AnswerTruthDetector: a combined cognitive load approach for separating truthful from deceptive answers in computer-administered questionnaires

Abstract

In human-computer interaction, much empirical research exists. Online questionnaires increasingly play an important role. Here the quality of the results depend strongly on the quality of the given answers, and it is essential to distinguish truthful from deceptive answers. There exist elegant single modalities for deception detection in the literature, such as mouse tracking and eye tracking (in this paper, respectively, measuring the pupil diameter). Yet, no combination of these two modalities is available. This paper presents a combined approach of two cognitive-load-based lie detection approaches. We address study administrators who conduct questionnaires in the HCI, wanting to improve the validity of questionnaires.