1 |
Comparing MOSAIC and the variational learning model of the optional infinitive stage in early child language
|
|
|
|
BASE
|
|
Show details
|
|
2 |
On the Utility of Conjoint and Compositional Frames and Utterance
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Understanding the Developmental Dynamics of Subject Omission: The Role of Processing Limitations in Learning
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Simulating the Noun-Verb Asymmetry in the Productivity of Children’s Speech
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Linking working memory and long-term memory: A computational model of the learning of new words
|
|
Jones, G; Gobet, F; Pine, J M. - : Blackwell Publishing. The definitive version is available at onlinelibrary.wiley.com, 2007
|
|
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.
|
|
Keyword:
computational modelling; language acquisition; non-word repetition; vocabulary
|
|
URL: http://bura.brunel.ac.uk/handle/2438/618
|
|
BASE
|
|
Hide details
|
|
8 |
Modelling the Development of Children’s use of Optional Infinitives in Dutch and English using MOSAIC
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Unifying cross-linguistic and within-language patterns of finiteness marking in MOSAIC
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Simulating the cross-linguistic development of optional infinitive errors in MOSAIC.
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Simulating optional infinitive errors in child speech through the omission of sentence-internal elements.
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Resolving ambiguities in the extraction of syntactic categories through chunking.
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Simulating the temporal reference of Dutch and English Root Infinitives.
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Modelling syntactic development in a cross-linguistic context
|
|
|
|
BASE
|
|
Show details
|
|
15 |
The role of input size and generativity in simulating language acquisition.
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Modelling children's negation errors using probabilistic learning in MOSAIC.
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Modelling the development of Dutch Optional Infinitives in MOSAIC.
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Subject omission in children's language; The case for performance limitations in learning.
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Modeling the optional infinite stage in MOSAIC: A generalization to Dutch
|
|
|
|
BASE
|
|
Show details
|
|
|
|