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Task classification model for visual fixation, exploration, and search

Authors:

Klaus MuellerAuthors Info & Claims

ETRA '19: Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications

Article No.: 65, Pages 1 - 4

https://doi.org/10.1145/3314111.3323073

Published: 25 June 2019 Publication History

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Abstract

Yarbus' claim to decode the observer's task from eye movements has received mixed reactions. In this paper, we have supported the hypothesis that it is possible to decode the task. We conducted an exploratory analysis on the dataset by projecting features and data points into a scatter plot to visualize the nuance properties for each task. Following this analysis, we eliminated highly correlated features before training an SVM and Ada Boosting classifier to predict the tasks from this filtered eye movements data. We achieve an accuracy of 95.4% on this task classification problem and hence, support the hypothesis that task classification is possible from a user's eye movement data.

References

[1]

Ali Borji and Laurent Itti. 2014. Defending Yarbus: Eye movements reveal observers' task. Journal of vision 14, 3 (2014), 29--29. Leon Bottou, Corinna Cortes, V Vapnik, JS Denker, H Drucker, I Guyon, LD Jackel, Y LeCun, UA Muller, E Sackinger, et al. 1994. Comparison of classifier methods: a case study in handwritten digit recognition. In Proceedings of 12th International Conference on Pattern Recognition. IEEE, 77--82.

Crossref

Google Scholar

[2]

Shenghui Cheng and Klaus Mueller. 2016. The data context map: Fusing data and attributes into a unified display. IEEE transactions on visualization and computer graphics 22, 1 (2016), 121--130.

Google Scholar

[3]

Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine learning 20, 3 (1995), 273--297.

Digital Library

Google Scholar

[4]

Yoav Freund, Robert E Schapire, et al. 1996. Experiments with a new boosting algorithm. In icml, Vol. 96. Citeseer, 148--156.

Digital Library

Google Scholar

[5]

Michelle R Greene, Tommy Liu, and Jeremy M Wolfe. 2012. Reconsidering Yarbus: A failure to predict observers' task from eye movement patterns. Vision research 62 (2012), 1--8.

Google Scholar

[6]

Chih-Wei Hsu and Chih-Jen Lin. 2002. A comparison of methods for multiclass support vector machines. IEEE transactions on Neural Networks 13, 2 (2002), 415--425.

Digital Library

Google Scholar

[7]

Joseph B Kruskal. 1964. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29, 1 (1964), 1--27.

Crossref

Google Scholar

[8]

Ayush Kumar, Rudolf Netzel, Michael Burch, Daniel Weiskopf, and Klaus Mueller. 2018. Visual Multi-Metric Grouping of Eye-Tracking Data. Journal ofEye Movement Research 10, 5 (2018), 11.

Google Scholar

[9]

David Opitz and Richard Maclin. 1999. Popular ensemble methods: An empirical study. Journal of artificial intelligence research 11 (1999), 169--198.

Digital Library

Google Scholar

[10]

Jorge Otero-Millan, Xoana G Troncoso, Stephen L Macknik, Ignacio Serrano-Pedraza, and Susana Martinez-Conde. 2008. Saccades and microsaccades during visual fixation, exploration, and search: foundations for a common saccadic generator. Journal of vision 8, 14 (2008), 21--21.

Crossref

Google Scholar

[11]

Anjul Tyagi, Ayush Kumar, Anshul Gandhi, and Klaus Mueller. 2018. Road Accidents in the UK (Analysis and Visualization). IEEE VIS.

Google Scholar

[12]

Alfred L Yarbus. 2013. Eye movements and vision. Springer.

Google Scholar

Cited By

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Klötzl DKrake THeyen FBecher MKoch MWeiskopf DKurzhals K(2024)NMF-Based Analysis of Mobile Eye-Tracking DataProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3653518(1-9)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3649902.3653518
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
Bhagat HJain SAbbott LSonth ASarkar A(2023)Driver Gaze Fixation and Pattern Analysis in Safety Critical Events2023 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55152.2023.10186718(1-8)Online publication date: 4-Jun-2023
https://doi.org/10.1109/IV55152.2023.10186718
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Index Terms

Task classification model for visual fixation, exploration, and search
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
2. Human-centered computing
  1. Visualization

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Published In

ETRA '19: Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications

June 2019

623 pages

ISBN:9781450367097

DOI:10.1145/3314111

Conference Chairs:
Krzysztof Krejtz
SWPS University, Poland
,
Bonita Sharif
University of Nebraska-Lincoln

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: 25 June 2019

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ETRA '19: 2019 Symposium on Eye Tracking Research and Applications

June 25 - 28, 2019

Colorado, Denver

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Overall Acceptance Rate 69 of 137 submissions, 50%

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Cited By

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Klötzl DKrake THeyen FBecher MKoch MWeiskopf DKurzhals K(2024)NMF-Based Analysis of Mobile Eye-Tracking DataProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3653518(1-9)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3649902.3653518
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
Bhagat HJain SAbbott LSonth ASarkar A(2023)Driver Gaze Fixation and Pattern Analysis in Safety Critical Events2023 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55152.2023.10186718(1-8)Online publication date: 4-Jun-2023
https://doi.org/10.1109/IV55152.2023.10186718
Tyagi AZhao JPatel PKhurana SMueller K(2022)Infographics Wizard: Flexible Infographics Authoring and Design ExplorationComputer Graphics Forum10.1111/cgf.1452741:3(121-132)Online publication date: 12-Aug-2022
https://doi.org/10.1111/cgf.14527
Tyagi AEstro TKuenning GZadok EMueller K(2022)PC-Expo: A Metrics-Based Interactive Axes Reordering Method for Parallel Coordinate DisplaysIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209392(1-11)Online publication date: 2022
https://doi.org/10.1109/TVCG.2022.3209392
Tyagi AXie CMueller K(2022)NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network SynthesisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209361(1-11)Online publication date: 2022
https://doi.org/10.1109/TVCG.2022.3209361
Bautista LNaval P(2021)GazeMAE: General Representations of Eye Movements using a Micro-Macro Autoencoder2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9412761(7004-7011)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICPR48806.2021.9412761
Kumar AHowlader PGarcia RWeiskopf DMueller K(2020)Challenges in Interpretability of Neural Networks for Eye Movement DataACM Symposium on Eye Tracking Research and Applications10.1145/3379156.3391361(1-5)Online publication date: 2-Jun-2020
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Tyagi ACao ZEstro TZadok EMueller K(2019)ICE: An Interactive Configuration Explorer for High Dimensional Categorical Parameter Spaces2019 IEEE Conference on Visual Analytics Science and Technology (VAST)10.1109/VAST47406.2019.8986923(23-34)Online publication date: Oct-2019
https://doi.org/10.1109/VAST47406.2019.8986923

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