Purpose: The purpose of the study was to compare the visual attention patterns of adults with aphasia and adults without neurological conditions when viewing visual scenes with 2 types of engagement.
Method: Eye-tracking technology was used to measure the visual attention patterns of 10 adults with aphasia and 10 adults without neurological conditions. Participants viewed camera-engaged (i.e., human figure facing camera) and task-engaged (i.e., human figure looking at and touching an object) visual scenes.
Results: Participants with aphasia responded to engagement cues by focusing on objects of interest more for task-engaged scenes than camera-engaged scenes; however, the difference in their responses to these scenes were not as pronounced as those observed in adults without neurological conditions. In addition, people with aphasia spent more time looking at background areas of interest and less time looking at person areas of interest for camera-engaged scenes than did control participants.
Conclusions: Results indicate people with aphasia visually attend to scenes differently than adults without neurological conditions. As a consequence, augmentative and alternative communication (AAC) facilitators may have different visual attention behaviors than the people with aphasia for whom they are constructing or selecting visual scenes. Further examination of the visual attention of people with aphasia may help optimize visual scene selection.
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http://dx.doi.org/10.1044/2015_JSLHR-L-14-0115 | DOI Listing |
Sci Rep
December 2024
College of Electrical Engineering, Northeast Electric Power University, Jilin, 132012, China.
The scattering of tiny particles in the atmosphere causes a haze effect on remote sensing images captured by satellites and similar devices, significantly disrupting subsequent image recognition and classification. A generative adversarial network named TRPC-GAN with texture recovery and physical constraints is proposed to mitigate this impact. This network not only effectively removes haze but also better preserves the texture information of the original remote sensing image, thereby enhancing the visual quality of the dehazed image.
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December 2024
Psychology Department, University of Illinois at Urbana Champaign, Champaign, United States.
Efficient searches are guided by target-distractor distinctiveness: the greater the distinctiveness, the faster the search. Previous research showed that when the target and distractors differ along both color and shape dimensions (i.e.
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December 2024
Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
Amblyopia affects more than visual acuity. To compare the performances of visual selective attention and numerical processing in children with anisometropic amblyopia and children with normal vision, and investigate whether performance would be improved after visual acuity recovery, we performed 3 visual attention tasks (identifying number location task, numerical comparison task, and specific number comparison task) in children with anisometropic amblyopia, children who had recovered from anisometropic amblyopia, and children with normal vision in 6-8 and 9-11 years groups. The numerical processing ability, visual selective attention, and numerical distance effect were assessed by their reaction time of different tasks.
View Article and Find Full Text PDFBMC Genomics
December 2024
College of Physics and Electronic Information, Gannan Normal University, Ganzhou, 341000, Jiangxi, China.
Long non-coding RNAs (lncRNAs) play crucial roles in numerous biological processes and are involved in complex human diseases through interactions with proteins. Accurate identification of lncRNA-protein interactions (LPI) can help elucidate the functional mechanisms of lncRNAs and provide scientific insights into the molecular mechanisms underlying related diseases. While many sequence-based methods have been developed to predict LPIs, efficiently extracting and effectively integrating potential feature information that reflects functional attributes from lncRNA and protein sequences remains a significant challenge.
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December 2024
School of Construction Machinery, Shandong Jiaotong University, Jinan, 250023, China.
Injection molded parts are increasingly utilized across various industries due to their cost-effectiveness, lightweight nature, and durability. However, traditional defect detection methods for these parts often rely on manual visual inspection, which is inefficient, expensive, and prone to errors. To enhance the accuracy of defect detection in injection molded parts, a new method called MRB-YOLO, based on the YOLOv8 model, has been proposed.
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