Using dual eye-tracking to unveil coordination and expertise in collaborative Tetris

Abstract

Using dual eye-tracking to unveil coordination and expertise in collaborative Tetris The use of dual eye-tracking is investigated in a collabora tive game setting. The automatic collection of information about partner's gaze will eventually serve to build adaptive interfaces. Following this agenda, and in order to identify stable gaze patterns, we investigate the impact of social and task related context upon individual gaze and action during a collaborative Tetris game. Results show that experts as well as novices adapt their playing style when interacting in mixed ability pairs. We also present machine learning results about the prediction of player's social context.

Toward a second-person neuroscience

“…Another important future extension of interactive paradigms will consist in making it possible for two participants to engage in a task and to interact in an experimentally controllable way in a mediated environment. Such a paradigm would enable one to control and change the bandwidth of the interaction and could help to systematically perturb an ongoing interaction, for example, by replaying responses of the other interactor from a previous interaction, and to investigate participants' ability to tell apart “whether the other can see me (or not).” One way, we suggest, this could be done would be to make use of a dual eyetracking setup, in which two interactors can be virtually present and perform a task together (e.g., Carletta et al 2010; Jermann et al 2010). Furthermore, such dual eyetracking paradigms might help to assess intra- and intersubject parameters during real-time interactions, which could be used to analyse neuroimaging data obtained from one or two brains.…”

On the relationship of online and offline social cognition

“…Konvalinka and Roepstorff, 2012 ). Here, a recent technical development in our group—inspired by previous work, which was successful in quantifying processes of interpersonal gaze coupling (e.g., Richardson et al, 2007 ; Jermann et al, 2010 )—has involved linking up two eyetrackers via a local area network to form a “dual eyetracking” system (Barisic et al, 2013 ; Pfeiffer et al, 2013b ), which enables real-time gaze-based social interactions of two individuals represented by virtual characters (Figure 3 ). In this setup, the gaze of two participants can be tracked continuously during the performance of joint tasks (e.g., two-person perceptual decision-making tasks).…”

Shaping learners’ attention in Massive Open Online Courses

“…previous studies (Jermann, Nüssli, & Li, 2010;Nüssli, Jermann, Sangin, & Dillenbourg, 2009) have shown that gaze is predictive of expertise and task performance, both for learning tasks and otherwise. In a collaborative Tetris task 1 , Jermann et al (2010) demonstrated that experts focus more on the stack than novices do.…”

“…previous studies (Jermann, Nüssli, & Li, 2010;Nüssli, Jermann, Sangin, & Dillenbourg, 2009) have shown that gaze is predictive of expertise and task performance, both for learning tasks and otherwise. In a collaborative Tetris task 1 , Jermann et al (2010) demonstrated that experts focus more on the stack than novices do. In a collaborative Raven and Bongard puzzle-solving task, Nüssli et al (2009) demonstrated that good performers switch more often than bad performers between problem figures and solution figures.…”