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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 49 1 Using computational modeling to understand the interaction between risk and protective factors in reading disability ... Lo, Jason C. M.; Rueckl, Jay G.; Zevin, Jason D.. - : Code Ocean, 2021 BASE Show details 2 Using computational modeling to understand the interaction between risk and protective factors in reading disability ... Lo, Jason C. M.; Rueckl, Jay G.; Zevin, Jason D.. - : Code Ocean, 2021 Abstract: Computational simulations of learning to read in the “triangle model” framework have contributed to our understanding of typical and atypical reading development. Manipulations of simulation parameters, such as the degree of noise in the phonological system, the number of processing (hidden) units, and the rate of learning have been used to show how differences in relevant perceptual and cognitive capacities can contribute to different forms of reading disability. Here we examine how graded, orthogonal variability in these “control parameters” influences performance across a broad range. We considered how variability in these parameters interact to produce different patterns of performance across a range of word and pseudoword stimuli over the course of training. Results were broadly consistent with expectations — noise in the phonological layer leads to worse performance on pseudowords and restricting the number of hidden units lead to worse performance for inconsistent words. One surprising result was that ... Keyword: Capsule; computational modeling; individual differences; reading disability; Social Sciences; triangle model URL: https://codeocean.com/capsule/4507067/tree/v2 https://dx.doi.org/10.24433/co.1821081.v2 BASE Hide details 3 Using information-theoretic measures to characterize the structure of the writing system: the case of orthographic-phonological regularities in English [<Journal>] Siegelman, Noam [Verfasser]; Kearns, Devin M. [Verfasser]; Rueckl, Jay G. [Verfasser] DNB Subject Category Language Show details 4 Using Information-Theoretic Measures to Characterize the Structure of the Writing System: The Case of Orthographic-Phonological Regularities in English Siegelman, Noam; Kearns, Devin M.; Rueckl, Jay G. In: Behav Res Methods (2020) BASE Show details 5 Friends in Low-Entropy Places: Orthographic Neighbor Effects on Visual Word Identification Differ Across Letter Positions Luthra, Sahil; You, Heejo; Rueckl, Jay G.... In: Cogn Sci (2020) BASE Show details 6 Is that a pibu or a pibo? Children with reading and language deficits show difficulties in learning and overnight consolidation of phonologically similar pseudowords Malins, Jeffrey G.; Landi, Nicole; Ryherd, Kayleigh... In: Dev Sci (2020) BASE Show details 7 Individual differences in learning the regularities between orthography, phonology and semantics predict early reading skills Siegelman, Noam; Rueckl, Jay G.; Steacy, Laura M.... In: J Mem Lang (2020) BASE Show details 8 Neurobiological signatures of L2 proficiency: Evidence from a bi-directional cross-linguistic study Brice, Henry; Mencl, William Einar; Frost, Stephen J.. - 2018 BASE Show details 9 Neural Representations for Newly Learned Words are Modulated by Overnight Consolidation, Reading skill, and Age Landi, Nicole; Malins, Jeffrey G.; Frost, Stephen J.. - 2018 BASE Show details 10 Development and Prediction of Context-Dependent Vowel Pronunciation in Elementary Readers Steacy, Laura M.; Compton, Donald L.; Petscher, Yaacov. - 2018 BASE Show details 11 Reading and the Neurocognitive Bases of Statistical Learning(1) Sawi, Oliver M.; Rueckl, Jay G.. - 2018 BASE Show details 12 Statistical and Cooperative Learning in Reading: An Artificial Orthography Learning Study Zhao, Jingjing; Li, Tong; Elliott, Mark A.. - 2017 BASE Show details 13 Dough, Tough, Cough, Rough: A “Fast” fMRI Localizer of Component Processes in Reading Malins, Jeffrey G.; Gumkowski, Nina; Buis, Bonnie. - 2016 BASE Show details 14 Advances in morphological processing : a special issue of language and cognitive processes Pollatsek, Alexander; Juhasz, Barbara J.; Morris, Joanna. - New York : Psychology Press, 2015 BLLDB UB Frankfurt Linguistik Show details 15 Universal brain signature of proficient reading: Evidence from four contrasting languages. Rueckl, Jay G; Paz-Alonso, Pedro M; Molfese, Peter J... In: Proceedings of the National Academy of Sciences of the United States of America, vol 112, iss 50 (2015) BASE Show details 16 Universal brain signature of proficient reading: Evidence from four contrasting languages Rueckl, Jay G.; Paz-Alonso, Pedro M.; Molfese, Peter J.. - : National Academy of Sciences, 2015 BASE Show details 17 Neural division of labor in reading is constrained by culture: A training study of reading Chinese characters Zhao, Jingjing; Wang, Xiaoyi; Frost, Stephen J.. - 2014 BASE Show details 18 Connectionism and the role of morphology in visual word recognition Rueckl, Jay G. In: Methodological and analytic frontiers in lexical research (2012) IDS Mannheim 19 How does the brain read words? Sandak, Rebecca; Frost, Stephen J.; Rueckl, Jay G.... In: The Cambridge handbook of psycholinguistics (Cambridge, 2012), p. 218-238 MPI für Psycholinguistik Show details 20 The limitations of the reverse-engineering approach to cognitive modeling Rueckl, Jay G.; Frost, Ram In: Behavioral and brain sciences. - New York, NY [u.a.] : Cambridge Univ. Press 35 (2012) 5, 305 OLC Linguistik Show details Page: 1 2 3

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