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On the Utility of Conjoint and Compositional Frames and Utterance
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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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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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Simulating the cross-linguistic development of optional infinitive errors in MOSAIC.
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Simulating optional infinitive errors in child speech through the omission of sentence-internal elements.
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Resolving ambiguities in the extraction of syntactic categories through chunking.
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Simulating the temporal reference of Dutch and English Root Infinitives.
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Modelling syntactic development in a cross-linguistic context
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The role of input size and generativity in simulating language acquisition.
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Modelling children's negation errors using probabilistic learning in MOSAIC.
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Modelling the development of Dutch Optional Infinitives in MOSAIC.
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Subject omission in children's language; The case for performance limitations in learning.
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Modeling the optional infinite stage in MOSAIC: A generalization to Dutch
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Learning novel sound patterns
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Abstract:
The acquisition of vocabulary represents a key phenomenon in language acquisition, yet it is still poorly understood. Gathercole and colleagues have recently provided a rigorous test of vocabulary knowledge (the nonword repetition test, Gathercole, Willis, Baddeley, & Emslie, 1994) and have adapted the phonological loop part of the working memory model (Baddeley & Hitch, 1974) to explain the nonword repetition findings (e.g. Gathercole & Baddeley, 1989). However, there are two major failings in their explanation: there is no description of how words are learned, and no definition of how the phonological loop interacts with long-term memory. We present an EPAM based computational model which overcomes these problems by combining the phonological loop approach with the EPAM/chunking approach (Feigenbaum & Simon, 1984). Trained on naturalistic phonemically coded speech (from mother’s utterances to 2-3 year old children), the model provides a good match to the nonword repetition data from 2-3 year old children. The model is also able to show the effect on nonword repetition when the model is trained using different sets of input. Implementing the phonological loop within EPAM represents a parsimonious approach to learning novel sound patterns and provides a more precise definition of how vocabulary acquisition may occur.
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Keyword:
Baddeley; EPAM; EPAM-VOC; Gathercole; language acquisition; long-term memory; nonword repetition test; phonological loop; vocabulary; working memory model
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URL: http://bura.brunel.ac.uk/handle/2438/2132
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