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Computational Models in Electroencephalography.
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In: Brain topography, vol 35, iss 1 (2022)
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Incremental Acquisition of a Minimalist Grammar using an SMT-Solver
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Learning Constraints on Wh-Dependencies by Learning How to Efficiently Represent Wh-Dependencies: A Developmental Modeling Investigation With Fragment Grammars
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Remodelling complement coercion interpretation
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Sociolinguistically-aware computational models of Mandarin-English codeswitching
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In: Proceedings of the Linguistic Society of America; Vol 7, No 1 (2022): Proceedings of the Linguistic Society of America; 5247 ; 2473-8689 (2022)
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Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement
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In: Alekseeva, Alexandra orcid:0000-0002-7990-4592 , Gladkoff, Serge, Sorokina, Irina and Han, Lifeng orcid:0000-0002-3221-2185 (2021) Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement. In: Metrics 2021: Workshop on Informetric and Scientometric Research (SIG-MET), 23-24 Oct 2021, Online. (2021)
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Is there a bilingual disadvantage for word segmentation? A computational modeling approach
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In: ISSN: 0305-0009 ; EISSN: 1469-7602 ; Journal of Child Language ; https://hal.archives-ouvertes.fr/hal-03498905 ; Journal of Child Language, Cambridge University Press (CUP), 2021, pp.1-28. ⟨10.1017/S0305000921000568⟩ (2021)
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SCALa: A blueprint for computational models of language acquisition in social context
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In: ISSN: 0010-0277 ; EISSN: 1873-7838 ; Cognition ; https://hal.inria.fr/hal-03373586 ; Cognition, Elsevier, 2021, 213, pp.104779. ⟨10.1016/j.cognition.2021.104779⟩ (2021)
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Do Infants Really Learn Phonetic Categories?
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In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-03550830 ; Open Mind, MIT Press, 2021, 5, pp.113-131. ⟨10.1162/opmi_a_00046⟩ (2021)
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The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using linear discriminative learning.
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In: Behavior research methods, vol 53, iss 3 (2021)
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Continuous developmental change explains discontinuities in word learning
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In: https://hal.archives-ouvertes.fr/hal-03191088 ; 2021 (2021)
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Modeling speech act development in early childhood: the role of frequency and linguistic cues
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In: Proceedings of the 43rd Annual Meeting of the Cognitive Science Society. ; 43rd Annual Meeting of the Cognitive Science Society. ; https://hal.archives-ouvertes.fr/hal-03236607 ; 43rd Annual Meeting of the Cognitive Science Society., Jul 2021, Vienna, Austria (2021)
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| 13 |
Data for: Effects of phonological neighbourhood density and frequency in picture naming. ...
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Data for: Effects of phonological neighbourhood density and frequency in picture naming. ...
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| 15 |
Using computational modeling to understand the interaction between risk and protective factors in reading disability ...
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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 ...
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Keyword:
Capsule; computational modeling; individual differences; reading disability; Social Sciences; triangle model
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URL: https://codeocean.com/capsule/4507067/tree/v1
https://dx.doi.org/10.24433/co.1821081.v1
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Using computational modeling to understand the interaction between risk and protective factors in reading disability ...
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A surprisal--duration trade-off across and within the world's languages ...
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Aligning Multidimensional Worldviews and Discovering Ideological Differences ...
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On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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