Author: Barnes, Jordan Idir
A technique for measuring the relative strengths of associative learning of eye- movements is introduced. In experiment designs that manipulate feedback signals, the strength of the learning associated with the signal can be measured by looking at the amount of perseveration to irrelevant information after a learning criterion is met. In contrast to previous models of attentional learning, shifts of attention are explained primarily by the joint influence of habituated motor movements and the gain derived from the anti-correlated components of abstract categories. For this to work, a realistic model of ocular-motor movements is required in order to ground the concept of attentional allocation. A dynamic neural field model of eye-movements is thus presented which captures a number of the mesoscopic neurodynamics known to influence visual attention during learning. Under a number of different simulation constraints, this model shows an ability to fit aspects of human performance.
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Thesis advisor: Blair, Mark
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