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Simulating Developmental Changes in Noun Richness through Performance-limited Distributional Analysis
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Defaulting effects contribute to the simulation of cross-linguistic differences in Optional Infinitive errors
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Sinuosity and the affect grid: A method for adjusting repeated mood scores
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Cluster damage robustness analysis and space independent community detection in complex networks
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Gegov, Emil. - : Brunel University School of Engineering and Design PhD Theses, 2012
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Transition expertise: Cognitive factors and developmental processes that contribute to repeated successful career transitions amongst elite athletes, musicians and business people
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Modelling language acquisition in children using network theory
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In: European Perspectives on Cognitive Sciences (2011)
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Comparing MOSAIC and the variational learning model of the optional infinitive stage in early child language
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On the Utility of Conjoint and Compositional Frames and Utterance
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Simulating the referential properties of Dutch, German and English Root Infinitives in MOSAIC
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Does chess need intelligence? – A study with young chess players
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Modelling the developmental patterning of finiteness marking in English, Dutch, German and Spanish using MOSAIC
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Understanding the Developmental Dynamics of Subject Omission: The Role of Processing Limitations in Learning
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Simulating the Noun-Verb Asymmetry in the Productivity of Children’s Speech
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Linking working memory and long-term memory: A computational model of the learning of new words
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Jones, G; Gobet, F; Pine, J M. - : Blackwell Publishing. The definitive version is available at onlinelibrary.wiley.com, 2007
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Abstract:
The nonword repetition (NWR) test has been shown to be a good predictor of children’s vocabulary size. NWR performance has been explained using phonological working memory, which is seen as a critical component in the learning of new words. However, no detailed specification of the link between phonological working memory and long-term memory (LTM) has been proposed. In this paper, we present a computational model of children’s vocabulary acquisition (EPAM-VOC) that specifies how phonological working memory and LTM interact. The model learns phoneme sequences, which are stored in LTM and mediate how much information can be held in working memory. The model’s behaviour is compared with that of children in a new study of NWR, conducted in order to ensure the same nonword stimuli and methodology across ages. EPAM-VOC shows a pattern of results similar to that of children: performance is better for shorter nonwords and for wordlike nonwords, and performance improves with age. EPAM-VOC also simulates the superior performance for single consonant nonwords over clustered consonant nonwords found in previous NWR studies. EPAM-VOC provides a simple and elegant computational account of some of the key processes involved in the learning of new words: it specifies how phonological working memory and LTM interact; makes testable predictions; and suggests that developmental changes in NWR performance may reflect differences in the amount of information that has been encoded in LTM rather than developmental changes in working memory capacity. Keywords: EPAM, working memory, long-term memory, nonword repetition, vocabulary acquisition, developmental change.
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Keyword:
computational modelling; language acquisition; non-word repetition; vocabulary
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URL: http://bura.brunel.ac.uk/handle/2438/618
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Modelling the Development of Children’s use of Optional Infinitives in Dutch and English using MOSAIC
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Unifying cross-linguistic and within-language patterns of finiteness marking in MOSAIC
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On the resolution of ambiguities in the extraction of syntactic categories through chunking
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