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Selective Attention and Decision-Making Have Separable Neural Bases in Space and Time. [PDF]
J NeurosciMoerel D, Rich AN, Woolgar A.
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Learning and selective attention
Nature Neuroscience, 2000Selective attention involves the differential processing of different stimuli, and has widespread psychological and neural consequences. Although computational modeling should offer a powerful way of linking observable phenomena at different levels, most work has focused on the relatively narrow issue of constraints on processing resources. By contrast,
Dayan, P., Kakade, S., Montague, P.
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Noradrenaline and selective attention
Life Sciences, 1979Abstract Rats depleted of forebrain noradrenaline by intracerebral injection of four micrograms of 6-hydroxydopamine into the dorsal noradrenergic bundle were examined on their ability to ignore irrelevant stimuli. In the latent inhibition paradigm normal rats were pre-exposed to visual and auditory stimuli in the absence of reward and such pre ...
S T, Mason, H C, Fibiger
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The neurobiology of selective attention
Current Biology, 1992Research in the field of selective visual attention has recently seen substantial progress in several areas. Neuroimaging and electrical recording results have indicated that selective attention amplifies neural activity in prestriate areas concerned with basic visual processing.
M I, Posner, J, Driver
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Selective Attention Improves Transformer
International Conference on Learning RepresentationsUnneeded elements in the attention's context degrade performance. We introduce Selective Attention, a simple parameter-free change to the standard attention mechanism which reduces attention to unneeded elements. Selective attention consistently improves
Yaniv Leviathan, Matan Kalman, Y. Matias
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Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification
IEEE Transactions on Medical Imaging, 2019Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the deep ...
Bolei Xu +8 more
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Learning Selective Self-Mutual Attention for RGB-D Saliency Detection
Computer Vision and Pattern Recognition, 2020Saliency detection on RGB-D images is receiving more and more research interests recently. Previous models adopt the early fusion or the result fusion scheme to fuse the input RGB and depth data or their saliency maps, which incur the problem of ...
Nian Liu, Ni Zhang, Junwei Han
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