We hypothesize that effective collaboration is facilitated when individuals and environmental components form a synergy where they work together and regulate one another to produce stable patterns of behavior, or regularity, as well as adaptively reorganize to form new behaviors, or irregularity. We tested this hypothesis in a study with 32 triads who collaboratively solved a challenging visual computer programming task for 20 min following an introductory warm‐up phase. Multidimensional recurrence quantification analysis was used to examine fine‐grained (i.e., every 10 s) collective patterns of regularity across team members' speech rate, body movement, and team interaction with the shared user interface. We found that teams exhibited significant patterns of regularity as compared to shuffled baselines, but there were no systematic trends in regularity across time. We also found that periods of regularity were associated with a reduction in overall behavior. Notably, the production of irregular behavior predicted expert‐coded metrics of collaborative activity, such as teams' ability to construct shared knowledge and effectively negotiate and coordinate execution of solutions, net of overall behavioral production and behavioral self‐similarity. Our findings support the theory that groups can interact to form interpersonal synergies and indicate that information about system‐level dynamics is a viable way to understand and predict effective collaborative processes.

Interaction intent prediction and the Midas touch have been a longstanding challenge for eye-tracking researchers and users of gazebased interaction. Inspired by machine learning approaches in biometric person authentication, we developed and tested an offline framework for task-independent prediction of interaction intents. We describe the principles of the method, the features extracted, normalization methods, and evaluation metrics. We systematically evaluated the proposed approach on an example dataset of gazeaugmented problem-solving sessions. We present results of three normalization methods, different feature sets and fusion of multiple feature types. Our results show that accuracy of up to 76% can be achieved with Area Under Curve around 80%. We discuss the possibility of applying the results for an online system capable of interaction intent prediction.

Multiparty collaborative problem solving-an increasingly important context in the 21st century workforcesuffers from a degradation of social and behavioral signals when attempted remotely, resulting in suboptimal outcomes. We investigate teams' multidimensional patterns of visual attention during a collaborative problemsolving task with an eye for leveraging insights to improve collaborative interfaces. Fifty-seven novices (forming 19 triads) engaged in a challenging programming task (Minecraft Hour of Code) using videoconferencing software with screen sharing. To discover patterns of individual-level gaze-UI coupling (coordination of a teammate's attention with respect to changes in the user interface) and team-level gaze-UI regularity (dynamics of teams' collective attention in context with changes in the user interface), we applied cross-and multidimensional recurrence quantification analyses, respectively. Individuals' eye gaze was significantly coupled with the ongoing screen activity whereas teams displayed

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