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Igor Farkas - One of the best experts on this subject based on the ideXlab platform.
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Early Lexical Development in a self-organizing neural network.
Neural Networks, 2004Co-Authors: Igor Farkas, Brian MacwhinneyAbstract:In this paper we present a self-organizing neural network model of early Lexical Development called DevLex. The network consists of two self-organizing maps (a growing semantic map and a growing phonological map) that are connected via associative links trained by Hebbian learning. The model captures a number of important phenomena that occur in early Lexical acquisition by children, as it allows for the representation of a dynamically changing linguistic environment in language learning. In our simulations, DevLex develops topographically organized representations for linguistic categories over time, models Lexical confusion as a function of word density and semantic similarity, and shows age-of-acquisition effects in the course of learning a growing lexicon. These results match up with patterns from empirical research on Lexical Development, and have significant implications for models of language acquisition based on self-organizing neural networks.
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2004 Special issue Early Lexical Development in a self-organizing neural network
2004Co-Authors: Igor Farkas, Brian MacwhinneyAbstract:In this paper we present a self-organizing neural network model of early Lexical Development called DevLex. The network consists of two self-organizing maps (a growing semantic map and a growing phonological map) that are connected via associative links trained by Hebbian learning. The model captures a number of important phenomena that occur in early Lexical acquisition by children, as it allows for the representation of a dynamically changing linguistic environment in language learning. In our simulations, DevLex develops topographically organized representations for linguistic categories over time, models Lexical confusion as a function of word density and semantic similarity, and shows age-of-acquisition effects in the course of learning a growing lexicon. These results match up with patterns from empirical research on Lexical Development, and have significant implications for models of language acquisition based on self-organizing neural networks.
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DevLex: a self-organizing neural network model of the Development of lexicon
Proceedings of the 9th International Conference on Neural Information Processing 2002. ICONIP '02., 2002Co-Authors: Igor FarkasAbstract:In this paper we present the DevLex model of language acquisition. DevLex consists of two self-organizing maps (a growing semantic map and a phonological map) that are connected via associative links. It simulates the early stages of Lexical Development in children, in particular, word confusion as evidenced in naming errors. The simulation results indicate that the rate of word confusion is modulated by Developmental profile of vocabulary increase, word density of competing neighbors, and rate of Lexical growth. These results match up with hypotheses from empirical research on Lexical Development.
Brian Macwhinney - One of the best experts on this subject based on the ideXlab platform.
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Early Lexical Development in a self-organizing neural network.
Neural Networks, 2004Co-Authors: Igor Farkas, Brian MacwhinneyAbstract:In this paper we present a self-organizing neural network model of early Lexical Development called DevLex. The network consists of two self-organizing maps (a growing semantic map and a growing phonological map) that are connected via associative links trained by Hebbian learning. The model captures a number of important phenomena that occur in early Lexical acquisition by children, as it allows for the representation of a dynamically changing linguistic environment in language learning. In our simulations, DevLex develops topographically organized representations for linguistic categories over time, models Lexical confusion as a function of word density and semantic similarity, and shows age-of-acquisition effects in the course of learning a growing lexicon. These results match up with patterns from empirical research on Lexical Development, and have significant implications for models of language acquisition based on self-organizing neural networks.
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2004 Special issue Early Lexical Development in a self-organizing neural network
2004Co-Authors: Igor Farkas, Brian MacwhinneyAbstract:In this paper we present a self-organizing neural network model of early Lexical Development called DevLex. The network consists of two self-organizing maps (a growing semantic map and a growing phonological map) that are connected via associative links trained by Hebbian learning. The model captures a number of important phenomena that occur in early Lexical acquisition by children, as it allows for the representation of a dynamically changing linguistic environment in language learning. In our simulations, DevLex develops topographically organized representations for linguistic categories over time, models Lexical confusion as a function of word density and semantic similarity, and shows age-of-acquisition effects in the course of learning a growing lexicon. These results match up with patterns from empirical research on Lexical Development, and have significant implications for models of language acquisition based on self-organizing neural networks.
Xiaowei Zhao - One of the best experts on this subject based on the ideXlab platform.
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Early Lexical Development: A corpus-based study of three languages
2008Co-Authors: Shuxia Liu, Xiaowei ZhaoAbstract:In this study, we present a crosslinguistic analysis of early Lexical Development in English, Mandarin and Cantonese based on large-scale child-adult interaction transcripts in the CHILDES database. We examined patterns of Lexical composition for several Lexical categories (nouns, verbs, and adjectives) in children and their caregivers’ vocabularies across eight different age groups from 13 to 60 months. A series of statistical methods were applied to analyze the Developmental trajectories of Lexical diversity of children’s speech. Our study overcomes several major methodological limitations of previous studies, and provides a clear and detailed picture of the similarities and differences in Lexical Development patterns: (1) in all three languages, children’s early language Development shows increasing diversity as a function of time, and the child lexicon becomes more similar to that of their parents over time; and (2) language-specific differences in the linguistic input have strong influence on children’s language output, which is reflected in the varying percentages of nouns, verbs, and adjectives in the child’s productive lexicon at different Developmental stages.
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Dynamic Self‐Organization and Early Lexical Development in Children
Cognitive Science, 2007Co-Authors: Xiaowei Zhao, Brian Mac WhinneyAbstract:In this study we present a self-organizing connectionist model of early Lexical Development. We call this model DevLex-II, based on the earlier DevLex model. DevLex-II can simulate a variety of empirical patterns in children's acquisition of words. These include a clear vocabulary spurt, effects of word frequency and length on age of acquisition, and individual differences as a function of phonological short-term memory and associative capacity. Further results from lesioned models indicate Developmental plasticity in the network's recovery from damage, in a non-monotonic fashion. We attribute the network's abilities in accounting for Lexical Development to interactive dynamics in the learning process. In particular, variations displayed by the model in the rate and size of early vocabulary Development are modulated by (a) input characteristics, such as word frequency and word length, (b) consolidation of Lexical-semantic representation, meaning-form association, and phonological short-term memory, and (c) delayed processes due to interactions among timing, severity, and recoverability of lesion. Together, DevLex and DevLex-II provide an accurate computational account of early Lexical Development.
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dynamic self organization and early Lexical Development in children
Cognitive Science, 2007Co-Authors: Xiaowei Zhao, Brian Mac WhinneyAbstract:In this study we present a self-organizing connectionist model of early Lexical Development. We call this model DevLex-II, based on the earlier DevLex model. DevLex-II can simulate a variety of empirical patterns in children's acquisition of words. These include a clear vocabulary spurt, effects of word frequency and length on age of acquisition, and individual differences as a function of phonological short-term memory and associative capacity. Further results from lesioned models indicate Developmental plasticity in the network's recovery from damage, in a non-monotonic fashion. We attribute the network's abilities in accounting for Lexical Development to interactive dynamics in the learning process. In particular, variations displayed by the model in the rate and size of early vocabulary Development are modulated by (a) input characteristics, such as word frequency and word length, (b) consolidation of Lexical-semantic representation, meaning-form association, and phonological short-term memory, and (c) delayed processes due to interactions among timing, severity, and recoverability of lesion. Together, DevLex and DevLex-II provide an accurate computational account of early Lexical Development.
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A Self-Organizing Connectionist Model of Bilingual Lexical Development - eScholarship
2006Co-Authors: Xiaowei ZhaoAbstract:A Self-Organizing Connectionist Model of Bilingual Lexical Development Xiaowei Zhao (xzhao2@richmond.edu) Ping Li (pli@richmond.edu) Department of Psychology, University of Richmond Richmond, VA 23173 USA Keywords: Connectionism; Bilingual Lexical Development distinct representations of the L2 lexicon may be established (Fig. 2a); when the learning of L2 was delayed relative to that of L1, the consolidation of L1 would significantly (sometimes dramatically) impact the representation of L2 (e.g., resulting in parasitic L2 lexicon; Fig. 2b). As seen in Fig. 2b, compared with L1 words, the L2 words occupied only small and fragmented regions on the semantic map (the shaded areas), and the small chunks isolated from the main part of the L2 words tended to be interspersed in L1 regions and close to those L1 words with similar meaning. Our results suggest a dynamic Developmental picture for bilingual Lexical acquisition: the acquisition of two languages entails strong competition in a highly interactive context and limited plasticity as a function of timing of learning; whether bilingual representations are distinct or shared will depend on important Developmental factors such as the history of learning. Our study illustrates computational mechanisms underlying L1-L2 competition, learning entrenchment, and plasticity of learning (see also Hernandez, Li, & MacWhinney, 2005). How do bilingual learners acquire a structured representation for the two lexicons at early stages of Lexical acquisition? In this study, we attempt to address this question with Devlex-II, a self-organizing connectionist model. Previous connectionist simulations suggest that bilingual Lexical representations may be functionally separate, but architecturally homogeneous (Li & Farkas, 2002). Here, we expand this single-mechanism- variable-representation view from a dynamical Developmental perspective. Results from our study will provide insights into the long-standing debate on distinct versus shared structure of bilingual Lexical representation. Figure 1: The DevLex-II model of Lexical Development. The DevLex-II model is a cognitively and neurally plausible connectionist model based on principle of self- organization and Hebbian learning rules (Zhao & Li, 2005). It consists of three self-organizing feature maps that are connected via associative links trained by Hebbian learning. Upon training, the meaning, phonology, and phonemic sequence of a word are processed by the network. The acquisition of Chinese and English is the bilingual context that the current model simulates. Here, each lexicon included 500 words, and was derived from CDI, the MacArthur-Bates Communicative Development Inventories. In the current study, we manipulated (1) the size of the input lexicon over different Developmental stages, and (2) the onset time of L2 Lexical learning – simultaneous: onset times of the two languages are identical; sequential: onset time of L2 (Chinese) is delayed relative to that of L1 (English). The sequential learning can be further divided into two situations, one as early L2 learning and the other late L2 learning. Our results show that: (1) distinct representations for the two lexicons can gradually develop in our network under simultaneous learning; (2) the representational structure is highly dependent on the onset time of L2 learning (Figure 2): when the learning of L2 was early relative to that of L1, (a) (b) Figure 2: Semantic organization of the bilingual lexicon acquired by DevLex-II. Shaded areas indicate L2 (Chinese) representations. (a) Early L2 learning. (b) Late L2 learning. Acknowledgments This research was supported by a grant from the National Science Foundation (BCS-0131829) to PL. References Hernandez, A., Li, P., & MacWhinney, B. (2005). The emergence of competing modules in bilingualism. Trends in Cognitive Sciences, 9, 220-225. Li, P., & Farkas, I. (2002). A self-organizing connectionist model of bilingual processing. In R. Heredia & J. Altarriba (Eds.), Bilingual sentence processing. North- Holland: Elsevier Science Publisher. Zhao, X., & Li. P. (2005) A self-organizing connectionist model of early word production, In Proceedings of the 27th Conference of Cognitive Science Society. Erlbaum.
Christian Champaud - One of the best experts on this subject based on the ideXlab platform.
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A naturalistic study of early Lexical Development: General processes and inter-individual variations in French children.
First Language, 2005Co-Authors: Dominique Bassano, Elsa Eme, Christian ChampaudAbstract:This study investigated early Lexical Development in French by analysing changes and variability in Lexical production and composition of children's spontaneous speech samples from three age groups: 1;8, 2;6 and 3;3 years (20 children in each).
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A naturalistic study of early Lexical Development: General processes and inter-individual variations in French children:
First Language, 2005Co-Authors: Dominique Bassano, Pascale-elsa Eme, Christian ChampaudAbstract:This study investigated early Lexical Development in French by analysing changes and variability in Lexical production and composition of children’s spontaneous speech samples from three age groups: 1;8, 2;6 and 3;3 years (20 children in each). Analyses of general Developmental changes showed that Lexical productivity increased strongly between 1;8 and 2;6 and between 2;6 and 3;3. Changes in Lexical composition mostly occurred between 1;8 and 2;6, indicating that the most important reorganizations are achieved by 2;6. The main changes observed (decreases in proportions of nouns and paraLexical classes, and increases in proportions of predicate and grammatical classes) fit overall the Developmental trajectories found for other languages, such as English and Italian. Two controversial issues were particularly examined and discussed with regard to cognitive, language-specific and methodological factors: noun-verb asynchrony and grammatical word explosion. Quantitative individual differences in Lexical compos...
Brian Mac Whinney - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Self‐Organization and Early Lexical Development in Children
Cognitive Science, 2007Co-Authors: Xiaowei Zhao, Brian Mac WhinneyAbstract:In this study we present a self-organizing connectionist model of early Lexical Development. We call this model DevLex-II, based on the earlier DevLex model. DevLex-II can simulate a variety of empirical patterns in children's acquisition of words. These include a clear vocabulary spurt, effects of word frequency and length on age of acquisition, and individual differences as a function of phonological short-term memory and associative capacity. Further results from lesioned models indicate Developmental plasticity in the network's recovery from damage, in a non-monotonic fashion. We attribute the network's abilities in accounting for Lexical Development to interactive dynamics in the learning process. In particular, variations displayed by the model in the rate and size of early vocabulary Development are modulated by (a) input characteristics, such as word frequency and word length, (b) consolidation of Lexical-semantic representation, meaning-form association, and phonological short-term memory, and (c) delayed processes due to interactions among timing, severity, and recoverability of lesion. Together, DevLex and DevLex-II provide an accurate computational account of early Lexical Development.
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dynamic self organization and early Lexical Development in children
Cognitive Science, 2007Co-Authors: Xiaowei Zhao, Brian Mac WhinneyAbstract:In this study we present a self-organizing connectionist model of early Lexical Development. We call this model DevLex-II, based on the earlier DevLex model. DevLex-II can simulate a variety of empirical patterns in children's acquisition of words. These include a clear vocabulary spurt, effects of word frequency and length on age of acquisition, and individual differences as a function of phonological short-term memory and associative capacity. Further results from lesioned models indicate Developmental plasticity in the network's recovery from damage, in a non-monotonic fashion. We attribute the network's abilities in accounting for Lexical Development to interactive dynamics in the learning process. In particular, variations displayed by the model in the rate and size of early vocabulary Development are modulated by (a) input characteristics, such as word frequency and word length, (b) consolidation of Lexical-semantic representation, meaning-form association, and phonological short-term memory, and (c) delayed processes due to interactions among timing, severity, and recoverability of lesion. Together, DevLex and DevLex-II provide an accurate computational account of early Lexical Development.