Evaluation of an eye tracking setup for studying visual attention in face-to-face conversations

Abstract

Many eye tracking studies use facial stimuli presented on a display to investigate attentional processing of social stimuli. To introduce a more realistic approach that allows interaction between two real people, we evaluated a new eye tracking setup in three independent studies in terms of data quality, short-term reliability and feasibility. Study 1 measured the robustness, precision and accuracy for calibration stimuli compared to a classical display-based setup. Study 2 used the identical measures with an independent study sample to compare the data quality for a photograph of a face (2D) and the face of the real person (3D). Study 3 evaluated data quality over the course of a real face-to-face conversation and examined the gaze behavior on the facial features of the conversation partner. Study 1 provides evidence that quality indices for the scene-based setup were comparable to those of a classical display-based setup. Average accuracy was better than 0.4° visual angle. Study 2 demonstrates that eye tracking quality is sufficient for 3D stimuli and robust against short interruptions without re-calibration. Study 3 confirms the long-term stability of tracking accuracy during a face-to-face interaction and demonstrates typical gaze patterns for facial features. Thus, the eye tracking setup presented here seems feasible for studying gaze behavior in dyadic face-to-face interactions. Eye tracking data obtained with this setup achieves an accuracy that is sufficient for investigating behavior such as eye contact in social interactions in a range of populations including clinical conditions, such as autism spectrum and social phobia.

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

“…One potential limitation might be that the present simulation was based on the assumption that gaze data follows a unique distribution for all generated data sets. Although, the overall parameters (deviation and skewness) are largely in accordance with data recorded in the real setup underlying the present simulation (12), it is possible that other recording conditions, populations or interaction paradigms will lead to different data distributions. Therefore, we make the gaze data simulation tool used in the present study freely available and encourage repeating or modifying the simulation process.…”

“…In recent years, several studies have questioned common reporting standards in this field, or the lack of data quality evaluations for the specific setup in use (8,9), e.g., some studies only report the overall accuracy of hardware determined by the manufacturer under ideal test conditions. The issue of data quality and inadequate reporting standards seems to be increasingly relevant as the field advances to develop more naturalistic, interactive or face-to-face eye tracking applications (10)(11)(12). Not only do these setups deviate further in design from the manufacturer's test conditions, but when used in naturalistic interactions, other factors can affect the accuracy of the eye tracking data, such as movements accompanying facial expressions or speech.…”

The effect of visual attention on stereoscopic lighting of museum ceramic exhibits： A virtual environment mixed with eye-tracking

“…It can greatly stimulate the users' visual sense and is used in different fields [40]. In the aspect of simulating physical lighting environment, VR is a better choice to replace the physical scene and many scholars have studied the human-factors lighting [41][42][43].…”

“…VR was applied in the research of museum stereoscopic lighting, which is not common in the past related research. Using eye-movement tracking technique to collect data can measure the salience indexes of human vision and improve the authenticity of the data [44].…”

How to choose the size of facial areas of interest in interactive eye tracking

“…In recent years, several studies have questioned common reporting standards in this field, or the lack of data quality evaluations regarding the specific setup in use [8,9], e.g., some studies only report the overall accuracy of hardware determined by the manufacturer under ideal test conditions. The issue of data quality and inadequate reporting standards seems to be increasingly relevant as the field advances to develop more naturalistic, interactive or face-to-face eye tracking applications [10][11][12]. Not only do these setups deviate further in design from the manufacturer's test conditions, but when used in naturalistic interactions, other factors can affect the accuracy, such as movements accompanying facial expressions, speech or varying viewing distances.…”