Trabulsi Julia, Norouzi Kian, Suurmets Seidi, Storm Mike, Ramsøy Thomas Zoëga
Facebook Inc, New York, NY, United States.
Neurons Inc., Department of Applied Neuroscience, Taastrup, Denmark.
Front Neurosci. 2021 Nov 8;15:578439. doi: 10.3389/fnins.2021.578439. eCollection 2021.
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.
对消费者广告反应的研究最近有所扩展，涵盖了使用眼动追踪来跟踪消费者的视线。眼动注视的校准和验证通常是在静态、可控环境下的大屏幕上进行测量的。然而，对于在较小屏幕（如智能手机）以及基于动态信息流的环境（如社交媒体网站上的情况）中，注视定位和眼睛注视的精确程度却知之甚少。我们测试了在自然滚动条件下，眼动追踪注视检测算法相对于原始注视映射的精度。我们的结果表明，硬件供应商通常采用的默认注视检测算法在手机上表现欠佳。在本文中，我们详细阐述了眼动追踪软件中的不同参数如何影响关键指标（如观看百分比和总注视持续时间）的有效性和可靠性。我们为在小屏幕（如智能手机）以及垂直移动环境（如社交媒体信息流）中生成改进的眼动追踪指标提供了建议。与一家领先的眼动追踪供应商的默认注视过滤设置相比，我们提出的对注视检测算法的调整将观看百分比的准确率提高了19%。本文提供的方法学方法还可以作为一个框架，用于评估除移动眼动追踪之外的应用神经科学方法和指标的有效性。
