Gaze-based Interest Detection on Newspaper Articles
Soumy Jacob

TU Kaiserslautern & German ResearchCenter for Artificial Intelligence (DFKI)
Kaiserslautern, Germany
Soumy.Jacob@dfki.de

Syed Saqib Bukhari

German Research Center for
Artificial Intelligence (DFKI)
Kaiserslautern, Germany
Saqib.Bukhari@dfki.de

Shoya Ishimaru

TU Kaiserslautern & German ResearchCenter for Artificial Intelligence (DFKI)
Kaiserslautern, Germany
Shoya.Ishimaru@dfki.de

Andreas Dengel

TU Kaiserslautern & German ResearchCenter for Artificial Intelligence (DFKI)
Kaiserslautern, Germany
Andreas.Dengel@dfki.de

ABSTRACT

1 INTRODUCTION

Eye tracking measures have been used to recognize cognitive states
involving mental workload, comprehension, and self-confidence inthe task of reading. In this paper, we present how these measures
can be used to detect the interest of a reader. From the reading behavior of 13 university students on 18 newspaper articles, we haveextracted features related to fixations, saccades, blinks and pupil
diameters to detect which documents each participant finds interesting or uninteresting. We have classified their level of interests
into four classes with an accuracy of 44% using eye movements,
and it has increased to 62% if a survey about subjective comprehension is included. This research can be incorporated in the real-time
prediction of a user’s interest while reading, for the betterment of
future designs of human-document interaction.

Reading has always been the foundation of all learning and thereby
education. This soft skill is a source of creative power but one
which is to be easily instilled in people. While reading, eyes perceive the content immediately and they form a vocabulary of theirown comprised of fixations, saccades, regressions, pupil diameterand blinks [Yang and McConkie 2001]. These measures behave differently in changing circumstances and give us an insight of cognitive states – thought processes and emotions – during the readingtask [Rayner et al. 2006]. Various studies have been done on using
these metrics to analyze a person’s attention, tiredness and cognitive load. In this paper, we pursue to study a reader’s interest andits association to eye-tracking metric behaviors.
Interest in reading can be motivated by concentration, curiosity,and demand. It may not rise out of habit but it will help motivate
the habit and subsequently the learning process. Interest motivates
earnestness in a person. According to Ibrahim Bafadal, "Reading is
a process of capturing or acquiring the concepts intended by theauthor, interpret, evaluate the author’s concepts, and reflect, or act
as intended of those concepts". Hence it not only depends on theability to interpret and evaluate the contents but also the will todo the same for comprehensive understanding [Hidi 2001; Raneyet al. 2014; Squires 2014].
This urge in reading, if recognized, can be used to improve thedata made available to the reader and also help in better humandocument interaction and the design. Predicting a reader’s interest
can help to make document more interactive or dynamic [Biedert
et al. 2010]. Research done on dynamically changing text showsthat reading dynamic text is much smoother and faster than reading static text [Uetsuki et al. 2017]. Eye gaze, if used to predict auser’s interest, comprehension and difficulty, can influence his/herinteraction with the learning environment and thereby affect thelearning process [Copeland et al. 2014]. This can further assist
teaching techniques and promote understanding and active interest in students.
On the basis of the motivation mentioned above, we propose
an interest detection method by utilizing an eye tracker. Researchquestions addressed in this study are two-fold. RQ1: How accurately can eye movements estimate a reader’s interest? RQ2: Since
reading behaviors are different for each reader, which measurement can be used as a common feature?

CCS CONCEPTS• Computing methodologies → Activity recognition and understanding;

KEYWORDS
Eye tracking, reading, interest, fixation, saccade, blink, pupil diameter, human-document interaction
ACM Reference Format:
Soumy Jacob, Shoya Ishimaru, Syed Saqib Bukhari, and Andreas Dengel.
2018. Gaze-based Interest Detection on Newspaper Articles. In PETMEI ’18:
7th Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction,
June 14–17, 2018, Warsaw, Poland. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3208031.3208034

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than
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PETMEI ’18, June 14–17, 2018, Warsaw, Poland© 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.
ACM ISBN 978-1-4503-5789-0/18/06. . . $15.00
https://doi.org/10.1145/3208031.3208034

PETMEI ’18, June 14–17, 2018, Warsaw, Poland

S. Jacob et al.

Figure 2: An example of a news article and eye gaze (circle:
fixation, line: saccade) on a document

Figure 1: Gaze events calculated by a signal of an eye tracker

2 PROPOSED METHOD
2.1 Gaze event detection
Figure 1 shows an example of the gaze events while reading anarticle. Eye movements while reading are composed of three basic metrics: fixations, saccades and blinks. A fixation occurs when
the gaze falls on something of interest to the screen area and usually lasts for about 100 - 150 ms. The rapid movement of the eye
between fixations is called a saccade. A blink is a semi-autonomic
rapid closing of the eyelid. Pupil diameters can be also obtained
from an eye tracker. We detect the gaze events by following steps.

As preprocessing, we filter raw eye movements to get fixations
and saccades on the basis of the approach proposed by Buscher et
al. [Buscher et al. 2008]. The midpoint of the left and the right gazecoordinates is taken as the gaze point, only if both values are nonzero, else the (left or right) non-zero coordinates is taken as thegaze point. A fixation typically consists of more than six successive
gaze locations grouped in succession. This makes the minimum
fixation duration 100ms as mentioned earlier for data recorded at
a rate of 60Hz. The successive gaze points making a new fixation
should fit inside a threshold rectangle of 30x30 pixels. All further
gaze points falling inside a 50x50 pixel rectangle is considered tobelong to the current fixation. This is done so that noise and small
eye movements are tolerated. If the gaze point does not fall in therectangle, it is either an outlier or the start of a new fixation, whichfurther merges with six other points. The fixation is considered tohave ended if at least six successive gaze points cannot be merged.
The movement or transition from one fixation to the other isrecorded as a saccade. Saccades are further divided into forward
saccades and backward saccades. The x-coordinate of successive
fixations denotes the direction of the saccade. Forward saccades
imply regular reading behavior, while backward saccades can either be regressions or line breaks. Regressions are backward eye
movements which allow re-reading of the text [Booth and Weger2013]. Line breaks are separated from regressions by analyzing thelength of the backward saccade. If the length is equal to or greaterthan the length of a line, then they are categorized as line breaks(observed as peaks in the saccade length in Figure 1).
Further, we use pupil diameter obtained from raw gaze data,
which is the average of left and right pupil diameter, if both are nonzero. Another characteristic eye behavior we record is blink. The
average duration for a blink of a human eye is 100-400ms. Hence,
6-24 consecutive zeroes in the left and the right gaze coordinates
are considered as one blink in our approach. The average latencyof two consecutive blinks is one second and blinks detected in between are considered as noise.

Gaze-based Interest Detection on Newspaper Articles

PETMEI ’18, June 14–17, 2018, Warsaw, Poland

Table 1: List of features
No.
1-2
3-4
5-6
7-8
9-10
11-12
13
14-15
16
17

feature{mean, standard deviation} of fixation duration{mean, standard deviation} of forward saccade length{mean, standard deviation} of forward saccade speed{mean, standard deviation} of regression length{mean, standard deviation} of regression speed
count of {forward saccades, regressions}regression ratio{mean, standard deviation} of pupil diameter
blink frequency
standard deviation of blink interval
Figure 3: An overview of the experimental setup. A participant is reading a news article on a display with SMI REDn
Scientific 60Hz remote eye tracker.

2.2

Feature calculation

On the basis of the gaze events, we extracted seventeen features
for further analysis, as listed in Table 1. Fixation duration is thetime taken for each fixation. Forward saccade length is the distance
between the two consecutive fixations that make the saccade. Forward saccade speed or Regression speed is the length of the saccade
divided by the time taken for the saccade. Regression ratio describes
the fraction of regressions out of the total number of saccades (i.e.,(No.11 / (No.11 + No. 12)). Regression length is the distance between
the two fixation coordinates that makes the regression. Pupil diameter is the diameter of the right pupil obtained from the raw gazedata, provided the x and y gaze coordinates are non-zero else taken
as zero. Since pupil diameter is user and environment dependent,
it was taken as a relative value compared to the pupil diameter
during the questionnaire which was taken as a baseline. Blink frequency is the number of blinks divided by the total time taken by
the reader (for each document). Blink interval is the time lag between two consecutive blinks.

2.3Classification

The aforementioned features were used for classification of the eye
tracking data into four levels of interest. We utilize either of Support Vector Machine (SVM) or Random Forest Classifier. SVM categorizes the test data with a separating optimal hyper plane which
is defined by the labeled training data. Random Forest Classifier
randomly selects subsets of training data and creates sets of decision trees and further uses the votes from the decision trees to findthe test data category. Selecting hyper parameters (C, kernel andgamma in SVM and the number of estimators, the maximum depthof the tree, the maximum features to consider, the minimum number of samples required to split an internal node, the minimum
number of samples required to be at a leaf node in Random Forest) is crucial and non-trivial. In our approach, we used 3-fold gridsearch cross-validation as a hyper parameter optimization technique. It searches exhaustively through a manually defined set ofparameters and finds those that achieved the highest score in thevalidation procedure. We separate training data into training forparameter optimization and the evaluation for each classification.

3 EXPERIMENTIn order to evaluate our proposed interest detection method, we
conducted an eye tracking experiment. The following section describes the experimental design and the analysis results.

3.1

Experimental design

Figure 3 shows an overview of the experimental setup. We used an
SMI REDn Scientific 60Hz remote eye tracker set up alongside anormal computer desktop to record gaze data.
Newspaper articles seemed to capture the purpose of the experiment better than any other text. To capture the reader’s interest,
we obtained a wide range of topics from different platforms like
technology, politics, sports, cooking etc. Thirteen university students (mean age: 25, std: 3, male: 6, female: 7, 2 of them are familiar with eye tracking) participated in the experiment where eachof them was asked to read eighteen newspaper articles comprisingof 403 - 649 words each (mean: 555, std: 70) as shown in Figure 2.
After reading each document, participants answered three questions. (1) the level of interest they had in the article, which was used
as ground truth (from 1 to 4, where 1 indicated “No interest” and4 indicated “High interest”), (2) a self-assessment about how muchof the content the reader understood (subjective comprehension,from 1 to 4, where 1 indicated “I couldn’t understand the article”
and 4 indicated “I could understand the article”), and (3) one question about of the article (i.e., objective comprehension).
The recordings were done in two sessions of one hour each, inorder to avoid eye-fatigue and these were conducted in a controlledenvironment. The tracker and the desktop were maintained in astable position. The lighting of the room was set so as not to affect
the gaze data (pupil diameter). Calibration was done after reading
every document to avoid error or shift of the gaze points.
We followed three different approaches to separate the traintest data before classification. Leave-one-recording-out uses eachrecording (data of each participant on each document) as test data,
the rest as training and the average of the accuracy in all cases
together is taken as the classification accuracy. Similar to this approach, leave-one-document-out approach exempts the data of adocument completely from the training set and uses it for testing.

PETMEI ’18, June 14–17, 2018, Warsaw, Poland

S. Jacob et al.

Table 2: Classification accuracies using SVM [%]leave-one-doc.-out
29
55
31
46
43
61
41
61

leave-one-rec.-out
28
52
34
42
41
62
39
58

Leave-one-participant-out approach uses one the data from all participants except one as training and uses the data from the left-out
participant as testing. It is quite significant as, in a realistic scenario; the system does not have training data from a new user.

3.2

Results

Table 2 represents the classification accuracies using SVM. The
most frequently selected hyper parameters are C: 32, gamma: 0.125,
kernel: Radial Basis Function. Table 3 shows the classification accuracies using Random Forest. The most frequently selected hyper
parameters are the number of estimators: 100, the maximum depthof the tree: 10, the maximum features to consider: square of thenumber of all features, the minimum number of samples required
to split an internal node: 10, the minimum number of samples required to be at a leaf node: 3. We also incorporated feature reduction techniques like PCA and LDA, but found no commendable
improvement in the classification.
The distribution of the predicted classes are observed as confusion matrixes shown in Figure 4 and Figure 5. The overall correlation between the various features used and the labels are stillquite small. However, when individual participants were considered, from correlations between features and interests shown in
Figure 6, we found that there was (1) a negative correlation between mean/standard deviations of fixation duration with the interest labels, (2) a considerably small positive correlation existed
for the standard deviation of regression speed, (3) also with thenumber of forward saccades. But this was observed for only half
the number of participants or less, the rest having no or very slight

1

36%

31%

26%

7%

2

20%

36%

24%

20%

3

19%

21%

38%

22%

4

Actual class

4

10%

14%

16%

59%

Figure 4: A confusion matrix of the SVM classifier withleave-one-recording-out training using only gaze features.
Mean accuracy: 44%.

Predicted class
1

2

3

4

1

1. reading speed
2. subjective comprehension
3. objective comprehension
4. eye movements
combination 1 and 4
combination 2 and 4
combination 3 and 4
combination allleave-one-part.-out
25
55
30
30
35
44
30
46

3

64%

36%

0%

0%

2

Table 3: Classification accuracies using Random Forest [%]

2

24%

45%

29%

2%

3

leave-one-rec.-out
30
58
33
44
44
59
43
57

Actual classleave-one-doc.-out
30
60
35
44
43
59
44
58

1%

16%

56%

26%

4

1. reading speed
2. subjective comprehension
3. objective comprehension
4. eye movements
combination 1 and 4
combination 2 and 4
combination 3 and 4
combination allleave-one-part.-out
31
59
34
28
26
40
28
41

Predicted class
1

3%

3%

16%

78%

Figure 5: A confusion matrix of the Random Forest classifier with leave-one-recording-out training using subjective
comprehension and gaze features. Mean accuracy: 62%.

correlations with the features. Feature importances were also computed using the Random Forest classifier as seen in the Table 4.
Mean forward-saccade speed, mean fixation duration and mean regression speed was found to have the highest importance (in thatorder) and number of regressions had the lowest.
We included non-eye related measures like reading speed andthe level of subjective/objective comprehension of the user. The
accuracies using SVM and Random Forest classifiers are tabulated
in Table 2 and Table 3. Figure 6 shows the Pearson’s correlationof the features for each participant with the level of interest. The
level of subjective comprehension of a person can be seen to havea very high effect on a person’s level of reading interest (denotedby red-high and blue-low). We got an accuracy of 60% when subjective comprehension was used for classification (Table 2). And, an
accuracy of 61% for leave-one-document-out when all measures reading speed, subjective and objective comprehension, eye gazefeatures, were used (Table 3). By using subjective comprehension
and eye-related features, we achieve an accuracy of 62%.

Gaze-based Interest Detection on Newspaper Articles

PETMEI ’18, June 14–17, 2018, Warsaw, Poland
Table 4: Importance of features in the Random Forest

Figure 6: Pearson correlations between features and interests of each participant

3.3

Discussion

Although the accuracies were not as high as expected, this research
threw light on using a remote eye tracker for cognitive state measurement. We found that mean forward-saccade speed, mean fixation duration and mean regression speed plays a vital role in predicting a reader’s interest. And that SVM with an RBF kernel isbest to classify gaze-based features.
However, a higher correlation of the features to the labels was
expected, though it was observed to be quite small. The correlation was quite different in the case of each participant for all features except for ones earlier mentioned. This led us to believe thatcognitive predictions are user-dependent and not just documentdependent. For example, pupil diameter may not undergo the samechanges in every user during the same psychological process. Thesefeatures are dependent on the user and his/her cognitive state. We
also found that the collection of ground truth related to interestand understanding are widely prone to human error and individual behavior.
Subjective comprehension of a person has a very high correlation to the level of interest while reading, which makes sense, sinceinterest can only be realized if the person truly understands the
text. But using eye measures while reading is an added advantage
to understand this and should be deeply explored, since it can berealistically collected while reading without reader intervention.

4

RELATED WORK

There has been a lot of study on designing systems which focus on
visualization and interaction methods that allows the user to better
attend to a task with minimal mental effort. Our research focuseson interpreting how interested the person is in reading or doing thetask at hand. This in turn can be used to provide more information
or better information that the user subconsciously requires.

featuresubjective comprehension
mean of forward saccade speed
mean of fixation duration
mean of regression speed
count of forward saccade
regression ratio
standard deviation of fixation duration
standard deviation of regression speed
standard deviation of blink interval
standard deviation of forward saccade length
objective comprehension
reading speed
mean of regression lengthmean of forward saccade length
blink frequency
standard deviation of regression length
standard deviation of forward saccade speed
mean of pupil diametercount of regression
standard deviation of pupil diameter

4.1

importance
0.27600.10190.0961
0.05240.0471
0.04130.04080.0386
0.03680.03650.03370.03360.03050.02130.02130.02050.02010.01910.01630.0161Brain sensors

Brain activity or cognitive processes can be measured through EEG
and MEG, by capturing changes in magnetic fields at the scalp
caused by electrical currents in brain neurons [Frey et al. 2016].
It refers to the recording of the brain’s spontaneous electrical activity over a period of time, as recorded from multiple electrodes
placed on the scalp [Lan et al. 2005]. fMRI or Functional magnetic
resonance imaging, measures the changes in blood flow to an areaof the brain to detect which part of the brain is in use. Functional
Near-Infrared Spectroscopy (fNIRS) uses near-infrared spectroscopy
for functional neuroimaging and measures brain activity throughhemodynamic responses [Ishimaru et al. 2014]. However, using
these techniques in day to day activities are quite intrusive. They
can neither be used in situ nor in public due to the time consuming
and expensive setup and the complex analysis.

4.2

Physiological sensors

There has been a growing interest in the study of the relation
between cognitive performance and heart rate variability (HRV).
HRV is a simple measurement of interactions between the autonomic nervous system (ANS) and the cardiovascular system. The
analysis of the HRV is based on the study of temporal oscillations
between heartbeats [Luque-Casado et al. 2013]. Electrodermal activity (EDA; changes in electrical conductance of the skin) is asensitive psychophysiological index of changes in autonomic sympathetic arousal that are integrated with emotional and cognitive
states [Brishtel et al. 2018]. HRV, EDA, respiration and brain signals have been used to predict mental stress and cognitive load invarious studies [Critchley 2002; Masood 2015]. The activity of ANS
can be also measured by the nose temperature. If autonomic nervous system is active than parasympathetic nervous system (i.e.,PETMEI ’18, June 14–17, 2018, Warsaw, Poland
a person feeds high workload), blood vessels constrict and therefore the nose temperature drops. It can be measured by an infrared
thermal camera [Ishimaru et al. 2017] Abdelrahman et al. haverecorded nose and forehead temperatures under different task difficulties and found significant changes [Abdelrahman et al. 2017].

4.3

Eye tracking and gaze oriented features

Various researches discuss eye tracking measures and its hand ininterpreting cognitive processes and cognitive load. The measuresused are voluntary – fixations and saccades, and involuntary –pupil diameter and blinking. According to Ruddmann et al., thedirection of gaze indicates repeated interest in an area and the importance of the area of interest in the current activity [Rudmann
et al. 2003]. Chen et al. have observed that fixation duration andfixation rate are indicators of an increase in attention on the current task [Chen et al. 2011]. They delved into the relevance of saccades in interpreting human mental effort in solving a task. They
have also found that an increase in blink interval and a decreasein blink rate indicated high mental effort and that studying the diameter of the pupil helps to realize the task difficulty and the cognitive effort. Blinking is more often an involuntary measure but
in certain cases it is a voluntary measure which can be studied tomeasure attention and tiredness [Zagermann et al. 2016]. Saccade
speed and length with other measures achieved high accuracy inmeasuring human performance. On the other hand, Manuel et al.
suggested a decrease in saccade speed indicated tiredness and anincrease in the same indicated task complexity [Barrios et al. 2004].
According to them, an increase in blink rate, a decrease in blinking speed and a decrease in the eyelid openness are indicators oftiredness and stress. Ishimaru et al. have investigated reading behaviors of students on a textbook in Physics [Ishimaru et al. 2018].
They have proposed two types of expertise prediction method: utilizing fixation durations on Areas of Interest and utilizing meanand standard deviation of fixations and saccades in a subsequence.
Porta et al. have further observed that decrease in pupil diameterat the end of the task indicated tiredness [Porta et al. 2012]. Gaze
has been used in adaptive scrolling algorithms to allow continuous
reading of newspaper articles in large public displays [Lander et al.
2015]. In this study, Lander et al. uses head mounted eye trackers
in a multi-user scenario to study the effect of this approach on theuser’s reading speed.

4.4

Position of our study

These papers give us a deep understanding of how eye movements
can be used to measure various cognitive factors like human mental effort, attention, cognitive load, tiredness etc. Our goal is forthe system to measure the degree of interest the reader has on thecontent, so that more data can be made available to the user and to
improve human-document interaction. Kunze et al. have explored
how to detect reader engagement from eye metrics and nose temperature with a dataset obtained from 5 users and achieved 100%
accuracy for binary classification [Kunze et al. 2015]. The features
used were blink frequency, nose temperature, number and duration of fixations, number and angle of saccades. We have sought
to improve this with a larger dataset of 13 users, each reading 18

S. Jacob et al.
documents. We further used features obtained from pupil diameterand regressions for a four-class classification.

5 CONCLUSION AND FUTURE WORKThis research demonstrated that eye measures from a remote eye
tracker can be successfully used to predict a reader’s interest. We
obtained data from experiments conducted with 13 students, whoread 18 newspaper articles each. We extracted seventeen featuresfrom raw gaze data obtained from the tracker and used it for further classification of the data into different levels of interest. Although the correlation of the features with the interest labels were
not as high as expected, forward-saccade speed, fixation duration
and regression speed were significant.
This work can be extended to include data from other sensors
like an infrared thermal camera to measure nose temperature anda physiological wristband to measure heart rate and skin electricalconductance. Data acquisition could also be improved by controlling the environment to present a stress-free or at-home experience for the reader. Also, unsupervised learning could be used fordata classification to avoid human error in the ground truth.

ACKNOWLEDGMENTS
This work was supported by JSPS KAKENHI Grant Numbers 17K12728,
17K00276.

REFERENCES

Yomna Abdelrahman, Eduardo Velloso, Tilman Dingler, Albrecht Schmidt, and Frank
Vetere. 2017. Cognitive heat: exploring the usage of thermal imaging to unobtrusively estimate cognitive load. Proceedings of the ACM on Interactive, Mobile,
Wearable and Ubiquitous Technologies 1, 3 (2017), 33.
Victor Manuel García Barrios, Christian Gütl, Alexandra M Preis, Keith Andrews,
Maja Pivec, Felix Mödritscher, and Christian Trummer. 2004. AdELE: A framework for adaptive e-learning through eye tracking. Inquiring Knowledge Networks
on the Web (2004), 609–616.
Ralf Biedert, Georg Buscher, Sven Schwarz, Manuel Möller, Andreas Dengel, andThomas Lottermann. 2010. The text 2.0 framework: writing web-based gazecontrolled realtime applications quickly and easily. In Proceedings of the 2010 workshop on Eye gaze in intelligent human machine interaction. ACM, 114–117.
Robert W Booth and Ulrich W Weger. 2013. The function of regressions in reading:
Backward eye movements allow rereading. Memory & cognition 41, 1 (2013), 82–
97.
Iuliia Brishtel, Shoya Ishimaru, Olivier Augereau, Koichi Kise, and Andreas Dengel.
2018. Assessing Cognitive Workload on Printed and Electronic Media using EyeTracker and EDA Wristband. In Proceedings of the 23rd International Conference onIntelligent User Interfaces Companion. ACM, 45.
Georg Buscher, Andreas Dengel, and Ludger van Elst. 2008. Eye movements as implicit
relevance feedback. In Extended abstracts of the 2008 CHI Conference on Human
Factors in Computing Systems. ACM, 2991–2996.
Siyuan Chen, Julien Epps, Natalie Ruiz, and Fang Chen. 2011. Eye activity as a measureof human mental effort in HCI. In Proceedings of the 16th international conferenceon Intelligent user interfaces. ACM, 315–318.
Leana Copeland, Tom Gedeon, and Sabrina Caldwell. 2014. Framework for Dynamic
Text Presentation in eLearning. Procedia Computer Science 39 (2014), 150–153.
Hugo D Critchley. 2002. Electrodermal responses: what happens in the brain. The
Neuroscientist 8, 2 (2002), 132–142.
Jérémy Frey, Maxime Daniel, Julien Castet, Martin Hachet, and Fabien Lotte. 2016.
Framework for electroencephalography-based evaluation of user experience. In
Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems.
ACM, 2283–2294.
Suzanne Hidi. 2001. Interest, reading, and learning: Theoretical and practical considerations. Educational Psychology Review 13, 3 (2001), 191–209.
Shoya Ishimaru, Syed Saqib Bukhari, Carina Heisel, Nicolas Großmann, Pascal Klein,
Jochen Kuhn, and Andreas Dengel. 2018. Augmented Learning on Anticipating
Textbooks with Eye Tracking. In Positive Learning in the Age of Information.
Springer, 387–398.
Shoya Ishimaru, Soumy Jacob, Apurba Roy, Syed Saqib Bukhari, Carina Heisel, Nicolas Großmann, Michael Thees, Jochen Kuhn, and Andreas Dengel. 2017. Cognitive

Gaze-based Interest Detection on Newspaper Articles
State Measurement on Learning Materials by Utilizing Eye Tracker and Thermal
Camera. In Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition, Vol. 8. IEEE, 32–36.
Shoya Ishimaru, Kai Kunze, Koichi Kise, and Masahiko Inami. 2014. Position Paper:
Brain Teasers - Toward Wearable Computing That Engages Our Mind. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous
Computing: Adjunct Publication. ACM, 1405–1408.
Kai Kunze, Susana Sanchez, Tilman Dingler, Olivier Augereau, Koichi Kise, Masahiko
Inami, and Terada Tsutomu. 2015. The augmented narrative: toward estimating
reader engagement. In Proceedings of the 6th augmented human international conference. ACM, 163–164.
Tian Lan, Andre Adami, Deniz Erdogmus, and Misha Pavel. 2005. Estimating cognitive
state using EEG signals. In Signal Processing Conference, 2005 13th European. IEEE,
1–4.
Christian Lander, Marco Speicher, Denise Paradowski, Norine Coenen, Sebastian
Biewer, and Antonio Krüger. 2015. Collaborative newspaper: Exploring an adaptive scrolling algorithm in a multi-user reading scenario. In Proceedings of the 4thInternational Symposium on Pervasive Displays. ACM, 163–169.
Antonio Luque-Casado, Mikel Zabala, Esther Morales, Manuel Mateo-March, andDaniel Sanabria. 2013. Cognitive performance and heart rate variability: the influence of fitness level. PloS one 8, 2 (2013), e56935.
Khalid Masood. 2015. EDA as a Discriminate Feature in Computation of Mental Stress.
In The Second International Conference on Digital Information Processing, Data Mining, and Wireless Communications. 199.
Marco Porta, Stefania Ricotti, and C Jimenez Perez. 2012. Emotional e-learning
through eye tracking. In Global Engineering Education Conference, 2012 IEEE. IEEE,
1–6.
Gary E Raney, Spencer J Campbell, and Joanna C Bovee. 2014. Using eye movements
to evaluate the cognitive processes involved in text comprehension. Journal ofvisualized experiments: JoVE 83 (2014).
Keith Rayner, Kathryn H Chace, Timothy J Slattery, and Jane Ashby. 2006. Eye movements as reflections of comprehension processes in reading. Scientific studies ofreading 10, 3 (2006), 241–255.
Darrell S Rudmann, George W McConkie, and Xianjun Sam Zheng. 2003. Eyetracking
in cognitive state detection for HCI. In Proceedings of the 5th international conference on Multimodal interfaces. ACM, 159–163.
Scott Squires. 2014. The effects of reading interest, reading purpose, and reading maturity on reading comprehension of high school students. Baker University.
Miki Uetsuki, Junji Watanabe, Hideyuki Ando, and Kazushi Maruya. 2017. Reading Traits for Dynamically Presented Texts: Comparison of the Optimum Reading
Rates of Dynamic Text Presentation and the Reading Rates of Static Text Presentation. Frontiers in Psychology 8 (2017), 1390.
S-N Yang and George W McConkie. 2001. Eye movements during reading: A theoryof saccade initiation times. Vision research 41, 25 (2001), 3567–3585.
Johannes Zagermann, Ulrike Pfeil, and Harald Reiterer. 2016. Measuring Cognitive
Load using Eye Tracking Technology in Visual Computing. In Proceedings of the
Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization. 78–85.

PETMEI ’18, June 14–17, 2018, Warsaw, Poland