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Executive Attention, Action Selection and Attention-Based Learning in Neurally Controlled Autonomous Agents

Garforth, Jason P. (2006). Executive Attention, Action Selection and Attention-Based Learning in Neurally Controlled Autonomous Agents. PhD thesis The Open University.

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Abstract

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: Faculty of Arts and Social Sciences (FASS) > History, Religious Studies, Sociology, Social Policy and Criminology
Item ID: 22333
Depositing User: Anthony Meehan
Date Deposited: 14 Apr 2011 14:52
Last Modified: 05 Oct 2016 07:53
URI: http://oro.open.ac.uk/id/eprint/22333
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