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      Superior colliculus encodes visual saliency before the primary visual cortex

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      Proceedings of the National Academy of Sciences
      Proceedings of the National Academy of Sciences

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          Abstract

          Models of visual attention postulate the existence of a bottom-up saliency map that is formed early in the visual processing stream. Although studies have reported evidence of a saliency map in various cortical brain areas, determining the contribution of phylogenetically older pathways is crucial to understanding its origin. Here, we compared saliency coding from neurons in two early gateways into the visual system: the primary visual cortex (V1) and the evolutionarily older superior colliculus (SC). We found that, while the response latency to visual stimulus onset was earlier for V1 neurons than superior colliculus superficial visual-layer neurons (SCs), the saliency representation emerged earlier in SCs than in V1. Because the dominant input to the SCs arises from V1, these relative timings are consistent with the hypothesis that SCs neurons pool the inputs from multiple V1 neurons to form a feature-agnostic saliency map, which may then be relayed to other brain areas.

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          A feature-integration theory of attention

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            A model of saliency-based visual attention for rapid scene analysis

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              State-of-the-art in visual attention modeling.

              Modeling visual attention--particularly stimulus-driven, saliency-based attention--has been a very active research area over the past 25 years. Many different models of attention are now available which, aside from lending theoretical contributions to other fields, have demonstrated successful applications in computer vision, mobile robotics, and cognitive systems. Here we review, from a computational perspective, the basic concepts of attention implemented in these models. We present a taxonomy of nearly 65 models, which provides a critical comparison of approaches, their capabilities, and shortcomings. In particular, 13 criteria derived from behavioral and computational studies are formulated for qualitative comparison of attention models. Furthermore, we address several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures. Finally, we highlight current research trends in attention modeling and provide insights for future.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                August 29 2017
                August 29 2017
                August 29 2017
                August 14 2017
                : 114
                : 35
                : 9451-9456
                Article
                10.1073/pnas.1701003114
                5584409
                28808026
                e230e65c-fc61-4793-8374-ceb65e183ea6
                © 2017

                Free to read

                http://www.pnas.org/site/misc/userlicense.xhtml

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