Optimizing Fixation Filters for Eye-Tracking on Small Screens.

Julia Trabulsi,Mike Storm,Seidi Suurmets,Thomas Zoëga Ramsøy,Kian Norouzi

doi:10.3389/fnins.2021.578439

Abstract

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.

Highlights

With the advent of smartphones around the turn of this decade, the use of mobile phones has expanded well beyond regular phone calls
Much work has been performed related to the validity and reliability of stationary eye-tracking measures (Blignaut, 2009; Nyström et al, 2013), and it has become clear that there is no universal definition for a fixation, which varies operationally in different settings
Visual attention is inferred from patterns of eye movements, where visual information is acquired during brief periods when the eye remains relatively stable, which are called fixations

Summary

Introduction

With the advent of smartphones around the turn of this decade, the use of mobile phones has expanded well beyond regular phone calls. The combination of small screens packed with information at a close focal distance, coupled with vertical movement during scrolling behaviors, poses a particular challenge to those who want to use methods such as eye-tracking glasses with off-the-shelf fixation detection software in assessing different aspects of customer attention. Much work has been performed related to the validity and reliability of stationary eye-tracking measures (Blignaut, 2009; Nyström et al, 2013), and it has become clear that there is no universal definition for a fixation, which varies operationally in different settings. In the context of this paper, we use the term ’fixation’ to signify a cluster of gaze samples during which the acquisition of visual information is likely to take place, and we suggest an improved approach for quantifying eye movement data for visual attention research on smartphones.

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