Eye-pointer Coordination in a Decision-making Task Under Uncertainty

Catia Cepeda

1,2

, Maria Camila Dias

, Dina Rindlisbacher

2,3

, Marcus Cheetham

2,3,∗

and Hugo Gamboa

1,∗

LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics),

Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal

Department of Internal Medicine, University Hospital Zurich, Zurich, Switzerland

University Research Priority Program "Dynamics of Healthy Aging", University Zurich, Zurich, Switzerland

hgamboa@fct.unl.pt

Keywords:

Eye-tracking, Pointer-tracking, Human Computer Interaction, Decision making, Uncertainty.

Abstract:

Eye-tracking (ET) systems, which capture eye movements, are often used to measure human behavior while

interacting with a user interface. Given the high costs and challenges of acquiring, installing and ensuring

good calibration of ET systems, the use of pointer (or mouse) tracking is gaining interest as a viable alter-

native in research on human-computer interaction. In this study, we measured and evaluated temporal and

spatial relationships between eye and pointer movements in a standardized task that allowed us to examine

the relationship between eye and pointer movements while participants made decisions under conditions of

high and low uncertainty. We collected data from N=81 participants and applied a range of metrics to a total

of 5205 decision trials. The overall ﬁndings show that the convergence between eye and pointer movements

is consistently high. Importantly, there are differences in levels of convergence depending on the temporal,

spatial and combined temporo-spatial metrics used. There are also differences in eye-pointer convergence

depending on the relative level of decision uncertainty in the task. In conclusion, the present ﬁndings favour

the use of pointer tracking to analyse human-computer interaction in more complex tasks.

1 INTRODUCTION

The individual style of interaction with a computer

can give insights into user experience, usability and

design of the user interface, and also about the users’

interests, preferences or personality (e.g., (Olson

and Olson, 2003; Dillon and Watson, 1996; Pocius,

1991)). The latter ﬁnding reﬂect the increasing in-

terest in understanding the inﬂuence of the user be-

haviour in human-computer interaction (HCI) (Dja-

masbi et al., 2008; Payne et al., 1988).

The eye-tracker (ET) technique is the usual ap-

proach for tracking human behavior since the mid-

1970s (Rayner, 1998). ET is thought to permit insight

into individuals’ cognitive states (Just and Carpenter,

1976; Olk and Kappas, 2011). A major drawback of

ET is the expensive equipment, challenges of calibra-

tion, and data loss. In addition, participants need to

be physically present for ET studies, which can lead

to smaller sample sizes (Chen et al., 2001; Rodden

and Fu, 2007).

∗

These authors have made an equal contribution.

More recently, several interesting approaches for

pointer tracking analysis have been developed. In

contrast to eye-tracking systems, pointer-tracking

data can be acquired easily and without extra equip-

ment. Taking into account that the results of both sys-

tems are similar, using x and y coordinates across the

screen, pointer-tracking is gaining interest as a viable

alternative to ET.

In the present study, we measured eye and pointer

movement, computed a range of metrics and evalu-

ated temporal and spatial relationships between eye

and pointer movements in a standardized decision

making task. This task, the Iowa Gambling Task

(IGT) (Bechara et al., 1994), allowed us to examine

these relationships while participants made decisions

under conditions of high and low uncertainty.

The IGT was originally devised as a well-

controlled laboratory simulation of real-life decision-

making under conditions of uncertainty. At the begin-

ning of the task, the participant is unfamiliar with the

probabilities of the negative and positive outcomes of

the different decision alternatives that are presented to

the participant. In the ﬁrst phase of the task, the de-

Cepeda, C., Dias, M., Rindlisbacher, D., Cheetham, M. and Gamboa, H.

Eye-pointer Coordination in a Decision-making Task Under Uncertainty.

DOI: 10.5220/0008867500300037

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS, pages 30-37

ISBN: 978-989-758-398-8; ISSN: 2184-4305

cision maker must search, explore and learn the pos-

sible contingencies of monetary gains and loss in or-

der to maximize the overall monetary outcome of the

task. In this sense the participant explores the task

contingencies and engages in decision making under

conditions of high uncertainty (cf. (White and Roth,

2009; Marchionini, 2006)). After gathering infor-

mation and acquiring an understanding of the task’s

reward and loss contingencies, participants learn to

maximize the overall monetary outcome and in effect

make decisions under conditions of higher certainty.

We use these phases of the task to differentiate and

analyse information search and decision making be-

havior under conditions of less compared with more

certainty. We examined whether there is a difference

in the temporal and spatial relationship between eye

gaze and pointer movements in the more uncertain

compared with the less uncertain phases of the task.

2 BACKGROUND

An issue associated with the eye-tracking equipment

is loss of calibration that can lead to missing miss-

ing data or deviation between the recorded and actual

eyed position. According to the context and task, it

is possible to correct this data. Hornof and Halverson

(2002) reported an approach that depends on required

ﬁxation locations to re-calibrate the eye tracker. In

their experiment, it was required to click on a spe-

ciﬁc target and, assuming that the participant looks

at the target during the click, to determine if the dis-

tance between the eye-tracking data and the click was

higher than a certain threshold, the eye tracker would

be automatically recalibrated after the click. An al-

ternative technique consists of two linear regressions

(one for horizontal dimension - x-axis - and another

for vertical dimension - y-axis) between the known

data points and the corresponding raw data (Blignaut

et al., 2014).

In HCI, most authors attempt to ﬁnd a relation-

ship between the eye movements and pointer move-

ments during web browsing using a computer mouse

(Chen et al., 2001; Cooke, 2006; Rodden and Fu,

2007; Rodden et al., 2008; Bieg et al., 2010; Huang

et al., 2011; Liebling and Dumais, 2014; Milisavljevic

et al., 2018), while others build predictive models of

the eye gaze based on mouse movements (Guo et al.,

2013; Huang et al., 2011; Navalpakkam et al., 2013).

A strong correlation between pointer position and

gaze position was found by Chen et al. (2001) in dif-

ferent styles of websites. The authors conclude that

when the mouse is moving to a meaningful region,

the eye gaze is very correlated with the pointer move-

ment. Cooke did in 2006 a similar approach, conclud-

ing that people move more the pointer while searching

information (Cooke, 2006).

Restricting the analysis, Rodden and Fu (2007)

considered the time spent in speciﬁc regions by

pointer and eye, ﬁnding proportions ranging from

25.8% and 59.9%. Later, in 2008, Rodden et al.

(2008) identiﬁed three patterns that seem to indicate

active mouse usage: following the eye vertically, fol-

lowing the eye horizontally, and marking a particular

result.

Whilst determining the inﬂuence of visual search

for a target in pointer-eye coordination, Bieg et al.

(2010) observed two behaviours: usually, the eye

reaches a targeted region before the mouse cursor

does, and if the participant knows the target location,

pointer movements begin without eye guidance. The

study of Liebling and Dumais (2014) also took into

account past experience on the interface in use, con-

cluding that the pointer and eye movements are not

coordinated during one-third of the time depending

on the type of target and familiarity with the task.

Some studies also followed a different approach

and, instead of comparing directly the eye and pointer

cursor, set up mouse features to predict gaze. A study

that improved the eye-pointer correlation using a vari-

ety of cursor behaviours and time-related pointer fea-

tures, also concludes that the future position of the

pointer has a strong correlation with the current eye

position (Huang et al., 2012). Navalpakkam et al.

(2013) predicted eye gaze with 67% accuracy using

mouse features and found a strong correlation be-

tween eye and pointer in areas of user main interest.

Believing that the relation between eye and pointer

movements is dependent on the task performed, Mil-

isavljevic et al. (2018) predicted the regions where the

pointer and eye are for different tasks with an accu-

racy of 70%.

The acquisition of eye-tracking data in the context

of the IGT has been mainly done to analyse pupil dila-

tion as a measure of cognitive effort and arousal dur-

ing the decision process (Fiedler and Glöckner, 2012;

Franco-Watkins and Johnson, 2011), as a marker for

uncertainty (Lavín et al., 2014) or as an anticipation

of disadvantage and advantageous decks (Simonovic

et al., 2017). Zommara et al. (2018) explored the exis-

tence of a gaze bias towards the chosen deck and con-

clude that this happens with or without using a mouse.

In contrast, pointer-tracking is increasingly being

used in psychological research, such as social cogni-

tion, decision-making and learning (for a review, see

(Freeman et al., 2011)). Some studies correlated the

mouse response dynamics with the subjects’ prefer-

ences (e.g. (Koop and Johnson, 2013; Chen and Fis-

Eye-pointer Coordination in a Decision-making Task Under Uncertainty

chbacher, 2016)). For example, Koop and Johnson

(2013) used the IGT and pointer-tracking and, using

metrics from mouse paths, showed that they could

reveal participants’ preferences for different decision

alternatives.

3 STUDY SETUP

3.1 Data Acquisition

To capture eye movements, we used a SMI Red250

eye tracker running IView software. The eye gaze

data ﬁle includes the person ID, the trial number, the

x and y position (in pixels) to where the right and left

eyes are looking, among other information.

The IGT was developed in Presentation software,

from Neurobehavioral Systems r and the pointer-

tracking data was being recorded to an independent

ﬁle in the local disk that is saved after the end of the

game. This ﬁle contains the person ID, the trial num-

ber, the x and y pointer’s position (in pixels) and time.

3.2 Participants

81 volunteers - 59 female and 22 male - participated

in this study, with ages between 16 and 34 years old.

Participants were students and recruited by mailing

list. All participants were native or ﬂuent speakers of

Standard German, consistently right-handed (Annett,

1970). All were healthy, with normal, or corrected-

to-normal, vision, no record of neurological or psy-

chiatric illness and no current medication use. None

reported gambling problems. Written informed con-

sent was obtained before participation according to

the guidelines of the Declaration of Helsinki. Each

volunteer received 20 Swiss Francs for participation.

All participants were tested individually in a small,

sound-attenuated, dimly lit experimental room.

3.3 Task

The IGT (Bechara, 2007) is a widely explored task

that simulates the daily decision-making under condi-

tions of uncertainty. It is a card game with four decks

that differs in the amount of money that could be won

or lost. The game starts by giving the player a ﬁc-

titious amount of money that should be increased as

much as possible. It covers 100 trials, which is un-

known to the player, and in each one of them, the

participant needs to choose one card out of four (by

clicking on it with the cursor). After each choice, it is

revealed the money won or lost. At a certain moment

of the IGT, the player should understand that there are

two advantageous decks (Buelow and Suhr, 2009).

3.4 Procedure

Participants attended a single testing session in a

quiet and comfortable laboratory room, lasting ap-

proximately 60 min. The experiment was conducted

in three phases: First, informed consent and demo-

graphic data were collected. Second, pointer data and

eye-tracking data were acquired during the IGT. Fi-

nally, subjects answered a questionnaire that is not re-

ported in the present study.

In Figure 1 is represented how the IGT was struc-

tured. As is conventionally done, a learning proﬁle

during the IGT can be discerned from an examina-

tion of the card selections in blocks of 20 cards across

the 100 card choices (block 1, cards 1-20; block 2,

cards 21-40 ... block 5, cards 81-100). In each trial,

the participant is instructed to focus on the question

mark. After a brief moment, a set of four cards is

shown and the question mark is replaced by the cur-

sor, that is only available from this moment. The dis-

tribution of the decks on the screen was adjusted to

acquire the eye and pointer-tracking data, with two

decks at the top and two decks at the bottom. The par-

ticipant is told to choose a card from one of the decks

by pressing the mouse button on the corresponding

deck (choice phase). After choosing a card, a red

rectangle appeared around the deck chosen and the

cursor disappears. Only after 1.5 seconds the win and

punishment are shown as feedback. Participants are

told that the goal of the game is to maximize their

winnings and that they are free to switch from any

deck to another at any time. The participants began

with a loan of 2000 CHF. Participants had no knowl-

edge of the distribution of probability and magnitude

of gains and losses over the decks or how many trials

they must play.

4 PRE-PROCESSING

As already referred, a problem that arises in every

study involving eye-tracking data is due to equipment

losses of calibration. Given that we aim to compare

the pointer data and eye-tracking data, we considered

the trials of all subjects and just removed the pointer-

tracking data correspondent to the lost data of the

eye-tracking. The average percentage of lost data for

all subjects was 23.83% and the values range from

0.15% to 99.99%. Besides, 10.7% of the trials had

no data recorded. Using the remaining data, there

was still some eye-tracking data out-of-calibration.

BIOSIGNALS 2020 - 13th International Conference on Bio-inspired Systems and Signal Processing

+ 50

- 50

total: 1250

next trial

previous trial

700 ms

~1000 ms

1500 ms

1 trial

fixation

choice

anticipation

feedback

Block

Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Trial 6

Trial 7

Trial 8

Trial 9

Trial 10

Trial 11

Trial 12

Trial 13

Trial 14

Trial 15

Trial 16

Trial 17

Trial 18

Trial 19

Trial 20

Block

Trial 1

Figure 1: IGT schematic representation of the sequential phases of each trial and respective duration. The total game was

divided into 5 different blocks, each one with 20 trials. A trial was composed by four phases, the ﬁxation, the choice while the

subject is deciding the deck he/she want to select, the anticipation phase to present the deck selected and, ﬁnally, the feedback

with the monetary win, loss and the current money.

The method applied in this work to correct the eye-

tracking data is an adaptation of the one presented by

Hornof and Halverson (2002).

In our experiment, this problem was essentially

solved with two linear regressions (one for X coor-

dinates and another for Y coordinates) between the

known data and the corresponding raw data in two

parts:

1. Adjustment of the eye-tracking data by translat-

ing the whole set of coordinates according to the

difference between a known ﬁxation position and

its given position. The IGT was programmed to

have a question mark in the centre of the screen,

and then the cursor replace the mark. This forces

the player to look initially to the middle of the

screen and therefore, having the initial position of

the eye-tracking for each player able us to trans-

late the x and y axes. This procedure requires the

correct acquisition of the initial ﬁxation of the eye,

otherwise, the calibration can not be done and the

respective trial should not be considered. 9.5% of

the initial trials have no ﬁxation at the beginning

and, consequently, they were not considered.

2. Adjustment of the eye-tracking data by scaling a

known distance. It was assumed that when the

participant clicks in a target, he tends to look at

it. The distance between the pointer and eye coor-

dinates during the click (the choice of the deck)

was accessed for each trial. The ratio between

the pointer and eye coordinates in the moment of

the click was computed and multiplied to the eye-

tracking data (this method was based on (Hornof

and Halverson, 2002)). This step is only applied

if, after the ﬁrst step, the distance between eye and

pointer at the click time was higher than 80 pixels.

This procedure requires the correct acquisition of

the ﬁnal ﬁxation of the eye, otherwise no conclu-

sion can be made and this trial should not be con-

sidered. 5% of the trials resulted from the ﬁrst cal-

ibration have no ﬁxation during the time of click-

ing and, consequently, were not considered. More

than 60% of the trials had not the ﬁnal ﬁxation

near the click area, so these trials were calibrated.

Figures 2 and 3 are an example of a trial that, to

be correctly compared to mouse movements, have

to be calibrated.

Even with the application of the two-phased lin-

ear regression, some trials still have not suitable eye

gaze data. To remove this data, the ratio between the

amount of data outside the region of interest (the cen-

Eye-pointer Coordination in a Decision-making Task Under Uncertainty

Figure 2: Example of a trial before calibration.

Figure 3: Example of a trial after calibration.

tral square where the decks are) and the total data -

outside ratio - was computed. 11.2% of trials were

discarded with this procedure, considering a thresh-

old higher than 20% of outside ratio.

A difference between this method to correct eye-

tracking data and the approaches presented in other

studies (for example, (Chen et al., 2001)) is this elim-

ination of the trials that, after the correction, are still

uncalibrated. If this procedure had been done in the

data without calibration 89.1% of the trials would be

eliminated. This process of calibration reduced our

dataset from 8100 trials to 5205.

5 COMPARISON METRICS AND

RESULTS

To compare the eye gaze and cursor movements dur-

ing a decision-making task, some measures were

computed to take into consideration the spatial and

temporal domain. With this purpose, it was consid-

ered a few regions inside the IGT - each deck consti-

tuted a region and the last region was constituted by

the space outside the decks. Two of the metrics cal-

culated, spatial coordination and temporal coordina-

tion, were adapted from Chen et al. (2001) and Rod-

den and Fu (2007). Another variable was introduced,

temporal-spatial coordination, to assess the amount of

time that the eye gaze and the cursor were visiting the

same region.

5.1 Spatial Coordination (SC)

Independent of time, this feature only evaluate if, dur-

ing a trial, the participant had a consistent interest in

the regions manifested both by pointer and eye move-

ments. This feature is the ratio between the number

of regions that were visited by both eye gaze and cur-

sor and the total number of regions visited by eye or

pointer, according to which of the two visited more

regions.

The considered regions were ”Deck 1”, ”Deck 2”,

”Deck 3” and ”Deck 4”. Region ”Outside the decks”

was not considered since it is a region visited by both

eye gaze and cursor in almost all trials, and, therefore,

the ratio would increase signiﬁcantly due to a region

which is nearly compulsory to visit. For example, to

go from a deck to another, it is required to visit the

region ”Outside the decks”, as well as at the beginning

of each trial, where the participants were instructed to

focus on the middle of the screen.

In terms of possibilities, if the eye gaze and cursor

visited the exactly same decks, this ratio has its max-

imum value of 1. A more speciﬁc example, if in a

certain trial the eye gaze visited ”Deck 1” and ”Deck

4” and the pointer visited only the ”Deck 4”, the value

of this relation would be 0.5.

The mean value of all trials for each participant

was determined. The values range from 0.00 to 1.00

and the mean value and its standard deviation are

0.78 ± 0.29. In our study, it was only considered, for

the eye gaze, the regions visited for more than 60 ms,

which corresponds the minimum ﬁxation duration ac-

cording to SMI (2010).

5.2 Temporal Coordination (TC)

In contrast with the previous feature described, the

one extracted from temporal coordination takes into

account the time spent in each region by the eye gaze

and by the cursor. For each trial, it is calculated the to-

tal duration spent by the pointer and eye gaze in each

region, to then ﬁnd the correlation between these two

measurements (Rodden and Fu, 2007). This correla-

tion was calculated for each region and this feature is

the result of its mean. Here, in addition to the decks’

area, also the area outside the decks is important to be

considered as a region. The correlation was measured

by the Pearson correlation coefﬁcient, which mea-

BIOSIGNALS 2020 - 13th International Conference on Bio-inspired Systems and Signal Processing

sures the linear relationship between the two datasets.

It varies between -1 and +1, with the extremes im-

plying the strongest linear relationships (negative and

positive, respectively) and 0 indicating no correlation

(Sedgwick, 2012).

The times’ correlation coefﬁcient ranges from

0.59 to 0.84, with a mean value and a standard de-

viation of 0.66 ± 0.09.

5.3 Temporal-Spatial Coordination

(TSC)

This variable covers the spatial information and the

temporal information, quantifying the ratio between

the time where the eye gaze and the cursor were in

the same region and the trial time. Similar to tempo-

ral coordination, this feature also considered the ﬁve

regions mentioned above. The mean value of all tri-

als was calculated and the values range from 0.00 to

1.00 with a mean value and a standard deviation of

0.65 ± 0.19.

5.4 High and Low Uncertainty

Conditions

When the participants know in advance the deck that

will be selected, they make a fast decision. There-

fore eye and mouse move directly from the centre

of the screen to the chosen deck. In this condition,

both movements should highly correlate. In contrast,

when the participants explore the options and waver

between different decks, those movements may not

be correlated. To differentiate these two conditions,

the less uncertain condition is established when the

eye gaze and the cursor only visit one region, and the

more uncertain condition is veriﬁed in the other tri-

als, in which the eye gaze and/or cursor examine more

than one deck. In our data set, 62% of the trials rep-

resent the low uncertainty condition.

The three metrics previously presented were as-

sessed for these two conditions and the results are

presented in Tables 1 and 2. As expected, the results

show a higher correlation between the eye and pointer

movements in non-exploratory condition. Neverthe-

less, although spatial coordination shows a decrease

of 48% from non-exploratory to exploratory trails, the

decline is inferior for temporal coordination (22%)

and temporal-spatial coordination (16%). This means

that the time spent by the eye gaze and cursor in the

same regions is comparable in both conditions, but

the visited regions diverge in the exploratory condi-

tion. This suggests that the common regions visited

by both pointer and eye gaze are the ones where the

participant spends the most of the time, and there are

regions that the eye gaze rapidly visits, and the cursor

does not visit at all.

Table 1: Relation between eye and pointer movements for

non-exploratory condition.

Mean STD Min Max

SC 0.97 0.17 0.00 1.00

TC 0.81 0.05 0.76 0.90

TSC 0.71 0.17 0.01 0.99

Table 2: Relation between eye and pointer movements for

exploratory condition.

Mean STD Min Max

SC 0.49 0.16 0.00 1.00

TC 0.59 0.11 0.48 1.00

TSC 0.55 0.18 0.00 0.97

6 CONCLUSIONS

The presented study compared eye and mouse move-

ments during a decision task to understand whether

pointer-tracking could serve as a useful alternative to

eye-tracking for assessing user behavior.

To ensure correct analysis and comparison of eye

and pointer data, it was necessary to resolve calibra-

tion issues and eye drift in the eye gaze data. The

design of the IGT in this study allowed us to deter-

mine the initial and ﬁnal eye ﬁxation in each trial and

therefore, to spatially correct the recorded eye posi-

tions within each decision trail. By applying this pro-

cedure, we were able to use 65% instead of just 11%

of the eye data in further analyses.

Our three approaches for evaluating the coordina-

tion between eye and pointer movements are consis-

tently high. Spatial coordination has a higher mean

correlation, which is in line with previous studies

that reported a stronger correlation of eye and mouse

movements in areas of interest and not necessarily

moving at the same time (Huang et al., 2011; Naval-

pakkam et al., 2013; Bieg et al., 2010). Still, the tem-

poral coordination is higher than the correlations re-

ported in previous studies (Rodden and Fu, 2007).

Regarding the relationship between eye and cur-

sor trajectories in conditions of higher and lower un-

certainty, the developed work demonstrated that there

is a high correlation when uncertainty is low. Never-

theless, when a person hesitates between different de-

cision alternatives this correlation decreases and, con-

trary to what was expected, the results of the temporal

coordination is higher than spatial coordination. This

could mean that in uncertain trials the pointer move-

ment in the area of interest is relatively consistent with

gaze.

Eye-pointer Coordination in a Decision-making Task Under Uncertainty

The temporal-spatial coordination, as a combina-

tion of coordination metrics, gives a clear picture of

the convergence between eye and mouse movements.

The level of correlation is somewhat lower in less un-

certain conditions. Nevertheless, the ﬁndings support

the idea that pointer tracking may be useful as an al-

ternative to eye-tracking.

REFERENCES

Annett, M. (1970). A classiﬁcation of hand preference by

association analysis. Br. J. Psychol., 61:303–321.

Bechara, A. (2007). Iowa Gambling Task: Professional

Manual. Lutz, FL: Psychological Assessment Re-

sources, Inc.

Bechara, A., Damasio, A. R., Damasio, H., and Anderson,

S. W. (1994). Insensitivity to future consequences fol-

lowing damage to human prefrontal cortex. Cognition,

50:7–15.

Bieg, H.-J., Chuang, L. L., Fleming, R. W., Reiterer, H., and

Bülthoff, H. H. (2010). Eye and pointer coordination

in search and selection tasks. In Proceedings of the

2010 Symposium on Eye-Tracking Research & Appli-

cations - ETRA ’10, page 89, New York, New York,

USA. ACM Press.

Blignaut, P., Holmqvist, K., Nyström, M., and Dewhurst,

R. (2014). Improving the accuracy of video-based

eye tracking in real time through post-calibration re-

gression. In Current Trends in Eye Tracking Re-

search, pages 77–100. Springer International Publish-

ing, Cham.

Buelow, M. T. and Suhr, J. A. (2009). Construct validity of

the Iowa gambling task.

Chen, F. and Fischbacher, U. (2016). Response time and

click position: cheap indicators of preferences. Jour-

nal of the Economic Science Association, 2(2):109–

126.

Chen, M. C., Anderson, J. R., and Sohn, M. H. (2001).

What can a mouse cursor tell us more? In CHI ’01 ex-

tended abstracts on Human factors in computing sys-

tems - CHI ’01, page 281, New York, New York, USA.

ACM Press.

Cooke, L. (2006). Is the Mouse a "Poor Man’s Eye

Tracker"? In Proceeding of the 53rd Annual Con-

ference of the Society for Technical Communication,

pages 252–255.

Dillon, A. and Watson, C. (1996). User analysis in HCI -

The historical lessons from individual differences re-

search. International Journal of Human Computer

Studies, 45(6):619–637.

Djamasbi, S., Tulu, B., Loiacono, E., and Shiteﬂeet-Smith,

J. (2008). Can a Reasonable Time Limit Improve

the Effective Usage of a Computerized Decision Aid?

Communications of the Association for Information

Systems, 23(22):393–408.

Fiedler, S. and Glöckner, A. (2012). The dynamics of deci-

sion making in risky choice: An eye-tracking analysis.

Frontiers in Psychology, 3(OCT):335.

Franco-Watkins, A. M. and Johnson, J. G. (2011). Decision

moving window: using interactive eye tracking to ex-

amine decision processes. Behavior Research Meth-

ods, 43(3):853–863.

Freeman, J. B., Dale, R., and Farmer, T. A. (2011). Hand in

motion reveals mind in motion.

Guo, Q., Jin, H., Lagun, D., Yuan, S., and Agichtein, E.

(2013). Mining touch interaction data on mobile de-

vices to predict web search result relevance. In Pro-

ceedings of the 36th international ACM SIGIR con-

ference on Research and development in information

retrieval - SIGIR ’13, page 153.

Hornof, A. J. and Halverson, T. (2002). Cleaning up sys-

tematic error in eye-tracking data by using required

ﬁxation locations. Behavior Research Methods, In-

struments, and Computers, 34(4):592–604.

Huang, J., White, R., and Buscher, G. (2012). User see,

user point. In Proceedings of the 2012 ACM annual

conference on Human Factors in Computing Systems

- CHI ’12, page 1341.

Huang, J., White, R. W., and Dumais, S. (2011). No clicks,

no problem. In Proceedings of the 2011 annual con-

ference on Human factors in computing systems - CHI

’11, page 1225, New York, New York, USA. ACM

Press.

Just, M. and Carpenter, P. (1976). Eye ﬁxations and cogni-

tive processes. Cognitive Psychology, 8(4):441–48.

Koop, G. J. and Johnson, J. G. (2013). The response dy-

namics of preferential choice. Cognitive Psychology,

67(4):151–185.

Lavín, C., San Martín, R., and Rosales Jubal, E. (2014).

Pupil dilation signals uncertainty and surprise in a

learning gambling task. Frontiers in Behavioral Neu-

roscience, 7:218.

Liebling, D. J. and Dumais, S. T. (2014). Gaze and mouse

coordination in everyday work. In Proceedings of the

2014 ACM International Joint Conference on Perva-

sive and Ubiquitous Computing Adjunct Publication -

UbiComp ’14 Adjunct, pages 1141–1150, New York,

New York, USA. ACM Press.

Marchionini, G. (2006). Exploratory search. Communica-

tions of the ACM, 49(4):41.

Milisavljevic, A., Hamard, K., Petermann, C., Gosselin, B.,

Doré-Mazars, K., and Mancas, M. (2018). Eye and

Mouse Coordination During Task: From Behaviour to

Prediction. In Proceedings of the 13th International

Joint Conference on Computer Vision, Imaging and

Computer Graphics Theory and Applications, pages

86–93. SCITEPRESS - Science and Technology Pub-

lications.

Navalpakkam, V., Jentzsch, L., Sayres, R., Ravi, S., Ahmed,

A., and Smola, A. (2013). Measurement and model-

ing of eye-mouse behavior in the presence of nonlin-

ear page layouts. In Proceedings of the 22nd inter-

national conference on World Wide Web - WWW ’13,

pages 953–964.

Olk, B. and Kappas, A. (2011). Eyetracking as a Tool

for Visual Research. In Margolis, E., Pauwels, L.

(eds) The Sage Handbook of Visual Research Meth-

ods, pages 433–51. Los Angeles: Sage.

BIOSIGNALS 2020 - 13th International Conference on Bio-inspired Systems and Signal Processing

Olson, G. M. and Olson, J. S. (2003). Human-Computer

Interaction: Psychological Aspects of the Human

Use of Computing. Annual Review of Psychology,

54(1):491–516.

Payne, J. W., Bettman, J. R., and Johnson, E. J. (1988).

Adaptive Strategy Selection in Decision Making.

Journal of Experimental Psychology: Learning, Mem-

ory, and Cognition, 14(3):534–552.

Pocius, K. E. (1991). Personality factors in human-

computer interaction: A review of the literature. Com-

puters in Human Behavior, 7(3):103–135.

Rayner, K. (1998). Eye movements in reading and informa-

tion processing: 20 years of research. Psychological

bulletin, 124(3):372–422.

Rodden, K. and Fu, X. (2007). Exploring how mouse move-

ments relate to eye movements on web search results

pages. In Special Interest Group on Information Re-

trieval Workshop on Web Information Seeking and In-

teraction, pages 29–32.

Rodden, K., Fu, X., Aula, A., and Spiro, I. (2008). Eye-

mouse coordination patterns on web search results

pages. In Proceeding of the twenty-sixth annual CHI

conference extended abstracts on Human factors in

computing systems - CHI ’08, page 2997, New York,

New York, USA. ACM Press.

Sedgwick, P. (2012). Pearson’s correlation coefﬁcient.

Simonovic, B., Stupple, E. J., Gale, M., and Shefﬁeld, D.

(2017). Stress and Risky Decision Making: Cognitive

Reﬂection, Emotional Learning or Both. Journal of

Behavioral Decision Making, 30(2):658–665.

SMI (2010). Begaze manual.

White, R. W. and Roth, R. A. (2009). Exploratory Search:

Beyond the Query-Response Paradigm. Synthesis

Lectures on Information Concepts, Retrieval, and Ser-

vices, 1(1):1–98.

Zommara, N. M., Takahashi, M., Ounjai, K., and Lauw-

ereyns, J. (2018). A gaze bias with coarse spatial in-

dexing during a gambling task. Cognitive Neurody-

namics, 12(2):171–181.

Eye-pointer Coordination in a Decision-making Task Under Uncertainty