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Closed-circuit television (CCTV) is increasingly used as a means to ensure the safety and security of critical infrastructure and public spaces. Operators in control rooms are responsible for monitoring multiple camera feeds that generally exceed the number of screens available. Using a realistic video surveillance simulation, the current study investigates strategies that untrained operators use to deal with this visual overload. The majority of participants adopted a priority strategy by fixating some scenes more than others, as opposed to a parity strategy of devoting roughly equal time across all screens—although participants were largely unaware of the strategy they had used. A parity approach led to better detection performance, perhaps because less time elapsed between viewing each available camera feed thus reducing the probability that an incident could pass unnoticed. The identification of successful scanning strategies can be used to inform operator training.

The failure to notice critical changes in both visual and auditory scenes may have important consequences for performance in complex dynamic environments, especially those related to security, such as aviation, surveillance during major events, and command and control of emergency response. Previous work has shown that a significant number of situation changes remain undetected by operators in such environments. In the current study, we examined the impact of using auditory warning messages to support the detection of critical situation changes and to a broader extent the decision making required by the environment. Twenty-two participants performed a radar operator task involving multiple subtasks while detecting critical task-related events that were cued by a specific type of audio message. Results showed that about 22% of the critical changes remained undetected by participants, a percentage similar to that found in previous work using visual cues to support change detection. However, we found that audio messages tended to bias threat evaluation toward perceiving objects as more threatening than they were in reality. Such findings revealed both benefits and costs associated with using audio messages to support change detection in complex dynamic environments.

Dynamic and complex command and control situations often require the timely recognition of changes in the environment in order to detect potentially malicious actions. Change detection can be challenging within a continually evolving scene, and particularly under multitasking conditions whereby attention is necessarily divided between several subtasks. On-screen tools can assist with detection (e.g., providing a visual record of changes, ensuring that none are overlooked), however, in a high workload environment, this may result in information overload to the detriment of the primary task. One alternative is to exploit the auditory modality as a means to support visual change detection. In the current study, we use a naval air-warfare simulation, and introduce an auditory alarm to coincide with critical visual changes (in aircraft speed/direction) on the radar. We found that participants detected a greater percentage of visual changes and were significantly quicker to detect these changes when they were accompanied by an auditory alarm than when they were not. Furthermore, participants reported that mental demand was lower in the auditory alarm condition, and this was reflected in reduced classification omissions on the primary task. Results are discussed in relation to Wickens’ multiple resource theory of attention and indicate the potential for using the auditory modality to facilitate visual change detection.

The key to the development of adaptive gameplay is the capability to monitor and predict in real time the players experience (or, herein, fun factor). To achieve this goal, we rely on biometrics and machine learning algorithms to capture a physiological signature that reflects the player's affective state during the game. In this paper, we report research and development effort into the real time monitoring of the player's level of fun during a commercially available video game session using physiological signals. The use of a triple-classifier system allows the transformation of players' physiological responses and their fluctuation into a single yet multifaceted measure of fun, using a non-linear gameplay. Our results suggest that cardiac and respiratory activities provide the best predictive power. Moreover, the level of performance reached when classifying the level of fun (70% accuracy) shows that the use of machine learning approaches with physiological measures can contribute to predicting players experience in an objective manner.

The intensive care unit (ICU) is a high-risk environment that requires cross-professional teams to provide life-saving patient care. There is ample evidence that poor communication creates situations where medical errors are likely to occur and affect patient safety. We tested whether communication-oriented debriefing following high-fidelity simulation improves quality of information exchange reflecting collaborative work in ICU teams. Ten teams of six cross-professional ICU workers participated in three simulation-based training sessions. After each training session, the experimental group was debriefed on communication-oriented skills (based on Crew Resource Management, CRM), while the control group was debriefed on technical skills. The analysis was double-blind; 30 videotaped sessions were coded for three types of communication measures by four observers showing adequate inter-rater reliability. Results suggest that high-fidelity simulation training can improve non-technical skills in cross-professional ICU teams. Further investigation is needed on the performance effects of communication-focused debriefing.

This study shows how the use of various measures of eye movement can serve to portray dynamic decision-making (DDM) in a coherent fashion. We extracted eye movement metrics relative to 1) scanpath, 2) eye fixations, and 3) pupillary response, to characterize DDM during the process of risk assessment. Results from Experiment 1 revealed that incorrect classifications were associated with 1) less efficient information search, 2) difficulties in making sense of critical information, and 3) a low level of cognitive load. In Experiment 2, we used eye tracking to assess the impact on DDM of introducing a decision support system. The addition of a temporal-overview display seems to affect processing time in DDM as indexed by shorter scanpaths and fixations during classifications. These findings illustrate how event-based eye movement measures can reveal characteristics and limitations of the ongoing cognitive processing involved in DDM and also contribute to usability testing.

Temporal awareness is pivotal to successful real-time dynamic decision making in a wide range of command and control situations; particularly in safety-critical environments. However, little explicit support for operators' temporal awareness is provided by decision support systems (DSS) for time-critical decisions. In the context of functional simulations of naval anti-air warfare and emergency response management, the present study compares operator support provided by two display formats. In both environments, we contrast a baseline condition to a condition in which a temporal display was integrated to the original interface to support operators' temporal awareness. We also wish to establish whether the implementation of time-based DSSs may also come with drawbacks on cognitive functioning and performance.