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• W2901085375 abstract "Attentional selection is a function of the brain that allocates computational resources momentarily to the most important part of a visual scene. Saliency map models have been used to predict the location of attentional selection and gaze. Border Ownership (BO) indicates the direction of the figure with respect to the border. I here propose a biologically plausible saliency model based on neural population for integrating the activities of intermediate-level visual areas with neurons selective to BO. A variety of BO organizations produces a population of model neurons that represent the grouping structure. In the model I propose, the interactions and the population responses of these model neurons underlie the determination of saliency and the accurate prediction of gaze location. I tested 100 patterns for BO organizations and found that the proposed saliency model not only reproduced the characteristics of perceptual organization but also captured object locations in natural images. Furthermore, the saliency model based on the population responses of the BO organization significantly improved the gaze prediction accuracy compared with previous saliency-based models. These results suggest a crucial role for a wide variety of BO organizations and neural population coding to determine saliency mediating attentional selection and to predict gaze location." @default.
• W2901085375 created "2018-11-29" @default.
• W2901085375 creator A5031369071 @default.
• W2901085375 date "2019-02-01" @default.
• W2901085375 modified "2023-10-12" @default.
• W2901085375 title "Saliency model based on a neural population for integrating figure direction and organizing Border Ownership" @default.
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• W2901085375 doi "https://doi.org/10.1016/j.neunet.2018.10.015" @default.
• W2901085375 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30481686" @default.
• W2901085375 hasPublicationYear "2019" @default.
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