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.  28
    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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  3. 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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  4.  52
    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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  5. 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.
  6.  30
    Auditory versus visual learning of temporal patterns.James R. Nazzaro & Jean N. Nazzaro - 1970 - Journal of Experimental Psychology 84 (3):477.
  7. Visual Learning: Time - Truth - Tradition.András Benedek & Agnes Veszelszki (eds.) - 2016 - Peter Lang.
     
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  8.  11
    Visual Learning.Matthew Crippen (ed.) - forthcoming
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  9. Perspectives on Visual Learning, vol. 3: Image and Metaphor in the New Century.András Benedek & Kristof Nyíri (eds.) - 2019
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  10. Electrifying the Future, 11th Budapest Visual Learning Conference.Kristof Nyiri (ed.) - 2024 - Budapest: Hungarian Academy of Science.
    The present online volume contains the papers prepared for the 11th Budapest Visual Learning Conference – ENVISIONING AN ELECTRIFYING FUTURE – held in a physical-online blended form on Nov. 13, 2024, organized by the University of Pécs (represented by Prof. Gábor Szécsi, Dean, Faculty of Cultural Sciences, Education and Regional Development), and the Hungarian Academy of Sciences (represented by Prof. Kristóf Nyíri, Member of the Hungarian Academy of Sciences). Nyíri and Szécsi were responsible for sending out the call for (...)
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  11. Perspective on Visual Learning, Vol. 1. The Victory of the Pictorial Age.Kristof Nyiri & Andras Benedek (eds.) - 2019
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  12.  26
    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.
  13. 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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  14. 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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  15.  58
    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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  16. The role of attention in visual learning.G. Wolford & Hy Kim - 1987 - Bulletin of the Psychonomic Society 25 (5):346-346.
  17.  21
    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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  18.  23
    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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  19.  35
    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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  20.  16
    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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  21.  29
    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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  22.  9
    Learning the Meanings of Function Words From Grounded Language Using a Visual Question Answering Model.Eva Portelance, Michael C. Frank & Dan Jurafsky - 2024 - Cognitive Science 48 (5):e13448.
    Interpreting a seemingly simple function word like “or,” “behind,” or “more” can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural‐network‐based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding (...)
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  23. 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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  24.  52
    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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  25.  25
    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.
  26.  14
    Learning Chinese visual culture in a transnational world.David Bell - 2018 - Educational Philosophy and Theory 50 (14):1402-1403.
  27.  22
    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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  28.  24
    How Interactive Visualizations Compare to Ethical Frameworks as Stand-Alone Ethics Learning Tools for Health Researchers and Professionals.Joanna Sleigh, Kelly Ormond, Manuel Schneider, Elsbeth Stern & Effy Vayena - 2023 - AJOB Empirical Bioethics 14 (4):197-207.
    Background Despite the bourgeoning of digital tools for bioethics research, education, and engagement, little research has empirically investigated the impact of interactive visualizations as a way to translate ethical frameworks and guidelines. To date, most frameworks take the format of text-only documents that outline and offer ethical guidance on specific contexts. This study’s goal was to determine whether an interactive-visual format supports frameworks in transferring ethical knowledge by improving learning, deliberation, and user experience.Methods An experimental comparative study was conducted (...)
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  29.  26
    What Determines Visual Statistical Learning Performance? Insights From Information Theory.Noam Siegelman, Louisa Bogaerts & Ram Frost - 2019 - Cognitive Science 43 (12):e12803.
    In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co‐occurrences between them are (...)
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  30.  28
    Visually controlled learning as a function of time and intensity of stimulation.W. S. Hunter - 1942 - Journal of Experimental Psychology 31 (5):423.
  31. Perceptual learning and memory in visual search.Marvin M. Chun - 2012 - In Jeremy Wolfe & Lynn Robertson (eds.), From Perception to Consciousness: Searching with Anne Treisman. Oxford University Press.
     
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  32. Priority-learning mediates age-related differences in visual-search.Ad Fisk & Wa Rogers - 1988 - Bulletin of the Psychonomic Society 26 (6):526-526.
     
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  33.  26
    Map Learning with a 3D Printed Interactive Small-Scale Model: Improvement of Space and Text Memorization in Visually Impaired Students.Stéphanie Giraud, Anke M. Brock, Marc J.-M. Macé & Christophe Jouffrais - 2017 - Frontiers in Psychology 8.
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  34.  28
    Visual selective attention in learning disabled and normal boys.Richard L. Long, Curtis W. McIntyre & Michael E. Murray - 1982 - Bulletin of the Psychonomic Society 19 (1):15-18.
  35.  30
    The Influence of Visual Guidance in Maze Learning.H. Carr - 1921 - Journal of Experimental Psychology 4 (6):399.
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  36. Motor learning and memory for visually guided reaching.R. Shadmehr & S. P. Wise - 2004 - In Michael S. Gazzaniga (ed.), The Cognitive Neurosciences III. MIT Press. pp. 353--375.
     
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  37.  62
    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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  38.  13
    Visual statistical learning is facilitated in Zipfian distributions.Ori Lavi-Rotbain & Inbal Arnon - 2021 - Cognition 206 (C):104492.
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  39. 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.
  40. Unconscious learning. Conditioning to subliminal visual stimuli.Juan P. Núñez & Francisco de Vicente - 2004 - Spanish Journal of Psychology 7 (1):13-28.
  41. Observational-learning of a visual-discrimination by pigeons.S. de HoganPriestle - 1987 - Bulletin of the Psychonomic Society 25 (5):342-342.
  42.  72
    Mechanisms of Visual Perceptual Learning in Macaque Visual Cortex.Rufin Vogels - 2010 - Topics in Cognitive Science 2 (2):239-250.
    The neural mechanisms underlying behavioral improvement in the detection or discrimination of visual stimuli following learning are still ill understood. Studies in nonhuman primates have shown relatively small and, across studies, variable effects of fine discrimination learning in primary visual cortex when tested outside the context of the learned task. At later stages, such as extrastriate area V4, extensive practice in fine discrimination produces more consistent effects upon responses and neural tuning. In V1 and V4, the effects of (...)
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  43.  9
    Learning the Concept of Function With Dynamic Visualizations.Tobias Rolfes, Jürgen Roth & Wolfgang Schnotz - 2020 - Frontiers in Psychology 11.
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  44.  10
    Perceptual learning and memory in visual search.M. Marvin - 2012 - In Jeremy Wolfe & Lynn Robertson (eds.), From Perception to Consciousness: Searching with Anne Treisman. Oxford University Press. pp. 227.
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  45.  49
    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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  46.  65
    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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  47.  23
    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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  48.  27
    The Effect of Visual Mnemonics and the Presentation of Character Pairs on Learning Visually Similar Characters for Chinese-As-Second-Language Learners.Li-Yun Chang, Yuan-Yuan Tang, Chia-Yun Lee & Hsueh-Chih Chen - 2022 - Frontiers in Psychology 13:783898.
    This study investigates the effects of visual mnemonics and the methods of presenting learning materials on learning visually similar characters for Chinese-as-second-language (CSL) learners. In supporting CSL learners to build robust orthographic representations in Chinese, addressing the challenges of visual similarity of characters (e.g., 理 and 埋) is an important issue. Based on prior research on perceptual learning, we tested three strategies that differ in the extent to which they promote interrelated attention to the form and meaning of (...)
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  49.  14
    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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  50. 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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