Connectionist Modelling in Cognitive Neuropsychology: A Case Study
Computational models offer tools for exploring the nature of human cognitive processes. In connectionist, neural network, or parallel distributed processing models, information processing takes the form of cooperative and competitive interactions among many simple, neuron-like processing units. These models provide new ways of thinking about the neural basis of cognitive processes, and how disorders of brain function lead to disorders of cognition.
This monograph is an expanded version of a recent issue of the journal Cognitive Neuropsychology. It presents the most comprehensive existing "case study" of how the effects of damage in connectionist models can replicate the detailed and diverse patterns of cognitive impairments that can arise in humans as a result of brain damage.
It begins with a review of the basic methodology of cognitive neuropsychology and of other attempts at modeling neuropsychological phenomena. It then focuses on a particular form of acquired reading disorder, "deep dyslexia," in which previously literate adults with brain damage exhibit a wide range of symptoms in pronouncing written words, the most striking of which is the production of semantic errors (e.g. reading RIVER as "ocean").
A series of simulations investigate the effects of damage in connectionist models that pronounce written words via their meaning. The work systematically explores each main aspect of the design of the models, identifying the basic computational properties that are responsible for the occurrence of deep dyslexia when the models are damaged.
Although the investigation concerns a specific form of reading impairment, the computational principles that emerge as critical are very general ones: representation of concepts as distributed patterns of activity, encoding of knowledge in terms of weights on connections between units, interactivity between units to form stable attractors for familiar activity patterns, and greater richness of concrete vs. abstract semantics. The fact that damage to models embodying these principles and damage to the brain can produce strikingly similar behaviour supports the view that the human cognitive system operates according to similar principles.
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Motivation of a connectionist account
Generating phonological responses
Effects of lesion severity
The network architecture
Confidence in visual vs semantic errors
Effects of concreteness in deep dyslexia
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abstract words aspects attractors back-propagation networks basin of attraction behaviour Behrmann Boltzmann Machines clean-up pathway clean-up units co-occurrence cognitive neuropsychology Coltheart computational concrete words connectionist modelling connectionist networks correct performance damage deep dyslexia deep dyslexic patients developed direct pathway dysgraphia dysphasia error pattern error rates error responses error types exception words grapheme units Hebbian learning Hinton and Shallice impaired input lesions input network interactions intermediate units iterations layers lesion location Lesion Severity lexical decision mapping orthography mixed visual-and-semantic errors Mozer neglect dyslexia network architectures nonwords orthography to phonology orthography to semantics output network output system pattern of activity phoneme units phonological Plaut presented processing pronunciation response criteria Rumelhart Saffran semantic activity semantic errors semantic features semantic representations semantic route semantic similarity sememe units settling Shallice & Warrington simulations stimulus surface dyslexia syndrome task visual and semantic visual errors visual similarity visual-then-semantic errors weights word set
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