{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/20137"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/20137","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"VISIT: An efficient computational model of human visual attention","abstract":"One of the challenges for models of cognitive phenomena is the development of efficient and flexible interfaces between low level sensory information and high level processes. For visual processing, researchers have long argued that an attentional mechanism is required to perform many of the tasks required by high level vision. This thesis presents VISIT, a connectionist model of covert visual attention that has been used as a vehicle for studying this interface. The model is efficient, flexible, and is biologically plausible. The complexity of the network is linear in the number of pixels. Effective parallel strategies are used to minimize the number of iterations required. The resulting system is able to efficiently solve two tasks that are particularly difficult for standard bottom-up models of vision: computing spatial relations and visual search. Simulations show that the network's behavior matches much of the known psychophysical data on human visual attention. The general architecture of the model also closely matches the known physiological data on the human attention system. Various extensions to VISIT are discussed, including methods for learning the component modules.","abstract_html":"One of the challenges for models of cognitive phenomena is the development of efficient and flexible interfaces between low level sensory information and high level processes. For visual processing, researchers have long argued that an attentional mechanism is required to perform many of the tasks required by high level vision. This thesis presents VISIT, a connectionist model of covert visual attention that has been used as a vehicle for studying this interface. The model is efficient, flexible, and is biologically plausible. The complexity of the network is linear in the number of pixels. Effective parallel strategies are used to minimize the number of iterations required. The resulting system is able to efficiently solve two tasks that are particularly difficult for standard bottom-up models of vision: computing spatial relations and visual search. Simulations show that the network's behavior matches much of the known psychophysical data on human visual attention. The general architecture of the model also closely matches the known physiological data on the human attention system. 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