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MARATSOS, M. P. "Problems of Connectionism: Connections and Symbols." Science 242, no. 4883 (December 2, 1988): 1316–17. http://dx.doi.org/10.1126/science.242.4883.1316.
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.
Fowler, David, and David W. Brooks. "Connectionism." Journal of Chemical Education 68, no. 9 (September 1991): 748. http://dx.doi.org/10.1021/ed068p748.
Gasser, Michael. "Connectionism and Universals of Second Language Acquisition." Studies in Second Language Acquisition 12, no. 2 (June 1990): 179–99. http://dx.doi.org/10.1017/s0272263100009074.
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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Beaman, C. Philip. "Neurons amongst the symbols?" Behavioral and Brain Sciences 23, no. 4 (August 2000): 468–70. http://dx.doi.org/10.1017/s0140525x00233359.
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, and Vanja Subotic. "The exploratory status of postconnectionist models." Theoria, Beograd 63, no. 2 (2020): 135–64. http://dx.doi.org/10.2298/theo2002135m.
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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Lones, Michael A., Alexander P. Turner, Luis A. Fuente, Susan Stepney, Leo S. D. Caves, and Andy M. Tyrrell. "Biochemical connectionism." Natural Computing 12, no. 4 (October 20, 2013): 453–72. http://dx.doi.org/10.1007/s11047-013-9400-y.
Berkeley, Istvan S. N. "The Curious Case of Connectionism." Open Philosophy 2, no. 1 (August 12, 2019): 190–205. http://dx.doi.org/10.1515/opphil-2019-0018.
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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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.
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.
Berkeley, István Stephen Norman. "On connectionism." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq21549.pdf.
As the technology of computer systems matures, it is becoming clear that conventional techniques are inadequate for complex applications, and attention is being increasingly directed at the use of Knowledge-Based Systems technology. Critical problems in Knowledge-Based Systems are the representation of the expertise from the selected application, and in harnessing sufficient computing power to utilise the stored expertise. An idea which is currently popular in Artificial Intelligence research is that of using parallel processing to ensure that the expertise or knowledge is used effectively, within realistic timescales. Proposals vary in the degree of parallelism, and in the distribution of the problem solving activities. The theory of connectionism, which proposes that the knowledge representation and the problem solving computations are distributed across a very large number of processors, has generated considerable interest and response. Connectionist machines, sometimes known as massively parallel processors, are not highly parallel versions of conventional problem solving engines, but combine the representation of the problem with the processing to produce what is known as an active memory network. Research is described which investigates the application of connectionist theory to various complex problems. These problems are investigated within the context of conventional knowledge-based systems, with the aim of establishing if massively parallel technology realises an efficient problem solving engine. The difficulties associated with the representation and use of numbers in connectionist networks are addressed, the problem of relating the knowledge representation in expert systems to that in connectionist networks is resolved, and the potential for medium scale parallelism in knowledge-based systems is contrasted with the parallelism of connectionism. Finally, the vision of an ideal problem solving engine is considered; some aspects of the evolving designs approach this ideal, and are described.
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Christiansen, Morten H. "Connectionism, learning and linguistics structure." Thesis, University of Edinburgh, 1995. http://hdl.handle.net/1842/526.
This thesis presents a connectionist theory of how infinite languages may fit within finite minds. Arguments are presented against the distinction between linguistic competence and observable language performance. It is suggested that certain kinds of finite state automata, i.e recurrent neural networks are likely to have suffcient computational power,and the necessary generalization capability,to serve as models for the procesing and acquisition of linguistic structure.
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Serchuk, Adam. "Connectionism, disciplinary identity and continuity." Thesis, Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/53246.
Connectionism, a new technique for modeling cognitive processes, has been presented by its supporters as a revolutionary advance that will soon replace conventional artificial intelligence (AI) research based on the serial computer. In this thesis, I identify three 'gambits' with which critics attempt to undermine connectionist claims, and show that use of these gambits depends on the status of the respondent's own discipline. I argue that in cases where the respondent's discipline has an accepted identity, for example biology and psychology, they take contradictory stances on the issue of continuity between their discipline and connectionism. By contrast, responses from supporters of AI, which has an uncertain status, insist on a continuous relationship between connectionism and AI. To account for this, I suggest that claims made by both supporters and critics of connectionism, which those actors would regard as purely cognitive, are tacitly structured by Kuhn's model of scientific change. As certain claims which the actors would describe as purely cognitive can be accounted for by the presence in common scholarly parlance of a particular philosophical model of scientific change, I conclude that in the confrontation between connectionism and conventional AI there exists a complex relationship between social and cognitive processes. Master of Science
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Krieger, Gordon S. F. "Connectionism, naturalized epistemology, and eliminative materialism." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=68112.
The aim of this essay is to explore the potential for an epistemology consistent with eliminative materialism based on work in connectionist modeling. I present a review of the connectionist approach to psychological models that contrasts it with the classical symbolic approach, focusing on the nature of their respective representations. While defending the legitimacy of the connectionist approach, I find that its most useful application is as a basis for neuroscientific investigation. Discussing connectionist psychology, I find it inconsistent with folk psychology and therefore consistent with eliminative materialism. I argue also for the naturalization of epistemology and thus for the relevance of psychology for epistemology. The conclusion of the essay is an outline of connectionist epistemology, which centres around two mathematical analyses of the global activity of connectionist networks; I argue that connectionist psychology leads to a version of epistemic pragmatism.
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Harrison, David J. "Connectionism, folk psychology and cognitive architecture." Thesis, University of Sheffield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322924.
Dayan, Peter Samuel. "Reinforcing connectionism : learning the statistical way." Thesis, University of Edinburgh, 1991. http://hdl.handle.net/1842/14754.
Connectionism's main contribution to cognitive science will prove to be the renewed impetus it has imparted to learning. Learning can be integrated into the existing theoretical foundations of the subject, and the combination, statistical computational theories, provide a framework within which many connectionist mathematical mechanisms naturally fit. Examples from supervised and reinforcement learning demonstrate this. Statistical computational theories already exist for certainn associative matrix memories. This work is extended, allowing real valued synapses and arbitrarily biased inputs. It shows that a covariance learning rule optimises the signal/noise ratio, a measure of the potential quality of the memory, and quantifies the performance penalty incurred by other rules. In particular two that have been suggested as occuring naturally are shown to be asymptotically optimal in the limit of sparse coding. The mathematical model is justified in comparison with other treatments whose results differ. Reinforcement comparison is a way of hastening the learning of reinforcement learning systems in statistical environments. Previous theoretical analysis has not distinguished between different comparison terms, even though empirically, a covariance rule has been shown to be better than just a constant one. The workings of reinforcement comparison are investigated by a second order analysis of the expected statistical performance of learning, and an alternative rule is proposed and empirically justified. The existing proof that temporal difference prediction learning converges in the mean is extended from a special case involving adjacent time steps to the general case involving arbitary ones. The interaction between the statistical mechanism of temporal difference and the linear representation is particularly stark. The performance of the method given a linearly dependent representation is also analysed.
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Casebeer, William D. "Natural ethical facts : evolution, connectionism and moral cognition /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2001. http://wwwlib.umi.com/cr/ucsd/fullcit?p3015847.
Guarini, Marcello 1970. "Rules and representations in the classicism-connectionism debate." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq31115.pdf.
Edwards, A. R. "Detection of instability in power systems using connectionism." Thesis, University of Bath, 1995. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760680.
Quinlan, Philip T. Connectionism and psychology: A psychological perspective on new connectionist research. Chicago: University of Chicago Press, 1991.
Karaminis, Themis N., and Michael S. C. Thomas. "Connectionism." In Encyclopedia of the Sciences of Learning, 767–71. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_397.
Wells, A. J. "Connectionism." In Rethinking Cognitive Computation, 209–23. London: Macmillan Education UK, 2006. http://dx.doi.org/10.1007/978-1-137-06661-9_18.
Weijters, Ton, and Antal van den Bosch. "Connectionism." In Handbook of Pragmatics, 165–71. Amsterdam: John Benjamins Publishing Company, 1995. http://dx.doi.org/10.1075/hop.m.con1.
Weijters, Ton, and Antal van den Bosch. "Connectionism." In Cognition and Pragmatics, 126–34. Amsterdam: John Benjamins Publishing Company, 2009. http://dx.doi.org/10.1075/hoph.3.08wei.
Weijters, Ton, and Antal van den Bosch. "Connectionism." In Handbook of Pragmatics, 295–302. Amsterdam: John Benjamins Publishing Company, 2022. http://dx.doi.org/10.1075/hop.m2.con1.
Sharkey, Amanda J. C., and Noel Sharkey. "Connectionism." In The Routledge Companion to Philosophy of Psychology, 180–92. Second edition. | Abingdon, Oxon ; New York, NY : Routledge, Taylor & Francis Group, 2020.: Routledge, 2019. http://dx.doi.org/10.4324/9780429244629-12.
Buckner, Cameron, and James Garson. "Connectionism and post-connectionist models." In The Routledge Handbook of the Computational Mind, 76–90. Milton Park, Abingdon, Oxon ; New York : Routledge, 2019. |: Routledge, 2018. http://dx.doi.org/10.4324/9781315643670-7.
Тези доповідей конференцій з теми "Connectionism":
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Charniak, Eugene. "Connectionism and explanation." In the 1987 workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1987. http://dx.doi.org/10.3115/980304.980319.
Lehnert, Wendy G. "Possible implications of connectionism." In the 1987 workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1987. http://dx.doi.org/10.3115/980304.980321.
Brachman, R. J., and D. L. McGuinness. "Knowledge representation, connectionism and conceptual retrieval." In the 11th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1988. http://dx.doi.org/10.1145/62437.62448.
Darenfed, Salah. "Genetic connectionism for computed tomographic reconstructions." In IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, edited by Raj S. Acharya and Dmitry B. Goldgof. SPIE, 1993. http://dx.doi.org/10.1117/12.148712.
Dvoryanchikova, A., A. Lobov, A. Capanji, and J. L. Martinez Lastra. "Connectionism-inspired knowledge modeling for industrial systems." In 2010 8th IEEE International Conference on Industrial Informatics (INDIN). IEEE, 2010. http://dx.doi.org/10.1109/indin.2010.5549587.
Xin Cui. "Theoretical basis of research program of new connectionism." In 2011 International Conference on Computer Science and Service System (CSSS). IEEE, 2011. http://dx.doi.org/10.1109/csss.2011.5974737.
Jixing, LI, WANG Yu, and QI Bin. "Discussion on Cyber Security Awareness and Awareness Model Building Based on Connectionism." In 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2018. http://dx.doi.org/10.1109/itoec.2018.8740446.
Zulu, Ephraim, and Theordore Haupt. "An Analysis of Connectionism and Schema Construction in Construction Studies Undergraduate Students." In 2017 7th World Engineering Education Forum (WEEF). IEEE, 2017. http://dx.doi.org/10.1109/weef.2017.8467129.
Xie, Xiuzhen. "Research on College English TPACK Framework Teaching from the Perspective of Connectionism." In Proceedings of the 2018 International Conference on Information Technology and Management Engineering (ICITME 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/icitme-18.2018.44.
Casebeer, William D. Natural Ethical Facts: Evolution, Connectionism, and Moral Cognition. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada387990.
Shastri, Lokendra. The Relevance of Connectionism to AI: A Representation and Reasoning Perspective. Fort Belvoir, VA: Defense Technical Information Center, September 1989. http://dx.doi.org/10.21236/ada225898.
Stanton, John F., Richard G. Anderson, Charles W. Dolan, and David E. McCleary. Moment Resistant Connections and Simple Connections. Precast/Prestressed Concrete Institute, 1986. http://dx.doi.org/10.15554/pci.rr.conn-008.
Wawrzynek, John. Connectionist Network Supercomputer Project. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada279206.
Pollack, Jordan B. High-Level Connectionist Models. Fort Belvoir, VA: Defense Technical Information Center, August 1989. http://dx.doi.org/10.21236/ada216581.
Bazzi, Samuel, and Claudio Labanca. Campaign Connections. Cambridge, MA: National Bureau of Economic Research, May 2023. http://dx.doi.org/10.3386/w31283.
Touretzky, David S., and Geoffrey E. Hinton. A Distributed Connectionist Production System. Fort Belvoir, VA: Defense Technical Information Center, December 1987. http://dx.doi.org/10.21236/ada188530.
Chen, H. H., and Y. C. Lee. Connectionist Models for Intelligent Computation. Fort Belvoir, VA: Defense Technical Information Center, July 1994. http://dx.doi.org/10.21236/ada286436.
Chen, H. H., and Y. C. Lee. Connectionist Models for Intelligent Computation. Fort Belvoir, VA: Defense Technical Information Center, July 1994. http://dx.doi.org/10.21236/ada296789.
Feldman, Jerome A., Dana H. Ballard, Christopher M. Brown, and Gary S. Dell. Rochester Connectionist Papers. 1979-1985. Fort Belvoir, VA: Defense Technical Information Center, December 1985. http://dx.doi.org/10.21236/ada170969.
