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  • Articles  (8)
  • Connectionism  (8)
  • 2015-2019
  • 1990-1994  (8)
  • 1915-1919
  • Philosophy  (8)
  • Political Science
  • 1
    Electronic Resource
    Electronic Resource
    Levels of description and explanation in cognitive science (1994)
    Springer
    Minds and machines 4 (1994), S. 1-25 
    Details
    ISSN: 1572-8641
    Keywords: Connectionism ; symbol processing ; levels of organization ; reduction ; mechanistic explanation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract 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 the level at which cognitive theories attempt to function: One is drawn from too low a level, the other from too high a level. If there is a distinctly cognitive level, then we still need to determine what are the basic organizational principles at that level.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00974201
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  • 2
    Electronic Resource
    Electronic Resource
    Representational trajectories in connectionist learning (1994)
    Springer
    Minds and machines 4 (1994), S. 317-332 
    Details
    ISSN: 1572-8641
    Keywords: Connectionism ; learning ; development ; recurrent networks ; unlearning ; catastrophic forgetting
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract The paper considers the problems involved in getting neural networks to learn about highly structured task domains. A central problem concerns the tendency of networks to learn only a set of shallow (non-generalizable) representations for the task, i.e., to ‘miss’ the deep organizing features of the domain. Various solutions are examined, including task specific network configuration and incremental learning. The latter strategy is the more attractive, since it holds out the promise of a task-independent solution to the problem. Once we see exactly how the solution works, however, it becomes clear that it is limited to a special class of cases in which (1) statistically driven undersampling is (luckily) equivalent to task decomposition, and (2) the dangers of unlearning are somehow being minimized. The technique is suggestive nonetheless, for a variety of developmental factors may yield the functional equivalent of both statistical AND ‘informed’ undersampling in early learning.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00974197
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  • 3
    Electronic Resource
    Electronic Resource
    Currents in connectionism (1993)
    Springer
    Minds and machines 3 (1993), S. 125-153 
    Details
    ISSN: 1572-8641
    Keywords: Connectionism ; neural networks ; expert networks ; recurrent networks ; RAAM networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract 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 compressed representations of strings in which there is no longer an explicit encoding of the components but where information about the structure of the original string can be recovered and so is present functionally. The final advance entails using connectionist learning procedures not just to change weights in networks but to change the patterns used as inputs to the network. These advances significantly increase the usefulness of connectionist networks for modeling human cognitive performance by, among other things, providing tools for explaining the productivity and systematicity of some mental activities, and developing representations that are sensitive to the content they are to represent.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00975529
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  • 4
    Electronic Resource
    Electronic Resource
    Connectionism, explicit rules, and symbolic manipulation (1993)
    Springer
    Minds and machines 3 (1993), S. 183-200 
    Details
    ISSN: 1572-8641
    Keywords: Connectionism ; representation ; explicit rules
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract 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 are presented herein that humanssometimes follow rules which arevery rapidly assignedexplicit internal representations, and that humans possessgeneral mechanisms capable of interpreting and following such rules. In particular, arguments are presented that thespeed with which humans are able to follow rules ofnovel structure demonstrates the existence of general-purpose rule following mechanisms. It is further argued that the existence of general-purpose rule following mechanisms strongly indicates that explicit rule following is not anisolated phenomenon, but may well be a common and important aspect of cognition. The relationship of the foregoing conclusions to Smolensky's view of explicit rule following is also explored. The arguments presented here are pragmatic in nature, and are contrasted with thekind of arguments developed by Fodor and Pylyshyn in their recent, influential paper.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00975531
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  • 5
    Electronic Resource
    Electronic Resource
    On the spuriousness of the symbolic/subsymbolic distinction (1993)
    Springer
    Minds and machines 3 (1993), S. 253-270 
    Details
    ISSN: 1572-8641
    Keywords: Connectionism ; symbolic/subsymbolic distinction ; TDIDT (top-down induction of decision trees) ; ID3 ; Smolensky ; Fodor ; Pylyshyn ; Quinlan
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract The article criticises the attempt to establish connectionism as an alternative theory of human cognitive architecture through the introduction of thesymbolic/subsymbolic distinction (Smolensky, 1988). The reasons for the introduction of this distinction are discussed and found to be unconvincing. It is shown that thebrittleness problem has been solved for a large class ofsymbolic learning systems, e.g. the class oftop-down induction of decision-trees (TDIDT) learning systems. Also, the process of articulating expert knowledge in rules seems quite practical for many important domains, including common sense knowledge. The article discusses several experimental comparisons betweenTDIDT systems and artificial neural networks using the error backpropagation algorithm (ANNs usingBP). The properties of one of theTDIDT systemsID3 (Quinlan, 1986a) are examined in detail. It is argued that the differences in performance betweenANNs usingBP andTDIDT systems reflect slightly different inductive biases but are not systematic; these differences do not support the view that symbolic and subsymbolic systems are fundamentally incompatible. It is concluded, that thesymbolic/subsymbolic distinction is spurious. It cannot establish connectionism as an alternative cognitive architecture.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00976281
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  • 6
    Electronic Resource
    Electronic Resource
    Superpositional connectionism: A reply to Marinov (1993)
    Springer
    Minds and machines 3 (1993), S. 271-281 
    Details
    ISSN: 1572-8641
    Keywords: Connectionism ; subsymbol ; symbol ; distribution
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract 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 differences become even harder to spot. To see them, it is necessary to consider not just the nature of an acquired input-output function but the nature of the representational scheme underlying it. Differences between such schemes make themselves best felt outside the domain of immediate problem solving. It is in the more extended contexts of performance DURING learning and cognitive change as a result of SUBSEQUENT training on new tasks (or simultaneous training on several tasks) that the effects of superpositional storage techniques come to the fore. I conclude that subsymbols, distribution and statistically driven learning alone are indeed not of the essence. But connectionism is not just about subsymbols and distribution. It is about the generation of whole subsymbol SYSTEMS in which multiple distributed representations are created and superposed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00976282
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  • 7
    Electronic Resource
    Electronic Resource
    Connectionism isn't magic (1991)
    Springer
    Minds and machines 1 (1991), S. 167-184 
    Details
    ISSN: 1572-8641
    Keywords: Connectionism ; eliminativism ; propositional attitudes ; representation ; symbols
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract 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 inside the connectionist network they discuss, and they suggest that the existence of a system which behaves in a cognitively interesting way, but which cannot be explained by appeal to internal symbol processing, falsifies central assumptions of folk psychology. My claim is that this argument is flawed, and that the theorist need not discard folk psychology in order to accept that the network illustrated exhibits cognitively interesting behaviour, even if it is conceded that symbols cannot be readily identified within the network.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00361035
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  • 8
    Electronic Resource
    Electronic Resource
    Fodor and Pylyshyn on connectionism (1991)
    Springer
    Minds and machines 1 (1991), S. 321-341 
    Details
    ISSN: 1572-8641
    Keywords: Cognitive architecture ; computationalism ; Connectionism ; implementation ; inference to the best explanation ; Language of Thought
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract Fodor and Pylyshyn (1988) have argued that the cognitive architecture is not Connectionist. Their argument takes the following form: (1) the cognitive architecture is Classical; (2) Classicalism and Connectionism are incompatible; (3) therefore the cognitive architecture is not Connectionist. In this essay I argue that Fodor and Pylyshyn's defenses of (1) and (2) are inadequate. Their argument for (1), based on their claim that Classicalism best explains the systematicity of cognitive capacities, is an invalid instance of inference to the best explanation. And their argument for (2) turns out to be question-begging. The upshot is that, while Fodor and Pylyshyn have presented Connectionists with the important empirical challenge of explaining systematicity, they have failed to provide sufficient reason for inferring that the cognitive architecture is Classical and not Connectionist.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00351183
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