Eye tracking metrics may provide unobtrusive measures of cognitive states such as workload and fatigue and can serve as useful inputs into future human computer interface technologies. To further explore the usefulness of eye tracking for the estimation of cognitive state, the current experiment evaluated saccade, fixation, and pupil-based measures to identify which metrics reliably indexed cognitive workload in a dynamic, unconstrained task (Tetris ®). In line with previous studies, our results show that some eye movement features are correlated with changes in workload, manipulated here via task difficulty. Among these were blink duration, saccade velocity, and tonic pupil dilation.

An emerging research agenda in Computer-Supported Cooperative Work focuses on human-agent teaming and AI agent's roles and effects in modern teamwork. In particular, one understudied key question centers around the construct of team cognition within human-agent teams. This study explores the unique nature of team dynamics in human-agent teams compared to human-human teams and the impact of team composition on perceived team cognition, team performance, and trust. In doing so, a mixed-method approach, including three team composition conditions (all human, human-human-agent, human-agent-agent), completed the team simulation NeoCITIES and completed shared mental model, trust, and perception measures. Results found that human-agent teams are similar to human-only teams in the iterative development of team cognition and the importance of communication to accelerating its development; however, human-agent teams are different in that action-related communication and explicitly shared goals are beneficial to developing team cognition. Additionally, human-agent teams trusted agent teammates less when working with only agents and no other humans, perceived less team cognition with agent teammates than human ones, and had significantly inconsistent levels of team mental model similarity when compared to human-only teams. This study contributes to Computer-Supported Cooperative Work in three significant ways: 1) advancing the existing research on human-agent teaming by shedding light on the relationship between humans and agents operating in collaborative environments, 2) characterizing team cognition development in human-agent teams; and 3) advancing real-world design recommendations that promote human-centered teaming agents and better integrate the two.

This paper presents a joint decision-making framework between human and artificial intelligent agents in an effort to create a cohesive team uninhibited by each other’s actions. Based on the well-known Recognition Primed Decision-Making Model, our framework expands upon RPD’s single decision maker to be more Human-Agent Teaming (HAT) oriented. Specifically, our framework includes three layers of shared cognition to ensure both a consistent level of transparency between members and the efficient completion of the task. The first layer provides itself as a foundation of expectations that provides familiarity recognition in a situation. The second layer categorizes the environmental features into relevant decisions informing the symbiotic nature of who should and how to enact decisions collaboratively, which is the third layer. Altogether, this mutually beneficial decision-making model emphasizes transparency so that both humans and artificial agents are equal partners in completing tasks in unique situations.

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