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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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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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8 |
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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Abstract:
Several theories have been put forward to explain the phenomenon that children who are learning to speak their native language tend to omit the subject of the sentence. According to the pro-drop hypothesis, children represent the wrong grammar. According to the performance limitations view, children represent the full grammar, but omit subjects due to performance limitations in production. This paper proposes a third explanation and presents a model which simulates the data relevant to subject omission. The model consists of a simple learning mechanism that carries out a distributional analysis of naturalistic input. It does not have any overt representation of grammatical categories, and its performance limitations reside mainly in its learning mechanism. The model clearly simulates the data at hand, without the need to assume large amounts of innate knowledge in the child, and can be considered more parsimonious on these grounds alone. Importantly, it employs a unified and objective measure of processing load, namely the length of the utterance, which interacts with frequency in the input. The standard performance limitations view assumes that processing load is dependent on a phrase’s syntactic role, but does not specify a unifying underlying principle.
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Keyword:
Bloom; computational modelling; distributional analysis; innate knowledge; learning; MOSAIC; naturalistic input; performance limitation; pro-drop hypothesis; Subject omission; syntax
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URL: http://bura.brunel.ac.uk/handle/2438/783
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Modeling the optional infinite stage in MOSAIC: A generalization to Dutch
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