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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.

 

Psychological science can benefit from and contribute to emerging approaches from the computing and information sciences driven by the availability of real-world data and advances in sensing and computing. We focus on one such approach, machine-learned computational models (MLCMs)—computer programs learned from data, typically with human supervision. We introduce MLCMs and discuss how they contrast with traditional computational models and assessment in the psychological sciences. Examples of MLCMs from cognitive and affective science, neuroscience, education, organizational psychology, and personality and social psychology are provided. We consider the accuracy and generalizability of MLCM-based measures, cautioning researchers to consider the underlying context and intended use when interpreting their performance. We conclude that in addition to known data privacy and security concerns, the use of MLCMs entails a reconceptualization of fairness, bias, interpretability, and responsible use.

 

Whereas social visual attention has been examined in computer-mediated (e.g., shared screen) or video-mediated (e.g., FaceTime) interaction, it has yet to be studied in mixed-media interfaces that combine video of the conversant along with other UI elements. We analyzed eye gaze of 37 dyads (74 participants) who were tasked with negotiating the price of a new car (as a buyer and seller) using mixed-media video conferencing under competitive or cooperative negotiation instructions (experimental manipulation). We used multidimensional recurrence quantification analysis to extract spatio-temporal patterns corresponding to mutual gaze (individuals look at each other), joint attention (individuals focus on the same elements of the interface), and gaze aversion (an individual looks at their partner, who is looking elsewhere). Our results indicated that joint attention predicted the sum of points attained by the buyer and seller (i.e., the joint score). In contrast, gaze aversion was associated with faster time to complete the negotiation, but with a lower joint score. Unexpectedly, mutual gaze was highly infrequent and unrelated to the negotiation outcomes and none of the gaze patterns predicted subjective perceptions of the negotiation. There were also no effects of gender composition or negotiation condition on the gaze patterns or negotiation outcomes. Our results suggest that social visual attention may operate differently in mixed-media collaborative interfaces than in face-to-face interaction. As mixed-media collaborative interfaces gain prominence, our work can be leveraged to inform the design of gaze-sensitive user interfaces that support remote negotiations among other tasks.