Team Combat Identification: Effects of Gender, Spatial Visualization, and Disagreement

OBJECTIVE

The combat identification (CID) abilities of same-gender and mixed-gender dyads were experimentally assessed, along with measures of spatial skills and team communication.

BACKGROUND

CID is a high-stakes decision-making task involving discrimination between friendly and enemy forces. Literature on CID is primarily focused on the individual, but the extensive use of teams in the military means that more team-based research is needed in this area.

METHOD

After a set of training sessions, 39 dyads were tasked with identifying 10 armored vehicles in a series of pictures and videos. Team communication was recorded, transcribed, and coded for instances of disagreements.

RESULTS

Analyses indicated that males scored higher on a spatial visualization measure than did females. M-M teams performed significantly better than M-F teams on the CID task, but when spatial ability and team disagreements were added as predictors, the effect of team gender composition became nonsignificant. Spatial ability and team disagreement were significant predictors of team CID performance.

CONCLUSION

Results suggest that spatial skills and team disagreement behaviors are more important for team CID performance than a team's gender composition. To our knowledge, this is the first lab study of team CID.

APPLICATION

This research highlights the importance of understanding both individual differences (e.g., spatial skills) and team processes (e.g., communication) within CID training environments in the military context.

Team performance in networked supervisory control of unmanned air vehicles: effects of automation, working memory, and communication content

OBJECTIVE

Assess team performance within a net-worked supervisory control setting while manipulating automated decision aids and monitoring team communication and working memory ability.

BACKGROUND

Networked systems such as multi-unmanned air vehicle (UAV) supervision have complex properties that make prediction of human-system performance difficult. Automated decision aid can provide valuable information to operators, individual abilities can limit or facilitate team performance, and team communication patterns can alter how effectively individuals work together. We hypothesized that reliable automation, higher working memory capacity, and increased communication rates of task-relevant information would offset performance decrements attributed to high task load.

METHOD

Two-person teams performed a simulated air defense task with two levels of task load and three levels of automated aid reliability. Teams communicated and received decision aid messages via chat window text messages.

RESULTS

Task Load x Automation effects were significant across all performance measures. Reliable automation limited the decline in team performance with increasing task load. Average team spatial working memory was a stronger predictor than other measures of team working memory. Frequency of team rapport and enemy location communications positively related to team performance, and word count was negatively related to team performance.

CONCLUSION

Reliable decision aiding mitigated team performance decline during increased task load during multi-UAV supervisory control. Team spatial working memory, communication of spatial information, and team rapport predicted team success.

APPLICATION

An automated decision aid can improve team performance under high task load. Assessment of spatial working memory and the communication of task-relevant information can help in operator and team selection in supervisory control systems.

Human Interaction With Multiple Remote Robots

In this chapter, I review research involving remote human supervision of multiple unmanned vehicles (UVs) using command complexity as an organizing construct. Multi-UV tasks range from foraging, requiring little coordination among UVs, to formation following, in which UVs must function as a cohesive unit. Command complexity, the degree to which operator effort increases with the number of supervised UVs, is used to categorize human interaction with multiple UVs. For systems in which each UV requires the same form of attention (O( n)), effort increases linearly with the number of UVs. For systems in which the control of one UV is dependent upon another (O(> n)), additional UVs impose greater than linear increases due to the expense of coordination. For other systems, an operator interacts with an autonomously coordinating group, and effort is unaffected by group size (O(1)). Studies of human/multi-UV interaction can be roughly grouped into O( n) supervision, involving one-to-one control of individual UVs, or O(1) commanding, in which higher-level commands are directed to a group. Research in O( n) command has centered on round-robin control, neglect tolerance, and attention switching. Approaches to O(1) command are divided into systems using autonomous path planning only, plan libraries, human-steered planners, and swarms. Each type of system has its advantages. Less complete work in scalable displays for multiple UVs is reviewed. Mixing levels of command is probably necessary to supervise multiple UVs performing realistic tasks. Research in O( n) control is mature and can provide quantitative and qualitative guidance for design. Interaction with planners and swarms is less mature but more critical to developing effective multi-UV systems capable of performing complex tasks.

The Effects of Autonomy and Cognitive Abilities on Workload and Supervisory Control of Unmanned Systems

In this paper, we examine the influence of autonomy and cognitive ability on workload in unmanned systems. First, we outline prior research regarding the role of autonomy and operator selection in decreasing workload in the realm of human-robot interaction. Next, we discuss two aspects of cognitive ability (i.e., visualization and perceptual speed) explain differences between these constructs, and their influence on workload. Then, we describe the current study and discuss the effects of varying levels of autonomy, visualization, and perceptual speed on workload in a simulated reconnaissance mission. Finally, we explore the implications of our findings in terms of the influence of autonomy and operator selection and provide suggestions for future research.