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Journal articles on the topic 'Low attention'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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1

Mäki-Marttunen, Verónica, Natasha Pickard, Anne-Kristin Solbakk, Keith H. Ogawa, Robert T. Knight, and Kaisa M. Hartikainen. "Low attentional engagement makes attention network activity susceptible to emotional interference." NeuroReport 25, no. 13 (2014): 1038–43. http://dx.doi.org/10.1097/wnr.0000000000000223.

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2

Williamson, Mate. "ENGAGING THE DISENGAGED: DIGITAL ADVERTISING'S IMPACT IN LOW-ATTENTION ENVIRONMENTS." International Journal of Economics Finance & Management Science 04, no. 01 (2019): 01–05. http://dx.doi.org/10.55640/ijefms-9138.

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In an era of constant digital distractions and short attention spans, the effectiveness of digital advertising in low-attention environments is a critical concern for marketers and advertisers. This study delves into the challenges and opportunities presented by such environments and examines the strategies and techniques that can engage audiences effectively. Through a comprehensive analysis of case studies and consumer behavior studies, we uncover insights that can reshape the way digital advertising is conceived and executed in today's fast-paced digital landscape.

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3

Min, Zeping. "Attention Link: An Efficient Attention-Based Low Resource Machine Translation Architecture." Procedia Computer Science 222 (2023): 284–92. http://dx.doi.org/10.1016/j.procs.2023.08.167.

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Williamson, Kyle. "NAVIGATING DISTRACTIONS: UNVEILING THE POWER OF DIGITAL ADVERTISING IN LOW-ATTENTION ENVIRONMENTS." International Journal of Economics Finance & Management Science 08, no. 12 (2023): 01–04. http://dx.doi.org/10.55640/ijefms-9144.

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Unveiling the Power of Digital Advertising in Low-Attention Environments," delves into the dynamics of digital advertising's effectiveness when faced with challenges posed by low-attention environments. In an era saturated with information and distractions, the study investigates strategies and elements within digital advertising that capture and retain audience focus. Through a comprehensive analysis of engagement metrics, user behaviors, and content attributes, the research aims to provide insights for advertisers seeking to optimize their campaigns in settings where attention is at a premiu

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Kim, Jun-Heok, Ju-Hyeon Noh, Jun-Young Jang, and Hee-Deok Yang. "Crack Detection in Low-Resolution Images Using Attention U-Net." Korean Institute of Smart Media 14, no. 4 (2025): 9–17. https://doi.org/10.30693/smj.2025.14.4.9.

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With the increasing importance of road and structural safety inspections, this study proposes a novel network for effectively detecting non-uniform cracks. Existing crack detection models often suffer from feature loss and performance degradation when learning the complex structures of irregular cracks. To address this issue, this study aims to develop a network that minimizes feature loss and maintains crack detection performance even in low-resolution images by utilizing receptive fields of various sizes. In this study, we design an Attention U-Net network incorporating a Large Receptive Fie

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6

Xia, Ruixue, Xuerong Zhao, Yang Liu, et al. "Attention network training promotes selective attention of children with low socioeconomic status." Journal of Experimental Child Psychology 239 (January 2024): 105807. http://dx.doi.org/10.1016/j.jecp.2023.105807.

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7

Millichap, J. Gordon. "Low Birth Weight and Attention Deficit Disorder." Pediatric Neurology Briefs 21, no. 3 (2007): 21. http://dx.doi.org/10.15844/pedneurbriefs-21-3-8.

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8

Gasparini, Francesca. "Low-quality image enhancement using visual attention." Optical Engineering 46, no. 4 (2007): 040502. http://dx.doi.org/10.1117/1.2721764.

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9

Bach Jensen, Morten, and Anna Lund Jepsen. "Low attention advertising processing in B2B markets." Journal of Business & Industrial Marketing 22, no. 5 (2007): 342–48. http://dx.doi.org/10.1108/08858620710773477.

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10

Yang, Howard, Peng Sun, Charles Chubb, and George Sperling. "Complex Attention Filters for Low Contrast Items." Journal of Vision 16, no. 12 (2016): 681. http://dx.doi.org/10.1167/16.12.681.

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11

Du, Wenchao, Hu Chen, Peixi Liao, Hongyu Yang, Ge Wang, and Yi Zhang. "Visual Attention Network for Low-Dose CT." IEEE Signal Processing Letters 26, no. 8 (2019): 1152–56. http://dx.doi.org/10.1109/lsp.2019.2922851.

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12

Santoso, Irene, Malcolm J. Wright, Giang Trinh, and Mark Avis. "Mind the attention gap: how does digital advertising impact choice under low attention?" European Journal of Marketing 56, no. 2 (2021): 442–66. http://dx.doi.org/10.1108/ejm-01-2021-0031.

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Purpose This study aims to investigate whether digital advertising can be effective despite consumer inattention and how certain common combinations of ad characteristics increase or decrease ad effectiveness under conditions of low attention. Design/methodology/approach Using two online experiments in naturalistic environment, the authors compare ad effects under focussed, divided and incidental attention, for certain ad characteristics, namely, appeal type and (mis)matching between appeal and brand type. The results are analysed using logistic regression. Findings Ad exposure under low atten

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13

Shin, Yehjin, Jeongwhan Choi, Hyowon Wi, and Noseong Park. "An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8984–92. http://dx.doi.org/10.1609/aaai.v38i8.28747.

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Sequential recommendation (SR) models based on Transformers have achieved remarkable successes. The self-attention mechanism of Transformers for computer vision and natural language processing suffers from the oversmoothing problem, i.e., hidden representations becoming similar to tokens. In the SR domain, we, for the first time, show that the same problem occurs. We present pioneering investigations that reveal the low-pass filtering nature of self-attention in the SR, which causes oversmoothing. To this end, we propose a novel method called Beyond Self-Attention for Sequential Recommendation

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14

Sokhadze, E. M., B. Hillard, M. Eng, A. S. El-Baz, A. Tasman, and L. Sears. "ELECTROENCEPHALOGRAPHIC BIOFEEDBACK IMPROVES FOCUSED ATTENTION IN ATTENTION DEFICIT/HYPERACTIVITY DISORDER." Bulletin of Siberian Medicine 12, no. 2 (2013): 182–94. http://dx.doi.org/10.20538/1682-0363-2013-2-182-194.

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EEG biofeedback (so called neurofeedback) is considered as an efficacious treatment for ADHD. We propose that operant conditioning of EEG in neurofeedback training mode, aimed to mitigate inattention and low arousal in ADHD, will be accompanied by changes in EEG bands' relative power. Patients were 18 children diagnosed with ADHD. The neurofeedback protocol (“Focus/Alertness” by Peak Achievement Trainer, Neurotek, KY) used to train patients has focused attention training procedure, which according to specifications, represents wide band EEG amplitude suppression training. Quantitative EEG anal

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15

Hoo, Seng Chun, Haidi Ibrahim, Shahrel Azmin Suandi, and Theam Foo Ng. "LCAM: Low-Complexity Attention Module for Lightweight Face Recognition Networks." Mathematics 11, no. 7 (2023): 1694. http://dx.doi.org/10.3390/math11071694.

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Inspired by the human visual system to concentrate on the important region of a scene, attention modules recalibrate the weights of either the channel features alone or along with spatial features to prioritize informative regions while suppressing unimportant information. However, the floating-point operations (FLOPs) and parameter counts are considerably high when one is incorporating these modules, especially for those with both channel and spatial attentions in a baseline model. Despite the success of attention modules in general ImageNet classification tasks, emphasis should be given to i

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16

Lee, Joonyeol, and John H. R. Maunsell. "The Effect of Attention on Neuronal Responses to High and Low Contrast Stimuli." Journal of Neurophysiology 104, no. 2 (2010): 960–71. http://dx.doi.org/10.1152/jn.01019.2009.

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It remains unclear how attention affects the tuning of individual neurons in visual cerebral cortex. Some observations suggest that attention preferentially enhances responses to low contrast stimuli, whereas others suggest that attention proportionally affects responses to all stimuli. Resolving how attention affects responses to different stimuli is essential for understanding the mechanism by which it acts. To explore the effects of attention on stimuli of different contrasts, we recorded from individual neurons in the middle temporal visual area (MT) of rhesus monkeys while shifting their

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17

Hammer, Jiri, Michaela Kajsova, Adam Kalina, et al. "Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal–external attention switching." Communications Biology 7 (September 9, 2024): 1105. https://doi.org/10.1038/s42003-024-06732-2.

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Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antago

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18

Scheel, Jennifer F., Karin Schielke, Stefan Lautenbacher, Sabine Aust, Simone Kremer, and Jörg Wolstein. "Low-Dose Alcohol Effects on Attention in Adolescents." Zeitschrift für Neuropsychologie 24, no. 2 (2013): 103–11. http://dx.doi.org/10.1024/1016-264x/a000094.

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Objective: Relatively little is known about the effects of low-dose alcohol effects on attentional performance in adolescents. In the present study, we examined these effects compared to those in adults. Method: 44 healthy subjects (21 adolescents and 23 adults) received either an alcoholic cocktail (average blood alcohol concentration: 0.054 %) or a placebo drink (randomized placebo-controlled). Before and after consuming the drink, participants completed tests on Alertness, Working Memory, Flexibility and Divided Attention. Results: Significant effects were found for the subtest Flexibility,

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19

Ruan, Yudi, Hao Ma, Di Ma, Weikai Li, and Xiao Wang. "Low-light image enhancement using dual cross attention." Engineering Applications of Artificial Intelligence 159 (November 2025): 111501. https://doi.org/10.1016/j.engappai.2025.111501.

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20

Jaworski, Jessica L. Bean, and Inge-Marie Eigsti. "Low-level visual attention and its relation to joint attention in autism spectrum disorder." Child Neuropsychology 23, no. 3 (2015): 316–31. http://dx.doi.org/10.1080/09297049.2015.1104293.

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21

Martin, Jean-Rémy, Jérôme Sackur, and Zoltan Dienes. "Attention or instruction: Do sustained attentional abilities really differ between high and low hypnotisable persons?" Psychological Research 82, no. 4 (2017): 700–707. http://dx.doi.org/10.1007/s00426-017-0850-1.

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22

Pomeshchikova, Irina, O. Kudimova, and S. Loman. "The level of attention selectivity of basketball players of student's teams." Спортивні ігри (SPORT GAMES), no. 1 (11) (March 7, 2019): 50–57. https://doi.org/10.5281/zenodo.2543577.

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<em>The question of psychological preparation is one of the main points of training of sportsmen-basketball players (team). Psychological features of competitions, regularities, reasons and dynamics of competitive states define high requirements to mentality of the sportsman. Distribution of attention allows the basketball player without being distracted by foreign irritants (shouts of fans, actions of the arbitrator) to watch the rival and partners, to trace movement of a ball, to distinguish false actions (feints) during the game. <strong>The purpose of the research</strong> – is to es

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23

Yang, Yadong, Xiaofeng Wang, Quan Zhao, and Tingting Sui. "Two-Level Attentions and Grouping Attention Convolutional Network for Fine-Grained Image Classification." Applied Sciences 9, no. 9 (2019): 1939. http://dx.doi.org/10.3390/app9091939.

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The focus of fine-grained image classification tasks is to ignore interference information and grasp local features. This challenge is what the visual attention mechanism excels at. Firstly, we have constructed a two-level attention convolutional network, which characterizes the object-level attention and the pixel-level attention. Then, we combine the two kinds of attention through a second-order response transform algorithm. Furthermore, we propose a clustering-based grouping attention model, which implies the part-level attention. The grouping attention method is to stretch all the semantic

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24

Gu, Guowei, Zhongchen Wang, Liguo Weng, Haifeng Lin, Zikai Zhao, and Liling Zhao. "Attention Guide Axial Sharing Mixed Attention (AGASMA) Network for Cloud Segmentation and Cloud Shadow Segmentation." Remote Sensing 16, no. 13 (2024): 2435. http://dx.doi.org/10.3390/rs16132435.

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Segmenting clouds and their shadows is a critical challenge in remote sensing image processing. The shape, texture, lighting conditions, and background of clouds and their shadows impact the effectiveness of cloud detection. Currently, architectures that maintain high resolution throughout the entire information-extraction process are rapidly emerging. This parallel architecture, combining high and low resolutions, produces detailed high-resolution representations, enhancing segmentation prediction accuracy. This paper continues the parallel architecture of high and low resolution. When handli

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25

Luo, Yu, Jiarong Zhou, Wei Bao, Jing Qiu, Jingqing Nian, and Yu Zhang. "Working memory content guides attention." Journal of Pacific Rim Psychology 15 (January 2021): 183449092110347. http://dx.doi.org/10.1177/18344909211034752.

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Previous studies showed that working memory (WM) content can guide attention; however, whether working memory capacity (WMC) and state anxiety could affect this remains unclear. This study aimed to examine the effect of WMC and state anxiety on attention guided by WM content. Participants with high and low WMC were assigned to either a neutral or an anxiety condition. They were asked to perform a modified change detection task with irrelevant singletons while their event-related potentials were recorded. N2pc and Pd were observed in the low-WMC and anxiety group, and Pd was observed in both th

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26

Wu, Mengfei, Taiji Lan, Xucheng Xue, and Xinwei Xu. "Unsupervised Image Enhancement Method Based on Attention Map Network Guidance and Attention Mechanism." Electronics 12, no. 8 (2023): 1887. http://dx.doi.org/10.3390/electronics12081887.

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Low-light image enhancement is a crucial preprocessing task in complex vision tasks. It directly impacts object detection, image segmentation, and image recognition outcomes. In recent years, with the continuous development of deep learning techniques, an increasing number of image enhancement methods based on deep learning have emerged. However, due to the high cost of data collection and the limited content of supervised learning datasets, more and more scholars have shifted their focus to the field of unsupervised image enhancement. Unsupervised image enhancement methods do not require pair

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27

Matsuo, Moemi, Takashi Higuchi, Taiyo Ichibakase, Hikaru Suyama, Runa Takahara, and Masatoshi Nakamura. "Differences in Electroencephalography Power Levels between Poor and Good Performance in Attentional Tasks." Brain Sciences 14, no. 6 (2024): 527. http://dx.doi.org/10.3390/brainsci14060527.

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Decreased attentional function causes problems in daily life. However, a quick and easy evaluation method of attentional function has not yet been developed. Therefore, we are searching for a method to evaluate attentional function easily and quickly. This study aimed to collect basic data on the features of electroencephalography (EEG) during attention tasks to develop a new method for evaluating attentional function using EEG. Twenty healthy young adults participated; we examined cerebral activity during a Clinical Assessment for Attention using portable EEG devices. The Mann–Whitney U test

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28

Yap, David, Samantha Denefrio, and Tracy A. Dennis-Tiwary. "Low working memory load facilitates attention bias modification training." Behaviour Research and Therapy 139 (April 2021): 103828. http://dx.doi.org/10.1016/j.brat.2021.103828.

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29

Guarnera, Maria, and Antonella D’Amico. "Training of Attention in Children With Low Arithmetical Achievement." Europe’s Journal of Psychology 10, no. 2 (2014): 277–90. http://dx.doi.org/10.5964/ejop.v10i2.744.

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This study focuses on the role of attentional processes in arithmetical skills and examines if training of basic attentive skills may improve also working memory abilities reducing arithmetic difficulties. In order to study the efficacy of attentional treatment in arithmetic achievement and in enhancing working memory abilities a test-treatment-retest quasi experimental design was adopted. The research involved 14 children, attending fourth and fifth grades, with Arithmetical Learning Disabilities (ALD) assigned to experimental and control conditions. The numerical comprehension and calculatio

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Lu, Bibo, Zebang Pang, Yanan Gu, and Yanmei Zheng. "Channel splitting attention network for low‐light image enhancement." IET Image Processing 16, no. 5 (2022): 1403–14. http://dx.doi.org/10.1049/ipr2.12418.

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31

Liang, Xiwen, Xiaoning Yan, Nenghua Xu, Xiaoyan Chen, and Hao Feng. "Low light enhancement CNN Network based on attention mechanism." Proceedings of International Conference on Artificial Life and Robotics 27 (January 20, 2022): 466–69. http://dx.doi.org/10.5954/icarob.2022.os11-2.

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32

Vaish, Rajan, Peter Organisciak, Kotaro Hara, Jeffrey Bigham, and Haoqi Zhang. "Low Effort Crowdsourcing: Leveraging Peripheral Attention for Crowd Work." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 2 (September 5, 2014): 62–63. http://dx.doi.org/10.1609/hcomp.v2i1.13191.

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Crowdsourcing systems leverage short bursts of focused attention from many contributors to achieve a goal. By requiring people’s full attention, existing crowdsourcing systems fail to leverage people’s cognitive surplus in the many settings for which they may be distracted, performing or waiting to perform another task, or barely paying attention. In this paper, we study opportunities for low-effort crowdsourcing that enable people to contribute to problem solving in such settings. We discuss the design space for low-effort crowdsourcing, and through a series of prototypes, demonstrate interac

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33

Verkerk, G., B. Houtzager, M. JeukensVisser, et al. "325 Attention Problems in Very Low Birth Weight Preschoolers." Archives of Disease in Childhood 97, Suppl 2 (2012): A95. http://dx.doi.org/10.1136/archdischild-2012-302724.0325.

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34

Bisserbe, J. C., E. De Rosa, J. P. Mialet, P. Boyer, and J. P. Boulenger. "VISUAL ATTENTION IN LOW AND HIGH ANXIETY TRAIT SUBJECTS." Clinical Neuropharmacology 15 (1992): 571B. http://dx.doi.org/10.1097/00002826-199202001-01113.

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35

Mitchell, J., K. Sundberg, and J. Reynolds. "Attention reduces low frequency correlated noise in macaque V4." Journal of Vision 9, no. 8 (2010): 93. http://dx.doi.org/10.1167/9.8.93.

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36

FIMM, B., K. WILLMES, and W. SPIJKERS. "The effect of low arousal on visuo-spatial attention." Neuropsychologia 44, no. 8 (2006): 1261–68. http://dx.doi.org/10.1016/j.neuropsychologia.2006.01.027.

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37

Santoso, Irene, Malcolm Wright, Giang Trinh, and Mark Avis. "Is digital advertising effective under conditions of low attention?" Journal of Marketing Management 36, no. 17-18 (2020): 1707–30. http://dx.doi.org/10.1080/0267257x.2020.1801801.

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38

Grimes, Anthony. "Towards an integrated model of low attention advertising effects." European Journal of Marketing 42, no. 1/2 (2008): 69–86. http://dx.doi.org/10.1108/03090560810840916.

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39

van der Meere, Jaap, Norbert A. Börger, Stephanus Theron Potgieter, Silja Pirila, and Paul De Cock. "Very Low Birth Weight and Attention Deficit/Hyperactivity Disorder." Child Neuropsychology 15, no. 6 (2009): 605–18. http://dx.doi.org/10.1080/09297040902984482.

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40

Kelly, Charlotte, and Juan Guevara Pinto. "Biasing Global and Local Attention During Low Prevalence Search." Journal of Vision 22, no. 14 (2022): 3221. http://dx.doi.org/10.1167/jov.22.14.3221.

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41

Amado Diago, Carlos A., and José A. Amado Señaris. "Should we pay more attention to low creatinine levels?" Endocrinología, Diabetes y Nutrición (English ed.) 67, no. 7 (2020): 486–92. http://dx.doi.org/10.1016/j.endien.2020.09.003.

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42

Wang, Yongnian, and Zhibin Zhang. "Global attention retinex network for low light image enhancement." Journal of Visual Communication and Image Representation 92 (April 2023): 103795. http://dx.doi.org/10.1016/j.jvcir.2023.103795.

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43

Lv, Feifan, Yu Li, and Feng Lu. "Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset." International Journal of Computer Vision 129, no. 7 (2021): 2175–93. http://dx.doi.org/10.1007/s11263-021-01466-8.

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44

sile, Hu, Takuya Onuma, and Nobuyuki Sakai. "Effect of “Low-Salt” Labels on Attention to and Cognition of Low-Salt Products." Proceedings of the Annual Convention of the Japanese Psychological Association 86 (2022): 4AM—044—PI—4AM—044—PI. http://dx.doi.org/10.4992/pacjpa.86.0_4am-044-pi.

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45

Lee, Dongwoo, Kyeongseok Jang, Soo Young Cho, Seunghyun Lee, and Kwangchul Son. "A Study on the Super Resolution Combining Spatial Attention and Channel Attention." Applied Sciences 13, no. 6 (2023): 3408. http://dx.doi.org/10.3390/app13063408.

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Existing CNN-based super resolution methods have low emphasis on high-frequency features, resulting in poor performance for contours and textures. To solve this problem, this paper proposes single image super resolution using an attention mechanism that emphasizes high-frequency features and a feature extraction process with different depths. In order to emphasize the high-frequency features of the channel and space, it is composed of CSBlock that combines channel attention and spatial attention. Attention block using 10 CSBlocks was used for high-frequency feature extraction. In order to extr

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46

Rahnev, Dobromir A., Linda Bahdo, Floris P. de Lange, and Hakwan Lau. "Prestimulus hemodynamic activity in dorsal attention network is negatively associated with decision confidence in visual perception." Journal of Neurophysiology 108, no. 5 (2012): 1529–36. http://dx.doi.org/10.1152/jn.00184.2012.

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Attention is thought to improve most aspects of perception. However, we recently showed that, somewhat surprisingly, endogenous attention can also lead to low subjective perceptual ratings ( Rahnev et al., 2011 ). Here we investigated the neural basis of this effect and tested whether spontaneous fluctuations of the attentional state can lead to low confidence in one's perceptual decision. We measured prestimulus functional magnetic resonance imaging activity in the dorsal attention network and used that activity as an index of the level of attention involved in a motion direction discriminati

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Serrano-Barroso, Almudena, Roma Siugzdaite, Jaime Guerrero-Cubero, et al. "Detecting Attention Levels in ADHD Children with a Video Game and the Measurement of Brain Activity with a Single-Channel BCI Headset." Sensors 21, no. 9 (2021): 3221. http://dx.doi.org/10.3390/s21093221.

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Attentional biomarkers in attention deficit hyperactivity disorder are difficult to detect using only behavioural testing. We explored whether attention measured by a low-cost EEG system might be helpful to detect a possible disorder at its earliest stages. The GokEvolution application was designed to train attention and to provide a measure to identify attentional problems in children early on. Attention changes registered with NeuroSky MindWave in combination with the CARAS-R psychological test were used to characterise the attentional profiles of 52 non-ADHD and 23 ADHD children aged 7 to 1

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48

Bahri, Toufik. "Covert Orienting of Attention Controls Vigilance Decrement at Low Event Rate." Perceptual and Motor Skills 79, no. 1 (1994): 83–92. http://dx.doi.org/10.2466/pms.1994.79.1.83.

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Factors controlling sustained visual orienting were investigated by combining the paradigms of covert orienting and vigilance. Analysis suggests a close relationship between orienting of attention and vigilance which is dependent on the event rare during the vigilance task. At a low event rate both facilitatory and inhibitory effects of orienting are found. Vigilance decrement is related to the accumulation of inhibition over time, supporting Posner, et al.'s 1984 theory. Invalid cues reduce the decrement. At a high event rate, however, neither facilitation nor inhibition effects are reliable,

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49

Shin, Sungho, Joosoon Lee, Junseok Lee, Seungjun Choi, and Kyoobin Lee. "Low-Resolution Image Classification Using Knowledge Distillation From High-Resolution Image Via Self-Attention Map." Journal of KIISE 47, no. 11 (2020): 1027–31. http://dx.doi.org/10.5626/jok.2020.47.11.1027.

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50

Mock, Vanessa L., Kimberly L. Luke, Jacqueline R. Hembrook-Short, and Farran Briggs. "Phase shifts in high-beta- and low-gamma-band local field potentials predict the focus of visual spatial attention." Journal of Neurophysiology 121, no. 3 (2019): 799–822. http://dx.doi.org/10.1152/jn.00469.2018.

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The local field potential (LFP) contains rich information about activity in local neuronal populations. However, it has been challenging to establish direct links between LFP modulations and task-relevant behavior or cognitive processes, such as attention. We sought to determine whether LFP amplitude or phase modulations are predictive of the allocation of visual spatial attention. LFPs were recorded simultaneously in multiple early visual brain structures of alert macaque monkeys performing attention-demanding detection and discrimination tasks. Attention directed toward the receptive field o

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