Eye-tracking Social Desirability Bias

Eye tracking is now a common technique studying the moment-by-moment cognition of those processing visual information. Yet this technique has rarely been applied to different survey modes. Our paper uses an innovative method of real-world eye tracking to look at attention to sensitive questions and response scale points, in Web, face-to-face and paper-and-pencil self-administered (SAQ) modes. We link gaze duration to responses in order to understand how respondents arrive at socially desirable or undesirable answers. Our novel technique sheds light on how social desirability biases arise from deliberate misreporting and/or satisficing, and how these vary across modes

Talk to the Virtual Hands: Self-Animated Avatars Improve Communication in Head-Mounted Display Virtual Environments

Background When we talk to one another face-to-face, body gestures accompany our speech. Motion tracking technology enables us to include body gestures in avatar-mediated communication, by mapping one's movements onto one's own 3D avatar in real time, so the avatar is self-animated. We conducted two experiments to investigate (a) whether head-mounted display virtual reality is useful for researching the influence of body gestures in communication; and (b) whether body gestures are used to help in communicating the meaning of a word. Participants worked in pairs and played a communication game, where one person had to describe the meanings of words to the other. Principal Findings In experiment 1, participants used significantly more hand gestures and successfully described significantly more words when nonverbal communication was available to both participants (i.e. both describing and guessing avatars were self-animated, compared with both avatars in a static neutral pose). Participants ‘passed’ (gave up describing) significantly more words when they were talking to a static avatar (no nonverbal feedback available). In experiment 2, participants' performance was significantly worse when they were talking to an avatar with a prerecorded listening animation, compared with an avatar animated by their partners' real movements. In both experiments participants used significantly more hand gestures when they played the game in the real world. Conclusions Taken together, the studies show how (a) virtual reality can be used to systematically study the influence of body gestures; (b) it is important that nonverbal communication is bidirectional (real nonverbal feedback in addition to nonverbal communication from the describing participant); and (c) there are differences in the amount of body gestures that participants use with and without the head-mounted display, and we discuss possible explanations for this and ideas for future investigation

Analyzing Social Interactions: Promises and Challenges of Cross Recurrence Quantification Analysis

The scientific investigation of social interactions presents substantial challenges: interacting agents engage each other at many different levels and timescales (motor and physiological coordination, joint attention, linguistic exchanges, etc.), often making their behaviors interdependent in non-linear ways. In this paper we review the current use of Cross Recurrence Quantification Analysis (CRQA) in the analysis of social interactions, and assess its potential and challenges. We argue that the method can sensitively grasp the dynamics of human interactions, and that it has started producing valuable knowledge about them. However, much work is still necessary: more systematic analyses and interpretation of the recurrence indexes and more consistent reporting of the results, more emphasis on theory-driven studies, exploring interactions involving more than 2 agents and multiple aspects of coordination, and assessing and quantifying complementary coordinative mechanisms. These challenges are discussed and operationalized in recommendations to further develop the field