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A thesis submitted in partial fulfilment of - Queen Margaret University

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module and how it communicates with other <strong>in</strong>ter-related modules is unknown<br />

(Ellis and Young, 1996). The development <strong>of</strong> computational concepts may be<br />

one avenue to help expla<strong>in</strong> the <strong>in</strong>ternal work<strong>in</strong>gs <strong>of</strong> the model through computer<br />

simulations (Cohen, 2000; Ellis and Young, 1996). Cognitive psychologists <strong>in</strong><br />

us<strong>in</strong>g a connectionist approach attempt to build computerised models <strong>of</strong><br />

cognition which are thought to have properties <strong>of</strong> neural <strong>in</strong>formation process<strong>in</strong>g<br />

(Braisby and Gellatly, 2005). Cohen (2000) cautions however that while<br />

connectionist models have some properties <strong>in</strong> common with neurones they are<br />

not neuronal equivalent. Connectionist models are thought to operate at a level<br />

<strong>of</strong> learned automatic processes without conscious thought (Smolensky, 1988)<br />

and while damaged process<strong>in</strong>g may be simulated it is not <strong>in</strong> itself a theory<br />

(McCloskey, 2004). However connectionist models provide <strong>in</strong>sights <strong>in</strong>to ways a<br />

damaged system could repair itself dur<strong>in</strong>g recovery as it can model the learn<strong>in</strong>g<br />

process <strong>in</strong>clud<strong>in</strong>g loss <strong>of</strong> learn<strong>in</strong>g through damage and relearn<strong>in</strong>g dur<strong>in</strong>g the<br />

recovery process (Cohen, 2000). The predictive power <strong>of</strong> connectionist<br />

modell<strong>in</strong>g is thought to be enhanced due to the explanation <strong>of</strong> cognitive<br />

process<strong>in</strong>g through learn<strong>in</strong>g (Lambon-Ralph, 2004).<br />

While the cognitive neuropsychological approach cannot adequately capture the<br />

dynamic process <strong>of</strong> learn<strong>in</strong>g, it does <strong>in</strong>form the outcome <strong>of</strong> the learn<strong>in</strong>g<br />

procedure <strong>in</strong> terms <strong>of</strong> provid<strong>in</strong>g <strong>in</strong>formation about the depth and characteristics<br />

<strong>of</strong> knowledge acquired dur<strong>in</strong>g the learn<strong>in</strong>g <strong>of</strong> skills (<strong>in</strong> this case language<br />

doma<strong>in</strong>-specific <strong>in</strong> terms <strong>of</strong> learn<strong>in</strong>g new vocabulary).<br />

6.5.6 Summary<br />

The discussion <strong>in</strong> Chapter 2 identified a number <strong>of</strong> requirements that a suitable<br />

theoretical model <strong>of</strong> language would need to provide <strong>in</strong> order to be a useful tool<br />

for this <strong>in</strong>vestigation. A cognitive neuropsychology model was chosen and its<br />

usefulness <strong>in</strong> fulfill<strong>in</strong>g the criteria for this <strong>in</strong>vestigation has been <strong>in</strong>dividually<br />

discussed with evidence above. The model provided a framework for assess<strong>in</strong>g<br />

274

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