Computer-generated facial areas of interest in eye tracking research: A simulation study

Abstract

Advances in eye tracking technology have enabled the development of interactive experimental setups to study social attention. Since these setups differ substantially from the eye tracker manufacturer’s test conditions, validation is essential with regard to data quality and other factors potentially threatening data validity. In this study, we evaluated the impact of data accuracy and areas of interest (AOIs) size on the classification of simulated gaze data. We defined AOIs of different sizes using the Limited-Radius Voronoi-Tessellation (LRVT) method, and simulated gaze data for facial target points with varying data accuracy. As hypothesized, we found that data accuracy and AOI size had strong effects on gaze classification. In addition, these effects were not independent and differed for falsely classified gaze inside AOIs (Type I errors) and falsely classified gaze outside the predefined AOIs (Type II errors). The results indicate that smaller AOIs generally minimize false classifications as long as data accuracy is good enough. For studies with lower data accuracy, Type II errors can still be compensated to some extent by using larger AOIs, but at the cost of an increased probability of Type I errors. Proper estimation of data accuracy is therefore essential for making informed decisions regarding the size of AOIs.

Areas of Interest (AOI) on marketing mix elements of green and non-green products in customer decision making

“…AOI is any visual stimulus object that the observer focuses on and that is identified by bounding boxes (Lagmay, & Rodrigo, 2022). It is a frequently used strategy to analyse eye tracking data, where this method assesses if a gaze point or fixation is focused at a designated zone surrounding a target point (Vehlen et al, 2021).…”