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Visual scanpath representation

Published: 22 March 2010 Publication History

Abstract

Eye tracking scanpaths contain information about how people see, but traditional tangled, overlapping scanpath representations provide little insight about scanning strategies. The present work describes and extends several compact visual scanpath representations that can provide additional insight about individual and aggregate/multiple scanning strategies. Three categories of representations are introduced: (1) Scaled traces are small images of scanpaths as connected saccades, allowing the comparison of relative fixation densities and distributions of saccades. (2) Time expansions, substituting ordinal position for either the scanpath's x or y-coordinates, can uncover otherwise subtle horizontal or vertical reversals in visual scanning. (3) Radial plots represent scanpaths as a set of radial arms about an origin, with each arm representing saccade counts or lengths within a binned set of absolute or relative angles. Radial plots can convey useful shape characteristics of scanpaths, and can provide a basis for new metrics. Nine different prototype scanning strategies were represented by these plots, then heuristics were developed to classify the major strategies. The heuristics were subsequently applied to real scanpath data, to identify strategy trends. Future work will further automate the identification of scanning strategies to provide researchers with a tool to uncover and diagnose scanning-related challenges.

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  1. Visual scanpath representation

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    cover image ACM Conferences
    ETRA '10: Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
    March 2010
    353 pages
    ISBN:9781605589947
    DOI:10.1145/1743666
    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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    Publication History

    Published: 22 March 2010

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    Author Tags

    1. eye tracking
    2. scanning strategy
    3. scanpath
    4. usability evaluation
    5. visualization

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    ETRA '10
    ETRA '10: Eye Tracking Research and Applications
    March 22 - 24, 2010
    Texas, Austin

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    Cited By

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    • (2024)Gaze-based Assessment of Expertise in ChessProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3655654(1-3)Online publication date: 4-Jun-2024
    • (2024)An Evaluation of Gaze-Based Person Identification with Different StimuliProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3655651(1-3)Online publication date: 4-Jun-2024
    • (2024)Gaze Patterns in Children With Autism Spectrum Disorder to Emotional Faces: Scanpath and SimilarityIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2024.336193532(865-874)Online publication date: 2024
    • (2024)tSPM-Net: A probabilistic spatio-temporal approach for scanpath predictionComputers & Graphics10.1016/j.cag.2024.103983122(103983)Online publication date: Aug-2024
    • (2024)The Linked Microposter Plots Family as New Means for the Visualization of Eye Tracking DataHuman Interface and the Management of Information10.1007/978-3-031-60114-9_6(65-82)Online publication date: 29-Jun-2024
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    • (2023)Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real‐World EnvironmentsComputer Graphics Forum10.1111/cgf.1483842:3(385-396)Online publication date: 27-Jun-2023
    • (2023)Prediction of Neuro Cognitive Disorders using Supervised Comparative Machine Learning Model & Scanpath Representations2023 IEEE 8th International Conference for Convergence in Technology (I2CT)10.1109/I2CT57861.2023.10126188(1-5)Online publication date: 7-Apr-2023
    • (2023)Applications of machine learning methods to assist the diagnosis of autism spectrum disorderNeural Engineering Techniques for Autism Spectrum Disorder, Volume 210.1016/B978-0-12-824421-0.00013-8(99-119)Online publication date: 2023
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