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On the difficulty of feature-based attentional modulations in visual object recognition: A modeling study.

dc.date.accessioned2005-12-22T01:18:52Z
dc.date.accessioned2018-11-24T10:24:00Z
dc.date.available2005-12-22T01:18:52Z
dc.date.available2018-11-24T10:24:00Z
dc.date.issued2004-01-14
dc.identifier.urihttp://hdl.handle.net/1721.1/30442
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/30442
dc.description.abstractNumerous psychophysical experiments have shown an important role for attentional modulations in vision. Behaviorally, allocation of attention can improve performance in object detection and recognition tasks. At the neural level, attention increases firing rates of neurons in visual cortex whose preferredstimulus is currently attended to. However, it is not yet known how these two phenomena are linked, i.e., how the visual system could be "tuned" in a task-dependent fashion to improve task performance. To answer this question, we performed simulations with the HMAX model of object recognition in cortex [45].We modulated firing rates of model neurons in accordance with experimental results about effects of feature-based attention on single neurons and measured changes in the model's performance in a variety of object recognition tasks. It turned out that recognition performance could only be improved under very limited circumstances and that attentional influences on the process of object recognition per se tend to display a lack of specificity or raise false alarm rates. These observations lead us to postulate a new role for the observed attention-related neural response modulations.
dc.format.extent38 p.
dc.format.extent65171868 bytes
dc.format.extent2503743 bytes
dc.language.isoen_US
dc.subjectAI
dc.subjectobject recognition
dc.subjectattention
dc.subjectvision
dc.subjectmodeling
dc.titleOn the difficulty of feature-based attentional modulations in visual object recognition: A modeling study.


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