Letteratura scientifica selezionata sul tema "Attention Network Test"
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Articoli di riviste sul tema "Attention Network Test"
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, n. 1 (7 dicembre 2009): 93–99. http://dx.doi.org/10.1177/1352458509350308.
Testo completoDuan, Keyi, Songyun Xie, Xin Zhang, Xinzhou Xie, Yujie Cui, Ruizhen Liu e Jian Xu. "Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT)". Brain Sciences 13, n. 2 (31 gennaio 2023): 247. http://dx.doi.org/10.3390/brainsci13020247.
Testo completoCohen, Noga, Avishai Henik e Nilly Mor. "Can Emotion Modulate Attention? Evidence for Reciprocal Links in the Attentional Network Test". Experimental Psychology 58, n. 3 (1 novembre 2011): 171–79. http://dx.doi.org/10.1027/1618-3169/a000083.
Testo completoHahn, Eric, Thi Minh Tam Ta, Constanze Hahn, Linn K. Kuehl, Claudia Ruehl, Andres H. Neuhaus e Michael Dettling. "Test–retest reliability of Attention Network Test measures in schizophrenia". Schizophrenia Research 133, n. 1-3 (dicembre 2011): 218–22. http://dx.doi.org/10.1016/j.schres.2011.09.026.
Testo completoWeaver, Bruce, Michel Bédard, Jim McAuliffe e Marie Parkkari. "Using the Attention Network Test to predict driving test scores". Accident Analysis & Prevention 41, n. 1 (gennaio 2009): 76–83. http://dx.doi.org/10.1016/j.aap.2008.09.006.
Testo completoFu, Jia, Guoming Yu e Lun Zhao. "Effect of aging on visual attention: Evidence from the Attention Network Test". Social Behavior and Personality: an international journal 49, n. 3 (10 marzo 2021): 1–8. http://dx.doi.org/10.2224/sbp.9806.
Testo completoWang, Bin, Jingjing Zhao, Zheng Wu, Wei Shang, Jie Xiang, Rui Cao, Haifang Li, Junjie Chen, Hui Zhang e Ting Yan. "Eccentricity Effects on the Efficiency of Attentional Networks: Evidence From a Modified Attention Network Test". Perception 45, n. 12 (11 luglio 2016): 1375–86. http://dx.doi.org/10.1177/0301006616658307.
Testo completoWestlye, Lars T., Håkon Grydeland, Kristine B. Walhovd e Anders M. Fjell. "Associations between Regional Cortical Thickness and Attentional Networks as Measured by the Attention Network Test". Cerebral Cortex 21, n. 2 (4 giugno 2010): 345–56. http://dx.doi.org/10.1093/cercor/bhq101.
Testo completoGreene, Deanna J., Anat Barnea, Kristin Herzberg, Anat Rassis, Maital Neta, Amir Raz e Eran Zaidel. "Measuring attention in the hemispheres: The lateralized attention network test (LANT)". Brain and Cognition 66, n. 1 (febbraio 2008): 21–31. http://dx.doi.org/10.1016/j.bandc.2007.05.003.
Testo completoPauletti, Caterina, Daniela Mannarelli, Maria Caterina De Lucia, Nicoletta Locuratolo, Antonio Currà, Paolo Missori, Lucio Marinelli e Francesco Fattapposta. "Selective attentional deficit in essential tremor: Evidence from the attention network test". Parkinsonism & Related Disorders 21, n. 11 (novembre 2015): 1306–11. http://dx.doi.org/10.1016/j.parkreldis.2015.08.035.
Testo completoTesi sul tema "Attention Network Test"
Ta, Thi Minh Tam [Verfasser]. "Test-Retest Reliabilität des Attention-Network-Test bei Schizophrenie / Thi Minh Tam Ta". Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2013. http://d-nb.info/1037725654/34.
Testo completoLehtonen, Sanna Elina. "Self-reported Inattention and Hyperactivity-impulsivity as Predictors of Attention Network Efficiency". Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/psych_diss/34.
Testo completoDimitriadis, Spyridon. "Multi-task regression QSAR/QSPR prediction utilizing text-based Transformer Neural Network and single-task using feature-based models". Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177186.
Testo completoTu, Guoyun. "Image Captioning On General Data And Fashion Data : An Attribute-Image-Combined Attention-Based Network for Image Captioning on Mutli-Object Images and Single-Object Images". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282925.
Testo completoBildtextning är ett avgörande fält för datorsyn och behandling av naturligt språk. Det kan tillämpas i stor utsträckning på högvolyms webbbilder, som att överföra bildinnehåll till synskadade användare. Många metoder antas inom detta område såsom uppmärksamhetsbaserade metoder, semantiska konceptbaserade modeller. Dessa uppnår utmärkt prestanda på allmänna bilddatamängder som MS COCO-dataset. Det lämnas dock fortfarande outforskat på bilder med ett objekt.I denna uppsats föreslår vi ett nytt attribut-information-kombinerat uppmärksamhetsbaserat nätverk (AIC-AB Net). I varje tidsteg läggs attributinformation till som ett komplement till visuell information. För sekventiell ordgenerering bestämmer rumslig uppmärksamhet specifika regioner av bilder som ska passera avkodaren. Sentinelgrinden bestämmer om den ska ta hand om bilden eller den visuella vaktposten (vad avkodaren redan vet, inklusive attributinformation). Text attributinformation matas synkront för att hjälpa bildigenkänning och minska osäkerheten.Vi bygger en ny modedataset bestående av modebilder för att skapa ett riktmärke för bilder med en objekt. Denna modedataset består av 144 422 bilder från 24 649 modeprodukter, med en beskrivningsmening för varje bild. Vår metod testas på MS COCO dataset och den föreslagna Fashion dataset. Resultaten visar den överlägsna prestandan hos den föreslagna modellen på både bilder med flera objekt och enbildsbilder. Vårt AIC-AB-nät överträffar det senaste nätverket Adaptive Attention Network med 0,017, 0,095 och 0,095 (CIDEr Score) i COCO-datasetet, modedataset (bästsäljare) respektive modedatasetet (alla leverantörer). Resultaten avslöjar också komplementet till uppmärksamhetsarkitektur och attributinformation.
Martínez-García, Marina. "Statistical analysis of neural correlates in decision-making". Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/283111.
Testo completoDurant aquesta tesi hem investigat els processos neuronals que es pro- dueixen durant tasques de presa de decisions, tasques basades en un ju- dici l ogic de classi caci o perceptual. Per a aquest prop osit hem analitzat tres paradigmes experimentals diferents (somatosensorial, visual i auditiu) en dues espcies diferents (micos i rates), amb l'objectiu d'il.lustrar com les neurones codi quen informaci on referents a les t asques. En particular, ens hem centrat en com certes informacions estan cod- i cades en l'activitat neuronal al llarg del temps. Concretament, com la informaci o sobre: la decisi o comportamental, els factors externs, i la con- ana en la resposta, b e codi cada en la mem oria. A m es a m es, quan el paradigma experimental ens ho va permetre, com l'atenci o modula aquests aspectes. Finalment, hem anat un pas m es enll a, i hem analitzat la comu- nicaci o entre les diferents arees corticals, mentre els subjectes resolien una tasca de presa de decisions.
Ishigami, Yoko. "THE ATTENTION NETWORK TEST (ANT): INDIVIDUAL DIFFERENCES? AND ?COMPONENTS OF ATTENTION ACROSS THE LIFE SPAN". 2011. http://hdl.handle.net/10222/13307.
Testo completoThis is a thesis that is manuscript-based.
Calvo, Alberto Lema. "Transcranial Random Noise Stimulation (tRNS) on Attention Network Test (ANT): an exploratory study". Master's thesis, 2017. http://hdl.handle.net/1822/48135.
Testo completoSpeed-accuracy tradeoff (SAT) refers to the adjustment between response speed and its accuracy, a process mediated through cortical and subcortical circuits. However, it remains unclear whether SAT can be modulated by the effects of tRNS and/or tDCS. In order to explore this effect, we used the attention network test (ANT). This tasks allowed us to test three independent networks related to both subcortical activity (alerting and orienting) and cortical level (executive). We hypothesized that performance would increase for alerting and orienting networks with tRNS whereas tDCS would increase performance in executive network. Moreover, to make the SAT effect more prominent, instructions that emphasized speed and accuracy were also introduced. Eighteen healthy students undertook three experimental sessions of tRNS, anodal tDCS and sham over the left dlPFC while performing the ANT. Results showed that no significant effect was found for stimulation. Instructions, however, showed a significant difference between speed condition and standard condition. Thus, SAT was successfully manipulated by instruction condition but not by stimulation.
O Speed-accuracy tradeoff (SAT) é definido como o ajustamento entre a velocidade e a exatidão de uma determinada resposta. Este processo é mediado por vias corticais e subcorticais suceptíveis de serem alteradas através de métodos de estimulação elétrica transcraniana. No presente estudo, dois tipos de estimulação, ETRN e ETCC, foram combinados com uma tarefa atencional (ANT) com o intuito de avaliar os seus efeitos nas vias corticais e subcorticais associadas ao SAT. Era esperado que a ETRN aumentasse o desempenho em vias atencionais subcorticais (de alerta e de orientação) enquanto que a ETCC melhorasse o desempenho na via atencional cortical (executiva). De igual modo, as instruções da tarefa também foram alteradas para manipular o SAT, favorecendo assim a velocidade ou a exatidão da resposta. Dezoito participantes saudáveis receberam de forma aleatorizada três sessões de estimulação (ETRN, ETCC, sham) unilateral sobre o CPFDL esquerdo. Os resultados mostram que a estimulação ETRN e ETCC não obteveram efeitos nas vias atentionais. As instruções, no entanto, revelam que quando o ênfase é colocado na velocidade da resposta, a via atencional executiva apresenta menor eficiência. Desta forma, O SAT apenas foi manipulado com sucesso através das intruções e não da estimulação.
Lei, Sot-Fu, e 李述富. "Performance of ADHD in the Attention Network Test(ANT) and Stop Signal Task(SST)". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/38382634929228758136.
Testo completo中山醫學大學
心理學系暨臨床心理學碩士班
103
The purpose of this study is to explore and compare the performance in Attention Network test (ANT) and the stop signal task (SST) between the Attention Deficit Hyperactivity Disorder(ADHD) children and normal children. The results will be used as references in developing clinical evaluation tools of neurological functions.This study uses ANT and SST to test subjects’ performances in different tasks. This study assumes ANT and SST are able to distinguish ADHA children and normal children. Study 1’s subjects are preschool stage children who are 6-12 years old ,23 subjects are suffering from ADHA and 23 subjects are normal, 46 subjects in total. Study 1 will use ANT to test subjects’ attention network function. Study 2 ’s subjects are preschool stage children who are 6-12 years old ,23 subjects are suffering from ADHA and 23 subjects are normal, 46 subjects in total. Study 2 will use SST to test subjects’ inhibit functions. The purpose of the studies mentioned above is to test the sensitivity of cognitive impairment. ANT in Study 1 shows that there is no obvious difference in 3 ANT scores (alert, orientation,executive control) between ADHA children and normal children. SST in Study 2 shows obvious difference in Stop Signal Reaction Time (SSRT) between ADHA children and normal children. To conclude, the sensitivity of SST testing impairment of inhibit function is higher the ANT testing cognitive impairment. As a result, SST will be more able to use in developing clinical evaluation tools. A discussion focus on ANT will be developed to find out the probable reason and related suggestions.
Brady, JJR. "The effect of mindfulness meditation on ERP measures of attention". Thesis, 2019. https://eprints.utas.edu.au/34692/1/Brady_whole_thesis.pdf.
Testo completoFlynn, SF. "Behavioural and neural correlates of orienting and executive control in high and low spider fear groups". Thesis, 2015. https://eprints.utas.edu.au/22616/1/Shelley%20Flynn%20Thesis.pdf.
Testo completoLibri sul tema "Attention Network Test"
Robbins, Trevor. The Neuropsycho–Pharmacology of Attention. A cura di Anna C. (Kia) Nobre e Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.028.
Testo completoDorogovtsev, Sergey N., e José F. F. Mendes. The Nature of Complex Networks. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780199695119.001.0001.
Testo completoScacco, Joshua M., e Kevin Coe. The Ubiquitous Presidency. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197520635.001.0001.
Testo completoWillie, Rachel. Translation. A cura di Andrew Hiscock e Helen Wilcox. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199672806.013.9.
Testo completoautores, Varios. A practical guidance on estimation of european wild ungulate population density. Ediciones de la Universidad de Castilla-La Mancha, 2022. http://dx.doi.org/10.18239/atenea_2022.41.00.
Testo completoUfimtseva, Nataliya V., Iosif A. Sternin e Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.
Testo completoCapitoli di libri sul tema "Attention Network Test"
Worden, Michael S. "Attention Network Test (ANT)". In Encyclopedia of Clinical Neuropsychology, 296–97. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1268.
Testo completoWorden, Michael S. "Attention Network Test (ANT)". In Encyclopedia of Clinical Neuropsychology, 1–3. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_1268-2.
Testo completoWorden, Michael S. "Attention Network Test (ANT)". In Encyclopedia of Clinical Neuropsychology, 405–7. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_1268.
Testo completoToa, Chean Khim, Kok Swee Sim e Shing Chiang Tan. "Emotiv Insight with Convolutional Neural Network: Visual Attention Test Classification". In Advances in Computational Collective Intelligence, 348–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88113-9_28.
Testo completoSeong, ChangMin, YoungRok Song, Jiwung Hyun e Yun-Gyung Cheong. "Towards Building Intrusion Detection Systems for Multivariate Time-Series Data". In Silicon Valley Cybersecurity Conference, 45–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96057-5_4.
Testo completoMash, Lisa E., Raymond M. Klein e Jeanne Townsend. "Attention Network Tests in ASD". In Encyclopedia of Autism Spectrum Disorders, 1–5. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-6435-8_102499-1.
Testo completoMash, Lisa E., Raymond M. Klein e Jeanne Townsend. "Attention Network Tests in ASD". In Encyclopedia of Autism Spectrum Disorders, 381–85. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_102499.
Testo completoYang, Ze, Pengfei Wang, Lei Zhang, Linjun Shou e Wenwen Xu. "A Recurrent Attention Network for Judgment Prediction". In Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series, 253–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30490-4_21.
Testo completoRizvi, Syed Zeeshan, Muhammad Umar Farooq e Rana Hammad Raza. "Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images: Case Study of Crowd Counting". In Digital Interaction and Machine Intelligence, 75–87. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_7.
Testo completoSun, Qiang, e Yue Wu. "A Multi-level Attention Model for Text Matching". In Artificial Neural Networks and Machine Learning – ICANN 2018, 142–53. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01418-6_15.
Testo completoAtti di convegni sul tema "Attention Network Test"
Yu, Fuxun, Chenchen Liu, Di Wang, Yanzhi Wang e Xiang Chen. "AntiDote: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency". In 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2020. http://dx.doi.org/10.23919/date48585.2020.9116416.
Testo completoMa, Junchi, Zongtao Duan e Lei Tang. "GATPS: An attention-based graph neural network for predicting SDC-causing instructions". In 2021 IEEE 39th VLSI Test Symposium (VTS). IEEE, 2021. http://dx.doi.org/10.1109/vts50974.2021.9441056.
Testo completoYang, Yiding, Xinchao Wang, Mingli Song, Junsong Yuan e Dacheng Tao. "SPAGAN: Shortest Path Graph Attention Network". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/569.
Testo completoWang, Yaxiong, Hao Yang, Xueming Qian, Lin Ma, Jing Lu, Biao Li e Xin Fan. "Position Focused Attention Network for Image-Text Matching". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/526.
Testo completoNikzad, Mohammad, Yongsheng Gao e Jun Zhou. "Attention-based Pyramid Dilated Lattice Network for Blind Image Denoising". In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/129.
Testo completoAbbas, Fadya. "An Improved NLP for Syntactic and Semantic Matching using Bidirectional LSTM and Attention Mechanism". In 9th International Conference on Artificial Intelligence and Applications (AIAPP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120906.
Testo completoLi, Xiao, Jiaxing Song e Weidong Liu. "Label-Attentive Hierarchical Attention Network for Text Classification". In ICBDC 2020: 2020 5th International Conference on Big Data and Computing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3404687.3404706.
Testo completoZhang, Huai Qiang, Qing Cheng Liu e Yu Juan Liu. "Study on Network Radiation Environmental Monitoring System". In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-15487.
Testo completoMughaz, Dror, Michael Cohen, Sagit Mejahez, Tal Ades e Dan Bouhnik. "From an Artificial Neural Network to Teaching [Abstract]". In InSITE 2020: Informing Science + IT Education Conferences: Online. Informing Science Institute, 2020. http://dx.doi.org/10.28945/4557.
Testo completoTay, Yi, Anh Tuan Luu e Siu Cheung Hui. "Hermitian Co-Attention Networks for Text Matching in Asymmetrical Domains". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/615.
Testo completoRapporti di organizzazioni sul tema "Attention Network Test"
Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang e Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), luglio 2021. http://dx.doi.org/10.21079/11681/41302.
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