research-article

Gaze Transition Entropy

Authors:

Krzysztof Krejtz,

Andrew Duchowski,

Izabela Krejtz,

Fernando González Perilli,

Natalia VillalobosAuthors Info & Claims

ACM Transactions on Applied Perception (TAP), Volume 13, Issue 1

Article No.: 4, Pages 1 - 20

https://doi.org/10.1145/2834121

Published: 10 December 2015 Publication History

Abstract

This article details a two-step method of quantifying eye movement transitions between areas of interest (AOIs). First, individuals' gaze switching patterns, represented by fixated AOI sequences, are modeled as Markov chains. Second, Shannon's entropy coefficient of the fit Markov model is computed to quantify the complexity of individual switching patterns. To determine the overall distribution of attention over AOIs, the entropy coefficient of individuals' stationary distribution of fixations is calculated. The novelty of the method is that it captures the variability of individual differences in eye movement characteristics, which are then summarized statistically. The method is demonstrated on gaze data collected from two studies, during free viewing of classical art paintings. Normalized Shannon's entropy, derived from individual transition matrices, is related to participants' individual differences as well as to either their aesthetic impression or recognition of artwork. Low transition and high stationary entropies suggest greater curiosity mixed with a higher subjective aesthetic affinity toward artwork, possibly indicative of visual scanning of the artwork in a more deliberate way. Meanwhile, both high transition and stationary entropies may be indicative of recognition of familiar artwork.

References

[1]

C. Acartürk and C. Habel. 2012. Eye tracking in multimodal comprehension of graphs. In Proceedings of the Workshop on Technology-Enhanced Diagrams Research (TEDR'12). 11--25.

[2]

Robert Russell Althoff. 1998. Eye Movement-Based Memory Assessment: The Use of Eye Movement Monitoring as an Indirect Measure of Memory. Ph.D. Dissertation. University of Illinois at Urbana-Champaign.

[3]

Roman Bednarik, Niko Myller, Erkki Sutinen, and Markku Tukiainen. 2005. Applying eye-movement tracking to program visualization. In Proceedings of the 2005 IEEE Symposium on Visual Languages and Human-Centric Computing (VLHCC'05). IEEE, Los Alamitos, CA, 302--304.

Digital Library

[4]

Julian Besag and Debashis Mondal. 2013. Exact goodness-of-fit tests for Markov chains. Biometrics 69, 2, 488--496.

[5]

Cezary Biele, Agata Kopacz, and Krzysztof Krejtz. 2013. Shall we care about the user's feelings? Influence of affect and engagement on visual attention. In Proceedings of the International Conference on Multimedia, Interaction, Design, and Innovation (MIDI'13). ACM, New York, NY, Article No. 7.

Digital Library

[6]

Gabriela Ciuperca and Valerie Girardin. 2005. On the estimation of the entropy rate of finite Markov chains. In Proceedings of the 11th International Symposium on Applied Stochastic Models and Data Analysis.

[7]

Thomas M. Cover and Joy A. Thomas. 2006. Elements of Information Theory (2nd ed.). John Wiley & Sons.

Digital Library

[8]

Jhani A. de Bruin, Katherine M. Malan, and Jan H. P. Eloff. 2013. Saccade deviation indicators for automated eye tracking analysis. In Proceedings of the 2013 Conference on Eye Tracking South Africa (ETSA'13). ACM, New York, NY, 47--54.

Digital Library

[9]

Andrew T. Duchowski, Sabarish V. Babu, Jeff Bertrand, and Krzysztof Krejtz. 2014. Gaze analytics pipeline for unity 3D integration: Signal filtering and analysis. In Proceedings of the 2nd International Workshop on Eye Tracking for Spatial Research (ET4S'14).

[10]

Andrew T. Duchowski, Jason Driver, Sheriff Jolaoso, William Tan, Beverly N. Ramey, and Ami Robbins. 2010. Scanpath comparison revisited. In Proceedings of the 2010 Symposium on Eye-Tracking Research and Applications (ETRA'10). ACM, New York, NY, 219--226.

Digital Library

[11]

Laura Ekroot and Thomas M. Cover. 1993. The entropy of Markov trajectories. IEEE Transactions on Information Theory 39, 4, 1418--1421.

Digital Library

[12]

Stephen R. Ellis and Lawrence Stark. 1986. Statistical dependency in visual scanning. Human Factors 28, 4, 421--438.

Digital Library

[13]

Miquel Feixas, Esteve del Acebo, Philippe Bekaert, and Mateu Sbert. 1999. An information theory framework for the analysis of scene complexity. In EuroGraphics, P. Brunet and R. Scopigno (Eds.), Vol. 18. EuroGraphics, 95--106.

[14]

Peter Fischer and Elizabeth Peinsipp-Byma. 2007. Eye tracking for objective usability evaluation. In Proceedings of the European Conference on Eye Movements (ECEM'07).

[15]

Joseph H. Goldberg and Xerxes P. Kotval. 1999. Computer interface evaluation using eye movements: Methods and constructs. International Journal of Industrial Ergonomics 24, 631--645.

[16]

Thomas Grindinger, Andrew T. Duchowski, and Michael Sawyer. 2010a. Group-wise similarity and classification of aggregate scanpaths. In Proceedings of the 2010 Symposium on Eye Tracking Research and Applications (ETRA'10). ACM, New York, NY, 101--104.

Digital Library

[17]

Thomas J. Grindinger, Vidya N. Murali, Stephen Tetreault, Andrew T. Duchowski, Stan T. Birchfield, and Pilar Orero. 2010b. Algorithm for discriminating aggregate gaze points: Comparison with salient regions-of-interest. In Proceedings of the International Workshop on Gaze Sensing and Interactions (IWCGI'10). 390--399.

Digital Library

[18]

Jean Hausser and Korbinian Strimmer. 2009. Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks. Journal of Machine Learning Research 1469--1484. http://jmlr.org/papers/v10/hausser09a.html.

Digital Library

[19]

Elizabeth Hume and Frédéric Mailhot. 2013. The role of entropy and surprisal in phonologization and language change. In Origins of Sound Change: Approaches to Phonologization, Alan C. L. Yu (Ed.). Oxford University Press, Oxford, UK, 29--47.

[20]

Alex D. Hwang, Hsueh-Cheng Wang, and Marc Pomplun. 2011. Semantic guidance of eye movements in real-world scenes. Vision Research 51, 1192--1205.

[21]

Halszka Jarodzka, Kenneth Holmqvist, and Marcus Nyström. 2010. A vector-based, multidimensional scanpath similarity measure. In Proceedings of the 2010 Symposium on Eye-Tracking Research and Applications (ETRA'10). ACM, New York, NY, 211--218.

Digital Library

[22]

Todd B. Kashdan, Matt W. Gallagher, Paul J. Silvia, Beate P. Winterstein, William E. Breen, Daniel Terhar, and Michael F. Steger. 2009. The curiosity and exploration inventory-II: Development, factor structure, and psychometrics. Journal of Research in Personality 43, 987--998.

[23]

Krzysztof Krejtz, Tomasz Szmidt, Andrew T. Duchowski, and Izabela Krejtz. 2014. Entropy-based statistical analysis of eye movement transitions. In Proceedings of the 2014 Symposium on Eye-Tracking Research and Applications (ETRA'14). ACM, New York, NY.

Digital Library

[24]

Anje Kruizinga, Ben Mulder, and Dick de Waard. 2006. Eye scan patterns in a simulated ambulance dispatchers task. In Developments in Human Factors in Transportation, Design, and Evaluation. Shaker Publishing, Orbassano, Italy.

[25]

Solomon Kullback and R. A. Leibler. 1951. On information and sufficiency. Annals of Mathematical Statistics 2222, 79--86.

[26]

John Liechty, Rik Pieters, and Michel Wedel. 2003. Global and local covert visual attention: Evidence from a Bayesian hidden Markov model. Psychometrika 68, 4, 519--541.

[27]

Vincente Ponsoda, D. Scott, and J. M. Findlay. 1995. A probability vector and transition matrix analysis of eye movements during visual search. Acta Psychologica 88, 167--185.

[28]

R Development Core Team. 2011. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ ISBN 3-900051-07-0.

[29]

Frederick Shic, Katarzyna Chawarska, Jessica, and Brian Scassellati. 2008. Autism, eye-tracking, entropy. In Proceedings of the 7th IEEE International Conference on Development and Learning (ICDL'08). 73--78.

[30]

Lisa Vandeberg, Samantha Bouwmeester, Bruno R. Bocanegra, and Rolf A. Zwaan. 2013. Detecting cognitive interactions through eye movement transitions. Journal of Memory and Language 69, 445--460.

[31]

Boris M. Velichkovsky, Markus Joos, Jens R. Helmert, and Sebastian Pannasch. 2005. Two visual systems and their eye movements: Evidence from static and dynamic scene perception. In Proceedings of the 27th Conference of the Cognitive Science Society (CogSci'05). 2283--2288.

[32]

Robin S. Weiss, Roger Remington, and Stephen R. Ellis. 1989. Sampling distributions of the entropy in visual scanning. Behavior Research Methods, Instruments, and Computers 21, 3, 348--352.

Cited By

Abeysinghe YMahanama BJayawardena GJayawardana YSunkara MDuchowski AAshok VJayarathna S(2024)A-DisETrac Advanced Analytic Dashboard for Distributed Eye TrackingInternational Journal of Multimedia Data Engineering and Management10.4018/IJMDEM.34179215:1(1-20)Online publication date: 2-Apr-2024
https://doi.org/10.4018/IJMDEM.341792
Liu ZLi JZhang YWu DHuo YYang JZhang MDong CJiang LSun RZhou RLi FYu XZhu DGuo YChen J(2024)Auxiliary Diagnosis of Children with Attention-Deficit/Hyperactivity Disorder: An Eye-Tracking Study with Novel Digital Biomarkers (Preprint)JMIR mHealth and uHealth10.2196/58927Online publication date: 29-Mar-2024
https://doi.org/10.2196/58927
Melnyk KFriedman LKomogortsev O(2024)What can entropy metrics tell us about the characteristics of ocular fixation trajectories?PLOS ONE10.1371/journal.pone.029182319:1(e0291823)Online publication date: 2-Jan-2024
https://doi.org/10.1371/journal.pone.0291823
Show More Cited By

Index Terms

Gaze Transition Entropy
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology

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Information & Contributors

Information

Published In

cover image ACM Transactions on Applied Perception

ACM Transactions on Applied Perception Volume 13, Issue 1

December 2015

112 pages

ISSN:1544-3558

EISSN:1544-3965

DOI:10.1145/2837040

Editors:
Victoria Interrante
University of Minnesota, USA
,
Diego Gutierrez
Universidad de Zaragoza, Spain

Issue’s Table of Contents

Copyright © 2015 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 December 2015

Accepted: 01 September 2015

Revised: 01 September 2015

Received: 01 September 2014

Published in TAP Volume 13, Issue 1

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Cited By

Abeysinghe YMahanama BJayawardena GJayawardana YSunkara MDuchowski AAshok VJayarathna S(2024)A-DisETrac Advanced Analytic Dashboard for Distributed Eye TrackingInternational Journal of Multimedia Data Engineering and Management10.4018/IJMDEM.34179215:1(1-20)Online publication date: 2-Apr-2024
https://doi.org/10.4018/IJMDEM.341792
Liu ZLi JZhang YWu DHuo YYang JZhang MDong CJiang LSun RZhou RLi FYu XZhu DGuo YChen J(2024)Auxiliary Diagnosis of Children with Attention-Deficit/Hyperactivity Disorder: An Eye-Tracking Study with Novel Digital Biomarkers (Preprint)JMIR mHealth and uHealth10.2196/58927Online publication date: 29-Mar-2024
https://doi.org/10.2196/58927
Melnyk KFriedman LKomogortsev O(2024)What can entropy metrics tell us about the characteristics of ocular fixation trajectories?PLOS ONE10.1371/journal.pone.029182319:1(e0291823)Online publication date: 2-Jan-2024
https://doi.org/10.1371/journal.pone.0291823
Wang YJiang YHu ZRuhdorfer CBâce MBulling A(2024)VisRecall++: Analysing and Predicting Visualisation Recallability from Gaze BehaviourProceedings of the ACM on Human-Computer Interaction10.1145/36556138:ETRA(1-18)Online publication date: 28-May-2024
https://dl.acm.org/doi/10.1145/3655613
Stark PJung AHahn JKasneci EGöllner R(2024)Using Gaze Transition Entropy to Detect Classroom Discourse in a Virtual Reality ClassroomProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3653335(1-11)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3649902.3653335
Zhang YYadav AHopko SMehta RGrollman DBroadbent EJu WSoh HWilliams T(2024)In Gaze We Trust: Comparing Eye Tracking, Self-report, and Physiological Indicators of Dynamic Trust during HRICompanion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610978.3640649(1188-1193)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610978.3640649
Edinger JHeck MBecker C(2024)Emotion Prediction Through Eye Tracking in Affective Computing Systems2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10502422(326-332)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerComWorkshops59983.2024.10502422
Jayawardena GAshok VJayarathna S(2024)Advanced Gaze Analytics Dashboard2024 IEEE International Conference on Information Reuse and Integration for Data Science (IRI)10.1109/IRI62200.2024.00034(114-119)Online publication date: 7-Aug-2024
https://doi.org/10.1109/IRI62200.2024.00034
Jayasuriya MHu GLe DAng KSankaran SLiu D(2024)A 3D Vector Field and Gaze Data Fusion Framework for Hand Motion Intention Prediction in Human-Robot Collaboration2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10609996(5637-5643)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10609996
Zhu YXu QZhen KZhang RWang S(2024)Quantitative Analysis of Eye-Tracking Data Based on Information-Theoretic Tools for Measuring Driver Drowsiness2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10687976(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10687976
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