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  • 1
    Electronic Resource
    Electronic Resource
    Expert networks: Paradigmatic conflict, technological rapproachement (1993)
    Springer
    Minds and machines 3 (1993), S. 53-71 
    Details
    ISSN: 1572-8641
    Keywords: Symbolic AI ; connectionist AI ; connectionism ; neural networks ; learning ; reasoning ; expert networks ; expert systems ; symbolic models ; sub-symbolic models
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract A rule-based expert system is demonstrated to have both a symbolic computational network representation and a sub-symbolic connectionist representation. These alternate views enhance the usefulness of the original system by facilitating introduction of connectionist learning methods into the symbolic domain. The connectionist representation learns and stores metaknowledge in highly connected subnetworks and domain knowledge in a sparsely connected expert network superstructure. The total connectivity of the neural network representation approximates that of real neural systems and hence avoids scaling and memory stability problems associated with other connectionist models.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00974305
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  • 2
    Electronic Resource
    Electronic Resource
    Interruptibility as a constraint on hybrid systems (1993)
    Springer
    Minds and machines 3 (1993), S. 73-96 
    Details
    ISSN: 1572-8641
    Keywords: Cognitive models ; interruptions ; hybrid systems ; connectionism ; symbolic systems ; constraints on hybrid systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract It is widely mooted that a plausible computational cognitive model should involve both symbolic and connectionist components. However, sound principles for combining these components within a hybrid system are currently lacking; the design of such systems is oftenad hoc. In an attempt to ameliorate this we provide a framework of types of hybrid systems and constraints therein, within which to explore the issues. In particular, we suggest the use of “system independent” constraints, whose source lies in general considerations about cognitive systems, rather than in particular technological or task-based considerations. We illustrate this through a detailed examination of an interruptibility constraint: handling interruptions is a fundamental facet of cognition in a dynamic world. Aspects of interruptions are delineated, as are their precise expression in symbolic and connectionist systems. We illustrate the interaction of the various constraints from interruptibility in the different types of hybrid systems. The picture that emerges of the relationship between the connectionist and the symbolic within a hybrid system provides for sufficient flexibility and complexity to suggest interesting general implications for cognition, thus vindicating the utility of the framework.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00974306
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  • 3
    Electronic Resource
    Electronic Resource
    Neural constraints in cognitive science (1994)
    Springer
    Minds and machines 4 (1994), S. 129-162 
    Details
    ISSN: 1572-8641
    Keywords: Cognitive science ; neuroscience ; connectionism ; classical cognitivism ; multiply realizability ; systematic explanation ; levels
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract The paper is an examination of the ways and extent to which neuroscience places constraints on cognitive science. In Part I, I clarify the issue, as well as the notion of levels in cognitive inquiry. I then present and address, in Part II, two arguments designed to show that facts from neuroscience are at a level too low to constrain cognitive theory in any important sense. I argue, to the contrary, that there are several respects in which facts from neurophysiology will constrain cognitive theory. Part III then turns to an examination of Connectionism and Classical Cognitivism to determine which, if either, is in a better position to accomodate neural constraints in the ways suggested in Part II.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00974143
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  • 4
    Electronic Resource
    Electronic Resource
    What the #$*%! is a Subsymbol? (2000)
    Springer
    Minds and machines 10 (2000), S. 1-14 
    Details
    ISSN: 1572-8641
    Keywords: connectionism ; logic ; subsymbols ; symbols ; trained network analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract In 1988, Smolensky proposed that connectionist processing systems should be understood as operating at what he termed the `subsymbolic' level. Subsymbolic systems should be understood by comparing them to symbolic systems, in Smolensky's view. Up until recently, there have been real problems with analyzing and interpreting the operation of connectionist systems which have undergone training. However, recently published work on a network trained on a set of logic problems originally studied by Bechtel and Abrahamsen (1991) seems to offer the potential to provide a detailed, empirically based answer to questions about the nature of subsymbols. In this paper, a network analysis procedure and the results obtained using it are discussed. This provides the basis for an insight into the nature of subsymbols, which is surprising.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008329513803
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  • 5
    Electronic Resource
    Electronic Resource
    The Causal and Explanatory Role of Information Stored in Connectionist Networks (2000)
    Springer
    Minds and machines 10 (2000), S. 361-380 
    Details
    ISSN: 1572-8641
    Keywords: connectionism ; mental representation ; neural networks ; causation ; explanation philosophy of mind
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract In this paper I defend the propriety of explaining the behavior of distributed connectionist networks by appeal to selected data stored therein. In particular, I argue that if there is a problem with such explanations, it is a consequence of the fact that information storage in networks is superpositional, and not because it is distributed. I then develop a “proto-account” of causation for networks, based on an account of Andy Clark's, that shows even superpositionality does not undermine information-based explanation. Finally, I argue that the resulting explanations are genuinely informative and not vacuous.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1026545231550
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  • 6
    Electronic Resource
    Electronic Resource
    “Words lie in our way” (1994)
    Springer
    Minds and machines 4 (1994), S. 421-437 
    Details
    ISSN: 1572-8641
    Keywords: Computation ; computationalism ; calculus ; analog computation ; digital computation ; continuous representation ; Chinese Room argument ; symbol grounding ; continuous formal system ; simulacrum ; intentionality ; connectionism
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract The central claim of computationalism is generally taken to be that the brain is a computer, and that any computer implementing the appropriate program would ipso facto have a mind. In this paper I argue for the following propositions: (1) The central claim of computationalism is not about computers, a concept too imprecise for a scientific claim of this sort, but is about physical calculi (instantiated discrete formal systems). (2) In matters of formality, interpretability, and so forth, analog computation and digital computation are not essentially different, and so arguments such as Searle's hold or not as well for one as for the other. (3) Whether or not a biological system (such as the brain) is computational is a scientific matter of fact. (4) A substantive scientific question for cognitive science is whether cognition is better modeled by discrete representations or by continuous representations. (5) Cognitive science and AI need a theoretical construct that is the continuous analog of a calculus. The discussion of these propositions will illuminate several terminology traps, in which it's all too easy to become ensnared.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00974168
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  • 7
    Electronic Resource
    Electronic Resource
    Brain symbols and computationalist explanation (1995)
    Springer
    Minds and machines 5 (1995), S. 25-44 
    Details
    ISSN: 1572-8641
    Keywords: Cognition ; connectionism ; computationalism ; mental representation ; philosophy of mind
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract Computationalist theories of mind require brain symbols, that is, neural events that represent kinds or instances of kinds. Standard models of computation require multiple inscriptions of symbols with the same representational content. The satisfaction of two conditions makes it easy to see how this requirement is met in computers, but we have no reason to think that these conditions are satisfied in the brain. Thus, if we wish to give computationalist explanations of human cognition, without committing ourselvesa priori to a strong and unsupported claim in neuroscience, we must first either explain how we can provide multiple brain symbols with the same content, or explain how we can abandon standard models of computation. It is argued that both of these alternatives require us to explain the execution of complex tasks that have a cognition-like structure. Circularity or regress are thus threatened, unless noncomputationalist principles can provide the required explanations. But in the latter case, we do not know that noncomputationalist principles might not bear most of the weight of explaining cognition. Four possible types of computationalist theory are discussed; none appears to provide a promising solution to the problem. Thus, despite known difficulties in noncomputationalist investigations, we have every reason to pursue the search for noncomputationalist principles in cognitive theory.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00974188
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  • 8
    Electronic Resource
    Electronic Resource
    Explaining Systematicity: A Reply to Kenneth Aizawa (1997)
    Springer
    Minds and machines 7 (1997), S. 571-579 
    Details
    ISSN: 1572-8641
    Keywords: systematicity ; connectionism ; cognitive architecture ; explanation ; structure-sensitivity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract In his discussion of results which I (with Michael Hayward) recently reported in this journal, Kenneth Aizawa takes issue with two of our conclusions, which are: (a) that our connectionist model provides a basis for explaining systematicity “within the realm of sentence comprehension, and subject to a limited range of syntax” (b) that the model does not employ structure-sensitive processing, and that this is clearly true in the early stages of the network's training. Ultimately, Aizawa rejects both (a) and (b) for reasons which I think are ill-founded. In what follows, I offer a defense of our position. In particular, I argue (1) that Aizawa adopts a standard of explanation that many accepted scientific explanations could not meet, and (2) that Aizawa misconstrues the relevant meaning of ‘structure-sensitive process’.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008252322227
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  • 9
    Electronic Resource
    Electronic Resource
    Explicitness With Psychological Ground (1998)
    Springer
    Minds and machines 8 (1998), S. 353-374 
    Details
    ISSN: 1572-8641
    Keywords: connectionism ; explicit ; implicit ; process ; representation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract Explicitness has usually been approached from two points of view, labelled by Kirsh the structural and the process view, that hold opposite assumptions to determine when information is explicit. In this paper, we offer an intermediate view that retains intuitions from both of them. We establish three conditions for explicit information that preserve a structural requirement, and a notion of explicitness as a continuous dimension. A problem with the former accounts was their disconnection with psychological work on the issue. We review studies by Karmiloff-Smith, and Shanks and St. John to show that the proposed conditions have psychological grounds. Finally, we examine the problem of explicit rules in connectionist systems in the light of our framework.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008275132559
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  • 10
    Electronic Resource
    Electronic Resource
    A comparison of connectionist models of music recognition and human performance (1992)
    Springer
    Minds and machines 2 (1992), S. 379-400 
    Details
    ISSN: 1572-8641
    Keywords: Music recognition ; connectionism ; neural networks ; pattern recognition ; features ; computer simulation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract Current artificial neural network or connectionist models of music cognition embody feature-extraction and feature-weighting principles. This paper reports two experiments which seek evidence for similar processes mediating recognition of short musical compositions by musically trained and untrained listeners. The experiments are cast within a pattern recognition framework based on the vision-audition analogue wherein music is considered an auditory pattern consisting of local and global features. Local features such as inter-note interval, and global features such as melodic contour, are derived from a two-dimensional matrix in which music is represented as a series of frequencies plotted over time. Manipulation of inter-note interval affected accuracy and reaction time measures in a discrimination task, whereas the same variables were affected by manipulation of melodic contour in a classification task. Musical training is thought of as a form of practice in musical pattern recognition and, as predicted, accuracy and reaction time measures of musically trained subjects were significantly better than those of untrained subjects. Given the evidence for feature-extraction and weighting processes in music recognition tasks, two connectionist models are discussed. The first is a single-layer perceptron which has been trained to discriminate between compositions according to inter-note interval. A second network, using the back-propagation algorithm and sequential input of patterns, is also discussed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00419420
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