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| http://dx.doi.org/10.1038/s41598-024-56475-7 | DOI Listing |
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Fixation-related potentials during a virtual navigation task: The influence of image statistics on early cortical processing.
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U.S. DEVCOM Army Research Laboratory, Humans in Complex Systems, Aberdeen Proving Ground, MD, USA.
Historically, electrophysiological correlates of scene processing have been studied with experiments using static stimuli presented for discrete timescales where participants maintain a fixed eye position. Gaps remain in generalizing these findings to real-world conditions where eye movements are made to select new visual information and where the environment remains stable but changes with our position and orientation in space, driving dynamic visual stimulation. Co-recording of eye movements and electroencephalography (EEG) is an approach to leverage fixations as time-locking events in the EEG recording under free-viewing conditions to create fixation-related potentials (FRPs), providing a neural snapshot in which to study visual processing under naturalistic conditions.
View Article and Find Full Text PDFShort-term pitch memory predicts both incidentally and intentionally acquired absolute pitch categories.
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Department of Psychology, Huron University College at Western, 1349 Western Road, London, ON, N6G 1H3, Canada.
Tonal short-term memory has been positively associated with both incidentally acquired absolute pitch memory (e.g., for popular songs) and explicitly learned absolute pitch (AP) categories; however, the relationship between these constructs has not been directly tested within the same individuals.
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Department of Wound Repair, The Second Affiliated Hospital of Hunan University of Chinese Medicine, Changsha City, Hunan Province, P.R. China.
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Sci Rep
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Swin-transformer for weak feature matching.
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Department of Computer Science and Technology, Qilu University of Technology, No. 3501 Daxue Road, Jinan, 250300, Shandong, China.
Feature matching in computer vision is crucial but challenging in weakly textured scenes due to the lack of pattern repetition. We introduce the SwinMatcher feature matching method, aimed at addressing the issues of low matching quantity and poor matching precision in weakly textured scenes. Given the inherently significant local characteristics of image features, we employ a local self-attention mechanism to learn from weakly textured areas, maximally preserving the features of weak textures.
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