Letter perception emerges from unsupervised deep learning and recycling of natural image features
暂无分享,去创建一个
[1] Jonathan Grainger,et al. References and Notes , 2022 .
[2] Marco Zorzi,et al. Do current connectionist learning models account for reading development in different languages? , 2004, Cognition.
[3] E. Candès,et al. Ridgelets: a key to higher-dimensional intermittency? , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[4] Jonathan Grainger,et al. A Vision of Reading , 2016, Trends in Cognitive Sciences.
[5] M. Sigman,et al. Opinion TRENDS in Cognitive Sciences Vol.9 No.7 July 2005 The neural code for written words: a proposal , 2022 .
[6] Alberto Testolin,et al. Modeling language and cognition with deep unsupervised learning: a tutorial overview , 2013, Front. Psychol..
[7] D. Pelli,et al. Measuring contrast sensitivity , 2013, Vision Research.
[8] J. Muise,et al. Alphabetic confusion: A clarification , 1985, Perception & psychophysics.
[9] S. Dehaene,et al. Cultural Recycling of Cortical Maps , 2007, Neuron.
[10] Thad A. Polk,et al. A Simple Common Contexts Explanation for the Development of Abstract Letter Identities , 1997, Neural Computation.
[11] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[12] Alessandro Sperduti,et al. Learning Orthographic Structure With Sequential Generative Neural Networks , 2016, Cogn. Sci..
[13] Brenda Rapp,et al. The effects of alphabet and expertise on letter perception. , 2016, Journal of experimental psychology. Human perception and performance.
[14] David B. Boles,et al. An upper- and lowercase alphabetic similarity matrix, with derived generation similarity values , 1989 .
[15] Karl J. Friston. The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.
[16] Manuel Perea,et al. Do serifs provide an advantage in the recognition of written words? , 2011 .
[17] Blair C. Armstrong,et al. The what, when, where, and how of visual word recognition , 2014, Trends in Cognitive Sciences.
[18] C Bundesen,et al. A template-matching pandemonium recognizes unconstrained handwritten characters with high accuracy , 1996, Memory & cognition.
[19] Shane T. Mueller,et al. identi fi cation : Effects of perceivability , similarity , and bias ☆ , 2011 .
[20] Jonathan Grainger,et al. Inverse discrimination time as a perceptual distance for alphabetic characters , 2004 .
[21] Bror Zachrisson,et al. Studies in the legibility of printed text , 1965 .
[22] Alberto Testolin,et al. Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions , 2016, Front. Comput. Neurosci..
[23] D. Bub,et al. Features for Identification of Uppercase and Lowercase Letters , 2008, Psychological science.
[24] S. Dehaene. Reading in the Brain: The New Science of How We Read , 2009 .
[25] Charles A. Perfetti,et al. Visual complexity in orthographic learning: Modeling learning across writing system variations , 2016 .
[26] Jonathan Grainger,et al. Testing computational models of letter perception with item-level event-related potentials , 2009, Cognitive neuropsychology.
[27] Marco Zorzi,et al. Emergence of a 'visual number sense' in hierarchical generative models , 2012, Nature Neuroscience.
[28] Ian C Simpson,et al. A letter visual-similarity matrix for Latin-based alphabets , 2013, Behavior research methods.
[29] Aapo Hyvärinen,et al. Natural Image Statistics - A Probabilistic Approach to Early Computational Vision , 2009, Computational Imaging and Vision.
[30] Garrison W. Cottrell,et al. Looking around the backyard helps to recognize faces and digits , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[31] J. Townsend. Theoretical analysis of an alphabetic confusion matrix , 1971 .
[32] Steven M. Seitz,et al. Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..
[33] Denis G. Pelli,et al. The visual filter mediating letter identification , 1994, Nature.
[34] D. Hubel,et al. Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.
[35] S. Dehaene,et al. The unique role of the visual word form area in reading , 2011, Trends in Cognitive Sciences.
[36] Rajesh P. N. Rao,et al. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. , 1999 .
[37] D. Pelli,et al. Feature detection and letter identification , 2006, Vision Research.
[38] Mark S. Seidenberg,et al. Phonology, reading acquisition, and dyslexia: insights from connectionist models. , 1999, Psychological review.
[39] Qiong Zhang,et al. The Structures of Letters and Symbols throughout Human History Are Selected to Match Those Found in Objects in Natural Scenes , 2006, The American Naturalist.
[40] Max Coltheart,et al. Letter recognition: From perception to representation , 2009, Cognitive neuropsychology.
[41] K O Johnson,et al. A comparison of visual and two modes of tactual letter resolution , 1983, Perception & psychophysics.
[42] D. J. Felleman,et al. Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.
[43] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[44] A. V. D. Heijden,et al. Anempirical interletter confusionmatrix for continuous-line capitals , 1984, Perception & psychophysics.
[45] Yoshua Bengio,et al. Deep Learning of Representations for Unsupervised and Transfer Learning , 2011, ICML Unsupervised and Transfer Learning.
[46] G C Gilmore,et al. Multidimensional letter similarity derived from recognition errors , 1979, Perception & psychophysics.
[47] William White,et al. A Proposal , 2008, Moon, Sun, and Witches.
[48] Orrin Devinsky,et al. Sequential then Interactive Processing of Letters and Words in the Left Fusiform Gyrus , 2012, Nature Communications.
[49] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[50] Michele De Filippo De Grazia,et al. Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists , 2013, Front. Psychol..
[51] G. Sperling,et al. Object spatial frequencies, retinal spatial frequencies, noise, and the efficiency of letter discrimination , 1991, Vision Research.
[52] B. Strom,et al. In clarification. , 2007, Pharmacoepidemiology and drug safety.
[53] J M Loomis,et al. Analysis of tactile and visual confusion matrices , 1982, Perception & psychophysics.
[54] Shane T. Mueller,et al. Alphabetic letter identification: effects of perceivability, similarity, and bias. , 2012, Acta psychologica.
[55] Régine Kolinsky,et al. Illiterate to literate: behavioural and cerebral changes induced by reading acquisition , 2015, Nature Reviews Neuroscience.
[56] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[57] Terrence J. Sejnowski,et al. The “independent components” of natural scenes are edge filters , 1997, Vision Research.
[58] Marco Zorzi,et al. Modelling reading development through phonological decoding and self-teaching: implications for dyslexia , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.
[59] Marco Zorzi,et al. Deep generative learning of location-invariant visual word recognition , 2013, Front. Psychol..
[60] Liang She,et al. Spatial structure of neuronal receptive field in awake monkey secondary visual cortex (V2) , 2016, Proceedings of the National Academy of Sciences.
[61] J. Ziegler,et al. Extra-large letter spacing improves reading in dyslexia , 2012, Proceedings of the National Academy of Sciences.
[62] Bruno A. Olshausen,et al. Highly overcomplete sparse coding , 2013, Electronic Imaging.
[63] Catherine E. Snow,et al. Preventing reading difficulties in young children , 1998 .
[64] J. Grainger,et al. Letter perception: from pixels to pandemonium , 2008, Trends in Cognitive Sciences.
[65] Geoffrey E. Hinton. Learning multiple layers of representation , 2007, Trends in Cognitive Sciences.
[66] Gordon E. Legge,et al. Psychophysics of Reading in Normal and Low Vision , 2006 .
[67] John B. Shoven,et al. I , Edinburgh Medical and Surgical Journal.
[68] W. R. Garner,et al. Reaction time as a measure of inter- and intraobject visual similarity: Letters of the alphabet , 1979 .
[69] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[70] D. Pelli,et al. The role of spatial frequency channels in letter identification , 2002, Vision Research.
[71] Michael L. Anderson. Neural reuse: A fundamental organizational principle of the brain , 2010, Behavioral and Brain Sciences.
[72] Eero P. Simoncelli,et al. Natural image statistics and neural representation. , 2001, Annual review of neuroscience.
[73] James L. McClelland,et al. An interactive activation model of context effects in letter perception: I. An account of basic findings. , 1981 .
[74] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[75] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[76] Stanislas Dehaene,et al. Adaptation of the human visual system to the statistics of letters and line configurations , 2015, NeuroImage.
[77] Denis G. Pelli,et al. The remarkable inefficiency of word recognition , 2003, Nature.