Date of Award


Degree Type


Degree Name

Doctor of Philosophy


The dissertation represents a critical evaluation of the major connectionist theories of human cognitive architecture. The central connectionist thesis that artificial neural networks (ANNs) can serve as finitary models of human cognizers is examined and rejected. Connectionist theories, in contrast to the classical symbol-processing theories of cognitive architecture, cannot explain the productivity and systematicity of mental states. The reason for this is that ANN-based cognitive architectures cannot maintain representational states with compositional structure. Chapter One analyzes the implementational connectionism's solution to the problem of compositionality. It is shown that neither the theory of weak nor of strong compositionality can solve this problem.;Chapter Two criticises the attempt to establish connectionism as an alternative theory of human cognitive architecture through the introduction of the symbolic/subsymbolic distinction. The reasons for the introduction of this distinction are examined and found to be unconvincing. Several experimental comparisons between the TDIDT class of symbolic learning systems and the class of artificial neural networks using the error backpropagation algorithm are discussed. It is argued that the differences in the performance of these two classes of learning systems are insignificant and are not systematic. Such evidence contradicts the view that ANNs define a new kind of "subsymbolic" computation.;Supporters of eliminative connectionism have argued for a pattern association and pattern recognition-based explanation of cognitive processes. They deny that explicit rules and symbolic representations play any role in cognition. Their argument is based to a large extent on Rumelhart and McClelland's and MacWhinney and Leinbach's connectionist models of learning of the past tenses of English verbs. Chapter Three presents an analysis of an experimental comparison between these models and the Symbolic Pattern Associator (SPA)--a learning system based on the classical architecture. It is shown that the SPA outperforms the connectionist models; moreover, the SPA can represent the acquired knowledge in the form of explicit rules. The analysis of this comparison leads to the conclusion that symbol-processing models have a far better chance of explaining complex cognitive phenomena in terms of rules and symbolic representations than eliminative connectionism.



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