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. / Psychology / D. Litt. et Phil. (Psychology)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:umkn-dsp01.int.unisa.ac.za:10500/635 |
| Date | 28 February 2003 |
| Creators | Janeke, Hendrik Christiaan |
| Contributors | Plug, Cornelis |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 1 online resource (224 leaves) |
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