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:	Julia Trabulsi, Kian Norouzi, Seidi Suurmets, Mike Storm, Thomas Zoëga Ramsøy
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	mobile eye-tracking smartphone mobile environment social media marketing validity reliability Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/7c9a6132fc214d4e9b3434157d4f203f
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id	oai:doaj.org-article:7c9a6132fc214d4e9b3434157d4f203f
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spelling	oai:doaj.org-article:7c9a6132fc214d4e9b3434157d4f203f2021-11-08T07:56:40ZOptimizing Fixation Filters for Eye-Tracking on Small Screens1662-453X10.3389/fnins.2021.578439https://doaj.org/article/7c9a6132fc214d4e9b3434157d4f203f2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.578439/fullhttps://doaj.org/toc/1662-453XThe 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.Julia TrabulsiKian NorouziKian NorouziSeidi SuurmetsMike StormThomas Zoëga RamsøyThomas Zoëga RamsøyFrontiers Media S.A.articlemobile eye-trackingsmartphonemobile environmentsocial media marketingvalidityreliabilityNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mobile eye-tracking smartphone mobile environment social media marketing validity reliability Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	mobile eye-tracking smartphone mobile environment social media marketing validity reliability Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Julia Trabulsi Kian Norouzi Kian Norouzi Seidi Suurmets Mike Storm Thomas Zoëga Ramsøy Thomas Zoëga Ramsøy Optimizing Fixation Filters for Eye-Tracking on Small Screens
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.
format	article
author	Julia Trabulsi Kian Norouzi Kian Norouzi Seidi Suurmets Mike Storm Thomas Zoëga Ramsøy Thomas Zoëga Ramsøy
author_facet	Julia Trabulsi Kian Norouzi Kian Norouzi Seidi Suurmets Mike Storm Thomas Zoëga Ramsøy Thomas Zoëga Ramsøy
author_sort	Julia Trabulsi
title	Optimizing Fixation Filters for Eye-Tracking on Small Screens
title_short	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_sort	optimizing fixation filters for eye-tracking on small screens
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/7c9a6132fc214d4e9b3434157d4f203f
work_keys_str_mv	AT juliatrabulsi optimizingfixationfiltersforeyetrackingonsmallscreens AT kiannorouzi optimizingfixationfiltersforeyetrackingonsmallscreens AT kiannorouzi optimizingfixationfiltersforeyetrackingonsmallscreens AT seidisuurmets optimizingfixationfiltersforeyetrackingonsmallscreens AT mikestorm optimizingfixationfiltersforeyetrackingonsmallscreens AT thomaszoegaramsøy optimizingfixationfiltersforeyetrackingonsmallscreens AT thomaszoegaramsøy optimizingfixationfiltersforeyetrackingonsmallscreens
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Optimizing Fixation Filters for Eye-Tracking on Small Screens

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