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  • Articles  (6)
  • connectionism  (6)
  • 2015-2019
  • 1990-1994  (6)
  • 1915-1919
  • Philosophy  (6)
  • 1
    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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    Paper (German National Licenses)
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  • 2
    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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  • 3
    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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  • 4
    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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  • 5
    Electronic Resource
    Electronic Resource
    Evolutionary epistemology as an overlapping, interlevel theory (1993)
    Springer
    Biology and philosophy 8 (1993), S. 173-192 
    Details
    ISSN: 1572-8404
    Keywords: Artificial life programming ; connectionism ; evolutionary epistemology ; genetic algorithm ; interlevel theories
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Philosophy
    Notes: Abstract I examine the branch of evolutionary epistemology which tries to account for the character of cognitive mechanisms in animals and humans by extending the biological theory of evolution to the neurophysiological substrates of cognition. Like Plotkin, I construe this branch as a struggling science, and attempt to characterize the sort of theory one might expect to find this truly interdisciplinary endeavor, an endeavor which encompasses not only evolutionary biology, cognitive psychology, and developmental neuroscience, but also and especially, the computational modeling of “artificial life” programming; I suggest that extending Schaffner's notion of interlevel theories to include both “horizontal” and “vertical” levels of abstraction best fits the theories currently being developed in cognitive science. Finally, I support this claim with examples drawn from computational modeling data using the genetic algorithm.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00850481
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  • 6
    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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