Slide for an Invited Talk at QMC22

1. Quantifying Eye Movement and Text Linguistic
Dada for Second Language Research
NAHATAME Shingo – University of Tsukuba, Japan

2. 1
0. Introduction
Brief Bio
Shingo Nahatame, Ph.D
Assistant Professor of Education
at Faculty of Human Sciences, University of Tsukuba
Specialization
-Language Education
-Teaching and Learning English as a Foreign
Language (EFL)
Research topic
-Studying cognitive processes of EFL reading using a
psycholinguistic method
-Analyzing EFL reading materials using computational
techniques

3. 1
0. Introduction
Brief Bio
Nahatame (2022)
Nahatame (2018)
Nahatame (2021)

4. 1
0. Introduction
1. Quantifying Eye Movement Data
2. Quantifying Text Linguistic Dada
3. Combining Quantitative Eye Movement and Text
Linguistic Data for L2 Reading Research
(Research in progress)
Outline of Today’s Talk

5. 1
0. Introduction
1. Quantifying Eye Movement Data
2. Quantifying Text Linguistic Dada
3. Combining Quantitative Eye Movement and Text
Linguistic Data for L2 Reading Research
(Research in progress)
Outline of Today’s Talk

6. 1
Why Eye-Tracking?
Eye-tracking research has become the “gold standard” for studying
cognitive processes of reading (Rayner, 1998, 2009)
1. Quantifying Eye Movement Data
• Eye-mind hypothesis: Eye movements are closely related to cognitive
processing (Just & Carpenter, 1980)
Basic Characteristics of Eye Movements:
• Fixations
• Saccades
Figure 1. Fixations and saccades during reading
(adapted from Conklin et al., 2018, Figure 1.3)

7. 1
Basic Characteristics of Eye Movements:
1. Quantifying Eye Movement Data

8. 1
Basic Characteristics of Eye Movements:
1. Quantifying Eye Movement Data

9. 1
Basic Characteristics of Eye Movements:
1. Quantifying Eye Movement Data

10. 1
1. Quantifying Eye Movement Data
Quantitative Eye-Movement Measures:
 Measures of global reading behavior
• Number of fixations
• Total fixation time
• Average fixation time (200-250ms for skilled English readers)
• Average saccade length (2 degrees or 7-9 letters for skilled English readers)
• Regression rate (10-15% of all fixations)
• Skipping rate (Cop et al., 2015; Rayner, 1998)
→ Assess “processing effort” or “reading difficulty” of individuals

11. 1
1. Quantifying Eye Movement Data
For Second Language Research:
Roberts & Siyanova-Chanturia (2013) Conklin et al. (2018) Godfroid (2019)
→ Use eye tracking to obtain insights into how we help students acquire an
additional language and perform better in the language

12. 1
1. Quantifying Eye Movement Data
For Second Language Research:
• How do L2 learners differ in reading behavior from L1 reads?
→ processing of an entire text
→ processing of unknown/infrequent words
→ processing of multi-word units (idioms, lexical bundles)
→ processing of syntactic sentence structures
• How do L2 learners process subtitles/captions of movies?
• What and how long do L2 learners look at during the reading,
listening, or writing test? (see Conklin et al., 2018; Godfroid, 2019, for a review)

13. 1
0. Introduction
1. Quantifying Eye Movement Data
2. Quantifying Text Linguistic Dada
3. Combining Quantitative Eye Movement and Text
Linguistic Data for L2 Reading Research
(Research in progress)
Outline of Today’s Talk

14. 1
2. Quantifying Text Linguistic Data
Text Readability:
• Multilevel Language and Comprehension (Crossley et al., 2019; McNamara et al., 2014)
word length
frequency
age of acquisition
familiarity
Multi-word expression
and so on...
1. Lexical property
2. Syntactic complexity
3, 4. Cohesion
3 Textbase
4 Situation model
lexical / semantic overlap between sentences
use of connectives
and so on...
sentence length
phrasal/clausal complexity
syntactic similarity
and so on...
5. Genre and rhetorical structure
Discourse category
Rhetorical composition
Theme, moral, or point of discourse
6. Pragmatic communication
Goals of speaker ⁄ writer and listener ⁄ reader

15. 1
2. Quantifying Text Linguistic Data
Text Readability:
• Multilevel Language and Comprehension (Crossley et al., 2019; McNamara et al., 2014)
word length
frequency
age of acquisition
familiarity
Multi-word expression
and so on...
1. Lexical property
2. Syntactic complexity
3, 4. Cohesion
3 Textbase
4 Situation model
lexical / semantic overlap between sentences
use of connectives
and so on...
sentence length
phrasal/clausal complexity
syntactic similarity
and so on...
5. Genre and rhetorical structure
Discourse category
Rhetorical composition
Theme, moral, or point of discourse
6. Pragmatic communication
Goals of speaker ⁄ writer and listener ⁄ reader

16. 1
2. Quantifying Text Linguistic Data
※AOA, FAM, and IMG range from 100 to 700
Coltheart, M. (1981). MRC Psycholinguistic Database [Computer software].
https://websites.psychology.uwa.edu.au/school/mrcdatabase/uwa_mrc.htm
age of acquisition
Lexical Property:
• Age of acquisition, familiarity, imageability and frequency

17. 1
2. Quantifying Text Linguistic Data
※AOA, FAM, and IMG range from 100 to 700
familiarity
Lexical Property:
• Age of acquisition, familiarity, imageability and frequency
Coltheart, M. (1981). MRC Psycholinguistic Database [Computer software].
https://websites.psychology.uwa.edu.au/school/mrcdatabase/uwa_mrc.htm

18. 1
2. Quantifying Text Linguistic Data
※AOA, FAM, and IMG range from 100 to 700
imageability
Lexical Property:
• Age of acquisition, familiarity, imageability and frequency
Coltheart, M. (1981). MRC Psycholinguistic Database [Computer software].
https://websites.psychology.uwa.edu.au/school/mrcdatabase/uwa_mrc.htm

19. 1
2. Quantifying Text Linguistic Data
※AOA, FAM, and IMG range from 100 to 700
frequency
Lexical Property:
• Age of acquisition, familiarity, imageability and frequency
→ Multidimensional features of words
Coltheart, M. (1981). MRC Psycholinguistic Database [Computer software].
https://websites.psychology.uwa.edu.au/school/mrcdatabase/uwa_mrc.htm

20. 1
2. Quantifying Text Linguistic Data
Eguchi, M. (2021). Multi-Word Units Profiler (Version 2.0.0) [Computer
software]. https://multiwordunitsprofiler.pythonanywhere.com
Lexical Property:
• Frequent word sequences (multi-word units)

21. 1
2. Quantifying Text Linguistic Data
Text Readability:
• Multilevel Language and Comprehension (Crossley et al., 2019; McNamara et al., 2014)
word length
frequency
age of acquisition
familiarity
Multi-word expression
and so on...
1. Lexical property
2. Syntactic complexity
3, 4. Cohesion
3 Textbase
4 Situation model
lexical / semantic overlap between sentences
use of connectives
and so on...
sentence length
phrasal/clausal complexity
syntactic similarity
and so on...
5. Genre and rhetorical structure
Discourse category
Rhetorical composition
Theme, moral, or point of discourse
6. Pragmatic communication
Goals of speaker ⁄ writer and listener ⁄ reader

22. 1
2. Quantifying Text Linguistic Data
(1) They all enjoyed the dinner.
Syntactic Complexity:
• Mean number of words before main verb
(2) When he was a child, he had a dog. 6
2
1
• Mean number of modifiers per noun phrase
(3) small cats
(4) Kate’s small cute old cats 4
McNamara et al. (2014)

23. 1
2. Quantifying Text Linguistic Data
Text Readability:
• Multilevel Language and Comprehension (Crossley et al., 2019; McNamara et al., 2014)
length
frequency
age of acquisition
familiarity
Multi-word expression
and so on...
1. Lexical property
2. Syntactic complexity
3, 4. Cohesion
3 Textbase
4 Situation model
lexical / semantic overlap between sentences
use of connectives
and so on...
length
phrasal/clausal complexity
syntactic similarity
and so on...
5. Genre and rhetorical structure
Discourse category
Rhetorical composition
Theme, moral, or point of discourse
6. Pragmatic communication
Goals of speaker ⁄ writer and listener ⁄ reader

24. 1
2. Quantifying Text Linguistic Data
Cohesion:
• Evaluate cohesion based on semantic similarity of words
Created by using R package LSAfun (Günther., & Kaup, 2015)
based on Touchstone Applied Science Associates, Inc. (TASA) corpus

25. 1
2. Quantifying Text Linguistic Data
Cohesion:
• Evaluate cohesion based on semantic similarity of words (McNamara et al.,
2014)
Computed by using R package LSAfun (Günther., & Kaup, 2015)
based on Touchstone Applied Science Associates, Inc. (TASA) corpus
 “eat” is more semantically similar to “bite” than “drive”
 The cosine between two sentences
 The cosine between two words
 The cosine between adjacent sentences; The first and second sentences are most semantically similar
 The mean value of cosines between the sentences

26. 1
2. Quantifying Text Linguistic Data
NLP Tools for the social sciences developed by Dr. Kristopher Kyle
https://www.linguisticanalysistools.org/
See also Crossly et al. (2019)

27. 1
0. Introduction
1. Quantifying Eye Movement Data
2. Quantifying Text Linguistic Dada
3. Combining Quantitative Eye Movement and Text
Linguistic Data for L2 Reading Research
(Research in progress)
Outline of Today’s Talk

28. 1
3. Combining Quantitative Eye Movement and Linguistic Data
Nahatame (under review):
• Using indices of linguistic features to predict fixation count and
duration during L1 and L2 reading (Dutch learners of English; Cop et al., 2015)
Most indices selected are lexical
features
RWA showed that frequency of
multiword units (bigram and
trigram) are most important
predictors

29. 1
3. Combining Quantitative Eye Movement and Linguistic Data
Nahatame (under review):
• Using indices of linguistic features to predict fixation count and
duration during L1 and L2 reading (Dutch learners of English; Cop et al., 2015)
→ Difficulty of word recognition
(decoding) is a key to explain both
L1 and L2 reading fluency
→ Processing of multi-word units play
an important role in fluent reading
Most indices selected are lexical
features
RWA showed that bigram and
trigram frequency are most
important predictors

30. 1
3. Combining Quantitative Eye Movement and Linguistic Data
Nahatame (in preparation):
• Using indices of linguistic features to predict eye movement data
obtained from Japanese learners of English
length
frequency
age of acquisition
familiarity
Multi-word expression
and so on...
Lexical property
Syntactic complexity
Cohesion
lexical / semantic overlap between sentences
use of connectives
and so on...
length
phrasal/clausal complexity
syntactic similarity
and so on...
→ Combining quantitative eye movement and text linguistic data will offer insights
into how we can assist ELLs with reading more fluently and how we can
improve reading materials for them.

31. 1
Conklin, K., Pellicer-Sánchez, A., & Carrol, G. (2018). Eye-tracking: A guide for applied linguistics research. Cambridge University Press.
Cop, U., Drieghe, D., & Duyck, W. (2015). Eye movement patterns in natural reading: A comparison of monolingual and bilingual reading of a
novel. PloS One, 10(8), e0134008. https://doi.org/10.1371/journal.pone.0134008
Crossley, S. A., Skalicky, S., & Dascalu, M. (2019). Moving beyond classic readability formulas: New methods and new models. Journal of
Research in Reading, 42, 541–561. https://doi.org/10.1111/1467-9817.12283
Godfroid, A. (2019). Eye tracking in second language acquisition and bilingualism: A research synthesis and methodological guide. Routledge.
Günther, F., Dudschig, C., & Kaup, B. (2015). LSAfun-An R package for computations based on Latent Semantic Analysis. Behavior research
methods, 47(4), 930-944.
Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87, 329–354.
https://doi.org/10.1037/0033-295X.87.4.329
McNamara, D. S., Graesser, A. C., McCarthy, P. M., & Cai, Z. (2014). Automated evaluation of text and discourse with Coh-Metrix. New York:
Cambridge University Press.
Nahatame, S. (2018). Comprehension and processing of paired sentences in second language reading: A comparison of causal and semantic
relatedness. Modern Language Journal, 102, 392-415. https://doi.org/10.1111/modl.12466
Nahatame, S. (2021). Text readability and processing effort in second language reading: A computational and eye-tracking investigation.
Language Learning, 71(4), 1004-1043. https://doi.org/10.1111/lang.1245
Nahatame, S. (2022). Causal and semantic relations in L2 text processing: An eye-tracking study. Reading in a Foreign Language, 34(1), 91-115.
https://nflrc.hawaii.edu/rfl/item/546
Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124, 372–422.
https://doi.org/10.1037/0033-2909.124.3.372
Rayner, K. (2009). Eye movements and attention in reading, scene perception, and visual search. The Quarterly Journal of Experimental
Psychology, 62, 1457–1506. https://doi.org/ 10.1080/17470210902816461
Roberts, L., & Siyanova-Chanturia, A. (2013). Using eye-tracking to investigate topics in L2 acquisition and L2 processing. Studies in Second
Language Acquisition, 35, 213–235. https://doi.org/10.1017/S0272263112000861
References

32. 1
Presenter information
Shingo Nahatame, Ph.D.
School of Education, Faculty of Human Sciences, University of Tsukuba, Japan
email: nahatame.shingo.gp@u.tsukuba.ac.jp
site: https://sites.google.com/site/snahatame/
Google scholar: https://scholar.google.co.jp/citations?user=qPSQHGsAAAAJ&hl=ja
References
Thank you for listening!
ありがとうございました！