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Connectionist modelling in cognitive science: an exposition and appraisal

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dc.contributor.advisor Plug, Cornelis en
dc.contributor.author Janeke, Hendrik Christiaan en
dc.date.accessioned 2009-08-25T10:45:23Z
dc.date.available 2009-08-25T10:45:23Z
dc.date.issued 2009-08-25T10:45:23Z
dc.date.submitted 2003-02-28 en
dc.identifier.citation Janeke, Hendrik Christiaan (2009) Connectionist modelling in cognitive science: an exposition and appraisal, University of South Africa, Pretoria, <http://hdl.handle.net/10500/635> en
dc.identifier.uri http://hdl.handle.net/10500/635
dc.description.abstract This thesis explores the use of artificial neural networks for modelling cognitive processes. It presents an exposition of the neural network paradigm, and evaluates its viability in relation to the classical, symbolic approach in cognitive science. Classical researchers have approached the description of cognition by concentrating mainly on an abstract, algorithmic level of description in which the information processing properties of cognitive processes are emphasised. The approach is founded on seminal ideas about computation, and about algorithmic description emanating, amongst others, from the work of Alan Turing in mathematical logic. In contrast to the classical conception of cognition, neural network approaches are based on a form of neurocomputation in which the parallel distributed processing mechanisms of the brain are highlighted. Although neural networks are generally accepted to be more neurally plausible than their classical counterparts, some classical researchers have argued that these networks are best viewed as implementation models, and that they are therefore not of much relevance to cognitive researchers because information processing models of cognition can be developed independently of considerations about implementation in physical systems. In the thesis I argue that the descriptions of cognitive phenomena deriving from neural network modelling cannot simply be reduced to classical, symbolic theories. The distributed representational mechanisms underlying some neural network models have interesting properties such as similarity-based representation, content-based retrieval, and coarse coding which do not have straightforward equivalents in classical systems. Moreover, by placing emphasis on how cognitive processes are carried out by brain-like mechanisms, neural network research has not only yielded a new metaphor for conceptualising cognition, but also a new methodology for studying cognitive phenomena. Neural network simulations can be lesioned to study the effect of such damage on the behaviour of the system, and these systems can be used to study the adaptive mechanisms underlying learning processes. For these reasons, neural network modelling is best viewed as a significant theoretical orientation in the cognitive sciences, instead of just an implementational endeavour. en
dc.format.extent 1 online resource (224 leaves)
dc.language.iso en en
dc.subject Artificial neural networks
dc.subject Attractor memory models
dc.subject Classical cognitive approach
dc.subject Coarse coding
dc.subject Cognition and computation
dc.subject Cognitive science
dc.subject Connectionist modelling
dc.subject Distributed representations
dc.subject Functionalism
dc.subject Mental representation
dc.subject Physical symbol system
dc.subject Turing machine
dc.subject.ddc 612.82
dc.subject.lcsh Connectionism
dc.subject.lcsh Cognitive science
dc.subject.lcsh Neural networks (Neurobiology)
dc.title Connectionist modelling in cognitive science: an exposition and appraisal en
dc.type Thesis en
dc.description.department Psychology en
dc.description.degree D. Litt. et Phil. (Psychology) en


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