Project Overview Team Situation Awareness (TSA), which is a part of team cognition, is a critical factor that influences team effectiveness. It can be defined as getting the right information from the right person within the right amount of time, in order to overcome an unexpected event (Gorman, Cooke, Pederson, Connor, & DeJoode, 2005). TSA is developed and maintained through team interactions, allowing for the measurement of TSA based on team interaction (Cooke & Gorman, 2009). In the current study, a specific measure, Coordinated Awareness of Situation by Teams (CAST) is used (Cooke & Gorman, 2009). CAST evaluates the effectiveness and efficiency of team interaction under “roadblock” scenarios (Gorman, Cooke, & Winner, 2006). These roadblocks represent novel situations in the task and require effective team communication and coordination. Team members must assess the situation according to their own specialized role and/or resources and coordinate with other team members to overcome each separate roadblock. In this task, effective communication refers to team anticipation. That is, each team member needs to anticipate each other’s needs by pushing information rather than pulling information during the task (Demir, McNeese, & Cooke, 2017). In this study, we examined how pushing and pulling information, and CAST were associated with Team Situation Awareness (TSA) in both Human-Autonomy (HAT) and all-human teams in simulated Remotely Piloted Aircraft System (RPAS) task environment. In this research, we integrated the synthetic agent to the Cognitive Engineering Research on Team Tasks Remotely Piloted Aircraft Systems - Synthetic Task Environment (CERTT-RPAS-STE) which was designed to be both a flexible research platform and a realistic task environment with a view to researching team performance and interaction-based measures of team cognition. In the simulated CERTT testbed, there are three heterogeneous teammates who need to take good photos of each target waypoint by communicating via text-chat: (1) the navigator who creates a dynamic flight plan and provides information about the waypoints, the RPA’s airspeed, and altitude restrictions to the pilot; (2) the pilot, who controls the RPA’s heading, altitude, and airspeed, and negotiates with the photographer in order to take a good photo; and (3) the photographer, who monitors sensor equipment in order to take photographs of target waypoints and sends feedback to the other team members about the quality of the photo. This project aimed to understand how team behaviors and team performance differed between HATs and all-human teams in RPAS operations: (1) the synthetic condition—the pilot role was given to the synthetic teammate, which was an ACT-R based cognitive model (which had a limited interaction ability, see Ball et al., 2010; Demir et al., 2015); (2) the control condition—the pilot was a randomly selected human participant, just like the other two participants; and (3) the experimenter condition—one of the experimenters served as an expert pilot. Experimenter condition utilized a Wizard of Oz paradigm in which a trained experimenter (located in a separate room) used a script to imitate a synthetic teammate and communicated with participants in limited communication behaviors but pushing and pulling information in a timely manner (robust coordination). Method There were 30 teams (10 for each condition): control teams consisted of three participants randomly assigned to each role; synthetic and experimenter teams included two participants randomly assigned to the navigator and photographer roles. The experiment took place over five 40-minute missions, and the goal was to take as many “good” photos of ground targets as possible while avoiding alarms and rule violations. During each mission, teams were presented with “roadblocks” by the introduction of a new, ad hoc target waypoint. We collected several measures, but we focused on: the proportion of roadblocks overcome per mission as an outcome measure of TSA; the CAST which is a coordination sequence of team interaction across the team members (i.e. which team members share with team members their experience during the roadblock); and verbal behaviors such as pushing and pulling information. Results and discussion In this team task, effective teamwork involves anticipating the needs of teammates, which in turn means pushing information before it is requested. However, in addition to anticipation, effective coordination is also needed during roadblocks. HATs demonstrated significantly lower levels of CAST than all-human teams. These results indicate that HATs’ lack of anticipation and coordination resulted in poorer TSA performance. These findings help HATs to grow its coordination and communication methodologies. Finally, future studies might examine the relationships highlighted in this study via nonlinear measures in terms of team stability and flexibility based on their communication and coordination patterns during the novel events. HAT is here to stay but improvements to human-machine interactions must continue if we are to improve team effectiveness.
Utilizing Virtual Crowd for Global Software Development
