition and Connectionism ». Language and Cognitive Processes 13, no 2-3 (juin 1998) : 97–104. http://dx.doi.org/10.1080/016909698386483.

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Tienson, John L. « AN INTRODUCTION TO CONNECTIONISM ». Southern Journal of Philosophy 26, S1 (26 mars 2010) : 1–16. http://dx.doi.org/10.1111/j.2041-6962.1988.tb00460.x.

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Graham, George. « CONNECTIONISM IN PAVLOVIAN HARNESS ». Southern Journal of Philosophy 26, S1 (26 mars 2010) : 73–91. http://dx.doi.org/10.1111/j.2041-6962.1988.tb00465.x.

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HARRIS, CATHERINE L. « Connectionism and Cognitive Linguistics ». Connection Science 2, no 1-2 (janvier 1990) : 7–33. http://dx.doi.org/10.1080/09540099008915660.

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CHRISTIANSEN, MORTEN H., et NICK CHATER. « Connectionism, Learning and Meaning ». Connection Science 4, no 3-4 (janvier 1992) : 227–52. http://dx.doi.org/10.1080/09540099208946617.

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48

North, Geoffrey. « A celebration of connectionism ». Nature 328, no 6126 (juillet 1987) : 107. http://dx.doi.org/10.1038/328107a0.

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49

Dulany, Donelson E. « Consciousness, connectionism, and intentionality ». Behavioral and Brain Sciences 22, no 1 (février 1999) : 154–55. http://dx.doi.org/10.1017/s0140525x99281794.

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50

Marcus, Gary F. « Can connectionism save constructivism ? » Cognition 66, no 2 (mai 1998) : 153–82. http://dx.doi.org/10.1016/s0010-0277(98)00018-3.

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