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Optimizing Fixation Filters for Eye-Tracking on Small Screens

The study of consumer responses to advertising has recently expanded to include the use of eye-tracking to track the gaze of consumers. The calibration and validation of eye-gaze have typically been measured on large screens in static, controlled settings. However, little is known about how precise...

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Autores principales: Trabulsi, Julia, Norouzi, Kian, Suurmets, Seidi, Storm, Mike, Ramsøy, Thomas Zoëga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606821/
https://www.ncbi.nlm.nih.gov/pubmed/34819830
http://dx.doi.org/10.3389/fnins.2021.578439
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author Trabulsi, Julia
Norouzi, Kian
Suurmets, Seidi
Storm, Mike
Ramsøy, Thomas Zoëga
author_facet Trabulsi, Julia
Norouzi, Kian
Suurmets, Seidi
Storm, Mike
Ramsøy, Thomas Zoëga
author_sort Trabulsi, Julia
collection PubMed
description The study of consumer responses to advertising has recently expanded to include the use of eye-tracking to track the gaze of consumers. The calibration and validation of eye-gaze have typically been measured on large screens in static, controlled settings. However, little is known about how precise gaze localizations and eye fixations are on smaller screens, such as smartphones, and in moving feed-based conditions, such as those found on social media websites. We tested the precision of eye-tracking fixation detection algorithms relative to raw gaze mapping in natural scrolling conditions. Our results demonstrate that default fixation detection algorithms normally employed by hardware providers exhibit suboptimal performance on mobile phones. In this paper, we provide a detailed account of how different parameters in eye-tracking software can affect the validity and reliability of critical metrics, such as Percent Seen and Total Fixation Duration. We provide recommendations for producing improved eye-tracking metrics for content on small screens, such as smartphones, and vertically moving environments, such as a social media feed. The adjustments to the fixation detection algorithm we propose improves the accuracy of Percent Seen by 19% compared to a leading eye-tracking provider’s default fixation filter settings. The methodological approach provided in this paper could additionally serve as a framework for assessing the validity of applied neuroscience methods and metrics beyond mobile eye-tracking.
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spelling pubmed-86068212021-11-23 Optimizing Fixation Filters for Eye-Tracking on Small Screens Trabulsi, Julia Norouzi, Kian Suurmets, Seidi Storm, Mike Ramsøy, Thomas Zoëga Front Neurosci Neuroscience The study of consumer responses to advertising has recently expanded to include the use of eye-tracking to track the gaze of consumers. The calibration and validation of eye-gaze have typically been measured on large screens in static, controlled settings. However, little is known about how precise gaze localizations and eye fixations are on smaller screens, such as smartphones, and in moving feed-based conditions, such as those found on social media websites. We tested the precision of eye-tracking fixation detection algorithms relative to raw gaze mapping in natural scrolling conditions. Our results demonstrate that default fixation detection algorithms normally employed by hardware providers exhibit suboptimal performance on mobile phones. In this paper, we provide a detailed account of how different parameters in eye-tracking software can affect the validity and reliability of critical metrics, such as Percent Seen and Total Fixation Duration. We provide recommendations for producing improved eye-tracking metrics for content on small screens, such as smartphones, and vertically moving environments, such as a social media feed. The adjustments to the fixation detection algorithm we propose improves the accuracy of Percent Seen by 19% compared to a leading eye-tracking provider’s default fixation filter settings. The methodological approach provided in this paper could additionally serve as a framework for assessing the validity of applied neuroscience methods and metrics beyond mobile eye-tracking. Frontiers Media S.A. 2021-11-08 /pmc/articles/PMC8606821/ /pubmed/34819830 http://dx.doi.org/10.3389/fnins.2021.578439 Text en Copyright © 2021 Trabulsi, Norouzi, Suurmets, Storm and Ramsøy. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Trabulsi, Julia
Norouzi, Kian
Suurmets, Seidi
Storm, Mike
Ramsøy, Thomas Zoëga
Optimizing Fixation Filters for Eye-Tracking on Small Screens
title Optimizing Fixation Filters for Eye-Tracking on Small Screens
title_full Optimizing Fixation Filters for Eye-Tracking on Small Screens
title_fullStr Optimizing Fixation Filters for Eye-Tracking on Small Screens
title_full_unstemmed Optimizing Fixation Filters for Eye-Tracking on Small Screens
title_short Optimizing Fixation Filters for Eye-Tracking on Small Screens
title_sort optimizing fixation filters for eye-tracking on small screens
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606821/
https://www.ncbi.nlm.nih.gov/pubmed/34819830
http://dx.doi.org/10.3389/fnins.2021.578439
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