Several models of team performance have suggested that a clearer understanding of team process is needed to determine better training formats and reduce crew-generated errors. The present study investigated the degree to which analyzing communication sequences would contribute to the understanding of effective crew process in two simulated flight tasks. The results indicate that pattern analyses reveal additional strong differences between performance groups that would have been overlooked by simple frequency counts of communication. In each case, the sequential analysis shed additional light on the communication patterns that characterize better-performing teams. These results are discussed in terms of their implications for team performance research and team training. Potential applications of this research include training needs assessment, training design, and performance measurement after training.

As robots are increasingly deployed in settings requiring social interaction, research is needed to examine the social signals perceived by humans when robots display certain social cues. In this paper, we report a study designed to examine how humans interpret social cues exhibited by robots. We first provide a brief overview of perspectives from social cognition in humans and how these processes are applicable to human–robot interaction (HRI). We then discuss the need to examine the relationship between social cues and signals as a function of the degree to which a robot is perceived as a socially present agent. We describe an experiment in which social cues were manipulated on an iRobot AvaTM mobile robotics platform in a hallway navigation scenario. Cues associated with the robot’s proxemic behavior were found to significantly affect participant perceptions of the robot’s social presence and emotional state while cues associated with the robot’s gaze behavior were not found to be significant. Further, regardless of the proxemic behavior, participants attributed more social presence and emotional states to the robot over repeated interactions than when they first interacted with it. Generally, these results indicate the importance for HRI research to consider how social cues expressed by a robot can differentially affect perceptions of the robot’s mental states and intentions. The discussion focuses on implications for the design of robotic systems and future directions for research on the relationship between social cues and signals.

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This study examined the effects of having experienced negative events related to the purpose of a training program on learning and retention. Participants were 32 private pilots who participated in an assertiveness-training study. The purpose of the training was to prevent aviation accidents caused by human error. Structured telephone interviews were conducted to determine whether participants had previously experienced 3 types of negative events related to the purpose of training. Results indicated a linear relationship between these negative events and assertive performance in a behavioral exercise 1 week after training. The same negative events, however, were not significantly related to the performance of untrained participants in the same behavioral exercise. It is suggested that previous experiences influenced posttraining performance by increasing motivation to learn. Training and development expenditures have continued to grow in recent years, as has research on variables influencing training effectiveness. In particular, a number of researchers have argued for the need for training research that goes beyond identifying what works to determine "why, when, and for whom a particular type of training is effective" (Tannenbaum & Yukl, 1992, p. 433). As such, researchers have begun to investigate pre

Increasingly autonomous robotic systems are expected to play a vital role in aiding humans in complex and dangerous environments. It is unlikely, however, that such systems will be able to consistently operate with perfect reliability. Even less than 100% reliable systems can provide a significant benefit to humans, but this benefit will depend on a human operator's ability to understand a robot's behaviors and states. The notion of system transparency is examined as a vital aspect of robotic design, for maintaining humans' trust in and reliance on increasingly automated platforms. System transparency is described as the degree to which a system's action, or the intention of an action, is apparent to human operators and/or observers. While the physical designs of robotic systems have been demonstrated to greatly influence humans' impressions of robots, determinants of transparency between humans and robots are not solely robot-centric. Our approach considers transparency as emergent property of the human-robot system. In this paper, we present insights from our interdisciplinary efforts to improve the transparency of teams made up of humans and unmanned robots. These nearfuturistic teams are those in which robot agents will autonomously collaborate with humans to achieve task goals. This paper demonstrates how factors such as human-robot communication and human mental models regarding robots impact a human's ability to recognize the actions or states of an automated system. Furthermore, we will discuss the implications of system transparency on other critical HRI factors such as situation awareness, operator workload, and perceptions of trust.