SlideShare a Scribd company logo
1 of 57
Download to read offline
Word Frequency
Dominance and L2
Word Recognition
September 12, 2016
Vocab@Tokyo
Meiji Gakuin University, Tokyo, Japan
1
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
2
Yu TAMURA
(Nagoya University)
Mitsuhiro MORITA
(Hiroshima University)
Yoshito NISHIMURA
(Nagoya University)
3
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
4
• Morphology
• Inflectional morphology
• -ed, -ing, 3rd-person -s, plural -s, -er
• Derivational morphology
• prefix
• pre- (e.g., precondition), dis- (e.g.,
disagree)
• suffix
• -able (e.g., wearable), -ish (e.g., boyish)
Introduction
5
Morphological Processing
• Morphology
• Inflectional morphology
• -ed, -ing, 3rd-person -s, plural -s, -er
• Derivational morphology
• prefix
• pre- (e.g., precondition), dis- (e.g.,
disagree)
• suffix
• -able (e.g., wearable), -ish (e.g., boyish)
Introduction
6
Morphological Processing
• Morphology
• Inflectional morphology
• -ed, -ing, 3rd-person -s, plural -s, -er
• Derivational morphology
• prefix
• pre- (e.g., precondition), dis- (e.g.,
disagree)
• suffix
• -able (e.g., wearable), -ish (e.g., boyish)
Introduction
7
Morphological Processing
• Recognition process
• Visual word recognition
• How morphology is processed in reading
• Auditory word recognition
• How morphology is processed in listening
Introduction
8
Morphological Processing
• Recognition process
• Visual word recognition
• How morphology is processed in reading
• Auditory word recognition
• How morphology is processed in listening
Introduction
9
Morphological Processing
Findings of This Study
• No evidence of direct access to the inflected
(plural) forms -> Morphological decomposition
10
Introduction
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
11
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
12
• The more frequent, the faster
• Three positions of the morphological processing
mechanism
• Full-form storage model (e.g., Sereno &
Jongman, 1997)
• Obligatory decomposition (e.g., Taft, 2004)
• Dual-route model (e.g., Baayen, Dijkstra, &
Schreuder, 1997)
Background
13
Frequency Effects
• The more frequent, the faster processing
• Three positions of the morphological processing
mechanism
• Full-form storage model (e.g., Sereno &
Jongman, 1997)
• Obligatory decomposition (e.g., Taft, 2004)
• Dual-route model (e.g., Baayen, Dijkstra, &
Schreuder, 1997)
Background
14
Frequency Effects
• Full-form storage model (e.g., Sereno &
Jongman, 1997)
• Base forms and inflected forms
• stored separately
• show frequency effects
Background
15
Frequency Effects
rule rules
rule rules
• The more frequent, the faster processing
• Three positions of the morphological processing
mechanism
• Full-form storage model (e.g.,Sereno &
Jongman, 1997)
• Obligatory decomposition (e.g., Taft, 2004)
• Dual-route model (e.g., Baayen, Dijkstra, &
Schreuder, 1997)
Background
16
Frequency Effects
• Obligatory decomposition (e.g., Taft, 2004)
• Inflected forms
• are always decomposed
• do not show frequency effects
Background
17
Frequency Effects
rule rules
rule rules
• The more frequent, the faster processing
• Three positions of the morphological processing
mechanism
• Full-form storage model (e.g., Sereno &
Jongman, 1997)
• Obligatory decomposition (e.g., Taft, 2004)
• Dual-route model (e.g., Baayen, Dijkstra, &
Schreuder, 1997)
Background
18
Frequency Effects
• Dual-route model (e.g., Baayen, Dijkstra, &
Schreuder, 1997)
• Frequently occurred inflected forms
• are processed as a whole
• show frequency effects
Background
19
Frequency Effects
kid kids
kid kids
rule rules
rule rules
High frequent inflected formsLow frequent inflected forms
faster
• Frequency difference between base forms and
inflected forms
• Singular-dominant nouns
• Singular (base) forms > plural (inflected) forms
• e.g., ball, box
• Plural-dominant nouns
• Plural (inflected) forms > singular forms (base)
• e.g., kids, tears
Background
20
Frequency Dominance
• Baayen et al. (1997)
• Dutch
• No Reaction Time (RT) difference between
• Plural dominant plurals and plural dominant
singulars
• Highly frequent inflected forms would not be
decomposed but processed as a whole
• Support dual-route model
• New et al. (2004)
• French and English
• Support Baayen et al. (1997)
Background
21
Frequency Dominance
• Morita (2007)
• Investigated whether the frequency of the
inflected words would affect the processing of the
base forms
• Cumulative frequency (sg + pl) predicts the
lexical decision time for native speakers of
English
• -> dual-route or decomposition
• Surface frequency (sg only) predicts the lexical
decision time for Japanese L2 learners of English
• -> full-form strage?
Background
22
Frequency Dominance
• How do L2 learners of English process and represent
regularly inflected words?
• Hypothesis
• If…
• frequent inflected forms < infrequent base forms
-> highly frequent inflected forms are processed as
a whole
• frequent inflected forms > infrequent base forms
-> inflected words are decomposed
• frequent inflected forms > infrequent inflected forms
-> frequency of the base forms matter
Background
23
Research Questions
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
24
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
25
• 72 Japanese undergraduate students
Table 1. Descriptive statistics of the TOEIC score
The Present Study
26
Participants
N M SD Min Max
TOEIC
score
72 575.42 104.19 325 800
1. Frequency list of nouns (both singular and plural
forms) from British National Corpus (BNC)
2. 18 words which double or triple in frequency of
singular form compared to plural form -> singular-
dominant words
The Present Study
27
Stimuli
3. 18 words which double or triple in frequency of
plural form compared to singular form -> plural
dominant words
4. 18 words whose frequency of singular and
plural form was almost same. -> control words
The Present Study
28
Stimuli
• The cumulative frequency (sg + pl) was
controlled among the three groups
Table 2. Mean Frequency and SD in Parentheses
The Present Study
29
Stimuli
k singular plural base
sg-domminant 18
69.865
(25.849)
21.684
(10.931)
91.549
(34.342)
pl-dominant 18
22.571
(18.661)
69.898
(43.345)
92.469
(59.779)
control 18
47.064
(23.202)
43.893
(24.664)
90.958
(46.185)
Note. frequency is based on per million
The Present Study
30
Stimuli
Table 3. List of Test Items
singular-dominant plural-dominant control
concept image parent proceeding topic element
film ball pound kid rabbit trend
science target standard tear bone secret
jacket video pupil resident store lesson
box hat individual finding principle firm
colour map detail critic horse step
bar context relation boot rule drug
network station resource participant function sport
college tower skill chemical plant document
• Judge whether the target words were real
English words or not
• 54 test items (18*3) presented either in
singular or plural form
• Carefully counterbalanced
• The same number of filler items were included
The Present Study
31
Lexical Decision Task
• Incorrect responses removed (6.6%)
• Outliers (M+3SD and RT below 200ms) removed (1.4%)
• Generalized linear mixed-effect model (GLMM)
• Response variable
• Raw RT
• Explanatory variable
• Presentation (2 levels)
• singular or plural
• Frequency dominance (3 levels)
• sg-dominant, pl-dominant, control
• Post-hoc multiple comparison
The Present Study
32
Analysis
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
33
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
34
35
Reaction Time
Results
k M SD
95%CI
LL UL
sg-domminant
pl 9 838 246 818 858
sg 9 765 232 747 783
pl-dominant
pl 9 922 324 896 949
sg 9 857 288 834 880
control
pl 9 824 280 802 846
sg 9 719 212 702 735
Table 4.
Descriptive Statistics of Reaction Time (ms)
Note. N = 72. CI= Confidence Interval; LL = lower limit; UL = upper limit
Results
36
Note. Error bar represents 95%CI
Results
37
Note. Error bar represents 95%CI
Significant differences
Results
38
Note. Error bar represents 95%CI
Results
39
Note. Error bar represents 95%CI
Significant differences
No significant differences
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
40
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
41
• Singular forms judged faster than plural forms
irrespective of the frequency dominance
• Singular forms
• sg-dominant = control < pl-dominant
• Plural forms
• sg-dominant = control < pl-dominant
Discussion
42
Summary of the Results
• Singular forms judged faster than plural forms
irrespective of the frequency dominance
• Singular forms
• sg-dominant = control < pl-dominant
• Plural forms
• sg-dominant = control < pl-dominant
Discussion
43
Summary of the Results
• Singular forms judged faster than plural forms
irrespective of the frequency dominance
• Pl-dominant plurals did not show frequency
advantage
• L2 learners always decompose plural
inflections
Discussion
44
Morphological Processing
• Singular forms judged faster than plural forms
irrespective of the frequency dominance
• Singular forms
• sg-dominant = control < pl-dominant
• Plural forms
• sg-dominant = control < pl-dominant
Discussion
45
Summary of the Results
• Singular forms
• sg-dominant = control < pl-dominant
• Surface frequency advantage was only found
between sg-dominant and pl-dominant
• No clear evidence of the surface frequency effect
• Frequency of the inflected forms had no effect on
the RT for the base forms
Discussion
46
Morphological Processing
• Singular forms judged faster than plural forms
irrespective of the frequency dominance
• Singular forms
• sg-dominant = control < pl-dominant
• Plural forms
• sg-dominant = control < pl-dominant
Discussion
47
Summary of the Results
• Plural forms
• sg-dominant = control < pl-dominant
• No frequency advantage for pl-dominant plurals
• No evidence of direct access to the plural forms
• High frequency inflected words were decomposed
• Access latency for inflected forms might be
affected by base form frequency
Discussion
48
Morphological Processing
• The experiment only focused on the surface
frequency (cumulative frequency was controlled)
• The results were entirely on the basis of lexical
decision task
-> priming task etc. might be needed
Discussion
49
Limitations
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
50
Overview
• Introduction
• Background
• The Present Study
• Results
• Discussion
• Conclusion
51
• How do L2 learners of English process and
represent regularly inflected words?
• They decompose the inflected words
irrespective of frequency dominance
-> Obligatory decomposition?
• No RT difference between control words and
sg-dominant words
• There still remains the possibility that L2
learners access abstract lexical entries which
include both singular and plural forms
Conclusion
52
Word Frequency Dominance and L2
Word Recognition
contact info
Yu Tamura
Nagoya University
yutamura@nagoya-u.jp
http://www.tamurayu.wordpress.com/
53
• Base form frequency seems to
matter
• Inflected words always
decomposed
• L2 learners access abstract
lexical entries (sg + pl forms)
Baayen, R. H., Lieber, R., & Schreuder, R. (1997). The morphological complexity
of simplex nouns. Linguistics, 35, 861–877. doi:10.1515/ling.1997.35.5.861
Morita, M. (2007) nihonjin eigo gakusyusya no meishi tansuukei ninshiki niokeru
hinndo kouka: hyousou hindo to ruiseki hindo. [Frequency effects on
recognition of singular nouns by Japanese learners of English: Surface
frequency and cumulative frequency]. Bulletin of the Graduate School of
Social & Cultural Systems at Yamagata University, 4, 9–19.
New, B., Brysbaert, M., Segui, J., Ferrand, L., & Rastle, K. (2004). The
processing of singular and plural nouns in French and English. Journal of
Memory and Language, 51, 568–585.
Sereno, J. A., & Jongman, A. (1997). Processing of English inflectional
morphology. Memory & Cognition, 25, 425–437. doi:10.3758/BF03201119
Taft, M. (2004). Morphological decomposition and the reverse base frequency
effect. The Quarterly Journal of Experimental Psychology. A, Human
Experimental Psychology, 57, 745–765.
References
54
55
GLMM
Results
Note. Number of observation = 3581. N = 72; K = 54.
Dominance: 1 = control, 2 = pl-dominant, 3 = sg-dominant
Random effects
Fixed effects By Subject By Items
Parameters Estima
te
SE t p SD SD
Intercept 925.32 23.12 40.03 <.001 67.18 52.15
Dominance2-1,3 85.87 23.60 3.64 <.001 — —
Dominance3-1,2 -27.10 20.92 -1.29 .195 — —
Presentation1-2 -70.23 5.57 -12.62 <.001 — —
Dom2-1,3:Pres 8.39 14.30 0.59 .557
Dom3-1,2:Pres -23.317 12.06 -1.93 .053 — —
56
Post-hoc Multiple Comparison
Results
Dominance Estimate SE z p
control 65.26 9.16 7.12 <.0001
pl-dominant 56.87 10.85 5.24 <.0001
sg-dominant 88.57 8.52 10.39 <.0001
Simple main-effect of presentation (pl vs sg)
57
Post-hoc Multiple Comparison
Results
Presentation comparison Estimate SE z p
plural
ctrl - pl -81.68 24.56 -3.33 .003
ctrl - sg 15.44 21.65 0.71 .756
pl - sg 97.12 30.64 3.17 .004
singular
ctrl - pl -90.06 24.76 -3.64 <.001
ctrl - sg 38.76 21.90 1.77 .179
pl - sg 88.57 8.52 10.39 <.001
Simple main-effect of frequency dominance

More Related Content

What's hot

Word2Vec: Learning of word representations in a vector space - Di Mitri & Her...
Word2Vec: Learning of word representations in a vector space - Di Mitri & Her...Word2Vec: Learning of word representations in a vector space - Di Mitri & Her...
Word2Vec: Learning of word representations in a vector space - Di Mitri & Her...Daniele Di Mitri
 
pptphrase-tagset-mapping-for-french-and-english-treebanks-and-its-application...
pptphrase-tagset-mapping-for-french-and-english-treebanks-and-its-application...pptphrase-tagset-mapping-for-french-and-english-treebanks-and-its-application...
pptphrase-tagset-mapping-for-french-and-english-treebanks-and-its-application...Lifeng (Aaron) Han
 
Integrating Incoming Information into Discourse Model in Tunisian Arabic
Integrating Incoming Information into Discourse Model in Tunisian ArabicIntegrating Incoming Information into Discourse Model in Tunisian Arabic
Integrating Incoming Information into Discourse Model in Tunisian ArabicDr. Marwa Mekni-Toujani
 
Bilingual terminology mining
Bilingual terminology miningBilingual terminology mining
Bilingual terminology miningEstelle Delpech
 
Natural Language Processing: Parsing
Natural Language Processing: ParsingNatural Language Processing: Parsing
Natural Language Processing: ParsingRushdi Shams
 
AsiaCALL 2017 presentation
AsiaCALL 2017 presentationAsiaCALL 2017 presentation
AsiaCALL 2017 presentationTakeshi Sato
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language ProcessingToine Bogers
 
Usage of regular expressions in nlp
Usage of regular expressions in nlpUsage of regular expressions in nlp
Usage of regular expressions in nlpeSAT Journals
 
Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Saurabh Kaushik
 
Descriptive Strategies Research: Survey Analysis
Descriptive Strategies Research: Survey AnalysisDescriptive Strategies Research: Survey Analysis
Descriptive Strategies Research: Survey AnalysisGwendolyn Yong
 
Word Embeddings, why the hype ?
Word Embeddings, why the hype ? Word Embeddings, why the hype ?
Word Embeddings, why the hype ? Hady Elsahar
 
Identification of Translationese: A Machine Learning Approach
Identification of Translationese: A Machine Learning ApproachIdentification of Translationese: A Machine Learning Approach
Identification of Translationese: A Machine Learning Approachiustinailisei
 
Tamura & Kusanagi (2014) CELES
Tamura & Kusanagi (2014) CELESTamura & Kusanagi (2014) CELES
Tamura & Kusanagi (2014) CELESYu Tamura
 
Representation Learning of Vectors of Words and Phrases
Representation Learning of Vectors of Words and PhrasesRepresentation Learning of Vectors of Words and Phrases
Representation Learning of Vectors of Words and PhrasesFelipe Moraes
 

What's hot (18)

GIGI 1229
GIGI 1229 GIGI 1229
GIGI 1229
 
L3 v2
L3 v2L3 v2
L3 v2
 
Word2Vec: Learning of word representations in a vector space - Di Mitri & Her...
Word2Vec: Learning of word representations in a vector space - Di Mitri & Her...Word2Vec: Learning of word representations in a vector space - Di Mitri & Her...
Word2Vec: Learning of word representations in a vector space - Di Mitri & Her...
 
pptphrase-tagset-mapping-for-french-and-english-treebanks-and-its-application...
pptphrase-tagset-mapping-for-french-and-english-treebanks-and-its-application...pptphrase-tagset-mapping-for-french-and-english-treebanks-and-its-application...
pptphrase-tagset-mapping-for-french-and-english-treebanks-and-its-application...
 
Integrating Incoming Information into Discourse Model in Tunisian Arabic
Integrating Incoming Information into Discourse Model in Tunisian ArabicIntegrating Incoming Information into Discourse Model in Tunisian Arabic
Integrating Incoming Information into Discourse Model in Tunisian Arabic
 
Bilingual terminology mining
Bilingual terminology miningBilingual terminology mining
Bilingual terminology mining
 
Natural Language Processing: Parsing
Natural Language Processing: ParsingNatural Language Processing: Parsing
Natural Language Processing: Parsing
 
AsiaCALL 2017 presentation
AsiaCALL 2017 presentationAsiaCALL 2017 presentation
AsiaCALL 2017 presentation
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language Processing
 
Usage of regular expressions in nlp
Usage of regular expressions in nlpUsage of regular expressions in nlp
Usage of regular expressions in nlp
 
Usage of regular expressions in nlp
Usage of regular expressions in nlpUsage of regular expressions in nlp
Usage of regular expressions in nlp
 
Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1
 
Descriptive Strategies Research: Survey Analysis
Descriptive Strategies Research: Survey AnalysisDescriptive Strategies Research: Survey Analysis
Descriptive Strategies Research: Survey Analysis
 
Word Embeddings, why the hype ?
Word Embeddings, why the hype ? Word Embeddings, why the hype ?
Word Embeddings, why the hype ?
 
Identification of Translationese: A Machine Learning Approach
Identification of Translationese: A Machine Learning ApproachIdentification of Translationese: A Machine Learning Approach
Identification of Translationese: A Machine Learning Approach
 
Tamura & Kusanagi (2014) CELES
Tamura & Kusanagi (2014) CELESTamura & Kusanagi (2014) CELES
Tamura & Kusanagi (2014) CELES
 
Networks and Natural Language Processing
Networks and Natural Language ProcessingNetworks and Natural Language Processing
Networks and Natural Language Processing
 
Representation Learning of Vectors of Words and Phrases
Representation Learning of Vectors of Words and PhrasesRepresentation Learning of Vectors of Words and Phrases
Representation Learning of Vectors of Words and Phrases
 

Viewers also liked

一般化線形混合モデル入門の入門
一般化線形混合モデル入門の入門一般化線形混合モデル入門の入門
一般化線形混合モデル入門の入門Yu Tamura
 
コンソールベタ打ち卒業:スクリプトエディタを使おう
コンソールベタ打ち卒業:スクリプトエディタを使おうコンソールベタ打ち卒業:スクリプトエディタを使おう
コンソールベタ打ち卒業:スクリプトエディタを使おうYu Tamura
 
理解型インプットタスクを用いた授業実践
理解型インプットタスクを用いた授業実践理解型インプットタスクを用いた授業実践
理解型インプットタスクを用いた授業実践Yu Tamura
 
文法性判断課題における反応時間と主観的測度は正答率を予測するか:文法項目の違いに焦点をあてて
文法性判断課題における反応時間と主観的測度は正答率を予測するか:文法項目の違いに焦点をあてて文法性判断課題における反応時間と主観的測度は正答率を予測するか:文法項目の違いに焦点をあてて
文法性判断課題における反応時間と主観的測度は正答率を予測するか:文法項目の違いに焦点をあててYu Tamura
 
rlistパッケージのススメ
rlistパッケージのススメrlistパッケージのススメ
rlistパッケージのススメYu Tamura
 
effectsパッケージを用いた一般化線形モデルの可視化
effectsパッケージを用いた一般化線形モデルの可視化effectsパッケージを用いた一般化線形モデルの可視化
effectsパッケージを用いた一般化線形モデルの可視化Yu Tamura
 
学振特別研究員になるために~知っておくべき10のTips~
学振特別研究員になるために~知っておくべき10のTips~学振特別研究員になるために~知っておくべき10のTips~
学振特別研究員になるために~知っておくべき10のTips~Masahito Ohue
 
Research review presentation_revised
Research review presentation_revisedResearch review presentation_revised
Research review presentation_revisedYu Tamura
 
Probabilistic algorithms for fun and pseudorandom profit
Probabilistic algorithms for fun and pseudorandom profitProbabilistic algorithms for fun and pseudorandom profit
Probabilistic algorithms for fun and pseudorandom profitTyler Treat
 
重回帰分析で交互作用効果
重回帰分析で交互作用効果重回帰分析で交互作用効果
重回帰分析で交互作用効果Makoto Hirakawa
 
Methodological options in grammar teaching materials
Methodological options in grammar teaching materialsMethodological options in grammar teaching materials
Methodological options in grammar teaching materialsYu Tamura
 
(実験心理学徒だけど)一般化線形混合モデルを使ってみた
(実験心理学徒だけど)一般化線形混合モデルを使ってみた(実験心理学徒だけど)一般化線形混合モデルを使ってみた
(実験心理学徒だけど)一般化線形混合モデルを使ってみたTakashi Yamane
 
混合モデルを使って反復測定分散分析をする
混合モデルを使って反復測定分散分析をする混合モデルを使って反復測定分散分析をする
混合モデルを使って反復測定分散分析をするMasaru Tokuoka
 
学振特別研究員になるために~知っておくべき10のTips~[平成28年度申請版]
学振特別研究員になるために~知っておくべき10のTips~[平成28年度申請版]学振特別研究員になるために~知っておくべき10のTips~[平成28年度申請版]
学振特別研究員になるために~知っておくべき10のTips~[平成28年度申請版]Masahito Ohue
 
エクセルで統計分析 統計プログラムHADについて
エクセルで統計分析 統計プログラムHADについてエクセルで統計分析 統計プログラムHADについて
エクセルで統計分析 統計プログラムHADについてHiroshi Shimizu
 
LinkedIn SlideShare: Knowledge, Well-Presented
LinkedIn SlideShare: Knowledge, Well-PresentedLinkedIn SlideShare: Knowledge, Well-Presented
LinkedIn SlideShare: Knowledge, Well-PresentedSlideShare
 

Viewers also liked (18)

一般化線形混合モデル入門の入門
一般化線形混合モデル入門の入門一般化線形混合モデル入門の入門
一般化線形混合モデル入門の入門
 
コンソールベタ打ち卒業:スクリプトエディタを使おう
コンソールベタ打ち卒業:スクリプトエディタを使おうコンソールベタ打ち卒業:スクリプトエディタを使おう
コンソールベタ打ち卒業:スクリプトエディタを使おう
 
理解型インプットタスクを用いた授業実践
理解型インプットタスクを用いた授業実践理解型インプットタスクを用いた授業実践
理解型インプットタスクを用いた授業実践
 
文法性判断課題における反応時間と主観的測度は正答率を予測するか:文法項目の違いに焦点をあてて
文法性判断課題における反応時間と主観的測度は正答率を予測するか:文法項目の違いに焦点をあてて文法性判断課題における反応時間と主観的測度は正答率を予測するか:文法項目の違いに焦点をあてて
文法性判断課題における反応時間と主観的測度は正答率を予測するか:文法項目の違いに焦点をあてて
 
rlistパッケージのススメ
rlistパッケージのススメrlistパッケージのススメ
rlistパッケージのススメ
 
effectsパッケージを用いた一般化線形モデルの可視化
effectsパッケージを用いた一般化線形モデルの可視化effectsパッケージを用いた一般化線形モデルの可視化
effectsパッケージを用いた一般化線形モデルの可視化
 
学振特別研究員になるために~知っておくべき10のTips~
学振特別研究員になるために~知っておくべき10のTips~学振特別研究員になるために~知っておくべき10のTips~
学振特別研究員になるために~知っておくべき10のTips~
 
Research review presentation_revised
Research review presentation_revisedResearch review presentation_revised
Research review presentation_revised
 
Gerunds
GerundsGerunds
Gerunds
 
Probabilistic algorithms for fun and pseudorandom profit
Probabilistic algorithms for fun and pseudorandom profitProbabilistic algorithms for fun and pseudorandom profit
Probabilistic algorithms for fun and pseudorandom profit
 
重回帰分析で交互作用効果
重回帰分析で交互作用効果重回帰分析で交互作用効果
重回帰分析で交互作用効果
 
Methodological options in grammar teaching materials
Methodological options in grammar teaching materialsMethodological options in grammar teaching materials
Methodological options in grammar teaching materials
 
(実験心理学徒だけど)一般化線形混合モデルを使ってみた
(実験心理学徒だけど)一般化線形混合モデルを使ってみた(実験心理学徒だけど)一般化線形混合モデルを使ってみた
(実験心理学徒だけど)一般化線形混合モデルを使ってみた
 
混合モデルを使って反復測定分散分析をする
混合モデルを使って反復測定分散分析をする混合モデルを使って反復測定分散分析をする
混合モデルを使って反復測定分散分析をする
 
学振特別研究員になるために~知っておくべき10のTips~[平成28年度申請版]
学振特別研究員になるために~知っておくべき10のTips~[平成28年度申請版]学振特別研究員になるために~知っておくべき10のTips~[平成28年度申請版]
学振特別研究員になるために~知っておくべき10のTips~[平成28年度申請版]
 
エクセルで統計分析 統計プログラムHADについて
エクセルで統計分析 統計プログラムHADについてエクセルで統計分析 統計プログラムHADについて
エクセルで統計分析 統計プログラムHADについて
 
reveal.js 3.0.0
reveal.js 3.0.0reveal.js 3.0.0
reveal.js 3.0.0
 
LinkedIn SlideShare: Knowledge, Well-Presented
LinkedIn SlideShare: Knowledge, Well-PresentedLinkedIn SlideShare: Knowledge, Well-Presented
LinkedIn SlideShare: Knowledge, Well-Presented
 

Similar to Word Frequency Dominance and L2 Word Recognition

Variations in citation practices across the scientific landscape: Analysis ba...
Variations in citation practices across the scientific landscape: Analysis ba...Variations in citation practices across the scientific landscape: Analysis ba...
Variations in citation practices across the scientific landscape: Analysis ba...Wout Lamers
 
Automatic Key Term Extraction from Spoken Course Lectures
Automatic Key Term Extraction from Spoken Course LecturesAutomatic Key Term Extraction from Spoken Course Lectures
Automatic Key Term Extraction from Spoken Course LecturesYun-Nung (Vivian) Chen
 
Learning consonant harmony in artificial languages
Learning consonant harmony in artificial languagesLearning consonant harmony in artificial languages
Learning consonant harmony in artificial languagesKevin McMullin
 
Ontology Design Patterns for Linked Data Tutorial at ISWC2016 - Introduction
Ontology Design Patterns for Linked Data Tutorial at ISWC2016 - IntroductionOntology Design Patterns for Linked Data Tutorial at ISWC2016 - Introduction
Ontology Design Patterns for Linked Data Tutorial at ISWC2016 - IntroductionAldo Gangemi
 
Testing for heterogeneity in rates of morphological evolution: discrete chara...
Testing for heterogeneity in rates of morphological evolution: discrete chara...Testing for heterogeneity in rates of morphological evolution: discrete chara...
Testing for heterogeneity in rates of morphological evolution: discrete chara...Graeme Lloyd
 
Lecture outline.09 02.key
Lecture outline.09 02.keyLecture outline.09 02.key
Lecture outline.09 02.keySteve Stein
 
Lecture outline.09 02.key
Lecture outline.09 02.keyLecture outline.09 02.key
Lecture outline.09 02.keySteve Stein
 
PPT-CCL: A Universal Phrase Tagset for Multilingual Treebanks
PPT-CCL: A Universal Phrase Tagset for Multilingual TreebanksPPT-CCL: A Universal Phrase Tagset for Multilingual Treebanks
PPT-CCL: A Universal Phrase Tagset for Multilingual TreebanksLifeng (Aaron) Han
 
Automatic speech recognition
Automatic speech recognitionAutomatic speech recognition
Automatic speech recognitionArif A.
 
Susan epstein at ibm csig speaker series
Susan epstein at ibm csig speaker seriesSusan epstein at ibm csig speaker series
Susan epstein at ibm csig speaker seriesdiannepatricia
 
Artificial Intelligence Notes Unit 4
Artificial Intelligence Notes Unit 4Artificial Intelligence Notes Unit 4
Artificial Intelligence Notes Unit 4DigiGurukul
 
An outline of Quantitative Research Methods
An outline of Quantitative Research MethodsAn outline of Quantitative Research Methods
An outline of Quantitative Research MethodsChristine Davies
 
Phylogenetic analysis
Phylogenetic analysis Phylogenetic analysis
Phylogenetic analysis Nitin Naik
 
Development and validation of a vocabulary size test of multiword expressions
Development and validation of a vocabulary size test of multiword expressionsDevelopment and validation of a vocabulary size test of multiword expressions
Development and validation of a vocabulary size test of multiword expressionsRon Martinez
 
Genetic algorithm raktim
Genetic algorithm raktimGenetic algorithm raktim
Genetic algorithm raktimRaktim Halder
 
Genetic algorithm_raktim_IITKGP
Genetic algorithm_raktim_IITKGP Genetic algorithm_raktim_IITKGP
Genetic algorithm_raktim_IITKGP Raktim Halder
 
Investigating Teachers' Perceptions of Fluency
Investigating Teachers' Perceptions of FluencyInvestigating Teachers' Perceptions of Fluency
Investigating Teachers' Perceptions of FluencyEllen Head
 
Computational accounts of human learning bias
Computational accounts of human learning biasComputational accounts of human learning bias
Computational accounts of human learning biasKevin McMullin
 

Similar to Word Frequency Dominance and L2 Word Recognition (20)

Variations in citation practices across the scientific landscape: Analysis ba...
Variations in citation practices across the scientific landscape: Analysis ba...Variations in citation practices across the scientific landscape: Analysis ba...
Variations in citation practices across the scientific landscape: Analysis ba...
 
Automatic Key Term Extraction from Spoken Course Lectures
Automatic Key Term Extraction from Spoken Course LecturesAutomatic Key Term Extraction from Spoken Course Lectures
Automatic Key Term Extraction from Spoken Course Lectures
 
Learning consonant harmony in artificial languages
Learning consonant harmony in artificial languagesLearning consonant harmony in artificial languages
Learning consonant harmony in artificial languages
 
Ontology Design Patterns for Linked Data Tutorial at ISWC2016 - Introduction
Ontology Design Patterns for Linked Data Tutorial at ISWC2016 - IntroductionOntology Design Patterns for Linked Data Tutorial at ISWC2016 - Introduction
Ontology Design Patterns for Linked Data Tutorial at ISWC2016 - Introduction
 
Testing for heterogeneity in rates of morphological evolution: discrete chara...
Testing for heterogeneity in rates of morphological evolution: discrete chara...Testing for heterogeneity in rates of morphological evolution: discrete chara...
Testing for heterogeneity in rates of morphological evolution: discrete chara...
 
Lecture outline.09 02.key
Lecture outline.09 02.keyLecture outline.09 02.key
Lecture outline.09 02.key
 
Lecture outline.09 02.key
Lecture outline.09 02.keyLecture outline.09 02.key
Lecture outline.09 02.key
 
PPT-CCL: A Universal Phrase Tagset for Multilingual Treebanks
PPT-CCL: A Universal Phrase Tagset for Multilingual TreebanksPPT-CCL: A Universal Phrase Tagset for Multilingual Treebanks
PPT-CCL: A Universal Phrase Tagset for Multilingual Treebanks
 
Automatic speech recognition
Automatic speech recognitionAutomatic speech recognition
Automatic speech recognition
 
Susan epstein at ibm csig speaker series
Susan epstein at ibm csig speaker seriesSusan epstein at ibm csig speaker series
Susan epstein at ibm csig speaker series
 
Artificial Intelligence Notes Unit 4
Artificial Intelligence Notes Unit 4Artificial Intelligence Notes Unit 4
Artificial Intelligence Notes Unit 4
 
An outline of Quantitative Research Methods
An outline of Quantitative Research MethodsAn outline of Quantitative Research Methods
An outline of Quantitative Research Methods
 
APA style
APA styleAPA style
APA style
 
Phylogenetic analysis
Phylogenetic analysis Phylogenetic analysis
Phylogenetic analysis
 
Development and validation of a vocabulary size test of multiword expressions
Development and validation of a vocabulary size test of multiword expressionsDevelopment and validation of a vocabulary size test of multiword expressions
Development and validation of a vocabulary size test of multiword expressions
 
Genetic algorithm raktim
Genetic algorithm raktimGenetic algorithm raktim
Genetic algorithm raktim
 
Genetic algorithm_raktim_IITKGP
Genetic algorithm_raktim_IITKGP Genetic algorithm_raktim_IITKGP
Genetic algorithm_raktim_IITKGP
 
Investigating Teachers' Perceptions of Fluency
Investigating Teachers' Perceptions of FluencyInvestigating Teachers' Perceptions of Fluency
Investigating Teachers' Perceptions of Fluency
 
Computational accounts of human learning bias
Computational accounts of human learning biasComputational accounts of human learning bias
Computational accounts of human learning bias
 
Automatic speech recognition
Automatic speech recognitionAutomatic speech recognition
Automatic speech recognition
 

Recently uploaded

UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024UKCGE
 
How to Add a many2many Relational Field in Odoo 17
How to Add a many2many Relational Field in Odoo 17How to Add a many2many Relational Field in Odoo 17
How to Add a many2many Relational Field in Odoo 17Celine George
 
Benefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationBenefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationMJDuyan
 
CAULIFLOWER BREEDING 1 Parmar pptx
CAULIFLOWER BREEDING 1 Parmar pptxCAULIFLOWER BREEDING 1 Parmar pptx
CAULIFLOWER BREEDING 1 Parmar pptxSaurabhParmar42
 
M-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxM-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxDr. Santhosh Kumar. N
 
Human-AI Co-Creation of Worked Examples for Programming Classes
Human-AI Co-Creation of Worked Examples for Programming ClassesHuman-AI Co-Creation of Worked Examples for Programming Classes
Human-AI Co-Creation of Worked Examples for Programming ClassesMohammad Hassany
 
AUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptxAUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptxiammrhaywood
 
CapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptxCapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptxCapitolTechU
 
How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17Celine George
 
What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?TechSoup
 
How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17Celine George
 
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptx
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptxPISA-VET launch_El Iza Mohamedou_19 March 2024.pptx
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptxEduSkills OECD
 
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRADUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRATanmoy Mishra
 
General views of Histopathology and step
General views of Histopathology and stepGeneral views of Histopathology and step
General views of Histopathology and stepobaje godwin sunday
 
In - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxIn - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxAditiChauhan701637
 
How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17Celine George
 
How to Manage Cross-Selling in Odoo 17 Sales
How to Manage Cross-Selling in Odoo 17 SalesHow to Manage Cross-Selling in Odoo 17 Sales
How to Manage Cross-Selling in Odoo 17 SalesCeline George
 
How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17Celine George
 
HED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfHED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfMohonDas
 

Recently uploaded (20)

UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024
 
How to Add a many2many Relational Field in Odoo 17
How to Add a many2many Relational Field in Odoo 17How to Add a many2many Relational Field in Odoo 17
How to Add a many2many Relational Field in Odoo 17
 
Benefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationBenefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive Education
 
CAULIFLOWER BREEDING 1 Parmar pptx
CAULIFLOWER BREEDING 1 Parmar pptxCAULIFLOWER BREEDING 1 Parmar pptx
CAULIFLOWER BREEDING 1 Parmar pptx
 
M-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxM-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptx
 
Human-AI Co-Creation of Worked Examples for Programming Classes
Human-AI Co-Creation of Worked Examples for Programming ClassesHuman-AI Co-Creation of Worked Examples for Programming Classes
Human-AI Co-Creation of Worked Examples for Programming Classes
 
AUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptxAUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptx
 
CapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptxCapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptx
 
How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17
 
What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?
 
How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17
 
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptx
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptxPISA-VET launch_El Iza Mohamedou_19 March 2024.pptx
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptx
 
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRADUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
 
General views of Histopathology and step
General views of Histopathology and stepGeneral views of Histopathology and step
General views of Histopathology and step
 
In - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxIn - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptx
 
How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17
 
How to Manage Cross-Selling in Odoo 17 Sales
How to Manage Cross-Selling in Odoo 17 SalesHow to Manage Cross-Selling in Odoo 17 Sales
How to Manage Cross-Selling in Odoo 17 Sales
 
How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17
 
Personal Resilience in Project Management 2 - TV Edit 1a.pdf
Personal Resilience in Project Management 2 - TV Edit 1a.pdfPersonal Resilience in Project Management 2 - TV Edit 1a.pdf
Personal Resilience in Project Management 2 - TV Edit 1a.pdf
 
HED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfHED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdf
 

Word Frequency Dominance and L2 Word Recognition

  • 1. Word Frequency Dominance and L2 Word Recognition September 12, 2016 Vocab@Tokyo Meiji Gakuin University, Tokyo, Japan 1
  • 2. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 2
  • 3. Yu TAMURA (Nagoya University) Mitsuhiro MORITA (Hiroshima University) Yoshito NISHIMURA (Nagoya University) 3
  • 4. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 4
  • 5. • Morphology • Inflectional morphology • -ed, -ing, 3rd-person -s, plural -s, -er • Derivational morphology • prefix • pre- (e.g., precondition), dis- (e.g., disagree) • suffix • -able (e.g., wearable), -ish (e.g., boyish) Introduction 5 Morphological Processing
  • 6. • Morphology • Inflectional morphology • -ed, -ing, 3rd-person -s, plural -s, -er • Derivational morphology • prefix • pre- (e.g., precondition), dis- (e.g., disagree) • suffix • -able (e.g., wearable), -ish (e.g., boyish) Introduction 6 Morphological Processing
  • 7. • Morphology • Inflectional morphology • -ed, -ing, 3rd-person -s, plural -s, -er • Derivational morphology • prefix • pre- (e.g., precondition), dis- (e.g., disagree) • suffix • -able (e.g., wearable), -ish (e.g., boyish) Introduction 7 Morphological Processing
  • 8. • Recognition process • Visual word recognition • How morphology is processed in reading • Auditory word recognition • How morphology is processed in listening Introduction 8 Morphological Processing
  • 9. • Recognition process • Visual word recognition • How morphology is processed in reading • Auditory word recognition • How morphology is processed in listening Introduction 9 Morphological Processing
  • 10. Findings of This Study • No evidence of direct access to the inflected (plural) forms -> Morphological decomposition 10 Introduction
  • 11. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 11
  • 12. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 12
  • 13. • The more frequent, the faster • Three positions of the morphological processing mechanism • Full-form storage model (e.g., Sereno & Jongman, 1997) • Obligatory decomposition (e.g., Taft, 2004) • Dual-route model (e.g., Baayen, Dijkstra, & Schreuder, 1997) Background 13 Frequency Effects
  • 14. • The more frequent, the faster processing • Three positions of the morphological processing mechanism • Full-form storage model (e.g., Sereno & Jongman, 1997) • Obligatory decomposition (e.g., Taft, 2004) • Dual-route model (e.g., Baayen, Dijkstra, & Schreuder, 1997) Background 14 Frequency Effects
  • 15. • Full-form storage model (e.g., Sereno & Jongman, 1997) • Base forms and inflected forms • stored separately • show frequency effects Background 15 Frequency Effects rule rules rule rules
  • 16. • The more frequent, the faster processing • Three positions of the morphological processing mechanism • Full-form storage model (e.g.,Sereno & Jongman, 1997) • Obligatory decomposition (e.g., Taft, 2004) • Dual-route model (e.g., Baayen, Dijkstra, & Schreuder, 1997) Background 16 Frequency Effects
  • 17. • Obligatory decomposition (e.g., Taft, 2004) • Inflected forms • are always decomposed • do not show frequency effects Background 17 Frequency Effects rule rules rule rules
  • 18. • The more frequent, the faster processing • Three positions of the morphological processing mechanism • Full-form storage model (e.g., Sereno & Jongman, 1997) • Obligatory decomposition (e.g., Taft, 2004) • Dual-route model (e.g., Baayen, Dijkstra, & Schreuder, 1997) Background 18 Frequency Effects
  • 19. • Dual-route model (e.g., Baayen, Dijkstra, & Schreuder, 1997) • Frequently occurred inflected forms • are processed as a whole • show frequency effects Background 19 Frequency Effects kid kids kid kids rule rules rule rules High frequent inflected formsLow frequent inflected forms faster
  • 20. • Frequency difference between base forms and inflected forms • Singular-dominant nouns • Singular (base) forms > plural (inflected) forms • e.g., ball, box • Plural-dominant nouns • Plural (inflected) forms > singular forms (base) • e.g., kids, tears Background 20 Frequency Dominance
  • 21. • Baayen et al. (1997) • Dutch • No Reaction Time (RT) difference between • Plural dominant plurals and plural dominant singulars • Highly frequent inflected forms would not be decomposed but processed as a whole • Support dual-route model • New et al. (2004) • French and English • Support Baayen et al. (1997) Background 21 Frequency Dominance
  • 22. • Morita (2007) • Investigated whether the frequency of the inflected words would affect the processing of the base forms • Cumulative frequency (sg + pl) predicts the lexical decision time for native speakers of English • -> dual-route or decomposition • Surface frequency (sg only) predicts the lexical decision time for Japanese L2 learners of English • -> full-form strage? Background 22 Frequency Dominance
  • 23. • How do L2 learners of English process and represent regularly inflected words? • Hypothesis • If… • frequent inflected forms < infrequent base forms -> highly frequent inflected forms are processed as a whole • frequent inflected forms > infrequent base forms -> inflected words are decomposed • frequent inflected forms > infrequent inflected forms -> frequency of the base forms matter Background 23 Research Questions
  • 24. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 24
  • 25. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 25
  • 26. • 72 Japanese undergraduate students Table 1. Descriptive statistics of the TOEIC score The Present Study 26 Participants N M SD Min Max TOEIC score 72 575.42 104.19 325 800
  • 27. 1. Frequency list of nouns (both singular and plural forms) from British National Corpus (BNC) 2. 18 words which double or triple in frequency of singular form compared to plural form -> singular- dominant words The Present Study 27 Stimuli
  • 28. 3. 18 words which double or triple in frequency of plural form compared to singular form -> plural dominant words 4. 18 words whose frequency of singular and plural form was almost same. -> control words The Present Study 28 Stimuli
  • 29. • The cumulative frequency (sg + pl) was controlled among the three groups Table 2. Mean Frequency and SD in Parentheses The Present Study 29 Stimuli k singular plural base sg-domminant 18 69.865 (25.849) 21.684 (10.931) 91.549 (34.342) pl-dominant 18 22.571 (18.661) 69.898 (43.345) 92.469 (59.779) control 18 47.064 (23.202) 43.893 (24.664) 90.958 (46.185) Note. frequency is based on per million
  • 30. The Present Study 30 Stimuli Table 3. List of Test Items singular-dominant plural-dominant control concept image parent proceeding topic element film ball pound kid rabbit trend science target standard tear bone secret jacket video pupil resident store lesson box hat individual finding principle firm colour map detail critic horse step bar context relation boot rule drug network station resource participant function sport college tower skill chemical plant document
  • 31. • Judge whether the target words were real English words or not • 54 test items (18*3) presented either in singular or plural form • Carefully counterbalanced • The same number of filler items were included The Present Study 31 Lexical Decision Task
  • 32. • Incorrect responses removed (6.6%) • Outliers (M+3SD and RT below 200ms) removed (1.4%) • Generalized linear mixed-effect model (GLMM) • Response variable • Raw RT • Explanatory variable • Presentation (2 levels) • singular or plural • Frequency dominance (3 levels) • sg-dominant, pl-dominant, control • Post-hoc multiple comparison The Present Study 32 Analysis
  • 33. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 33
  • 34. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 34
  • 35. 35 Reaction Time Results k M SD 95%CI LL UL sg-domminant pl 9 838 246 818 858 sg 9 765 232 747 783 pl-dominant pl 9 922 324 896 949 sg 9 857 288 834 880 control pl 9 824 280 802 846 sg 9 719 212 702 735 Table 4. Descriptive Statistics of Reaction Time (ms) Note. N = 72. CI= Confidence Interval; LL = lower limit; UL = upper limit
  • 36. Results 36 Note. Error bar represents 95%CI
  • 37. Results 37 Note. Error bar represents 95%CI Significant differences
  • 38. Results 38 Note. Error bar represents 95%CI
  • 39. Results 39 Note. Error bar represents 95%CI Significant differences No significant differences
  • 40. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 40
  • 41. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 41
  • 42. • Singular forms judged faster than plural forms irrespective of the frequency dominance • Singular forms • sg-dominant = control < pl-dominant • Plural forms • sg-dominant = control < pl-dominant Discussion 42 Summary of the Results
  • 43. • Singular forms judged faster than plural forms irrespective of the frequency dominance • Singular forms • sg-dominant = control < pl-dominant • Plural forms • sg-dominant = control < pl-dominant Discussion 43 Summary of the Results
  • 44. • Singular forms judged faster than plural forms irrespective of the frequency dominance • Pl-dominant plurals did not show frequency advantage • L2 learners always decompose plural inflections Discussion 44 Morphological Processing
  • 45. • Singular forms judged faster than plural forms irrespective of the frequency dominance • Singular forms • sg-dominant = control < pl-dominant • Plural forms • sg-dominant = control < pl-dominant Discussion 45 Summary of the Results
  • 46. • Singular forms • sg-dominant = control < pl-dominant • Surface frequency advantage was only found between sg-dominant and pl-dominant • No clear evidence of the surface frequency effect • Frequency of the inflected forms had no effect on the RT for the base forms Discussion 46 Morphological Processing
  • 47. • Singular forms judged faster than plural forms irrespective of the frequency dominance • Singular forms • sg-dominant = control < pl-dominant • Plural forms • sg-dominant = control < pl-dominant Discussion 47 Summary of the Results
  • 48. • Plural forms • sg-dominant = control < pl-dominant • No frequency advantage for pl-dominant plurals • No evidence of direct access to the plural forms • High frequency inflected words were decomposed • Access latency for inflected forms might be affected by base form frequency Discussion 48 Morphological Processing
  • 49. • The experiment only focused on the surface frequency (cumulative frequency was controlled) • The results were entirely on the basis of lexical decision task -> priming task etc. might be needed Discussion 49 Limitations
  • 50. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 50
  • 51. Overview • Introduction • Background • The Present Study • Results • Discussion • Conclusion 51
  • 52. • How do L2 learners of English process and represent regularly inflected words? • They decompose the inflected words irrespective of frequency dominance -> Obligatory decomposition? • No RT difference between control words and sg-dominant words • There still remains the possibility that L2 learners access abstract lexical entries which include both singular and plural forms Conclusion 52
  • 53. Word Frequency Dominance and L2 Word Recognition contact info Yu Tamura Nagoya University yutamura@nagoya-u.jp http://www.tamurayu.wordpress.com/ 53 • Base form frequency seems to matter • Inflected words always decomposed • L2 learners access abstract lexical entries (sg + pl forms)
  • 54. Baayen, R. H., Lieber, R., & Schreuder, R. (1997). The morphological complexity of simplex nouns. Linguistics, 35, 861–877. doi:10.1515/ling.1997.35.5.861 Morita, M. (2007) nihonjin eigo gakusyusya no meishi tansuukei ninshiki niokeru hinndo kouka: hyousou hindo to ruiseki hindo. [Frequency effects on recognition of singular nouns by Japanese learners of English: Surface frequency and cumulative frequency]. Bulletin of the Graduate School of Social & Cultural Systems at Yamagata University, 4, 9–19. New, B., Brysbaert, M., Segui, J., Ferrand, L., & Rastle, K. (2004). The processing of singular and plural nouns in French and English. Journal of Memory and Language, 51, 568–585. Sereno, J. A., & Jongman, A. (1997). Processing of English inflectional morphology. Memory & Cognition, 25, 425–437. doi:10.3758/BF03201119 Taft, M. (2004). Morphological decomposition and the reverse base frequency effect. The Quarterly Journal of Experimental Psychology. A, Human Experimental Psychology, 57, 745–765. References 54
  • 55. 55 GLMM Results Note. Number of observation = 3581. N = 72; K = 54. Dominance: 1 = control, 2 = pl-dominant, 3 = sg-dominant Random effects Fixed effects By Subject By Items Parameters Estima te SE t p SD SD Intercept 925.32 23.12 40.03 <.001 67.18 52.15 Dominance2-1,3 85.87 23.60 3.64 <.001 — — Dominance3-1,2 -27.10 20.92 -1.29 .195 — — Presentation1-2 -70.23 5.57 -12.62 <.001 — — Dom2-1,3:Pres 8.39 14.30 0.59 .557 Dom3-1,2:Pres -23.317 12.06 -1.93 .053 — —
  • 56. 56 Post-hoc Multiple Comparison Results Dominance Estimate SE z p control 65.26 9.16 7.12 <.0001 pl-dominant 56.87 10.85 5.24 <.0001 sg-dominant 88.57 8.52 10.39 <.0001 Simple main-effect of presentation (pl vs sg)
  • 57. 57 Post-hoc Multiple Comparison Results Presentation comparison Estimate SE z p plural ctrl - pl -81.68 24.56 -3.33 .003 ctrl - sg 15.44 21.65 0.71 .756 pl - sg 97.12 30.64 3.17 .004 singular ctrl - pl -90.06 24.76 -3.64 <.001 ctrl - sg 38.76 21.90 1.77 .179 pl - sg 88.57 8.52 10.39 <.001 Simple main-effect of frequency dominance