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Analyzing gaze transition behavior using bayesian mixed effects Markov models

Authors:

Islam Akef Ebeid,

Nilavra Bhattacharya,

Jacek Gwizdka,

Abhra SarkarAuthors Info & Claims

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

Article No.: 5, Pages 1 - 5

https://doi.org/10.1145/3314111.3319839

Published: 25 June 2019 Publication History

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Abstract

The complex stochastic nature of eye tracking data calls for exploring sophisticated statistical models to ensure reliable inference in multi-trial eye-tracking experiments. We employ a Bayesian semi-parametric mixed-effects Markov model to compare gaze transition matrices between different experimental factors accommodating individual random effects. The model not only allows us to assess global influences of the external factors on the gaze transition dynamics but also provides comprehension of these effects at a deeper local level. We experimented to explore the impact of recognizing distorted images of artwork and landmarks on the gaze transition patterns. Our dataset comprises sequences representing areas of interest visited when applying a content independent grid to the resulting scan paths in a multi-trial setting. Results suggest that image recognition to some extent affects the dynamics of the transitions while image type played an essential role in the viewing behavior.

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Cited By

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Teng CSharma HDrukker LPapageorghiou ANoble J(2021)Towards Scale and Position Invariant Task Classification Using Normalised Visual Scanpaths in Clinical Fetal UltrasoundSimplifying Medical Ultrasound10.1007/978-3-030-87583-1_13(129-138)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-87583-1_13
Pan YLyu RNie QMeng L(2020)Study on the Emotional Image of Calligraphy Strokes based on Sentiment Analysis2020 5th International Conference on Communication, Image and Signal Processing (CCISP)10.1109/CCISP51026.2020.9273474(264-269)Online publication date: Nov-2020
https://doi.org/10.1109/CCISP51026.2020.9273474

Index Terms

Analyzing gaze transition behavior using bayesian mixed effects Markov models
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
    2. HCI theory, concepts and models

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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

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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

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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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Teng CSharma HDrukker LPapageorghiou ANoble J(2021)Towards Scale and Position Invariant Task Classification Using Normalised Visual Scanpaths in Clinical Fetal UltrasoundSimplifying Medical Ultrasound10.1007/978-3-030-87583-1_13(129-138)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-87583-1_13
Pan YLyu RNie QMeng L(2020)Study on the Emotional Image of Calligraphy Strokes based on Sentiment Analysis2020 5th International Conference on Communication, Image and Signal Processing (CCISP)10.1109/CCISP51026.2020.9273474(264-269)Online publication date: Nov-2020
https://doi.org/10.1109/CCISP51026.2020.9273474

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Gaze Transition Entropy

Beyond gaze: preliminary analysis of pupil dilation and blink rates in an fMRI study of program comprehension

Enhanced gaze interaction using simple head gestures