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Vocabulary Development inFirst and Second Language Acquisition
Ping LiCognitive Science Lab
Department of PsychologyUniversity of RichmondRichmond, VA 23173
[email protected]://cogsci.richmond.edu/
Overview of research
Does it differ in which language we speak? Crosslinguistic studies of Chinese and English
Does it differ in how many languages we speak? Bilingual language representation
Does it differ across a developmental trajectory? Child language acquisition
How does
process and organize
??
Approaches: behavioral, computational, & neural
Outline The lexicon in acquisition: Major issues
Vocabulary spurt Bilingual lexical representation Age of acquisition/critical periods
Connectionism and development
Self-organizing neural network models DevLex, DevLex-II, SOMBiP
Conclusions
Lexical Development in Monolinguals
Lexical development in children: it’s fast 14,000 words by age 6
Changes across time: not spread out evenly 0-18 months: 50 words 18-30 months: 500 words
Vocabulary spurt: rapid learning following slow growth Word-learning intrinsic factors
Phonological memory, word retrieval, lexical organization Cognitive and social abilities
Naming insight, communicative awakening
Lexical Development in Bilinguals
Simultaneous bilingual acquisition No spurt found for both L1 and L2 Individual differences: size, rate, and frequency of L2 input
Sequential bilingual acquisition Shared versus distinct lexical memory
(one store vs. two stores)
Learning (SLA) and representation (Bilingualism)
A computational perspective
Why computational modeling? Flexibility in parameter variation and
hypothesis testinge.g., Timing, size, and rate of input
Computational mechanisms required forlexical acquisition
e.g., association, organization, and competition
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Localization vs.Organization
Neurons in the brainconnect with one
another to form networks
The brain learns by modifyingcertain connections in
response to inputs
Neural Networks
units
weights
learning rule
activations
connections
Neurons in the brain connect with one
another to form networks
The brain learns by modifyingcertain connections in
response to inputs
Neural Networks
Neurons in the brain connect with one
another to form networks
The brain learns by modifyingcertain connections in
response to inputs
Information is distributed through large groups of connected units
Knowledge is represented bypatterns of activation
Learning is accomplished by adjustment of the weights that connect the units
Emergent Properties
Connectionist language learning models
Limitation of previous computational models
Artificial input patterns Small size of lexicon Supervised learning (e.g., BP network) Monolingual learning & representation
Connectionist language learning models
Our Model
Realistic input (based on parental speech) Early child lexicon (500 words based on CDI) Unsupervised learning (Self-Organizing Maps) Mono-&-bilingual learning and representation
This has been made possible by the availability of large-scale speech corporaonline (e.g., CHILDES, CDI, HAL) and the computational tools therein
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Computational principles of our model
Self-Organizing Map (SOM; Kohonen, 1982, 2001) Unsupervised Learning Topography-preserving Gradual formation of structures with soft boundaries
Forming lexical categories
Computational principles of our model
Self-Organizing Map (SOM; Kohonen, 1982, 2001) Unsupervised Learning Topography-preserving Gradual formation of structures with soft boundaries
Hebbian Learning Different maps can be connected via Hebbian learning,
according to which associative strengths of the correspondingnodes increase through co-activation
Li, P., Farkas, I., & MacWhinney, B. (2004). Early Lexical Development in a Self- Organizing Neural Network. Neural Networks, 17, 1345-1362.
Li, P., Zhao, X., & MacWhinney, B. (2007). Dynamic self-organization and early lexicaldevelopment in children. Cognitive Science, 31.
DevLex: a Developmental Lexicon model (Li et al. 2004)
The DevLex Bilingual Model (SOMBiP; Li & Farkas, 2004)
ENGLISH SEMANTICS
CHINESE SEMANTICS
CHINESE PHONOLOGY
ENGLISH PHONOLOGY
ASSOCIATIVE CONNECTIONS (Hebbian
learning)
Self-organization
Self-organization
Wo
r d F
or m
P
hon olo gi c a
l W
or d
Mea n
i ng
C
o- oc c ur re nc e- bas e d re pr es ent a
ti on ( deri ved fr om
separ ate com
pon ent
expos ed to bil ingua
l corp us)
Phonological Map
Semantic Map
Input phonology map(SOM)
Self-Organization
Word Form(Phonetic features)
Output sequence map(SARDNET)
Self-Organization
Word Sequence(Phonetic features)
Hebbian Learning(Comprehension)
Hebbian Learning(Production)
Semantic map(SOM)
Word Meaning Representation(WCD)
Self-Organization
The DevLex-II model (Li et al. 2007)
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Simulating lexical disorders(Miikkulainen, 1997; Silberman et al., 2007) Input Representations
Phonological representations PatPho - a phonological pattern generator
(Li & MacWhinney, 2002)
Phonemic representations Articulatory features of phonemes
(Ladefoged, 1982)
Semantic representations WCD - a word co-occurrence detector
(Farkas & Li, 2002; Li et al., 2004)
Emergence of Lexical Categories in DevLex Emergence of structured semantic representations
50 150
250 500
Rapid learning in early vocabulary
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Rapid learning and lexical organization
Vocabulary spurt occurs when Structured representations in word meaning
and phonological shape are established Associative mapping is consolidated “Setting up the basic framework”
Word confusion rates across time
Effects of word frequency and length Effects of phonological working memory
Effects of associative capacity Effects of simulated lesion
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Summary of DevLex Results for L1
Developmental changes in word learning are modulated bylexical organization; Lexical organization is reflected in thedevelopment of structured representation
Individual differences with respect to the shape and function ofearly vocabulary are explained by the interaction betweenseveral parameters, including phonological short-term memoryand associative capacity
Learning itself can determine the shape of change, anddiscontinuous patterns of development can emerge from thesame underlying mechanisms
Emergence of lexical representations based onbilingual input
ENGLISH SEMANTICS
CHINESE SEMANTICS
CHINESE PHONOLOGY
ENGLISH PHONOLOGY
ASSOCIATIVE CONNECTIONS (Hebbian
learning)
Self-organization
Self-organization
Wo r d Fo r m
P
h on olog ica l W
o r d Me an in g
Co- oc cu rr en ce -b as ed
re pr es en t at ion ( der i ved fr om
s epar ate componen t
expo sed t o bil ingu al c or pus)
Phonological Map
Semantic Map
Emergence of lexical representations(Simultaneous Learning)
Effects of age of learning
Early L2 learning Late L2 learning
Effects of age of learning
32:10.3:1L2:L1
2%3244English
64%956ChineseLateL2 Learning
1.87:10.75:1L2:L1
11%2397English
20.6%1803ChineseEarlyL2 learning
1:10.94:1L2:L1
12.8%2162English
12.8%2038ChineseSimultaneouslearning
Confusion rateLexical space
Table 1: Size of lexical space and rate of confusion for Chinese (L2)vs. English (L1) on the semantic map
Summary of DevLex Results for L2
When the learning of L2 is early relative to that of L1,functionally distinct representations of the two lexiconsmay be established.
When the learning of L2 is delayed relative to that ofL1, the structural consolidation of L1 will significantly(sometimes dramatically) impact on the representationof L2 (e.g., resulting in parasitic L2).
Plasticity and competition: The network’ ability toreorganize its structure decreases as the structure ofL1 has consolidated
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Li, et al. 2004
Neural correlates ofnouns and verbs inChinese
Neural correlates of nouns and verbs in early bilingualsChan, et al. (2006)
ERP signatures of subject-verb agreement in L2 learningChen, et al. (2006) Conclusions
DevLex is a developmentally plausible and computationallyrealistic model designed to account for the dynamic self-organization in lexical learning and representation
The model captures important mechanisms underlyingdevelopmental phenomena (e.g., early plasticity, competition,and experience-dependent structural changes; Bates, 1999).
Our model projects lexical development at a dynamicalsystems level, in which phonological and semanticrepresentations of words continuously interact and evolve.
New research in language acquisition point to directionsbeyond the nature-nurture debate (e.g., infant studies; cf. Kuhl;Saffran), and our research exemplifies such directions.
ReferencesLi, P. (2006). Modeling language acquisition and processing: Connectionist networks. In
Li, P., Tan, L.H., Bates, E., & Tzeng, O. (eds.), Handbook of East Asian Psycholinguistics(Vol. 1: Chinese). Cambridge, UK: Cambridge University Press.
Li, P., Zhao, X., & MacWhinney, B. (2007). Dynamic self-organization and children’s wordlearning. Cognitive Science, 31.
Li, P., Farkas, I., & MacWhinney, B. (2004). Early Lexical Development in a Self-OrganizingNeural Network. Neural Networks, 17, 1345-1362.
Li, P. (2003). Language acquisition in a self-organizing neural network. In P. Quinlan (ed.)Connectionist Models of Development: Insights from real and artificial neural networks.Taylor & Francis: Psychology Press.
Li, P., & Farkas, I. (2002). A self-organizing connectionist model of bilingual processing. InR. Heredia & J. Altarriba (eds.), Bilingual sentence processing. North-Holland: ElsevierScience Publisher.
Hernandez, A., Li, P., & MacWhinney, B. (2005). The emergence of competing modules inbilingualism. Trends in Cognitive Sciences, 9, 220-225.
Hernandez, A., & Li, P. (2007). Age of acquisition: Its neural and computational mechanisms.Psychological Bulletin.
For reprints or preprints, visit http://cogsci.richmond.edu/