research-article

Using Eye Gaze Data and Visual Activities to Infer Human Cognitive Styles: Method and Feasibility Studies

Authors:

George E. Raptis,

Christina Katsini,

Christos Fidas,

George Samaras,

Nikolaos AvourisAuthors Info & Claims

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 164 - 173

https://doi.org/10.1145/3079628.3079690

Published: 09 July 2017 Publication History

Abstract

Recent research provides evidence that individual differences in human cognitive styles affect user performance and experience in diverse application domains. However, state-of-the-art elicitation methods of cognitive styles require researchers to apply explicit, in-lab, and time-consuming "paper-and-pencil" techniques, rendering real-time integration of cognitive styles? elicitation impractical in interactive system design. Aiming to elaborate an implicit elicitation method of cognitive styles, this paper reports two feasibility studies based on an eye-tracking multifactorial model. In both studies, participants performed visual activities of varying characteristics, and the eye-tracking analysis revealed quantitative differences on visual behavior among individuals with different cognitive styles. Based on these differences, a series of classification experiments were conducted, and the results revealed that gaze-based implicit elicitation of cognitive styles in real-time is feasible, which could be used by interactive systems to adapt to the users' cognitive needs and preferences, to better assist them, and improve their performance and experience.

References

[1]

Maria Kozhevnikov 2007. Cognitive styles in the context of modern psychology: Toward an integrated framework of cognitive style. Psychological Bulletin. 133, 3 (2007), 464--481.

[2]

Nikos Tsianos, Panagiotis Germanakos, Zacharias Lekkas, Costas Mourlas, and George Samaras 2009. Eye-Tracking Users' Behavior in Relation to Cognitive Style within an E-learning Environment. 2009 Ninth IEEE International Conference on Advanced Learning Technologies (Jul. 2009), 329--333.

Digital Library

[3]

Dereck Toker, Cristina Conati, Ben Steichen, and Giuseppe Carenini 2013. Individual user characteristics and information visualization: Connecting the dots through eye tracking. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. (2013), 295--304.

Digital Library

[4]

Marios Belk, Christos Fidas, Panagiotis Germanakos, and George Samaras 2015. Do human cognitive differences in information processing affect preference and performance of CAPTCHA? International Journal of Human-Computer Studies. 84, (2015), 1--18.

Digital Library

[5]

Franco Mawad, Marcela Trías, Ana Giménez, Alejandro Maiche, and Gastón Ares 2015. Influence of cognitive style on information processing and selection of yogurt labels: Insights from an eye-tracking study. Food Research International. 74, (2015), 1--9.

[6]

Khamsum Kinley, Dian Tjondronegoro, Helen Partridge, and Sylvia Edwards 2012. Human-computer interaction: the impact of users' cognitive styles on query reformulation behaviour during web searching. Proceedings of the 24th Australian Computer-Human Interaction Conference on - OzCHI '12 (New York, New York, USA, 2012), 299--307.

Digital Library

[7]

George E. Raptis, Christos A. Fidas, and Nikolaos M. Avouris 2016. Do Field Dependence-Independence Differences of Game Players Affect Performance and Behaviour in Cultural Heritage Games? Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play - CHI PLAY '16 (New York, New York, USA, 2016), 38--43.

Digital Library

[8]

Richard Riding and Indra Cheema 1991. Cognitive Styles--an overview and integration. Educational Psychology. 11, 3--4 (Jan. 1991), 193--215.

[9]

Herman A. Witkin 1950. Individual Differences in Ease of Perception of Embedded Figures. Journal of Personality. 19, 1 (Sep. 1950), 1--15.

[10]

Herman. A. Witkin, Carol A. Moore, Donald R. Goodenough, and Patricia W. Cox 1975. Field-Dependent and Field-Independent Cognitive Styles and their Educational Implications. ETS Research Bulletin Series. 1975, 2 (Dec. 1975), 1--64.

[11]

Christopher W. Allinson and John Hayes 1996. The Cognitive Style Index: A Measure of Intuition-Analysis For Organizational Research. Journal of Management Studies. 33, 1 (Jan. 1996), 119--135.

[12]

Daniel Patanella et al. 2011. Cognitive Styles. Encyclopedia of Child Behavior and Development. Springer US. 382--383.

[13]

Philip K. Oltman, Evelyn Raskin, and Herman A. Witkin 1971. Group Embedded Figures Test. Consulting Psychologists Press.

[14]

George E. Raptis, Christos A. Fidas, and Nikolaos M. Avouris 2016. Using Eye Tracking to Identify Cognitive Differences: A Brief Literature Review. 20th Pan-Hellenic Conference in Informatics (Patras, Greece, 2016).

Digital Library

[15]

Christian Gütl, Maja Pivec, Christian Trummer, Victor Manuel García-Barrios, Felix Mödritscher, Juergen Pripfl, and Martin Umgeher 2005. Adele (adaptive e-learning with eye-tracking): Theoretical background, system architecture and application scenarios. European Journal of Open, Distance and E-Learning. 8, 2 (2005).

[16]

Efi A. Nisiforou and Andrew Laghos 2013. Do the eyes have it? Using eye tracking to assess students cognitive dimensions. Educational Media International. 50, 4 (Dec. 2013), 247--265.

[17]

Efi Nisiforou and Andrew Laghos 2016. Field Dependence--Independence and Eye Movement Patterns: Investigating Users' Differences Through an Eye Tracking Study. Interacting with Computers. 28, 4 (Jun. 2016), 407--420.

[18]

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. Applied Sciences. (2004).

[19]

Efi A. Nisiforou, Eleni Michailidou, and Andrew Laghos 2014. Using Eye Tracking to Understand the Impact of Cognitive Abilities on Search Tasks. 46--57.

[20]

Ben Steichen, Giuseppe Carenini, and Cristina Conati 2013. User-adaptive information visualization. Proceedings of the 2013 international conference on Intelligent user interfaces - IUI '13 (New York, New York, USA, 2013), 317.

Digital Library

[21]

Marios Belk, Efi Papatheocharous, Panagiotis Germanakos, and George Samaras 2013. Modeling users on the World Wide Web based on cognitive factors, navigation behavior and clustering techniques. Journal of Systems and Software. 86, 12 (Dec. 2013), 2995--3012.

Digital Library

[22]

Sherry Y. Chen and Xiaohui Liu 2008. An Integrated Approach for Modeling Learning Patterns of Students in Web-Based Instruction. ACM Transactions on Computer-Human Interaction. 15, 1 (May 2008), 1--28.

Digital Library

[23]

Yi Wang, Jalal Mahmud, and Taikun Liu 2016. Understanding Cognitive Styles from User-Generated Social Media Content. Proceedings of the Tenth International AAAI Conference on Web and Social Media (ICWSM 2016) (Cologne, Germany, 2016).

[24]

Chu-Han Chan, Chen-Wei Hsieh, and Sherry Y. Chen 2014. Cognitive styles and the use of electronic journals in a mobile context. Journal of Documentation. 70, 6 (2014), 997--1014.

[25]

Ben Steichen, Michael M.a Wu, Dereck Toker, Cristina Conati, and Giuseppe Carenini 2014. Te,Te,Hi,Hi: Eye gaze sequence analysis for informing user-adaptive information visualizations. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8538, (2014), 183--194.

[26]

Anatoly Yelizarov and Dennis Gamayunov 2014. Adaptive Visualization Interface That Manages User's Cognitive Load Based on Interaction Characteristics. Proceedings of the 7th International Symposium on Visual Information Communication and Interaction - VINCI '14 (New York, New York, USA, 2014), 1--8.

Digital Library

[27]

Andreas Demetriou, George Spanoudis, Michael Shayer, Antigoni Mouyi, Smaragda Kazi, and Maria Platsidou 2013. Cycles in speed-working memory-G relations: Towards a developmental--differential theory of the mind. Intelligence. 41, 1 (Jan. 2013), 34--50.

[28]

Michael Kirton 1976. Adaptors and innovators: A description and measure. Journal of Applied Psychology. 61, 5 (1976), 622--629.

[29]

Charoula Angeli, Nicos Valanides, and Paul Kirschner 2009. Field dependence--independence and instructional-design effects on learners' performance with a computer-modeling tool. Computers in Human Behavior. 25, 6 (Nov. 2009), 1355--1366.

Digital Library

[30]

Dane M. Chapman and Judith G. Calhoun 2006. Validation of learning style measures: implications for medical education practice. Medical Education. 40, 6 (Jun. 2006), 576--583.

[31]

D. Shinar, E.D. McDowell, M.J. Rackoff, and T.H. Rockwell 1978. Field dependence and driver visual search behavior. Human Factors. 20, 5 (1978), 553--560.

[32]

Peter Wittek, Ying-Hsang Liu, Sándor Darányi, Tom Gedeon, and Ik Soo Lim 2016. Risk and Ambiguity in Information Seeking: Eye Gaze Patterns Reveal Contextual Behavior in Dealing with Uncertainty. Frontiers in Psychology. 7, (Nov. 2016).

[33]

Andrew T. Duchowski 2007. Eye tracking methodology: Theory and practice. Springer London.

Digital Library

[34]

Licheng Shi, Yuling Hu, and Jiali Wang 2016. Effects of cognitive styles on eye fixations: Evidence from drivers' eye movements. International Journal of Psychology. 51, 1153 (2016).

[35]

Shi Zhuomin and Zheng Wanyi 2014. An Eye-tracking Study on the Differences Between Two Cognitive Styles of Individuals in Reading Advertising. Journal of Marketing Science. 10, 3 (2014), 128--145.

[36]

James L.C. Wijnen and Cees J. Groot 1984. An eye movement analysis system (EMAS) for the identification of cognitive processes on figural tasks. Behavior Research Methods, Instruments, & Computers. 16, 3 (May 1984), 277--281.

[37]

Ming-Shiunn Huang and Brian Byrne 1978. Cognitive style and lateral eye movements. British Journal of Psychology. 69, 1 (1978), 85--90.

[38]

Maria Solé Puig, Laura Puigcerver, J. Antonio Aznar-Casanova, and Hans Supèr 2013. Difference in Visual Processing Assessed by Eye Vergence Movements. PLoS ONE. 8, 9 (Sep. 2013), e72041.

[39]

Kerstin Gidlöf, Annika Wallin, Richard Dewhurst, and Kenneth Holmqvist 2013. Using eye tracking to trace a cognitive process: Gaze behaviour during decision making in a natural environment. Journal of Eye Movement Research. 6, 1 (2013).

[40]

George E. Raptis, Christos A. Fidas, and Nikolaos M. Avouris 2016. Differences of Field Dependent/Independent Gamers on Cultural Heritage Playing: Preliminary Findings of an Eye--Tracking Study. 199--206.

[41]

Martha E. Crosby and W. Wesley Peterson 1991. Using Eye Movements to Classify Search Strategies. Proceedings of the Human Factors Society Annual Meeting. 35, 20 (Sep. 1991), 1476--1480.

[42]

Rodney C. Conklin, Walter Muir, and Frederick J. Boersma 1968. Field Dependency-Independency and Eye-Movement Patterns. Perceptual and Motor Skills. 26, 1 (Feb. 1968), 59--65.

[43]

Sukru Eraslan, Yeliz Yesilada, and Simon Harper 2016. Eye tracking scanpath analysis on web pages. Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications - ETRA '16 (New York, New York, USA, 2016), 103--110.

Digital Library

[44]

Krzysztof Krejtz, Andrew Duchowski, Tomasz Szmidt, Izabela Krejtz, Fernando González Perilli, Ana Pires, Anna Vilaro, and Natalia Villalobos 2015. Gaze Transition Entropy. ACM Transactions on Applied Perception. 13, 1 (Dec. 2015), 1--20.

Digital Library

[45]

Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten 2009. The WEKA data mining software. ACM SIGKDD Explorations Newsletter. 11, 1 (Nov. 2009), 10.

Digital Library

[46]

Oleg V Komogortsev, Denise V Gobert, Sampath Jayarathna, Do Hyong Koh, and Sandeep M. Gowda 2010. Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors. IEEE Transactions on Biomedical Engineering. 57, 11 (Nov. 2010), 2635--2645.

[47]

Mohammad Khatib and Rasoul Mohammad Hosseinpur 2011. On the Validity of the Group Embedded Figure Test (GEFT). Journal of Language Teaching and Research. 2, 3 (May 2011).

[48]

Kurt Gottschaldt 1938. Gestalt factors and repetition. A source book of Gestalt psychology. Kegan Paul, Trench, Trubner & Company. 109--135.

[49]

George E. Raptis, Christos A. Fidas, and Nikolaos M. Avouris 2017. On Implicit Elicitation of Cognitive Strategies using Gaze Transition Entropies in Pattern Recognition Tasks. Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '17 (New York, New York, USA, 2017), 1993--2000.

Digital Library

[50]

Rachna Dhamija and Adrian Perrig 2000. Deja Vu-A User Study: Using Images for Authentication. USENIX Security Symposium (2000), 4.

Digital Library

[51]

Martin Mihajlov and Borka Jerman-Blažič 2011. On designing usable and secure recognition-based graphical authentication mechanisms. Interacting with Computers. 23, 6 (Nov. 2011), 582--593.

Digital Library

[52]

Robert Biddle, Sonia Chiasson, and P.C. Van Oorschot 2012. Graphical passwords. ACM Computing Surveys. 44, 4 (Aug. 2012), 1--41.

Digital Library

[53]

Yao Ma, Jinjuan Feng, Libby Kumin, and Jonathan Lazar 2013. Investigating User Behavior for Authentication Methods. ACM Transactions on Accessible Computing. 4, 4 (Jul. 2013), 1--27.

Digital Library

[54]

Christina Katsini, Christos Fidas, Marios Belk, Nikolaos Avouris, and George Samaras 2017. Influences of Users' Cognitive Strategies on Graphical Password Composition. Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '17 (New York, New York, USA, 2017), 2698--2705.

Digital Library

Cited By

Mohamed Selim ABarz MBhatti OAlam HSonntag D(2024)A review of machine learning in scanpath analysis for passive gaze-based interactionFrontiers in Artificial Intelligence10.3389/frai.2024.13917457Online publication date: 5-Jun-2024
https://doi.org/10.3389/frai.2024.1391745
Anastasiou DCoppens AMaquil V(2024)Gesture combinations during collaborative decision-making at wall displaysi-com10.1515/icom-2023-003723:1(57-69)Online publication date: 25-Mar-2024
https://doi.org/10.1515/icom-2023-0037
Langner MToreini PMaedche A(2024)Cognitive state detection with eye tracking in the field: an experience sampling study and its lessons learnedi-com10.1515/icom-2023-003523:1(109-129)Online publication date: 15-Apr-2024
https://doi.org/10.1515/icom-2023-0035
Show More Cited By

Index Terms

Using Eye Gaze Data and Visual Activities to Infer Human Cognitive Styles: Method and Feasibility Studies

Recommendations

Inferring Cognitive Style from Eye Gaze Behavior During Information Visualization Usage
UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Information Visualization is a key technique to assist users in data analysis tasks, by creating visual representations of data to amplify human cognition. However, while human cognitive abilities and styles have been shown to differ significantly, ...
Subtle gaze direction

This article presents a novel technique that combines eye-tracking with subtle image-space modulation to direct a viewer's gaze about a digital image. We call this paradigm subtle gaze direction. Subtle gaze direction exploits the fact that our ...
Visual Cognitive Radio

Cognitive radio is always based on spectrum sensing to cognize the physical characteristics of the wireless channel and carry out wireless communication resource scheduling. This kind of method makes it hard for cognitive radio to respond to a change in ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

420 pages

ISBN:9781450346351

DOI:10.1145/3079628

General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Federica Cena
University of Torino, Italy
,
Michel Desmarais
Polytechnique University, Montreal, Canada

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for 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 ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UMAP '17

Sponsor:

UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

Acceptance Rates

UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

74
Total Citations
View Citations
952
Total Downloads

Downloads (Last 12 months)92
Downloads (Last 6 weeks)6

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mohamed Selim ABarz MBhatti OAlam HSonntag D(2024)A review of machine learning in scanpath analysis for passive gaze-based interactionFrontiers in Artificial Intelligence10.3389/frai.2024.13917457Online publication date: 5-Jun-2024
https://doi.org/10.3389/frai.2024.1391745
Anastasiou DCoppens AMaquil V(2024)Gesture combinations during collaborative decision-making at wall displaysi-com10.1515/icom-2023-003723:1(57-69)Online publication date: 25-Mar-2024
https://doi.org/10.1515/icom-2023-0037
Langner MToreini PMaedche A(2024)Cognitive state detection with eye tracking in the field: an experience sampling study and its lessons learnedi-com10.1515/icom-2023-003523:1(109-129)Online publication date: 15-Apr-2024
https://doi.org/10.1515/icom-2023-0035
Caruso M(2024)A Multimodal Understanding of the Eye-Mind LinkProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3688617(632-636)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3688617
Tang HBenerradi JMaior HPike MLandowska AWilson M(2024)Exploring the Impact of Verbal-Imagery Cognitive Style on Web Search Behaviour and Mental WorkloadProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638313(303-316)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638313
Hosseini MMueller HBoll S(2024)Controlling the Rooms: How People Prefer Using Gestures to Control Their Smart HomesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642687(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642687
Shen YXu MLiang W(2024)Context-Aware Head-and-Eye Motion Generation with Diffusion Model2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR)10.1109/VR58804.2024.00039(157-167)Online publication date: 16-Mar-2024
https://doi.org/10.1109/VR58804.2024.00039
Castilla DDel Tejo Catalá OPons PSignol FRey BSuso-Ribera CPerez-Cortes J(2024)Improving the understanding of web user behaviors through machine learning analysis of eye-tracking dataUser Modeling and User-Adapted Interaction10.1007/s11257-023-09373-y34:2(293-322)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11257-023-09373-y
Pacol CRodrigo MTablatin C(2024)A Comparative Study of High and Low Performing Students’ Visual Effort and Attention When Identifying Syntax ErrorsAugmented Cognition10.1007/978-3-031-61569-6_6(77-94)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-61569-6_6
Steichen B(2024)Computational Methods to Infer Human Factors for Adaptation and Personalization Using Eye TrackingA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_7(183-204)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_7
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents