Task classification model for visual fixation, exploration, and search

Ayush Kumar; Anjul Tyagi; Michael Burch; Daniel Weiskopf; Klaus Mueller

doi:10.1145/3314111.3323073

Task classification model for visual fixation, exploration, and search

Ayush Kumar, Anjul Tyagi, Michael Burch, Daniel Weiskopf, Klaus Mueller

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Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

10 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - ETRA 2019
Subtitle of host publication	2019 ACM Symposium On Eye Tracking Research and Applications
Editors	Stephen N. Spencer
Place of Publication	New York
Publisher	Association for Computing Machinery, Inc
Number of pages	4
ISBN (Electronic)	978-1-4503-6709-7
DOIs	https://doi.org/10.1145/3314111.3323073
Publication status	Published - 25 Jun 2019
Event	11th ACM Symposium on Eye Tracking Research and Applications, ETRA 2019 - Denver, United States Duration: 25 Jun 2019 → 28 Jun 2019

Conference

Conference	11th ACM Symposium on Eye Tracking Research and Applications, ETRA 2019
Country/Territory	United States
City	Denver
Period	25/06/19 → 28/06/19

Keywords

Classifier
Eye movements
Task decoding
Visual attention
Yarbus

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10.1145/3314111.3323073

Cite this

@inproceedings{f854637c6c3b41819c64d1220c5afcf5,

title = "Task classification model for visual fixation, exploration, and search",

abstract = "Yarbus{\textquoteright} claim to decode the observer{\textquoteright}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{\textquoteright}s eye movement data.",

keywords = "Classifier, Eye movements, Task decoding, Visual attention, Yarbus",

author = "Ayush Kumar and Anjul Tyagi and Michael Burch and Daniel Weiskopf and Klaus Mueller",

year = "2019",

month = jun,

day = "25",

doi = "10.1145/3314111.3323073",

language = "English",

editor = "Spencer, {Stephen N.}",

booktitle = "Proceedings - ETRA 2019",

publisher = "Association for Computing Machinery, Inc",

address = "United States",

note = "11th ACM Symposium on Eye Tracking Research and Applications, ETRA 2019 ; Conference date: 25-06-2019 Through 28-06-2019",

}

Kumar, A, Tyagi, A, Burch, M, Weiskopf, D & Mueller, K 2019, Task classification model for visual fixation, exploration, and search. in SN Spencer (ed.), Proceedings - ETRA 2019: 2019 ACM Symposium On Eye Tracking Research and Applications., 65, Association for Computing Machinery, Inc, New York, 11th ACM Symposium on Eye Tracking Research and Applications, ETRA 2019, Denver, United States, 25/06/19. https://doi.org/10.1145/3314111.3323073

Task classification model for visual fixation, exploration, and search. / Kumar, Ayush; Tyagi, Anjul; Burch, Michael et al.
Proceedings - ETRA 2019: 2019 ACM Symposium On Eye Tracking Research and Applications. ed. / Stephen N. Spencer. New York: Association for Computing Machinery, Inc, 2019. 65.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Task classification model for visual fixation, exploration, and search

AU - Kumar, Ayush

AU - Tyagi, Anjul

AU - Burch, Michael

AU - Weiskopf, Daniel

AU - Mueller, Klaus

PY - 2019/6/25

Y1 - 2019/6/25

N2 - 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.

AB - 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.

KW - Classifier

KW - Eye movements

KW - Task decoding

KW - Visual attention

KW - Yarbus

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DO - 10.1145/3314111.3323073

M3 - Conference contribution

AN - SCOPUS:85069517339

BT - Proceedings - ETRA 2019

A2 - Spencer, Stephen N.

PB - Association for Computing Machinery, Inc

CY - New York

T2 - 11th ACM Symposium on Eye Tracking Research and Applications, ETRA 2019

Y2 - 25 June 2019 through 28 June 2019

ER -

Task classification model for visual fixation, exploration, and search

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