Garforth, Jason P.
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I describe the design and implementation of an integrated neural architecture, modelled on human executive attention, which is used to control both automatic (reactive) and willed action selection in a simulated robot. The model, based upon Norman and Shallice's supervisory attention system, incorporates important features of human attentional control: selection of an intended task over a more salient automatic task; priming of future tasks that are anticipated; and appropriate levels of persistence of focus of attention. Recognising that attention-based learning, mediated by the limbic system, and the hippocampus in particular, plays an important role in adaptive learning, I extend the Norman and Shallice model, introducing an intrinsic, attention-based learning mechanism that enhances the automaticity of willed actions and reduces future need for attentional effort required for dealing with distractions. These enhanced features support a new level of attentional autonomy in the operation of the simulated robot. Some properties of the model are explored using lesion studies, leading to the identification of a correspondence between the behavioural pathologies of the simulated robot and those seen in human patients suffering dysfunction of executive attention.
|Item Type:||Thesis (PhD)|
|Copyright Holders:||2006 Jason P. Garforth|
|Keywords:||executive attention; action selection; autonomous robotics; cognitive robotics; adaptive learning; neural network|
|Academic Unit/Department:||Social Sciences > Sociology|
|Depositing User:||Anthony Meehan|
|Date Deposited:||14 Apr 2011 14:52|
|Last Modified:||24 Feb 2016 02:02|
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