Results for 'Visual learning. '

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  1. bayesvl: Visually learning the graphical structure of Bayesian networks and performing MCMC with ‘Stan’.Viet-Phuong La & Quan-Hoang Vuong - 2019 - Vienna, Austria: The Comprehensive R Archive Network (CRAN).
    La, V. P., & Vuong, Q. H. (2019). bayesvl: Visually learning the graphical structure of Bayesian networks and performing MCMC with ‘Stan’. The Comprehensive R Archive Network (CRAN).
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  2. bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'.Quan-Hoang Vuong & Viet-Phuong La - 2019 - Open Science Framework 2019:01-47.
  3. Visual Learning: Time - Truth - Tradition.András Benedek & Agnes Veszelszki (eds.) - 2016 - Peter Lang.
     
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  4.  21
    Implicit visual learning and the expression of learning.Hilde Haider, Katharina Eberhardt, Alexander Kunde & Michael Rose - 2013 - Consciousness and Cognition 22 (1):82-98.
    Although the existence of implicit motor learning is now widely accepted, the findings concerning perceptual implicit learning are ambiguous. Some researchers have observed perceptual learning whereas other authors have not. The review of the literature provides different reasons to explain this ambiguous picture, such as differences in the underlying learning processes, selective attention, or differences in the difficulty to express this knowledge. In three experiments, we investigated implicit visual learning within the original serial reaction time task. We used different (...)
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  5. Visual Learning in Multisensory Environments.Robert A. Jacobs & Ladan Shams - 2010 - Topics in Cognitive Science 2 (2):217-225.
    We study the claim that multisensory environments are useful for visual learning because nonvisual percepts can be processed to produce error signals that people can use to adapt their visual systems. This hypothesis is motivated by a Bayesian network framework. The framework is useful because it ties together three observations that have appeared in the literature: (a) signals from nonvisual modalities can “teach” the visual system; (b) signals from nonvisual modalities can facilitate learning in the visual (...)
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  6. Perspectives on Visual Learning, vol. 3: Image and Metaphor in the New Century.András Benedek & Kristof Nyíri (eds.) - 2019
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  7.  21
    Auditory versus visual learning of temporal patterns.James R. Nazzaro & Jean N. Nazzaro - 1970 - Journal of Experimental Psychology 84 (3):477.
  8.  43
    Spontaneous and Training‐Induced Visual Learning in Cortical Blindness: Characteristics and Neural Substrates.Tim Martin & Krystel R. Huxlin - 2010 - Topics in Cognitive Science 2 (2):306-319.
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  9. Perspective on Visual Learning, Vol. 1. The Victory of the Pictorial Age.Kristof Nyiri & Andras Benedek (eds.) - 2019
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  10.  19
    Cortical dynamics of contextually cued attentive visual learning and search: Spatial and object evidence accumulation.Tsung-Ren Huang & Stephen Grossberg - 2010 - Psychological Review 117 (4):1080-1112.
  11. The role of attention in visual learning.G. Wolford & Hy Kim - 1987 - Bulletin of the Psychonomic Society 25 (5):346-346.
  12. How Images Behave: 9th Budapest Visual Learning Conference, Budapest, 26 November 2020.Kristof Nyiri, András Benedek & Petra Aczel (eds.) - 2020 - Hungarian Academy of Sciences.
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  13.  32
    Visual statistical learning in the newborn infant.Hermann Bulf, Scott P. Johnson & Eloisa Valenza - 2011 - Cognition 121 (1):127-132.
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  14.  43
    Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?Noam Siegelman, Louisa Bogaerts, Ofer Kronenfeld & Ram Frost - 2018 - Cognitive Science 42 (S3):692-727.
    From a theoretical perspective, most discussions of statistical learning have focused on the possible “statistical” properties that are the object of learning. Much less attention has been given to defining what “learning” is in the context of “statistical learning.” One major difficulty is that SL research has been monitoring participants’ performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory (...)
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  15.  30
    Learning rapidly about the relevance of visual cues requires conscious awareness.Eoin Travers, Chris D. Frith & Nicholas Shea - 2018 - Quarterly Journal of Experimental Psychology 71 (8):1698–1713.
    Humans have been shown capable of performing many cognitive tasks using information of which they are not consciously aware. This raises questions about what role consciousness actually plays in cognition. Here, we explored whether participants can learn cue-target contingencies in an attentional learning task when the cues were presented below the level of conscious awareness, and how this differs from learning about conscious cues. Participants’ manual (Experiment 1) and saccadic (Experiment 2) response speeds were influenced by both conscious and unconscious (...)
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  16.  13
    Visual Heuristics for Verb Production: Testing a Deep‐Learning Model With Experiments in Japanese.Franklin Chang, Tomoko Tatsumi, Yuna Hiranuma & Colin Bannard - 2023 - Cognitive Science 47 (8):e13324.
    Tense/aspect morphology on verbs is often thought to depend on event features like telicity, but it is not known how speakers identify these features in visual scenes. To examine this question, we asked Japanese speakers to describe computer‐generated animations of simple actions with variation in visual features related to telicity. Experiments with adults and children found that they could use goal information in the animations to select appropriate past and progressive verb forms. They also produced a large number (...)
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  17.  56
    Visual statistical learning in infancy: evidence for a domain general learning mechanism.Natasha Z. Kirkham, Jonathan A. Slemmer & Scott P. Johnson - 2002 - Cognition 83 (2):B35-B42.
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  18.  33
    Visual statistical learning in children and young adults: how implicit?Julie Bertels, Emeline Boursain, Arnaud Destrebecqz & Vinciane Gaillard - 2014 - Frontiers in Psychology 5.
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  19.  17
    Learning and performance in a complex tracking task as a function of visual noise.George E. Briggs, Paul M. Fitts & Harry P. Bahrick - 1957 - Journal of Experimental Psychology 53 (6):379.
  20.  13
    Visual Statistical Learning With Stimuli Presented Sequentially Across Space and Time in Deaf and Hearing Adults.Beatrice Giustolisi & Karen Emmorey - 2018 - Cognitive Science 42 (8):3177-3190.
    This study investigated visual statistical learning (VSL) in 24 deaf signers and 24 hearing non‐signers. Previous research with hearing individuals suggests that SL mechanisms support literacy. Our first goal was to assess whether VSL was associated with reading ability in deaf individuals, and whether this relation was sustained by a link between VSL and sign language skill. Our second goal was to test the Auditory Scaffolding Hypothesis, which makes the prediction that deaf people should be impaired in sequential processing (...)
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  21. Unsupervised learning of visual structure.Shimon Edelman - unknown
    To learn a visual code in an unsupervised manner, one may attempt to capture those features of the stimulus set that would contribute significantly to a statistically efficient representation. Paradoxically, all the candidate features in this approach need to be known before statistics over them can be computed. This paradox may be circumvented by confining the repertoire of candidate features to actual scene fragments, which resemble the “what+where” receptive fields found in the ventral visual stream in primates. We (...)
     
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  22.  7
    Learning from lines: Critical COVID data visualizations and the quarantine quotidian.Shannon Mattern, Erin Simmons & Emily Bowe - 2020 - Big Data and Society 7 (2).
    In response to the ubiquitous graphs and maps of COVID-19, artists, designers, data scientists, and public health officials are teaming up to create counter-plots and subaltern maps of the pandemic. In this intervention, we describe the various functions served by these projects. First, they offer tutorials and tools for both dataviz practitioners and their publics to encourage critical thinking about how COVID-19 data is sourced and modeled—and to consider which subjects are not interpellated in those data sets, and why not. (...)
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  23.  95
    Visual speech contributes to phonetic learning in 6-month-old infants.Tuomas Teinonen, Richard N. Aslin, Paavo Alku & Gergely Csibra - 2008 - Cognition 108 (3):850-855.
  24. Observational-learning of a visual-discrimination by pigeons.S. de HoganPriestle - 1987 - Bulletin of the Psychonomic Society 25 (5):342-342.
  25. Perceptual learning and memory in visual search.Marvin M. Chun - 2012 - In Jeremy M. Wolfe & Lynn C. Robertson (eds.), From Perception to Consciousness: Searching with Anne Treisman. Oxford University Press.
     
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  26. Integrating visual and verbal knowledge during classroom learning with computer tutors.Kirsten R. Butcher & Vincent Aleven - 2007 - In McNamara D. S. & Trafton J. G. (eds.), Proceedings of the 29th Annual Cognitive Science Society. Cognitive Science Society. pp. 137--142.
  27.  19
    Learning Visual Units After Brief Experience in 10‐Month‐Old Infants.Amy Needham, Robert L. Goldstone & Sarah E. Wiesen - 2014 - Cognitive Science 38 (7):1507-1519.
    How does perceptual learning take place early in life? Traditionally, researchers have focused on how infants make use of information within displays to organize it, but recently, increasing attention has been paid to the question of how infants perceive objects differently depending upon their recent interactions with the objects. This experiment investigates 10-month-old infants' use of brief prior experiences with objects to visually organize a display consisting of multiple geometrically shaped three-dimensional blocks created for this study. After a brief exposure (...)
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  28.  12
    Learning abstract visual concepts via probabilistic program induction in a Language of Thought.Matthew C. Overlan, Robert A. Jacobs & Steven T. Piantadosi - 2017 - Cognition 168 (C):320-334.
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  29.  10
    Visual borderlands: Visuality, performance, fluidity and art-science learning.Kathryn Grushka, Miranda Lawry, Ari Chand & Andy Devine - 2022 - Educational Philosophy and Theory 54 (4):404-421.
    The image is the raw material of the twenty-first century. Images infiltrate all social and cultural spaces. Its digital-mediated realities drive communication, industry and knowledge. Images saturate life and adolescent learners are familiar with the participatory nature of image production and its social, educational and personal communicative realities. Vision and visibility, seeing and being now dominate how we inter-subjectively recognise ourselves and perform our world. We also find our aesthetic and embodied self increasingly constituted within imaging acts that are relational. (...)
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  30.  6
    Visual statistical learning is facilitated in Zipfian distributions.Ori Lavi-Rotbain & Inbal Arnon - 2021 - Cognition 206 (C):104492.
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  31. How Infants Learn About the Visual World.Scott P. Johnson - 2010 - Cognitive Science 34 (7):1158-1184.
    The visual world of adults consists of objects at various distances, partly occluding one another, substantial and stable across space and time. The visual world of young infants, in contrast, is often fragmented and unstable, consisting not of coherent objects but rather surfaces that move in unpredictable ways. Evidence from computational modeling and from experiments with human infants highlights three kinds of learning that contribute to infants’ knowledge of the visual world: learning via association, learning via active (...)
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  32.  21
    Serial learning as a function of meaningfulness and mode of presentation with audio and visual stimuli of equivalent duration.Rudolph W. Schulz & Richard A. Kasschau - 1966 - Journal of Experimental Psychology 71 (3):350.
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  33.  16
    Visual and pronouncing responses, and the relation between orienting task and presentations in incidental learning.Arnold Mechanic & Joanne D'Andrea - 1966 - Journal of Experimental Psychology 71 (3):343.
  34.  40
    Effects of Visual Information on Adults' and Infants' Auditory Statistical Learning.Erik D. Thiessen - 2010 - Cognitive Science 34 (6):1093-1106.
    Infant and adult learners are able to identify word boundaries in fluent speech using statistical information. Similarly, learners are able to use statistical information to identify word–object associations. Successful language learning requires both feats. In this series of experiments, we presented adults and infants with audio–visual input from which it was possible to identify both word boundaries and word–object relations. Adult learners were able to identify both kinds of statistical relations from the same input. Moreover, their learning was actually (...)
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  35.  5
    Learning Chinese visual culture in a transnational world.David Bell - 2018 - Educational Philosophy and Theory 50 (14):1402-1403.
  36.  19
    When learning goes beyond statistics: Infants represent visual sequences in terms of chunks.Lauren K. Slone & Scott P. Johnson - 2018 - Cognition 178 (C):92-102.
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  37. Visual orienting, learning and conscious awareness.T. Lambert - 2003 - In Luis Jimenez (ed.), Attention and Implicit Learning. John Benjamins.
  38. Visual spatial learning of complex object structures through virtual and real-world data.Chiara Silvestri, Rene Motro, Bernard Maurin & Birgitta Dresp-Langley - 2010 - Design Studies 31:364-380.
    This article probes the visual spatial représentations underlying the creative conceptual design of complex objects.
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  39.  5
    Learning to track the visual motion of contours.Andrew Blake, Michael Isard & David Reynard - 1995 - Artificial Intelligence 78 (1-2):179-212.
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  40.  9
    Assessing Visual Statistical Learning in Early-School-Aged Children: The Usefulness of an Online Reaction Time Measure.Merel van Witteloostuijn, Imme Lammertink, Paul Boersma, Frank Wijnen & Judith Rispens - 2019 - Frontiers in Psychology 10.
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  41.  51
    Visual Semantic Navigation Based on Deep Learning for Indoor Mobile Robots.Li Wang, Lijun Zhao, Guanglei Huo, Ruifeng Li, Zhenghua Hou, Pan Luo, Zhenye Sun, Ke Wang & Chenguang Yang - 2018 - Complexity 2018:1-12.
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  42. Unconscious learning. Conditioning to subliminal visual stimuli.Juan P. Núñez & Francisco de Vicente - 2004 - Spanish Journal of Psychology 7 (1):13-28.
  43.  45
    Implicit learning for probable changes in a visual change detection task.Melissa R. Beck, Bonnie L. Angelone, Daniel T. Levin, Matthew S. Peterson & D. Alexander Varakin - 2008 - Consciousness and Cognition 17 (4):1192-1208.
    Previous research demonstrates that implicitly learned probability information can guide visual attention. We examined whether the probability of an object changing can be implicitly learned and then used to improve change detection performance. In a series of six experiments, participants completed 120–130 training change detection trials. In four of the experiments the object that changed color was the same shape on every trial. Participants were not explicitly aware of this change probability manipulation and change detection performance was not improved (...)
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  44.  12
    Learning to Look: A Handbook for the Visual Arts.Joshua C. Taylor - 1981 - University of Chicago Press.
    An illustrated beginner's guide to the visual arts examines specific art works, studies expression and construction of art, and discusses creative and technical processes of art.
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  45.  23
    Kinesthetic-visual matching, perspective-taking and reflective self-awareness in cultural learning.Robert W. Mitchell - 1993 - Behavioral and Brain Sciences 16 (3):530-531.
    Tomasello, Kruger & Ratner deserve congratulations for their well-reasoned ideas on the development of cultural learning. Their arguments are generally convincing, perhaps because their distinctions and developmental relations among types of cultural learning and agency mirror concepts of my own.
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  46.  27
    Visual Object Tracking in RGB-D Data via Genetic Feature Learning.Ming-xin Jiang, Xian-Xian Luo, Tao Hai, Hai-yan Wang, Song Yang & Ahmed N. Abdalla - 2019 - Complexity 2019:1-8.
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  47.  32
    Visual attention to features by associative learning.Davood G. Gozli, Joshua B. Moskowitz & Jay Pratt - 2014 - Cognition 133 (2):488-501.
  48.  19
    On learning and shift (in)variance of pattern recognition across the visual field.Martin Jüttner - 1997 - Behavioral and Brain Sciences 20 (4):751-752.
    Ballard et al.'s principle of deictic coding as exemplified in the analysis of fixation patterns relies on a functional dichotomy between foveal and extrafoveal vision based on the well-known dependency of spatial resolution on eccentricity. Experimental evidence suggests that for processes of pattern learning and recognition such a dichotomy may be less warranted because its manifestation depends on the learning state of the observer. This finding calls for an explicit consideration of learning mechanisms within deictic coding schemes.
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  49.  21
    Visually controlled learning as a function of time and intensity of stimulation.W. S. Hunter - 1942 - Journal of Experimental Psychology 31 (5):423.
  50. Locus of learning in visual search.V. Walsh & A. Ellison - 1996 - In Enrique Villanueva (ed.), Perception. Ridgeview. pp. 1374-1374.
     
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