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Journal articles on the topic 'Attention Network Test'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Urbanek, Carsten, Nicholetta Weinges-Evers, Judith Bellmann-Strobl, Markus Bock, Jan Dörr, Eric Hahn, Andres H. Neuhaus, et al. "Attention Network Test reveals alerting network dysfunction in multiple sclerosis." Multiple Sclerosis Journal 16, no. 1 (December 7, 2009): 93–99. http://dx.doi.org/10.1177/1352458509350308.

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Attention is one of the cognitive domains typically affected in multiple sclerosis. The Attention Network Test was developed to measure the function of the three distinct attentional networks, alerting, orienting, and executive control. The Attention Network Test has been performed in various neuropsychiatric conditions, but not in multiple sclerosis. Our objective was to investigate functions of attentional networks in multiple sclerosis by means of the Attention Network Test. Patients with relapsing—remitting multiple sclerosis (n = 57) and healthy controls (n = 57) matched for age, sex, and education performed the Attention Network Test. Significant differences between patients and controls were detected in the alerting network (p = 0.003), in contrast to the orienting (p = 0.696) and the conflict (p = 0.114) network of visual attention. Mean reaction time in the Attention Network Test was significantly longer in multiple sclerosis patients than in controls (p = 0.032), Multiple sclerosis patients benefited less from alerting cues for conflict resolution compared with healthy controls. The Attention Network Test revealed specific alterations of the attention network in multiple sclerosis patients which were not explained by an overall cognitive slowing.

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Duan, Keyi, Songyun Xie, Xin Zhang, Xinzhou Xie, Yujie Cui, Ruizhen Liu, and Jian Xu. "Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT)." Brain Sciences 13, no. 2 (January 31, 2023): 247. http://dx.doi.org/10.3390/brainsci13020247.

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The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis. A modified version of the attention network test (ANT) with an EEG recording was adopted to probe the dynamic topology propagation in the three attention networks. First, the event-related potentials (ERP) analysis was used to extract sub-stage networks corresponding to the role of each attention network. Then, the dynamic network model of each attention network was constructed by post hoc test between conditions followed by the short-time-windows fitting model and brain network construction. We found that the alerting involved long-range interaction among the prefrontal cortex and posterior cortex of brain. The orienting elicited more sparse information flow after the target onset in the frequency band 1–30 Hz, and the executive control contained complex top-down control originating from the frontal cortex of the brain. Moreover, the switch of the activated regions in the associated time courses was elicited in attention networks contributing to diverse processing stages, which further extends our knowledge of the mechanism of attention networks.

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Cohen, Noga, Avishai Henik, and Nilly Mor. "Can Emotion Modulate Attention? Evidence for Reciprocal Links in the Attentional Network Test." Experimental Psychology 58, no. 3 (November 1, 2011): 171–79. http://dx.doi.org/10.1027/1618-3169/a000083.

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Evolution theory suggests that adaptive behavior depends on our ability to give preferential attention to emotional information when it is necessary for our survival, and to down-regulate irrelevant emotional influence. However, empirical work has shown that the interaction between emotion and attention varies, based on the attentional network in question. The aim of the current research was to examine the influence of stimulus emotionality on attention in three attentional networks: alerting, orienting, and executive functions. In two studies, using negative and neutral cues in a modified version of the Attention Network Test, it was found that negative cues impaired task performance in the absence of executive conflict, but not when executive processes were activated. Moreover, it was found that the influence of negative cues on task performance in a given trial was attenuated following activation of executive processes in the previous trial. These results suggest that when executive resources are required, inhibitory mechanisms are recruited to decrease the disruptive effect of emotional stimuli. More importantly, these findings indicate that the effect of emotional stimuli on attention is down-regulated both during cognitive conflict and after the conflict has already ended.

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Hahn, Eric, Thi Minh Tam Ta, Constanze Hahn, Linn K. Kuehl, Claudia Ruehl, Andres H. Neuhaus, and Michael Dettling. "Test–retest reliability of Attention Network Test measures in schizophrenia." Schizophrenia Research 133, no. 1-3 (December 2011): 218–22. http://dx.doi.org/10.1016/j.schres.2011.09.026.

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Weaver, Bruce, Michel Bédard, Jim McAuliffe, and Marie Parkkari. "Using the Attention Network Test to predict driving test scores." Accident Analysis & Prevention 41, no. 1 (January 2009): 76–83. http://dx.doi.org/10.1016/j.aap.2008.09.006.

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6

Fu, Jia, Guoming Yu, and Lun Zhao. "Effect of aging on visual attention: Evidence from the Attention Network Test." Social Behavior and Personality: an international journal 49, no. 3 (March 10, 2021): 1–8. http://dx.doi.org/10.2224/sbp.9806.

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We investigated the effects of aging on attentional functions using the Attention Network Test (ANT), which enables simultaneous testing of alerting, orienting, and executive networks, and their interactions. Participants were 38 young adults (Mage = 21.35 years) and 36 older adults (Mage = 71.17 years). Although the older adults exhibited a slower overall response, the three attentional functions showed different modulation according to age group and the trial block being completed. Older adults exhibited significant impairment in the alerting function, regardless of whether they were completing the first or second block of trials, whereas their executive function decreased significantly only in Block 2 owing to cognitive fatigue. Both age groups performed similarly for the orienting function. Future researchers should seek to further clarify the specificity of attention function with people aged over 70 years to address their attention disturbance.

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7

Wang, Bin, Jingjing Zhao, Zheng Wu, Wei Shang, Jie Xiang, Rui Cao, Haifang Li, Junjie Chen, Hui Zhang, and Ting Yan. "Eccentricity Effects on the Efficiency of Attentional Networks: Evidence From a Modified Attention Network Test." Perception 45, no. 12 (July 11, 2016): 1375–86. http://dx.doi.org/10.1177/0301006616658307.

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8

Westlye, Lars T., Håkon Grydeland, Kristine B. Walhovd, and Anders M. Fjell. "Associations between Regional Cortical Thickness and Attentional Networks as Measured by the Attention Network Test." Cerebral Cortex 21, no. 2 (June 4, 2010): 345–56. http://dx.doi.org/10.1093/cercor/bhq101.

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9

Greene, Deanna J., Anat Barnea, Kristin Herzberg, Anat Rassis, Maital Neta, Amir Raz, and Eran Zaidel. "Measuring attention in the hemispheres: The lateralized attention network test (LANT)." Brain and Cognition 66, no. 1 (February 2008): 21–31. http://dx.doi.org/10.1016/j.bandc.2007.05.003.

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Pauletti, Caterina, Daniela Mannarelli, Maria Caterina De Lucia, Nicoletta Locuratolo, Antonio Currà, Paolo Missori, Lucio Marinelli, and Francesco Fattapposta. "Selective attentional deficit in essential tremor: Evidence from the attention network test." Parkinsonism & Related Disorders 21, no. 11 (November 2015): 1306–11. http://dx.doi.org/10.1016/j.parkreldis.2015.08.035.

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11

Neuhaus, Andres H., Carsten Urbanek, Carolin Opgen-Rhein, Eric Hahn, Thi Minh Tam Ta, Simone Koehler, Melanie Gross, and Michael Dettling. "Event-related potentials associated with Attention Network Test." International Journal of Psychophysiology 76, no. 2 (May 2010): 72–79. http://dx.doi.org/10.1016/j.ijpsycho.2010.02.005.

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12

Redick, Thomas S., and Randall W. Engle. "Working memory capacity and attention network test performance." Applied Cognitive Psychology 20, no. 5 (2006): 713–21. http://dx.doi.org/10.1002/acp.1224.

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Pauletti, Caterina, Daniela Mannarelli, Nicoletta Locuratolo, Luca Pollini, Antonio Currà, Lucio Marinelli, Steno Rinalduzzi, and Francesco Fattapposta. "Attention in Parkinson’s disease with fatigue: evidence from the attention network test." Journal of Neural Transmission 124, no. 3 (October 25, 2016): 335–45. http://dx.doi.org/10.1007/s00702-016-1637-z.

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14

Mahoney, Jeannette R., Joe Verghese, Kristina Dumas, Cuiling Wang, and Roee Holtzer. "Multisensory cueing and the attention network test in aging." Seeing and Perceiving 25 (2012): 20. http://dx.doi.org/10.1163/187847612x646424.

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The Attention Network Test (ANT) assesses the effect of alerting and orienting cues on a visual flanker task measuring executive attention. Previous findings revealed that older adults demonstrate greater RT benefits when provided with visual orienting cues that offer both spatial and temporal information of an ensuing target. Given the overlap of neural correlates involved in multisensory processing and cueing (i.e., alerting and orienting), especially in the superior colliculus, thalamus, superior temporal and parietal regions, an investigation of multisensory cueing effects was warranted. The current study was designed to determine whether participants, both old and young, benefited from receiving multisensory alerting and orienting cues on a visual flanker task. Eighteen young (M = 19.17 yrs) and eighteen old (M = 76.44 yrs) individuals that were determined to be non-demented and without any medical or psychiatric conditions that would affect their performance were included. Results revealed main effects for the executive attention and orienting networks, but not for the alerting network. In terms of orienting, both old and young adults demonstrated significant orienting effects for auditory–somatosensory (AS), auditory–visual (AV), and visual–somatosensory (VS) cues. Benefits of multisensory compared to unisensory averaged orienting effects differed by cue type and age group; younger adults demonstrated significantly greater RT benefits for AS orienting cues whereas older adults demonstrated significantly greater RT benefits for AV orienting cues. Both groups, however, demonstrated significant RT benefits for VS orienting cues. These findings provide evidence for the facilitative effect of multisensory orienting cues, and not multisensory alerting cues, in old and young adults.

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15

Gamboz, Nadia, Stefania Zamarian, and Corrado Cavallero. "Age-Related Differences in the Attention Network Test (ANT)." Experimental Aging Research 36, no. 3 (June 7, 2010): 287–305. http://dx.doi.org/10.1080/0361073x.2010.484729.

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16

Salzer, Yael, Tal Oron-Gilad, and Avishai Henik. "Evaluation of the attention network test using vibrotactile stimulations." Behavior Research Methods 47, no. 2 (May 31, 2014): 395–408. http://dx.doi.org/10.3758/s13428-014-0479-6.

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17

Backes, Volker, Thilo Kellermann, Bianca Voss, Jörn Krämer, Conny Depner, Frank Schneider, and Ute Habel. "Neural correlates of the attention network test in schizophrenia." European Archives of Psychiatry and Clinical Neuroscience 261, S2 (September 29, 2011): 155–60. http://dx.doi.org/10.1007/s00406-011-0264-9.

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18

Yaakoby-Rotem, Sarit, and Ronny Geva. "Asymmetric Attention Networks: The Case of Children." Journal of the International Neuropsychological Society 20, no. 4 (March 11, 2014): 434–43. http://dx.doi.org/10.1017/s1355617714000150.

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AbstractVisuospatial attention-networks are represented in both hemispheres, with right-hemisphere dominance in adults. Little is known about the lateralization of the attentional-networks in children. To assess the lateralization of attentional-networks in children aged 5 years, performance on a Lateralized-Attention-Network-Test specifically designed for children (LANT-C) was compared with performance on the Attention-Network-Test for children (ANT-C). Participants were 82 children, aged 5–6 years (55% boys, middle–class, mainstream schooling). They were examined with both the ANT-C and the LANT-C along with evaluation of intelligence and attention questionnaires. Multiple analysis of variance showed a main effect for network, with high efficiency for orienting and lower executive efficiency (accuracy; p < .001; η2 = .282). An effect for procedure, elucidated higher efficiency in the ANT-C relatively to the LANT-C (accuracy; p < .01; η2 = .097). A procedure × network interaction effect was also found, showing that this procedure difference is present in the alerting and executive networks (accuracy; p < .05; η2 = .096). LANT-C analysis showed a left visual-field advantage in alerting, (accuracy; p < .05; η2 = .066), while executing with the right hand benefitted executive performance (response-time; p < .05; η2 = .06). Results extend previous findings manifesting a right-hemisphere advantage in children's alerting-attention, pointing to the importance of lateralization of brain function to the understanding of the integrity of attention-networks in children. (JINS, 2014, 20, 1–10)

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19

Federico, Francesca, Andrea Marotta, Diana Martella, and Maria Casagrande. "Development in attention functions and social processing: Evidence from the Attention Network Test." British Journal of Developmental Psychology 35, no. 2 (August 4, 2016): 169–85. http://dx.doi.org/10.1111/bjdp.12154.

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20

Mannarelli, D., C. Pauletti, C. Panzini, A. Corrado, R. Delle Chiaie, and F. Fattapposta. "Cerebellum and attention networks functioning: findings from a cerebellar transcranial Direct Current Stimulation and Attention Network Test study." International Journal of Psychophysiology 131 (October 2018): S113—S114. http://dx.doi.org/10.1016/j.ijpsycho.2018.07.309.

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21

Huang, Wanling, Long Zhang, Yaoting Sun, Fangfang Chen, and Kai Wang. "The Prediction Analysis of Autistic and Schizotypal Traits in Attentional Networks." Psychiatry Investigation 18, no. 5 (May 25, 2021): 417–25. http://dx.doi.org/10.30773/pi.2020.0251.

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Objective Empirical findings confirmed that autistic and schizotypal traits are associated with attentional function as well as include various dimensions. So far, no study has reported which dimension of these traits relates to attentional networks. This study aimed to find out whether there are associations between attentional networks and autistic traits; and between attentional networks and schizotypal traits.Methods A total of 449 volunteers was included in this study, and autism-spectrum quotient (AQ), schizotypal personality questionnaire (SPQ), and attention network test (ANT) were used to measure autistic traits and schizotypal traits. The three independent attentional networks, including alerting network, orienting network, and executive control network, were also measured.Results Autistic traits were associated with the orienting network, whereas schizotypal traits were associated with the orienting network and executive control network. Furthermore, attentional networks could be predicted by specific dimensions of autistic and schizotypal traits. AQ-attention switching [0.104 (-1.175– -0.025), p=0.041] and AQ-attention to detail [-0.097 (-0.798– -0.001), p=0.049] were significant predictors of orienting network and gender were significant predictor of executive network (Beta=0.107; 95% CI=-0.476–10.139; p=0.031). Whereas, schizotypal dimension “interpersonal” was a significant predictor of all three attentional networks [Alerting: 0.147 (-0.010–0.861), p=0.045; Orienting: 0.147 (0.018–0.733), p=0.040; Executive: 0.198 (0.215–1.309), p=0.006].Conclusion This study demonstrated that autistic and schizotypal traits were associated with attentional networks. The specific dimensions of autistic and schizotypal traits could predict attentional networks. Nevertheless, the attentional networks predicted with these two traits were different.

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22

Roth, Alexandra K., Douglas R. Denney, and Sharon G. Lynch. "Information processing speed and attention in multiple sclerosis: Reconsidering the Attention Network Test (ANT)." Journal of Clinical and Experimental Neuropsychology 37, no. 5 (May 26, 2015): 518–29. http://dx.doi.org/10.1080/13803395.2015.1037252.

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23

Rajan, Abhijit, Sreenivasan Meyyappan, Yuelu Liu, Immanuel Babu Henry Samuel, Bijurika Nandi, George R. Mangun, and Mingzhou Ding. "The Microstructure of Attentional Control in the Dorsal Attention Network." Journal of Cognitive Neuroscience 33, no. 6 (May 1, 2021): 965–83. http://dx.doi.org/10.1162/jocn_a_01710.

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Abstract The top–down control of attention involves command signals arising chiefly in the dorsal attention network (DAN) in frontal and parietal cortex and propagating to sensory cortex to enable the selective processing of incoming stimuli based on their behavioral relevance. Consistent with this view, the DAN is active during preparatory (anticipatory) attention for relevant events and objects, which, in vision, may be defined by different stimulus attributes including their spatial location, color, motion, or form. How this network is organized to support different forms of preparatory attention to different stimulus attributes remains unclear. We propose that, within the DAN, there exist functional microstructures (patterns of activity) specific for controlling attention based on the specific information to be attended. To test this, we contrasted preparatory attention to stimulus location (spatial attention) and to stimulus color (feature attention), and used multivoxel pattern analysis to characterize the corresponding patterns of activity within the DAN. We observed different multivoxel patterns of BOLD activation within the DAN for the control of spatial attention (attending left vs. right) and feature attention (attending red vs. green). These patterns of activity for spatial and feature attentional control showed limited overlap with each other within the DAN. Our findings thus support a model in which the DAN has different functional microstructures for distinctive forms of top–down control of visual attention.

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McConnell, Meghan M., and David I. Shore. "Mixing measures: testing an assumption of the attention network test." Attention, Perception, & Psychophysics 73, no. 4 (January 15, 2011): 1096–107. http://dx.doi.org/10.3758/s13414-010-0085-3.

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25

Sun, Gang, Xiao Yang, Qingjun Jiang, Kai Liu, Bo Li, Li Li, Lun Zhao, and Min Li. "Hyperthermia impairs the executive function using the Attention Network Test." International Journal of Hyperthermia 28, no. 7 (September 4, 2012): 621–26. http://dx.doi.org/10.3109/02656736.2012.705217.

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26

Crivelli, Lucía, Mauricio F. Farez, Claudio D. González, Marcela Fiol, Alejandra Amengual, Ramón Leiguarda, and Jorge Correale. "Alerting Network Dysfunction in Early Multiple Sclerosis." Journal of the International Neuropsychological Society 18, no. 4 (May 24, 2012): 757–63. http://dx.doi.org/10.1017/s1355617712000410.

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AbstractThe objective of this study is to assess attention in recently diagnosed relapsing-remitting multiple sclerosis patients. Twenty-seven patients with early multiple sclerosis and low clinical disability scores (EDSS<2) and 27 sex-, age-, and education-matched healthy controls underwent attention assessment using the Attentional Network Test, a computerized task designed to measure efficiency independently in 3 attentional networks (Alerting, Orienting and Executive Control). MS patients had significantly less efficiency in the Alerting network (p = .006). In contrast, in the Orienting and Executive Control networks, they did not differ from controls. A significant interaction between Alerting and Executive Control was also found in the MS patients (p = .007). Early relapsing-remitting multiple sclerosis particularly affects the Alerting domain of attention, whereas the Orienting and Executive Control domains are not affected. (JINS, 2012, 18, 1–7)

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Song, Qi, Jie Li, Chenghong Li, Hao Guo, and Rui Huang. "Fully Attentional Network for Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 2280–88. http://dx.doi.org/10.1609/aaai.v36i2.20126.

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Recent non-local self-attention methods have proven to be effective in capturing long-range dependencies for semantic segmentation. These methods usually form a similarity map of R^(CxC) (by compressing spatial dimensions) or R^(HWxHW) (by compressing channels) to describe the feature relations along either channel or spatial dimensions, where C is the number of channels, H and W are the spatial dimensions of the input feature map. However, such practices tend to condense feature dependencies along the other dimensions, hence causing attention missing, which might lead to inferior results for small/thin categories or inconsistent segmentation inside large objects. To address this problem, we propose a new approach, namely Fully Attentional Network (FLANet), to encode both spatial and channel attentions in a single similarity map while maintaining high computational efficiency. Specifically, for each channel map, our FLANet can harvest feature responses from all other channel maps, and the associated spatial positions as well, through a novel fully attentional module. Our new method has achieved state-of-the-art performance on three challenging semantic segmentation datasets, i.e., 83.6%, 46.99%, and 88.5% on the Cityscapes test set, the ADE20K validation set, and the PASCAL VOC test set, respectively.

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Mannarelli, Daniela, Caterina Pauletti, Antonio Currà, Lucio Marinelli, Alessandra Corrado, Roberto Delle Chiaie, and Francesco Fattapposta. "The Cerebellum Modulates Attention Network Functioning: Evidence from a Cerebellar Transcranial Direct Current Stimulation and Attention Network Test Study." Cerebellum 18, no. 3 (February 23, 2019): 457–68. http://dx.doi.org/10.1007/s12311-019-01014-8.

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Markett, Sebastian, Martin Reuter, Christian Montag, Gesine Voigt, Bernd Lachmann, Sarah Rudorf, Christian E. Elger, and Bernd Weber. "Assessing the function of the fronto-parietal attention network: Insights from resting-state fMRI and the attentional network test." Human Brain Mapping 35, no. 4 (May 14, 2013): 1700–1709. http://dx.doi.org/10.1002/hbm.22285.

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Sinha, Nidhi, Swasti Arora, Priyanka Srivastava, and Raymond M. Klein. "What networks of attention are affected by depression? A meta-analysis of studies that used the attention network test." Journal of Affective Disorders Reports 8 (April 2022): 100302. http://dx.doi.org/10.1016/j.jadr.2021.100302.

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Zhang, Zhiqin, Bo Zhang, Fen Li, and Dehua Kong. "Multihead Self Attention Hand Pose Estimation." E3S Web of Conferences 218 (2020): 03023. http://dx.doi.org/10.1051/e3sconf/202021803023.

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In This paper, we propose a hand pose estimation neural networks architecture named MSAHP which can improve PCK (percentage correct keypoints) greatly by fusing self-attention module in CNN (Convolutional Neural Networks). The proposed network is based on a ResNet (Residual Neural Network) backbone and concatenate discriminative features through multiple different scale feature maps, then multiple head self-attention module was used to focus on the salient feature map area. In recent years, self-attention mechanism was applicated widely in NLP and speech recognition, which can improve greatly key metrics. But in compute vision especially for hand pose estimation, we did not find the application. Experiments on hand pose estimation dataset demonstrate the improved PCK of our MSAHP than the existing state-of-the-art hand pose estimation methods. Specifically, the proposed method can achieve 93.68% PCK score on our mixed test dataset.

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Feltmate, Brett B. T., Austin J. Hurst, and Raymond M. Klein. "Effects of fatigue on attention and vigilance as measured with a modified attention network test." Experimental Brain Research 238, no. 11 (August 29, 2020): 2507–19. http://dx.doi.org/10.1007/s00221-020-05902-y.

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Togo, Fumiharu, Gudrun Lange, Benjamin H. Natelson, and Karen S. Quigley. "Attention network test: Assessment of cognitive function in chronic fatigue syndrome." Journal of Neuropsychology 9, no. 1 (September 24, 2013): 1–9. http://dx.doi.org/10.1111/jnp.12030.

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Weaver, Bruce, Michel Bédard, and Jim McAuliffe. "Evaluation of a 10-minute Version of the Attention Network Test." Clinical Neuropsychologist 27, no. 8 (November 2013): 1281–99. http://dx.doi.org/10.1080/13854046.2013.851741.

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Wang, Weixuan, Zhihong Chen, and Haifeng Hu. "Hierarchical Attention Network for Image Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8957–64. http://dx.doi.org/10.1609/aaai.v33i01.33018957.

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Recently, attention mechanism has been successfully applied in image captioning, but the existing attention methods are only established on low-level spatial features or high-level text features, which limits richness of captions. In this paper, we propose a Hierarchical Attention Network (HAN) that enables attention to be calculated on pyramidal hierarchy of features synchronously. The pyramidal hierarchy consists of features on diverse semantic levels, which allows predicting different words according to different features. On the other hand, due to the different modalities of features, a Multivariate Residual Module (MRM) is proposed to learn the joint representations from features. The MRM is able to model projections and extract relevant relations among different features. Furthermore, we introduce a context gate to balance the contribution of different features. Compared with the existing methods, our approach applies hierarchical features and exploits several multimodal integration strategies, which can significantly improve the performance. The HAN is verified on benchmark MSCOCO dataset, and the experimental results indicate that our model outperforms the state-of-the-art methods, achieving a BLEU1 score of 80.9 and a CIDEr score of 121.7 in the Karpathy’s test split.

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Kim, Rae-Eun, and Sang-Mee Koo. "Differences in Attention Levels between Preliminary Nurses and Pre-service Early Childhood Teachers Using ANT (Attentional Network Test) Computer Test." Medico-Legal Update 19, no. 2 (2019): 660. http://dx.doi.org/10.5958/0974-1283.2019.00251.2.

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Lu, Hanna, Sandra S. M. Chan, Ada W. T. Fung, and Linda C. W. Lam. "Efficiency of Attentional Components in Elderly with Mild Neurocognitive Disorders Shown by the Attention Network Test." Dementia and Geriatric Cognitive Disorders 41, no. 1-2 (2016): 93–98. http://dx.doi.org/10.1159/000441350.

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Aims: Complex attention, serving as a main diagnostic item of mild neurocognitive disorders (NCD), has been reported to be susceptible to pathological ageing. This study aimed to evaluate the attention network functions in older adults with subtypes of NCD. Methods: 36 adults with NCD due to Alzheimer's disease (NCD-AD), 31 adults with NCD due to vascular disease (NCD-vascular) and 137 healthy controls were recruited. Attention Network Test (ANT) was conducted to assess the efficiency of alerting, orienting and executive control. Results: Significant between-group differences were found in executive control (conventional score: F = 11.472, p < 0.001; ratio score: F = 8.430, p < 0.001) and processing speed (F = 4.958, p = 0.008). NCD subgroups demonstrated poorer performance on the ANT, particularly on executive control (healthy 59.9 ± 45.9, NCD-vascular 88.9 ± 44.8, NCD-AD 97.0 ± 53.9). Moreover, the NCD-AD group showed both less efficient executive control and prominent slowing processing speed (reaction time: healthy 687.5 ± 106.0 ms, NCD-vascular 685.3 ± 97.1 ms, NCD-AD 750.6 ± 132.6 ms). Conclusions: The NCD-vascular group appeared to be less efficient in executive control, while the NCD-AD group demonstrated less effective executive control and also slower processing speed. These results suggest that the characterized performance of ANT, processing speed and executive control in particular, might help differentiate adults at risk of different forms of cognitive impairment.

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Dye, Matthew W. G., Dara E. Baril, and Daphne Bavelier. "Which aspects of visual attention are changed by deafness? The case of the Attentional Network Test." Neuropsychologia 45, no. 8 (2007): 1801–11. http://dx.doi.org/10.1016/j.neuropsychologia.2006.12.019.

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Kratz, Oliver, Petra Studer, Susanne Malcherek, Karlheinz Erbe, Gunther H. Moll, and Hartmut Heinrich. "Attentional processes in children with ADHD: An event-related potential study using the attention network test." International Journal of Psychophysiology 81, no. 2 (August 2011): 82–90. http://dx.doi.org/10.1016/j.ijpsycho.2011.05.008.

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Dai, Biyun, Jinlong Li, and Ruoyi Xu. "Multiple Positional Self-Attention Network for Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7610–17. http://dx.doi.org/10.1609/aaai.v34i05.6261.

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Self-attention mechanisms have recently caused many concerns on Natural Language Processing (NLP) tasks. Relative positional information is important to self-attention mechanisms. We propose Faraway Mask focusing on the (2m + 1)-gram words and Scaled-Distance Mask putting the logarithmic distance punishment to avoid and weaken the self-attention of distant words respectively. To exploit different masks, we present Positional Self-Attention Layer for generating different Masked-Self-Attentions and a following Position-Fusion Layer in which fused positional information multiplies the Masked-Self-Attentions for generating sentence embeddings. To evaluate our sentence embeddings approach Multiple Positional Self-Attention Network (MPSAN), we perform the comparison experiments on sentiment analysis, semantic relatedness and sentence classification tasks. The result shows that our MPSAN outperforms state-of-the-art methods on five datasets and the test accuracy is improved by 0.81%, 0.6% on SST, CR datasets, respectively. In addition, we reduce training parameters and improve the time efficiency of MPSAN by lowering the dimension number of self-attention and simplifying fusion mechanism.

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Liu, Xiongfei, Bengao Li, Xin Chen, Haiyan Zhang, and Shu Zhan. "Content-Based Attention Network for Person Image Generation." Journal of Circuits, Systems and Computers 29, no. 15 (July 6, 2020): 2050250. http://dx.doi.org/10.1142/s0218126620502503.

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This paper proposes a novel method for person image generation with arbitrary target pose. Given a person image and an arbitrary target pose, our proposed model can synthesize images with the same person but different poses. The Generative Adversarial Networks (GANs) are the major part of the proposed model. Different from the traditional GANs, we add attention mechanism to the generator in order to generate realistic-looking images, we also use content reconstruction with a pretrained VGG16 Net to keep the content consistency between generated images and target images. Furthermore, we test our model on DeepFashion and Market-1501 datasets. The experimental results show that the proposed network performs favorably against state-of-the-art methods.

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MAHONEY, JEANNETTE R., JOE VERGHESE, YELENA GOLDIN, RICHARD LIPTON, and ROEE HOLTZER. "Alerting, orienting, and executive attention in older adults." Journal of the International Neuropsychological Society 16, no. 5 (July 27, 2010): 877–89. http://dx.doi.org/10.1017/s1355617710000767.

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AbstractThe Attention Network Test (ANT) assesses alerting, orienting, and executive attention. The current study was designed to achieve three main objectives. First, we determined the reliability, effects, and interactions of attention networks in a relatively large cohort of non-demented older adults (n= 184). Second, in the context of this aged cohort, we examined the effect of chronological age on attention networks. Third, the effect of blood pressure on ANT performance was evaluated. Results revealed high-reliability for the ANT as a whole, and for specific cue and flanker types. We found significant main effects for the three attention networks as well as diminished alerting but enhanced orienting effects during conflict resolution trials. Furthermore, increased chronological age and low blood pressure were both associated with significantly worse performance on the executive attention network. These findings are consistent with executive function decline in older adults and the plausible effect of reduced blood flow to the frontal lobes on individual differences in attention demanding tasks. (JINS, 2010,16, 877–889.)

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Pêcher, C., C. Quaireau, C. Lemercier, and J. M. Cellier. "The effects of inattention on selective attention: How sadness and ruminations alter attention functions evaluated with the Attention Network Test." European Review of Applied Psychology 61, no. 1 (January 2011): 43–50. http://dx.doi.org/10.1016/j.erap.2010.10.003.

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Ding, Enjie, Yuhao Cheng, Chengcheng Xiao, Zhongyu Liu, and Wanli Yu. "Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network." Applied Sciences 11, no. 7 (March 31, 2021): 3111. http://dx.doi.org/10.3390/app11073111.

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Light-weight convolutional neural networks (CNNs) suffer limited feature representation capabilities due to low computational budgets, resulting in degradation in performance. To make CNNs more efficient, dynamic neural networks (DyNet) have been proposed to increase the complexity of the model by using the Squeeze-and-Excitation (SE) module to adaptively obtain the importance of each convolution kernel through the attention mechanism. However, the attention mechanism in the SE network (SENet) selects all channel information for calculations, which brings essential challenges: (a) interference caused by the internal redundant information; and (b) increasing number of network calculations. To address the above problems, this work proposes a dynamic convolutional network (termed as EAM-DyNet) to reduce the number of channels in feature maps by extracting only the useful spatial information. EAM-DyNet first uses the random channel reduction and channel grouping reduction methods to remove the redundancy in the information. As the downsampling of information can lead to the loss of useful information, it then applies an adaptive average pooling method to maintain the information integrity. Extensive experimental results on the baseline demonstrate that EAM-DyNet outperformed the existing approaches, thus it can achieve higher accuracy of the network test and less network parameters.

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Tan, Yi-Fei, Soon-Chang Poh, Chee-Pun Ooi, and Wooi-Haw Tan. "Human activity recognition with self-attention." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (April 1, 2023): 2023. http://dx.doi.org/10.11591/ijece.v13i2.pp2023-2029.

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In this paper, a self-attention based neural network architecture to address human activity recognition is proposed. The dataset used was collected using smartphone. The contribution of this paper is using a multi-layer multi-head self-attention neural network architecture for human activity recognition and compared to two strong baseline architectures, which are convolutional neural network (CNN) and long-short term network (LSTM). The dropout rate, positional encoding and scaling factor are also been investigated to find the best model. The results show that proposed model achieves a test accuracy of 91.75%, which is a comparable result when compared to both the baseline models.

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Xiang, Zhijie, Weijia Gong, Zehui Li, Xue Yang, Jihua Wang, and Hong Wang. "Predicting Protein–Protein Interactions via Gated Graph Attention Signed Network." Biomolecules 11, no. 6 (May 28, 2021): 799. http://dx.doi.org/10.3390/biom11060799.

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Protein–protein interactions (PPIs) play a key role in signal transduction and pharmacogenomics, and hence, accurate PPI prediction is crucial. Graph structures have received increasing attention owing to their outstanding performance in machine learning. In practice, PPIs can be expressed as a signed network (i.e., graph structure), wherein the nodes in the network represent proteins, and edges represent the interactions (positive or negative effects) of protein nodes. PPI predictions can be realized by predicting the links of the signed network; therefore, the use of gated graph attention for signed networks (SN-GGAT) is proposed herein. First, the concept of graph attention network (GAT) is applied to signed networks, in which “attention” represents the weight of neighbor nodes, and GAT updates the node features through the weighted aggregation of neighbor nodes. Then, the gating mechanism is defined and combined with the balance theory to obtain the high-order relations of protein nodes to improve the attention effect, making the attention mechanism follow the principle of “low-order high attention, high-order low attention, different signs opposite”. PPIs are subsequently predicted on the Saccharomyces cerevisiae core dataset and the Human dataset. The test results demonstrate that the proposed method exhibits strong competitiveness.

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Tang, Yi-Jun, Yi-He Pang, and Bin Liu. "DeepIDP-2L: protein intrinsically disordered region prediction by combining convolutional attention network and hierarchical attention network." Bioinformatics 38, no. 5 (December 2, 2021): 1252–60. http://dx.doi.org/10.1093/bioinformatics/btab810.

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Abstract Motivation Intrinsically disordered regions (IDRs) are widely distributed in proteins. Accurate prediction of IDRs is critical for the protein structure and function analysis. The IDRs are divided into long disordered regions (LDRs) and short disordered regions (SDRs) according to their lengths. Previous studies have shown that LDRs and SDRs have different proprieties. However, the existing computational methods fail to extract different features for LDRs and SDRs separately. As a result, they achieve unstable performance on datasets with different ratios of LDRs and SDRs. Results In this study, a two-layer predictor was proposed called DeepIDP-2L. In the first layer, two kinds of attention-based models are used to extract different features for LDRs and SDRs, respectively. The hierarchical attention network is used to capture the distribution pattern features of LDRs, and convolutional attention network is used to capture the local correlation features of SDRs. The second layer of DeepIDP-2L maps the feature extracted in the first layer into a new feature space. Convolutional network and bidirectional long short term memory are used to capture the local and long-range information for predicting both SDRs and LDRs. Experimental results show that DeepIDP-2L can achieve more stable performance than other exiting predictors on independent test sets with different ratios of SDRs and LDRs. Availability and implementation For the convenience of most experimental scientists, a user-friendly and publicly accessible web-server for the new predictor has been established at http://bliulab.net/DeepIDP-2L/. It is anticipated that DeepIDP-2L will become a very useful tool for identification of intrinsically disordered regions. Supplementary information Supplementary data are available at Bioinformatics online.

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Bedard, M., H. Maxwell, B. Weaver, S. Marshall, G. Naglie, M. J. Rapoport, and H. A. Tuokko. "REACTION TIMES ON THE ATTENTION NETWORK TEST ARE ASSOCIATED WITH TRAFFIC VIOLATIONS." Innovation in Aging 1, suppl_1 (June 30, 2017): 741. http://dx.doi.org/10.1093/geroni/igx004.2672.

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MacLeod, Jeffrey W., Michael A. Lawrence, Meghan M. McConnell, Gail A. Eskes, Raymond M. Klein, and David I. Shore. "Appraising the ANT: Psychometric and theoretical considerations of the Attention Network Test." Neuropsychology 24, no. 5 (2010): 637–51. http://dx.doi.org/10.1037/a0019803.

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Lundervold, Astri J., Steinunn Adolfsdottir, Helene Halleland, Anne Halmøy, Kerstin Plessen, and Jan Haavik. "Attention Network Test in adults with ADHD - the impact of affective fluctuations." Behavioral and Brain Functions 7, no. 1 (2011): 27. http://dx.doi.org/10.1186/1744-9081-7-27.

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