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Evaluation of an eye tracking setup for studying visual attention in face-to-face conversations
(2021)
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.
Software and interactive systems that adapt their behavior to the user are often referred to as Adaptive Systems. These systems infer the user's goals, knowledge or preferences by observing the user's actions. A synposis of 43 published studies demonstrated that only few of the existing systems are evaluated empirically. Most studies failed to show an advantage of the user model. A new framework is proposed that categorizes existing studies and defines an evaluation procedure which is able to uncover failures and maladaptations in the user model. It consists of four layers: evaluation of input data, evaluation of inference, evaluation of adaptation decision and evaluation of total interaction. Exemplary, the framework has been applied to the HTML-Tutor, an online-course that adapts to the learners' knowledge. Several empirical studies are described that test the accuracy of the user models, and explore the effects of adaptation to knowledge respectively prior knowledge. Generalization issues of the approach are discussed.