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Title

Analysis of Eye Movements and Linguistic
Boundaries in a Text for the Investigation ofJapanese Reading Processes

Author(s)TERA, Akemi; SHIRAI, Kiyoaki; YUIZONO, Takaya;
SUGIYAMA, Kozo

Citation

IEICE TRANSACTIONS on Information and Systems,
E91‑D(11): 2560‑2567

Issue Date

2008‑11‑01

Type

Journal Article

Text version

publisher

URL

http://hdl.handle.net/10119/8556

RightsCopyright (C)2008 IEICE. Akemi TERA, Kiyoaki
SHIRAI, Takaya YUIZONO, Kozo SUGIYAMA, IEICE
TRANSACTIONS on Information and Systems, E91‑
D(11), 2008, 2560‑2567.
http://www.ieice.org/jpn/trans̲online/

Description

Japan Advanced Institute of Science and Technology

IEICE TRANS. INF. & SYST., VOL.E91–D, NO.11 NOVEMBER 2008

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PAPER

Special Section on Knowledge, Information and Creativity Support System

Analysis of Eye Movements and Linguistic Boundaries in a Text
for the Investigation of Japanese Reading Processes
Akemi TERA†a) , Member, Kiyoaki SHIRAI†† , Nonmember, Takaya YUIZONO† ,and Kozo SUGIYAMA† , Members

SUMMARY In order to investigate reading processes of Japanese language learners, we have conducted an experiment to record eye movements
during Japanese text reading using an eye-tracking system. We showedthat Japanese native speakers use “forward and backward jumping eye
movements” frequently [13], [14]. In this paper, we analyzed further thesame eye tracking data. Our goal is to examine whether Japanese learners
fix their eye movements at boundaries of linguistic units such as words,
phrases or clauses when they start or end “backward jumping”. We consider conventional linguistic boundaries as well as boundaries empiricallydefined based on the entropy of the N-gram model. Another goal is to examine the relation between the entropy of the N-gram model and the depthof syntactic structures of sentences. Our analysis shows that (1) Japaneselearners often fix their eyes at linguistic boundaries, (2) the average of theentropy is the greatest at the fifth depth of syntactic structures.
key words: eye tracking, saccade, fixation, N-gram model, entropy

1. Introduction
In the field of Japanese language education for non-native
speakers, many Japanese language learners (Japanese learners) feel that a Japanese text is diﬃcult to read. The reasons
are generally summarized as follows: there is no space between words, a Japanese text consists of four diﬀerent kindsof characters (Hiragana, Katakana, Kanji and Roma-ji), and
it is diﬃcult to look up words represented by Kanji in a dictionary since Kanji words have several possible readings. In
order to support the learning of Japanese, an investigationof Japanese learners’ reading processes plays an importantrole.
Several studies aimed at analyzing reading processesof Japanese learners have been conducted. Tera et al. reported that “Na-adjectives”, “sahen” and complex verbs areoften consulted by Japanese learners via a reading support
system developed in [5]–[7], [9]∗ . These studies indicatedthat Japanese learners especially want to know information
about predicates in the Japanese text.
Eye movements of language learners are also analyzed.
Here we briefly introduce “fixation” and “saccade” in eye
movements. When we read a text, we use our eyes to get
Manuscript received April 4, 2008.
Manuscript revised July 4, 2008.
†
The authors are with School of Knowledge Science, Japan
Advanced Institute of Science and Technology, Nomi-shi, 923–
1292 Japan.
††
The author is with School of Information Science, Japan Advanced Institute of Science and Technology, Nomi-shi, 923–1292
Japan.
a) E-mail: tera@jaist.ac.jp
DOI: 10.1093/ietisy/e91–d.11.2560

information from the text. It is already known that people do
not move their eyes smoothly or constantly while reading,but repeat fixation and saccade. “Fixation” means that theeyeballs stop moving for the eye to glance at the same point
for a while in order to carefully read a text, while “saccade”
means that the eyeballs move from a fixation point to thenext fixation point. We call a saccade in the forward direction a “forward saccade”, while a saccade in the backward
direction is a “backward saccade”.
So far, numerous attempts have been made to analyze
fixation or saccade, as summarized in Rayner [8]. From the
beginning of 1990, research on reading processes Japanese
texts using an eye-tracking system were conducted, using
an eye-tracking system. Osaka [2] reported diﬀerences in
the locations where subjects fix their eyes according to thetypes of characters in the text. Shigematsu et al. [4] discussed diﬀerences of fixation of eyes between Japanese andChinese students. Saida [3] proposed a new method that restricted learners’ field of view using cutoﬀ frames in order
to measure the range of field of view in the reading process.
We also tried to investigate reading processes using an eyetracking system [13], [14]. We found that (1) when reading
text in native or familiar languages, fixation time tended to
be short, (2) Japanese native speakers often showed “backward jumping eye movements” and “forward jumping eye
movements”. It is well known that “backward jumping eye
movements” occurs when a reader finds out some discrepancies while reading with respect to the meanings he holds,
and is often used by skilled language learners [1].
Although many research studies investigated fixationor saccade in reading process, there has been no attempt
to reveal the relation between saccade and the linguistic
boundaries in a text in the reading process. Especially,
it is still uncertain if learners of Japanese tend to start orend their backward saccade near linguistic boundaries. In
this paper, we analyzed relation between backward jumping
and linguistic boundaries with eye tracking data obtainedin [13], [14]. In addition to conventional linguistic boundaries such as word or clause boundaries, we also consider
the clear boundaries empirically detected based on the entropy of the N-gram model. Thus far, in the research to reveal reading processes, the eye fixation time or the fixation∗“Na-adjective” and “saahen” are word classes in Japanese.
The Na-adjective is one kind of adjective, while sahen is a word
which functions both as a noun and as a verb.

c 2008 The Institute of Electronics, Information and Communication Engineers
Copyright TERA et al.: ANALYSIS OF EYE MOVEMENTS AND LINGUISTIC BOUNDARIES IN A TEXT

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position is analyzed, but not the saccade time [4]. Fixationis the work required for getting information from characters,
while saccade is the work required for searching the positions at which the characters exist. Thus we pay attention
not only to eye fixation but also to saccade time.
The paper is organized as follows: our goal is explainedin more detail in Sect. 2. Section 3 describes the detailed
procedures of our analysis. Results of our analysis are reported in Sect. 4, and we discuss these results in Sect. 5.
A preliminary survey of the relation between syntactic structures and entropy is presented in Sect. 6. We conclude thepaper in Sect. 7.
2. GoalThe goal of this paper is to investigate the reading process ofJapanese learners. More specifically, we investigate where
backward saccades occur in the text. As described in Sect. 1,
a backward saccade (backward jumping eye movement) occurs when a learner cannot understand the text smoothly,
and wants to read previous sentences again to make surethat what he/she understood before is correct [1]. If this iscorrect, we suppose that Japanese learners would start orend a backward saccade not within a word but at linguistic
boundaries. So we propose the following hypothesis:[Hypothesis]
When reading a Japanese text, a backward saccade starts orends around linguistic boundaries.
In this paper, we will empirically prove the above hypothesis through an analysis of real eye-tracking data.
In our analysis, linguistic boundaries in a text are defined in two ways. One is by borrow the concepts of conventional linguistic boundaries such as words, bunsetsus† orclauses. The other is empirically defining linguistic boundaries by measuring the entropy of the N-gram model trained
from a large number of real texts. One of the advantagesof the second approach is that we can consider the degreeof intensity of linguistic boundaries. As we will introduce
in Sect. 3.2, the entropy of the N-gram model reflects how
likely the position in a text might be linguistic boundaries.
That is, if the entropy of a certain position is great, it might
be a clear linguistic boundary. We would like to analyze if
backward saccade is more likely to occur at clear linguistic
boundaries, which can be statistically detected.
Another goal is to examine the relation between the
backward saccade and syntactic structures of sentences. We
would like to know if Japanese learners start or end backward saccades at deep positions in syntactic structures. As
a preliminary survey for this investigation, we examine thecorrelation between the entropy of the N-gram model andthe depth of syntactic structures.
3. Methodology
In this section, procedures to verify our hypothesis are described. The experiment for recording eye-tracking data isbriefly described in Sect. 3.1, while the verification of our

Fig. 1

Experimental set-up.

hypothesis by use of the obtained eye-tracking data is discussed in Sect. 3.2.
3.1 Collecting Eye Tracking Data
The following equipment is used to obtain eye-tracking dataof Japanese learners:
(1)
(2)
(3)
(4)
(5)

An eye-mark recorder EMR-8 (NAC)
A 44-degree lensAn analysis system (NAC)
A windows NT, 17-inch monitor
A head standSince subjects are required to have eyesight over 1.0 torecord eye movements with our eye-mark recorder, an eyesight check is performed before the experiment. An illustration of the recording of the eye tracking data with the above
equipment is shown in Fig. 1.
The procedures for obtaining eye-tracking data consistof the following 7 steps:(1) Choose intermediate learners as subjects according to
the results of the Standard Japanese Ability Test andthe English TOEFL/TOEIC.
(2) Explain an overview of the experiments and give instructions to the subjects, and then let them do preliminary experiments.
(3) Ask the subjects to read texts displayed randomly on
a PC screen, one after another. Time for reading is unlimited.
(4) Do calibration for eye tracking.
(5) Record eye tracking data during reading.
(6) Ask the subjects which texts are most diﬃcult and easiest to understand, and the reason why.
(7) Analyze the obtained eye tracking data as well as otherdata (Questionnaires (6), etc.).
The Japanese text used in the experiment is a newspaper article excerpted from the Chunichi Newspaper [12],shown in Fig. 2. It contains 178 characters. Figure 3 showsa schematic representation of the obtained eye tracking data
overlapped with the text. In Fig. 3, circles indicate fixations,†
Bunsetsu is a linguistic unit widely accepted in Japanese:a chunk of words consisting of content words accompanied bysome functional words.

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Table 1

Fig. 2

Fig. 3

Japanese text used for the experiment.

Saccades and fixations on the text.

Nationality of subjects.

In this experiment, we divide the Non-Kanji-area into 2 subgroups, “Non-kanji-Asia” and “Non-Kanji-Europe”, since
their native languages, Asian languages and European languages, are rather diﬀerent. That is, subjects are categorized
into 5 groups.
We collected eye-tracking data for 20 subjects. They
are classified into the following 5 groups according to theirnative language and familiarity with Kanji [9]:• Japanese-Native-Speaker(J)
Subjects who are Japanese native speakers.
• Kanji-area(K)
Subjects who use Kanji in their native language, except
Japanese.
• Middle-area(M)
Subjects who know Kanji but do not usually use them.
• Non-Kanji-area-Asia(NA)
Subjects who do not use Kanji and are from Asia.
• Non-Kanji-area-Europe(NE)
Subjects who do not use Kanji and are from Europe.
The Numbers of subjects in each group as well as theirnationalities are summarized in Table 1.
3.2 Analysis of Eye Tracking Data

Fig. 4

Japanese texts used for the experiment.

while lines indicate saccades. The size of the circles represents the time of the fixations.
Furthermore, we use 3 more texts in this experiment(See Fig. 4). Using these texts, we analyze the diﬀerence ineye movements for the five diﬀerent groups of learners thatare mentioned below.
All of the subjects in this experiment are graduate students, 4 subjects are Japanese native speakers and others are
Japanese language learners, from 22 to 28 years old. All of
the foreign subjects except 3 get 60–80 scores for 100 fullscore Japanese ability test. For the investigation of reading processes, the categorization of subjects is a very important problem. Until recently, in the field of Japanese
language education, subjects in experiments were usuallycategorized into 2 groups, “Kanji-area” and “Non-Kanjiarea”. In 1999, Tera et al. divided subjects into 3 groups
considering the familiarity of the subjects with Kanji. For
example, Korean people use Kanji in their name or signboard, but they don’t use Kanji but Hangul in their writing. Hence, Tera et al. divided Kanji-area into 2 sub-groups,
and then categorized all subject into 3 groups, whichare “Kanji-area”, “Middle-area” and “Non-Kanji-area”.

The major goal in this paper is to examine if learners often
start or end saccades near linguistic boundaries. First, we
define three kinds of linguistic boundaries in a text: (1) wordboundaries, (2) bunsetsu boundaries and (3) clause boundaries. A ‘clause boundary’ is a boundary of subordinate
clauses or sentences. In our experiment, these boundaries
in the text are manually annotated.
In addition to these linguistic boundaries, we try todefine boundaries by the entropy of the N-gram model.
The N-gram model is a well-known probabilistic language
model widely used in the research field of natural language
processing [10]. The N-gram model of characters is theprobabilistic model predicting the appearance of a character (ci ) given its preceding N − 1 characters (ci−N+1 ..ci−1 ). It
is defined as follows:P(ci | ci−N+1 · · · ci−1 )(1)

The N-gram model can be automatically trained from a large
amount of corpora [10]. Next, the entropy E of the N-grammodel is given as below:
P(ci | ci−N+1 · · · ci−1 ) log P(ci | ci−N+1 · · · ci−1 )
E=−
ci

(2)

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In general, the entropy of the N-gram model is relevant tolinguistic boundaries such as boundaries of words, phrases
or clauses. For example, let us consider the cases wherecharacters ci−N+1 . . . ci are in a word. In such cases, only
a limited number of possible characters would appear after the string ci−N+1 . . . ci−1 . Thus the entropy would be
low since the probability distribution P(ci | ci−N+1 · · · ci−1 )is not uniform, or is skewed. On the other hand, if there
is a linguistic boundary between ci−1 and ci , various characters could appear after the string ci−N+1 . . . ci−1 . In suchcases, the entropy would be high since P(ci | ci−N+1 · · · ci−1 )tends to be uniform. To summarize, if the entropy of the
N-gram model is high, we can assume that there is a linguistic boundary at a position between characters ci−1 and ci .
More concretely, if the entropy at a position is greater thana certain threshold T , we regard that position as a ‘clear’linguistic boundary.
When training the N-gram model of characters, we
set N equal to 5, that is, we estimate a 5-gram model which
predicts the probabilities of appearance of characters given
4 preceding characters. The model is trained by maximum likelihood estimation from newspaper articles published over 13 years. Then, the entropy of the 5-gram modelis calculated at all positions in the text that subjects read in
our experiment. The ‘Position in the text’ refers to a position between characters, and the entropy at a position between ci−1 and ci is the entropy of the conditional probability distribution P(ci | ci−4 ci−3 ci−2 ci−1 ), where ci−4 , ci−3 , ci−2 ,and ci−1 are the 4 characters appearing before that position.
Unfortunately, the entropy cannot be calculated for all positions due to the data sparseness, since the entropy is notdetermined when the string ci−4 ci−3 ci−2 ci−1 does not occur
in the training corpus. Hereafter we call such positions “uncertain entropy positions”.
Next, we extract fixations at the start and at the end of
backward saccades from the eye tracking data. The positions of extracted fixations in the text area were identified,since our current goal is to examine if fixations happen at
linguistic boundaries or at high entropy positions. However,it was rather diﬃcult to decide the exact positions of fixations. Osaka [2] reported that readers saw between 2 and 5,
mostly 3 and 4 characters when they fixed their eyes. Thismeans that Japanese learners may see, not a point, but an
area including several characters at fixations. Therefore, we
suppose that subjects see 3 or 4 characters when they fix
their eyes, and identify characters when they glance at fixations as shown in Fig. 5.
In this figure, a star shows the center of eye glanceat fixation, while ‘C’ stands for characters supposed to be
seen by subjects. That is, when the center of eye glanceat fixation is on a character, we suppose that subjects see
3 characters, the character on eye glance and its previous

Fig. 5

Saccades and fixations on the text.

and following characters (Fig. 5 (a)). Alternately, when thecenter of eye glance is between characters, we suppose that
subjects see 4 characters around the center of eye glance(Fig. 5 (b)). Hereafter we call the area including charactersglanced at by subjects “fixation area”.
After identifying the fixation area for each fixation, we
manually check if linguistic boundaries exist in the fixation
area. More concretely, we look for linguistic boundaries
among positions indicated by circles in Fig. 5. Then the linguistic boundary ratio LBR, defined by the formula below,
is calculated.
No. of fixations such that at least one linguistic
boundary exists in the fixation area
LBR =Total No. of fixations
LBR evaluates how likely fixations are to happen
around linguistic boundaries. If the LBR is high enough,our hypothesis proposed in Sect. 2 is verified.
4. Results of the Analysis
4.1 Analysis of Eye Tracking Data
At first, we can have a look at the typical eye-fixation patternof each group which we used in this experiment. Figure 6
shows the patterns of eye fixation when reading Japanesetext, the size of each circle indicating the time of fixation.
From this figure, we found that fixations of a Japanese subject occur for almost 1 in 10 characters. Fixations of a Kanjiarea subject occur about every 3 or 4 characters, and mostof the fixations occur on Kanji characters. Fixations of
a Middle-area group occur also almost every 3 or 4 characters, like a Kanji-area group, but not only on Kanji characters, also on Hiragana characters. The Fixations of a NonKanji-Asia or a Non-Kanji-Europe group occur almost every 1 or 2 characters.
Figure 7 shows the averages of the saccade time andof the fixation time for each group† . Concerning the saccade time, Japanese native speaker group is longer. Also,
the average saccade time of the Middle-area group is similar, although the standard deviation is much larger. Other
groups’ saccade times are shorter. Concerning the fixation
time, for Japanese-native speaker group, it is the shortest,
while for the other groups, it is longer.
In order to evaluate the diﬃculty of the 4 texts used inthis experiment, we asked all subjects to choose the mostdiﬃcult and the easiest text to read. Table 2 shows the numbers of subjects who chose each text as the most diﬃcult or
the easiest one. Each circle indicates the highest or higher
numbers of objects. 9 subjects chose the Text-4 as the mostdiﬃcult text. 9 subjects, most of who belong to the Kanjiarea chose Text-1 as the easiest text. According to theseobservations, we roughly estimate the order of 4 texts as
Text-1, Text-3, Text-2 and Text-4 from the easiest text, asshown in Table 2.
†
Note that the saccade and fixation times shown here are roughestimations, since the sampling rate of EMR-8 is 30 Hz for binocular record.

IEICE TRANS. INF. & SYST., VOL.E91–D, NO.11 NOVEMBER 2008

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Table 2

Fig. 6

Typical pattern of eye fixation for each group.

Fig. 8Average of saccade time and fixation time for each text.
Table 3Fig. 7

Subjective diﬃculty of text.

Number of fixation times.

Averages of saccade time and fixation time for each group.

Figure 8 shows the averages time of the saccade
time and of the fixation time for each group (indicated inalphabetic order on the horizontal axis) and on each text (indicated its number). The saccade or the fixation time goesnot seem to vary significantly from one text to another. Thus
we cannot find any strong correlation between the saccadeor the fixation time and the subjective diﬃculty of texts.

To summarize, the types of eye movements, thesaccade time and the fixation time diﬀer according to thegroup of learners, namely their native languages. On theother hand, the diﬃculty of texts seems not to influence theeye movements of learners.
Table 3 shows the times of fixation. Each column indi-

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Table 4

Analysis of backward saccade and entropy.

cates the text, while each row indicates the group of learners.
The last row shows the number of characters in texts.
Table 3 shows that Japanese native speakers read texts
with fewer fixations than other groups. The number of fixation times that of foreigners are about twice or 2.5 times
that of Japanese. The number of fixation times is highest
for Text-3, and lowest for Text-2. But we cannot find any
significant diﬀerences among the 4 texts.
4.2 Analysis of Backward Saccade
In this section, we report the results of our analysis ofbackward saccades and linguistic boundaries. We found
367 backward saccades from the eye-tracking data of
20 subjects. The LBR is calculated for fixations at the startand at the end of these backward saccades. Results are summarized in Table 4. It shows the LBR for several cases, classified according to the following aspects:• Start or end
Fixations are distinguished if they are at the start or at
the end of backward saccades. The LBR for start fixations
are shown in the tables on the left, and the LBR for end fixations in the tables on the right.
• Types of linguistic boundaries
In Table 4, we showed the results for bunsetsu andclause boundaries as well as two kinds of boundaries definedby entropy (‘Entropy 2.5’ and ‘Entropy 3.0’). When we examined the relation between backward saccades and wordboundaries, LBR was equal to 1 for almost all subjects. Thisis because we tried to find boundaries around 3 or 4 characters from the center of fixations (see Fig. 5). Therefore weomitted showing detail results for word boundaries.
• Threshold of entropy (T )When we define linguistic boundaries according toentropy, the number of linguistic boundaries in the text canbe controlled by the threshold T . In this experiment, we
set the threshold T to 2.5 and 3. Among 177 positions in

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the text† , 52 (29.4%) and 35 (19.8%) positions are regarded
as linguistic boundaries when T = 2.5 and T = 3.0, respectively. We manually checked these 52 positions and foundthat 10 are clause boundaries, 29 bunsetsu boundaries and
50 word boundaries. While only two positions are wrongly
identified as linguistic boundaries. We suppose that theseare more clear boundaries than others.
• Groups of subjects
We separately calculate the LBR for fixations of subjects in the 5 diﬀerent groups described in Sect. 3.1. In
Table 4, the average and standard deviation (SD) of the LBR
for each group is shown in each row. The same data for all
subjects is also shown in the ‘All-ave’ row.
• Uncertain entropy position
When we define linguistic boundaries according to theentropy, the entropy at some positions cannot be calculated
due to data sparseness as described in Sect. 3.2. If such
uncertain entropy positions are located in the fixation area,
the judgment of whether a linguistic boundary exists in the
fixation area is also uncertain. So we calculated the LBR
when the entropy at all positions in the fixation area can becalculated (in ‘Certain’ column), and the LBR for all fixations, including ones such that uncertain entropy positions
exist in the fixation area (‘All’ column). In the latter case,the LBR is just an approximation. Note that it is underestimated since uncertain entropy positions may be real linguistic boundaries.
The expectation of the LBR is also shown in the ‘E’ row
in Table 4. This is defined as the proportion of points suchthat linguistic boundaries exist in the neighborhood of all
points in the text. Here, points in the text mean both centersof characters as indicated by the star in Fig. 5 (a), and positions between characters as indicated by the star in Fig. 5 (b).
That is, the expectation of the LBR represents the probability that when a point in the text is randomly chosen as thecenter of fixation, one or more linguistic boundaries exist atthat point.

boundaries. It may indicates that start points of backward
saccades more likely agree with clear linguistic boundaries
empirically estimated from a large corpus than bunsetsu orclause boundaries.
Finally, we will discuss the diﬀerences of groups ofsubjects. The LBR of group J (Japanese-Native-Speaker)
for start fixations is relatively high: it is the greatest among
all groups for bunsetsu and entropy (T = 2.5). We guess that
this is because Japanese native speakers have rich knowledge about Japanese, and can recognize linguistic boundaries correctly. On the other hand, the LBR of group K(Kanji-area) is the lowest for all types of linguistic boundaries except for entropy (T = 3.0). Surprisingly, the LBR ofgroup NE (Non-Kanji-area-Europe) is relatively high for allcases. Although Europeans might not be familiar with Kanjicharacters, they often start their backward saccades aroundlinguistic boundaries. The above observations may suggestthat the reading process of Japanese learners is not strongly
related to their familiarity with Japanese or Kanji.
6. Syntactic Structure and Entropy
Another question about backward saccades is whether
Japanese learners tend to start or end their backward eye
movements at deep positions in syntactic structures of sentences. As a preliminary survey, we investigated the relationship between the depth of syntactic structures and theentropy of the N-gram model. First, we obtained syntactic
structures of sentences in the text used in our experiment,using the Japanese CALL system Asunaro†† . For each character in sentences, we obtained the entropy of the 5-grammodel at the position after the characters and its depth ina syntactic tree, where the depth is the distance from theroot to the character. We obtained the statistics shown in
Table 5.
The correlation between the depth and the average ofentropy is −0.0941. This means that there is no relationshipbetween the two variables.

5. Discussion
We here discuss the results shown in Table 4. First, the
LBR for start points of backward saccades are greater thanthe expected ratio (E). On the other hand, the LBR for
end points are lower than E excepting for the case of ‘Entropy 2.5’. These results indicate that our hypothesis is valid
for start points, but not for end points. That is, language
learners tend to start their backward saccades at linguistic
boundaries. This suggests that learners might recognize linguistic boundaries when they start to move their eyes backward. Hereafter, we focus on only start points of backward
saccades.
Next, we will compare types of linguistic boundaries.
We found that the diﬀerence between the average of the LBR
and E for start points is 0.034 for bunsetsu, 0.009 for clause,
0.042 for entropy (T = 2.5) and 0.061 for entropy (T = 3.0),
respectively. Thus, the diﬀerences for boundaries definedby entropy are greater than those for bunsetsu or clause

Table 5

Averages of saccade time and fixation time for each group.

†
Note that the text used in this experiment contains 178 characters.
††
http://hinoki.ryu.titech.ac.jp/

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7. Conclusion
In this paper, we analyzed eye-tracking data to investigate
the reading process of Japanese learners. We proposed the
hypothesis that learners tend to start and end their backward
saccades around linguistic boundaries. The analysis on thereal eye tracking data showed that our hypothesis is valid tosome degree.
In future research, large-scale experiments are required. The size of the experiment reported here is not suﬃcient in terms of both the number of texts and the number ofsubjects. It is necessary to conduct a larger experiment andto analyze the results further in order to reveal the reading
process of Japanese learners. We have already obtained eyetracking data for 3 other texts with the same subjects. The
analysis of these 3 texts will be performed shortly. A detailed comparison between the nationalities and languagesof Japanese learners will also be made. Another problemof the current experiment is that the sampling rate of eye
tracking system is not so high, 30 Hz for binocular record.
Analysis on the eye tracking data obtained by a more sophisticated recorder is desired. Furthermore, we plan to empirically investigate the relation between backward saccades
and the depth in syntactic structures.
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[13] A. Tera and K. Sugiyama, “Eye-tracking analyses of Japanese reading processes (IV) — Forward and backward jumping eye movements,” Proc. IEICE Gen. Conf. 2006, TL2005-35-48, pp.43–48,

2006.
[14] A. Tera and K. Sugiyama, “Eye-tracking analyses of Japanese reading processes,” Proc. KICSS, pp.108–114, 2006.

Akemi Terareceived the M.S. degreein Knowledge Science from School of Knowledge Science, Japan Advanced Institute of Science and Technology. Her main field is theJapanese language education and Japanese language learning support system. She is now
a PhD student of Japan Advanced Institute of
Science and Technology.

Kiyoaki Shirai
received the B.S (1993),
M.S (1995) and D.S (1998) degrees in Engineering from Tokyo Institute of Technology. He wasa research assistant at Tokyo Institute of Technology from 1998 to 2001. He has been an associate professor at School of Information Science, Japan Advanced Institute of Science andTechnology since 2001. His research interests
lie in the fields of natural language processing.

Takaya Yuizono
received the B.E., M.E.,
and Dr. of Engineering from Kagoshima University, in 1994, 1996, 1999, respectively. He wasa research associate in Kagoshima University,a lecturer and an associate professor in Shimane
University, respectively. He has been an associate professor at School of Knowledge Science, Japan Advanced Institute of Science andTechnology since 2005. His research interests
include in groupware, computer-supported cooperative work and knowledge medium. He isa member of ACM and IEEE.

Kozo Sugiyama
received the M.S. and D.S
degrees in Earth Sciences from Nagoya University in 1971 and 1974, respectively. During 1974–1997, he worked for Fujitsu Ltd andFujitsu Laboratories Ltd to study system engineering, information visualization, and creativity support. He stayed in International Institute of Applied Systems Analysis, Austria in
1982–1983. He is now a professor of School of
Knowledge Science, Japan Advanced Instituteof Science and Technology.