Journal articles: 'Visual learning'

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Sze, Daniel Y. "Visual Learning." Journal of Vascular and Interventional Radiology 32, no. 3 (March 2021): 331. http://dx.doi.org/10.1016/j.jvir.2021.01.265.

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Liu, Yan, Yang Liu, Shenghua Zhong, and Songtao Wu. "Implicit Visual Learning." ACM Transactions on Intelligent Systems and Technology 8, no. 2 (January 18, 2017): 1–24. http://dx.doi.org/10.1145/2974024.

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Cruz, Rodrigo Santa, Basura Fernando, Anoop Cherian, and Stephen Gould. "Visual Permutation Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 12 (December 1, 2019): 3100–3114. http://dx.doi.org/10.1109/tpami.2018.2873701.

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Jones, Rachel. "Visual learning visualized." Nature Reviews Neuroscience 4, no. 1 (January 2003): 10. http://dx.doi.org/10.1038/nrn1014.

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Lu, Zhong-Lin, Tianmiao Hua, Chang-Bing Huang, Yifeng Zhou, and Barbara Anne Dosher. "Visual perceptual learning." Neurobiology of Learning and Memory 95, no. 2 (February 2011): 145–51. http://dx.doi.org/10.1016/j.nlm.2010.09.010.

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Richler, Jennifer J., and Thomas J. Palmeri. "Visual category learning." Wiley Interdisciplinary Reviews: Cognitive Science 5, no. 1 (November 26, 2013): 75–94. http://dx.doi.org/10.1002/wcs.1268.

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Nida, Diini Fitrahtun, Muhyiatul Fadilah, Ardi Ardi, and Suci Fajrina. "CHARACTERISTICS OF VISUAL LITERACY-BASED BIOLOGY LEARNING MODULE VALIDITY ON PHOTOSYNTHESIS LEARNING MATERIALS." JURNAL PAJAR (Pendidikan dan Pengajaran) 7, no. 4 (July 29, 2023): 785. http://dx.doi.org/10.33578/pjr.v7i4.9575.

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Visual literacy is the skill to interpret and give meaning to information in the form of images or visuals. Visual literacy is included in the list of 21st-century skills. The observation results indicate that most of the students have not mastered visual literacy well. One of the efforts that can be made to improve visual literacy is the provision of appropriate and right teaching materials. The research is an R&D (Research and Development) using a 4-D model, which is modified to 3-D (define, design, develop). The instruments used were content analysis sheets and validation questionnaires. The results of the research imply that there are three characteristics of the validity of the developed module. First, visual literacy produces students’ critical thinking and communication skills by building their own meaning or conclusions regarding the given image object. Second, visual literacy produces students' creative thinking by recreating it in the form of images or other visual objects from the provided visual information. Third, visual literacy produces students' critical thinking skills by connecting visual objects or images that are distributed to them. The module is considered to be very valid (feasible) to use with a percentage of 94.23%.

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Guinibert, Matthew. "Learn from your environment: A visual literacy learning model." Australasian Journal of Educational Technology 36, no. 4 (September 28, 2020): 173–88. http://dx.doi.org/10.14742/ajet.5200.

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Based on the presupposition that visual literacy skills are not usually learned unaided by osmosis, but require targeted learning support, this article explores how everyday encounters with visuals can be leveraged as contingent learning opportunities. The author proposes that a learner’s environment can become a visual learning space if appropriate learning support is provided. This learning support may be delivered via the anytime and anywhere capabilities of mobile learning (m-learning), which facilitates peer learning in informal settings. The study propositioned a rhizomatic m-learning model of visual skills that describes how the visuals one encounters in their physical everyday environment can be leveraged as visual literacy learning opportunities. The model was arrived at by following an approach based on heuristic inquiry and user-centred design, including testing prototypes with representative learners. The model describes one means visual literacy could be achieved by novice learners from contingent learning encounters in informal learning environments, through collaboration and by providing context-aware learning support. Such a model shifts the onus of visual literacy learning away from academic programmes and, in this way, opens an alternative pathway for the learning of visual skills. Implications for practice or policy: This research proposes a means for learners to leverage visuals they encounter in their physical everyday environment as visual literacy learning opportunities. M-learning software developers may find the pedagogical model useful in informing their own software. Educators teaching visual skills may find application of the learning model’s pedagogical assumptions in isolation in their own formal learning settings.

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Taga, Tadashi, Kazuhito Yoshizaki, and Kimiko Kato. "Visual field difference in visual statistical learning." Proceedings of the Annual Convention of the Japanese Psychological Association 79 (September 22, 2015): 2EV—074–2EV—074. http://dx.doi.org/10.4992/pacjpa.79.0_2ev-074.

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Holland, Keith. "Visual skills for learning." Set: Research Information for Teachers, no. 2 (August 1, 1996): 1–4. http://dx.doi.org/10.18296/set.0900.

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MuhiAl-Din, Shaima, and Siddeeq Al - Bana. "Learning Visual Basic Reactively." TANMIYAT AL-RAFIDAIN 30, no. 92 (December 1, 2008): 130–49. http://dx.doi.org/10.33899/tanra.2008.161729.

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Fuchs, R., J. Waser, and M. E. Groller. "Visual Human+Machine Learning." IEEE Transactions on Visualization and Computer Graphics 15, no. 6 (November 2009): 1327–34. http://dx.doi.org/10.1109/tvcg.2009.199.

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Turk-Browne, Nicholas B., Phillip J. Isola, Brian J. Scholl, and Teresa A. Treat. "Multidimensional visual statistical learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 34, no. 2 (2008): 399–407. http://dx.doi.org/10.1037/0278-7393.34.2.399.

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Mountstephen, Mary. "SEN special: Visual learning." Primary Teacher Update 2011, no. 1 (October 2011): 38–39. http://dx.doi.org/10.12968/prtu.2011.1.1.38.

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Bischof, Walter F. "Visual Learning: An Overview." Swiss Journal of Psychology 63, no. 3 (September 2004): 151–64. http://dx.doi.org/10.1024/1421-0185.63.3.151.

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A review is presented of modern approaches to the learning and recognition of complex patterns, including discriminant functions, neural networks, decision trees, and hidden Markov models. Next, several relational learning systems are introduced and discussed, in detail one specific technique, conditional rule generation. This technique is shown to be very flexible and useful for the learning of static patterns, such as objects, as well as dynamic patterns, such as movement patterns. The technique is illustrated with a number of very difficult visual learning problems.

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Shams, L., A. Seitz, and V. van Wassenhove. "Audio-visual statistical learning." Journal of Vision 6, no. 6 (March 18, 2010): 152. http://dx.doi.org/10.1167/6.6.152.

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Sanderson, Katharine. "Learning tools: Visual aids." Nature 477, no. 7366 (September 2011): 621–22. http://dx.doi.org/10.1038/nj7366-621a.

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Taylor, Sarah, Taehwan Kim, Yisong Yue, Ben Milner, and Iain Matthews. "Learning from visual speech." Journal of the Acoustical Society of America 140, no. 4 (October 2016): 3004. http://dx.doi.org/10.1121/1.4969315.

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Seitz, Aaron R., Robyn Kim, and Ladan Shams. "Sound Facilitates Visual Learning." Current Biology 16, no. 14 (July 2006): 1422–27. http://dx.doi.org/10.1016/j.cub.2006.05.048.

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Malec, James F., Robert J. Ivnik, and Nancy S. Hinkeldey. "Visual Spatial Learning Test." Psychological Assessment: A Journal of Consulting and Clinical Psychology 3, no. 1 (March 1991): 82–88. http://dx.doi.org/10.1037/1040-3590.3.1.82.

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Ji, Ruolei, and Lina J. Karam. "Learning-based Visual Compression." Foundations and Trends® in Computer Graphics and Vision 15, no. 1 (2023): 1–112. http://dx.doi.org/10.1561/0600000101.

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Muslim, Fachruddiansyah, Ekawarna Ekawarna, Aminah Ramalia, Ricky Purnama Wirayuda, and Diki Chen. "Learning Intensity and Visual Learning Style on Learning Outcomes." Journal of Education Research and Evaluation 6, no. 2 (June 28, 2022): 385–96. http://dx.doi.org/10.23887/jere.v6i2.40312.

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Learning activities do not have to be done for a long time. Good learning intensity is carried out regularly will make learning activities a habit. The learning process also needs to be supported by a learning style that suits the characteristics of students. This study aimed to analyze the intensity of learning-on-learning outcomes, visual learning styles on learning outcomes, and learning intensity and visual learning styles on learning outcomes. This type of research is quantitative with a descriptive quantitative approach. The sample used was 65 students with purposive sampling. Data collection method using questionnaire and documentation. The instrument used is a questionnaire. Data analysis techniques are qualitative descriptive analysis, quantitative, and inferential statistics. The results showed a significant effect of learning intensity on learning outcomes in macroeconomic theory courses. There is a significant effect of visual learning style on learning outcomes in macroeconomic theory courses. There is a significant influence between visual learning styles on learning outcomes of macroeconomic theory courses. There are significant effects of visual learning style on learning outcomes of macroeconomic theory courses. It is concluded that there is a simultaneous influence between learning intensity and visual learning style on learning outcomes in macroeconomic theory courses.

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Yang, Chuanguang, Zhulin An, Linhang Cai, and Yongjun Xu. "Mutual Contrastive Learning for Visual Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 3045–53. http://dx.doi.org/10.1609/aaai.v36i3.20211.

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We present a collaborative learning method called Mutual Contrastive Learning (MCL) for general visual representation learning. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributions among a cohort of networks. A crucial component of MCL is Interactive Contrastive Learning (ICL). Compared with vanilla contrastive learning, ICL can aggregate cross-network embedding information and maximize the lower bound to the mutual information between two networks. This enables each network to learn extra contrastive knowledge from others, leading to better feature representations for visual recognition tasks. We emphasize that the resulting MCL is conceptually simple yet empirically powerful. It is a generic framework that can be applied to both supervised and self-supervised representation learning. Experimental results on image classification and transfer learning to object detection show that MCL can lead to consistent performance gains, demonstrating that MCL can guide the network to generate better feature representations. Code is available at https://github.com/winycg/MCL.

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Gole, Sule. "Museums as Visual Laboratories in the Learning-Teaching Process." New Trends and Issues Proceedings on Humanities and Social Sciences 2, no. 7 (January 27, 2016): 48–53. http://dx.doi.org/10.18844/gjhss.v2i7.1179.

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Hong, Richang, Yang Yang, Meng Wang, and Xian-Sheng Hua. "Learning Visual Semantic Relationships for Efficient Visual Retrieval." IEEE Transactions on Big Data 1, no. 4 (December 1, 2015): 152–61. http://dx.doi.org/10.1109/tbdata.2016.2515640.

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HP, Bambang Setiyo, Hartati Mochtar, and Atwi Suparman. "The Effect of Blended Learning Approach and Visual-Spatial Ability on Learning Outcomes." JETL (Journal of Education, Teaching and Learning) 5, no. 1 (March 31, 2020): 193. http://dx.doi.org/10.26737/jetl.v5i1.1150.

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The purpose of this study was to conduct an empirical study to find out the aspects that influence the basic CNC learning outcomes, in this case regarding the application of the approach blended learning, and the abilities visual-spatial possessed by students who take the course. Based on the types of research variables that exist, then this experimental research is appropriate to be carried out using the experimental Treatment by Level design. Data analysis in this experimental study used 2-way ANOVA with one treatment variable and one attribute variable. This research was carried out using experimental research methods. This research was conducted at the CNC/CADCAM Laboratory, Department of Mechanical Education, Faculty of Engineering, Yogyakarta State University. Research Results 1) Basic CNC learning outcomes of students who take part in learning using the approach Blended Learning higher than students who take learning using the Conventional Approach; 2) Basic CNC learning outcomes of students with ability spatial-visual high, higher than students with abilities spatial-visual low who jointly follow Basic CNC learning; 3) There is an influence of the interaction between learning approaches and spatial visual abilities on basic CNC learning outcomes; 4) Basic CNC learning outcomes of students capable of high spatial visuals who take part in learning with approach blended learning, higher than students with high spatial-visual abilities who follow learning with conventional approaches; 5) Basic CNC learning outcomes of students with low spatial-visual abilities who take part in learning with approach blended learning, lower than students with low spatial-visual abilities who follow learning with conventional approaches. The conclusion of this research is the basic CNC learning outcomes of students who take part in learning using the Approach <em>Blended Learning</em> higher than students who take learning using the Conventional Approach.

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Sahuni, Sahuni, Iffah Budiningsih, and Lisna Marwani P. "INTERACTION OF LEARNING MEDIA WITH LEARNING INTEREST IN ARABIC LEARNING OUTCOMES." Akademika 9, no. 02 (November 30, 2020): 43–52. http://dx.doi.org/10.34005/akademika.v9i02.871.

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The research aims to determine the influence of visual media, print media and the interest in learning outcomes in Arabic. The research method used is the method of experiment with sample two classes, which amounted to 39 students. Research samples were taken in a simple randomized basis. The Data was analyzed in descriptive and ANAVA. The results are: a) the learning outcome of Arabic students who are taught using visual media is higher than using print media; b) there is interaction between the media and learning interest in Arabic language learning outcomes; c) on students who have a high learning interest, the learning outcomes of Arabic students who use visual media is higher than use print media; d) on students who have low learning interests, the learning outcomes of Arabic students who use visual media are lower than use print media.

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Sacha, Dominik, Matthias Kraus, Daniel A. Keim, and Min Chen. "VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning." IEEE Transactions on Visualization and Computer Graphics 25, no. 1 (January 2019): 385–95. http://dx.doi.org/10.1109/tvcg.2018.2864838.

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Maulida, Sarah, and Muhamad Sofian Hadi. "Using Audio Visual Media to Improve English Learning Outcomes." Jurnal Studi Guru dan Pembelajaran 5, no. 1 (April 30, 2022): 11–15. http://dx.doi.org/10.30605/jsgp.5.1.2022.1297.

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Video recordings, slides, and sound are examples of audio-visual media. It is thought that learning to use audio visuals in English lessons will help students better understand the problems or lessons presented. Because listeners are encouraged to use their imagination and optimize their left and right brain function. Audio-visual media in the form of animated learning videos and power points are used in English subjects at SMK Grafika that are conducted online or online. The purpose of this study was to see if audio visuals could boost students' motivation and enthusiasm for learning during online classes.

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Aji, Daru Tunggul. "Literasi Visual sebagai Pendekatan dalam Pembelajaran Fotografi." Rekam 17, no. 2 (October 30, 2021): 123–34. http://dx.doi.org/10.24821/rekam.v17i2.5660.

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Visual Literacy As an Approach To Learning Photography. This article is an overview of the current photographic phenomena. Visual literacy as an approach becomes an offer in the development of photography learning science. As a of discipline, photography has the complexity of learning, just like other scientific disciplines. In photography learning, visual literacy is a significant capital. Visual literacy can be understood as a person's ability to respond to phenomena. It's not just the ability to switch media (design); from the oral to the visual, from the textual to the visual, from the audio to the visiual or from the visual to the other visual forms, and the ability to conduct studies of existing visual works. In photography, it is necessary not only to be processed artistically but also processed that has critical considerations, both from ethics, aesthetics, and perspective, to a phenomenon

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Nagata, Takeshi, and Daiki Hashimoto. "Visual Inspection by Deep Learning and Machine Learning." Journal of The Japan Institute of Electronics Packaging 23, no. 4 (July 1, 2020): 271–74. http://dx.doi.org/10.5104/jiep.23.271.

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Haider, Hilde, Katharina Eberhardt, Alexander Kunde, and Michael Rose. "Implicit visual learning and the expression of learning." Consciousness and Cognition 22, no. 1 (March 2013): 82–98. http://dx.doi.org/10.1016/j.concog.2012.11.003.

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Vogels, Rufin. "Mechanisms of Visual Perceptual Learning in Macaque Visual Cortex." Topics in Cognitive Science 2, no. 2 (April 2010): 239–50. http://dx.doi.org/10.1111/j.1756-8765.2009.01051.x.

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Sithole, Seedwell, Ragini Datt, Paul de Lange, and Meredith Tharapos. "Learning accounting through visual representations." Accounting Research Journal 34, no. 4 (May 18, 2021): 365–84. http://dx.doi.org/10.1108/arj-06-2018-0100.

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Purpose The purpose of this study is to investigate the effectiveness of diagrammatic visualisation techniques versus sentential learning contexts in an accounting subject using the theoretical lens of cognitive load theory (CLT). Design/methodology/approach The present study used four groups of students; two groups completed a task using diagrammatic visualisation learning materials, with one of the groups undertaking their leaning activities collaboratively and another on an individual basis, whereas two comparison groups were given a sentential learning context without diagrams, with one group undertaking their leaning activities collaboratively and the other individually. In addition to performance grades, cognitive load self-report scores were also elicited from participants. Findings The findings of this study indicate support for diagrammatic visualisation techniques for students working collaboratively. Compared with sentential learners, the authors find significantly improved test performance for students who work collaboratively in a diagrammatic visualisation environment. Students in the visualisation environments obtained higher grades than those in the sentential group. In terms of mental effort, students in the visualisation conditions reported the lowest cognitive load. Practical implications The authors conclude that diagrammatic visualisation learning techniques enhance student performance outcomes, particularly for those who work collaboratively. CLT assists in the understanding of the mental processes involved in learning. Instructional designers need to consider CLT when developing diagrammatic visualisation material to enable students to obtain the best possible learning outcomes. Originality/value This study addresses a gap in the literature by examining the use of diagrammatic visualisation materials as an alternative to text when learning accounting. The study explores the effect of visualisation material on students’ cognitive load by analysing their mental effort. The study contributes useful findings on visualisation as a conduit to enhancing the understanding of accounting using CLT principles.

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Hout, M. C., and S. D. Goldinger. "Learning in repeated visual search." Attention, Perception & Psychophysics 72, no. 5 (June 30, 2010): 1267–82. http://dx.doi.org/10.3758/app.72.5.1267.

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Copperman, Elana, Catriel Beeri, and Nava Ben‐Zvi. "Visual modelling of learning processes." Innovations in Education and Teaching International 44, no. 3 (August 2007): 257–72. http://dx.doi.org/10.1080/14703290701486571.

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Dosher, Barbara, and Zhong-Lin Lu. "Visual Perceptual Learning and Models." Annual Review of Vision Science 3, no. 1 (September 15, 2017): 343–63. http://dx.doi.org/10.1146/annurev-vision-102016-061249.

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Laamerad, Pooya, Daniel Guitton, and Christopher C. Pack. "Eye movements shape visual learning." Proceedings of the National Academy of Sciences 117, no. 14 (March 24, 2020): 8203–11. http://dx.doi.org/10.1073/pnas.1913851117.

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Most people easily learn to recognize new faces and places, and with more extensive practice they can become experts at visual tasks as complex as radiological diagnosis and action video games. Such perceptual plasticity has been thoroughly studied in the context of training paradigms that require constant fixation. In contrast, when observers learn under more natural conditions, they make frequent saccadic eye movements. Here we show that such eye movements can play an important role in visual learning. Observers performed a task in which they executed a saccade while discriminating the motion of a cued visual stimulus. Additional stimuli, presented simultaneously with the cued one, permitted an assessment of the perceptual integration of information across visual space. Consistent with previous results on perisaccadic remapping [M. Szinte, D. Jonikaitis, M. Rolfs, P. Cavanagh, H. Deubel,J. Neurophysiol.116, 1592–1602 (2016)], most observers preferentially integrated information from locations representing the presaccadic and postsaccadic retinal positions of the cue. With extensive training on the saccade task, these observers gradually acquired the ability to perform similar motion integration without making eye movements. Importantly, the newly acquired pattern of spatial integration was determined by the metrics of the saccades made during training. These results suggest that oculomotor influences on visual processing, long thought to subserve the function of perceptual stability, also play a role in visual plasticity.

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Chetverikov, Andrey, Gianluca Campana, and Árni Kristjánsson. "Rapid learning of visual ensembles." Journal of Vision 17, no. 2 (February 28, 2017): 21. http://dx.doi.org/10.1167/17.2.21.

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Turk-Browne, Nicholas. "Hippocampal contributions to visual learning." Journal of Vision 18, no. 10 (September 1, 2018): 1365. http://dx.doi.org/10.1167/18.10.1365.

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Koller, Harold P. "Visual processing and learning disorders." Current Opinion in Ophthalmology 23, no. 5 (September 2012): 377–83. http://dx.doi.org/10.1097/icu.0b013e32835720e2.

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Beymer, D., and T. Poggio. "Image Representations for Visual Learning." Science 272, no. 5270 (June 28, 1996): 1905–9. http://dx.doi.org/10.1126/science.272.5270.1905.

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Hiles, B. P., N. Intrator, and S. Edelman. "Unsupervised learning of visual structure." Journal of Vision 2, no. 7 (March 15, 2010): 74. http://dx.doi.org/10.1167/2.7.74.

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Li, Jia. "Learning-based visual saliency computation." ACM SIGMultimedia Records 2, no. 4 (December 2010): 8–9. http://dx.doi.org/10.1145/2039331.2039336.

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Camachon, Cyril, Gilles Montagne, Martinus Buekers, and Michel Laurent. "Learning to Use Visual Information." Ecological Psychology 16, no. 2 (April 2004): 115–28. http://dx.doi.org/10.1207/s15326969eco1602_2.

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Slemmer, J. A., N. Z. Kirkham, and S. P. Johnson. "Visual statistical learning in infancy." Journal of Vision 1, no. 3 (March 14, 2010): 25. http://dx.doi.org/10.1167/1.3.25.

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Jacobs, Robert A., and Ladan Shams. "Visual Learning in Multisensory Environments." Topics in Cognitive Science 2, no. 2 (April 2010): 217–25. http://dx.doi.org/10.1111/j.1756-8765.2009.01056.x.

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Chen, D., and Y. V. Jiang. "Culture and visual context learning." Journal of Vision 7, no. 9 (March 30, 2010): 800. http://dx.doi.org/10.1167/7.9.800.

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Hinton, Geoffrey E. "Learning to represent visual input." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1537 (January 12, 2010): 177–84. http://dx.doi.org/10.1098/rstb.2009.0200.

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One of the central problems in computational neuroscience is to understand how the object-recognition pathway of the cortex learns a deep hierarchy of nonlinear feature detectors. Recent progress in machine learning shows that it is possible to learn deep hierarchies without requiring any labelled data. The feature detectors are learned one layer at a time and the goal of the learning procedure is to form a good generative model of images, not to predict the class of each image. The learning procedure only requires the pairwise correlations between the activations of neuron-like processing units in adjacent layers. The original version of the learning procedure is derived from a quadratic ‘energy’ function but it can be extended to allow third-order, multiplicative interactions in which neurons gate the pairwise interactions between other neurons. A technique for factoring the third-order interactions leads to a learning module that again has a simple learning rule based on pairwise correlations. This module looks remarkably like modules that have been proposed by both biologists trying to explain the responses of neurons and engineers trying to create systems that can recognize objects.

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Li, Wu, Valentin Piëch, and Charles D. Gilbert. "Learning to Link Visual Contours." Neuron 57, no. 3 (February 2008): 442–51. http://dx.doi.org/10.1016/j.neuron.2007.12.011.

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Journal articles on the topic 'Visual learning'

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