Prospects for Augmenting Team Interactions with Real‐Time Coordination‐Based Measures in Human‐Autonomy Teams

Abstract

Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human‐autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams’ coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human‐autonomy teams that augment team interactions using coordination‐based measures.

A Computational Approach to Examining Team Coordination Breakdowns During Crisis Situations

“…These teams were informed that this participant could not play the game anymore but could communicate with the team. Differences between conditions are to be examined in another study that is part of the overarching project (other work related to this project are Hałgas et al, 2022; Wiltshire et al, 2022). After the first half and at the end of the game, participants completed a series of surveys that were not the focus of the current study.…”

“…Consequently, by identifying team CBs, significant moments within team processes underlying ineffective team adaptive performance can be located, providing a means to further analyze these points in time. Moreover, if team coordination can be measured in real time, and CBs can be identified through computational methods rather than manually, relevant and timely feedback regarding team coordination can be delivered to improve effective team adaptive performance during crisis situations (Wiltshire et al, 2022).…”

Harmonizing human-AI synergy: behavioral science in AI-integrated design

“…Monitoring cognitive workload with physiological metrics like eye gaze (Aygun et al, 2022) and using real-time feedback (Knisely et al, 2021;Pomranky & Wojciechowski, 2007), alongside autonomous systems that respond to user states (Yuh et al, 2022;Wiltshire et al, 2014Wiltshire et al, , 2022, improve performance.…”

Examining Physiological Features Underlying Team Coordination Breakdowns

“…Identifying TCBs provides us with the opportunity to examine the underlying behavioral, cognitive, and/or physiological conditions that occur during suboptimal team performance. Moreover, if we can develop computational systems that can monitor teamwork and reliably identify current or impending TCBs, feedback based on team coordination might even be delivered in real-time [5][6][7] .…”

“…To achieve effective team functioning and performance, teams have to respond adaptively to changes in their working environment 28 . Team adaptations can be re ected in the team's coordination, which exhibits various states 7 . For example, a state in which signals exhibit spatiotemporal similarity is referred to as synchrony, and a state in which signals exhibit disorder is referred to as high entropy 29 .…”