Modeling the Cognitive Task Load and Performance of Naval Operators

Neerincx, Mark A.; Kennedie, Stefan; Grootjen, Marc; Grootjen, Franc

doi:10.1007/978-3-642-02812-0_31

Cited by 11 publications

(6 citation statements)

References 5 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other studies have done similar research, but within different context and with different methods. As a first example, Neerincx et al (2009) created a naïve Bayesian network to predict performance of naval operators. The COPE model includes Neerincx' model, addressing more factors and distinguishing several error types, and can therefore be used for training purposes.…”

Section: Discussionmentioning

confidence: 99%

Modelling environmental and cognitive factors to predict performance in a stressful training scenario on a naval ship simulator

2015

Self Cite

View full text Add to dashboard Cite

Professionals working in risky or emergency situations have to make very accurate decisions, while the quality of the decisions might be affected by the stress that these situations bring about. Integrating task feedback and biofeedback into computer-based training environments could improve trainees' stress-coping behaviour. This paper presents and assesses a refined version of the cognitive performance and error (COPE) model that describes the effects of stressful events on decisions as a foundation for such a support tool. Within a high-fidelity simulator of a ship's bridge at the Royal Netherlands Naval College, students of the naval college (n = 10) were observed while completing a 2-h-long shadowing and boarding operation combined with a search-and-rescue operation. For every action, variables were measured: objective and subjective task demand, challenge and threat appraisal, and arousal based on heart rate and heart rate variability. The data supported the COPE model and were used to create predictive models. The variables could provide minute-byminute predictions of performance that can be divided into performance rated by experts and errors. The predictions for performance rated by experts correlated with the observed data (r = 0.77), and 68.3 % of the predicted errors were correct. The error predictions concern the chances of making specific errors of communication, planning, speed, and task allocation. These models will be implemented into a real-time feedback system for trainees performing in stressful simulated training tasks.

show abstract

Section: Discussionmentioning

confidence: 99%

Modelling environmental and cognitive factors to predict performance in a stressful training scenario on a naval ship simulator

2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…Neerincx et al [20] showed that the cognitive states recognized by this model (optimal, underload, overload, cognitive lock-up and vigilance) affected performance, and that the model could be successfully used to estimate performance, with an accuracy of 86% in a laboratory setup, and 74% aboard a naval ship. (Neerincx, 2003) Using only three metrics makes it easier for an expert to rate the tasks, and allows for automatic estimation of the cognitive state.…”

Section: The Ctl-model: Overviewmentioning

confidence: 98%

Real time modeling of the cognitive load of an Urban Search And Rescue robot operator

Colin

Smets²,

Mioch³

et al. 2014

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

Self Cite

View full text Add to dashboard Cite

Urban Search And Rescue (USAR) robots are used to find and save victims in the wake of disasters such as earthquakes or terrorist attacks. The operators of these robots are affected by high cognitive load; this hinders effective robot usage. This paper presents a cognitive task load model for real-time monitoring and, subsequently, balancing of workload on three factors that affect operator performance and mental effort: time occupied, level of information processing, and number of task switches. To test an implementation of the model, five participants drove a shape-shifting USAR robot, accumulating over 16 hours of driving time in the course of 485 USAR missions with varying objectives and difficulty. An accuracy of 69% was obtained for discrimination between low and high cognitive load; higher accuracy was measured for discrimination between extreme cognitive loads. This demonstrates that such a model can contribute, in a non-invasive manner, to estimating an operator's cognitive state. Several ways to further improve accuracy are discussed, based on additional experimental results.

show abstract

“…An important challenge in adaptive automation is deciding when to change the level of autonomy of the robot, and to which level. This can be done based on the cognitive task load of the operator (Neerincx, 2003), as cognitive task load has an influence on performance (Neerincx et al, 2009). In addition, cognitive task load itself is influenced by changing levels of automation, as the level of autonomy and operator task load are inversely correlated if other factors remain stable (Steinfeld et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Being in such a state has a negative influence on performance. The CTL model has been experimentally validated in the naval domain (Neerincx et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Task Allocation for Human-robot Teams

Giele

Mioch

Neerincx

et al. 2015

Proceedings of the International Conference on Agents and Artificial Intelligence

View full text Add to dashboard Cite

Artificial agents, such as robots, are increasingly deployed for teamwork in dynamic, high-demand environments. This paper presents a framework, which applies context information to establish task (re)allocations that improve human-robot team's performance. Based on the framework, a model for adaptive automation was designed that takes the cognitive task load (CTL) of a human team member and the coordination costs of switching to a new task allocation into account. Based on these two context factors, it tries to optimize the level of autonomy of a robot for each task. The model was instantiated for a single human agent cooperating with a single robot in the urban search and rescue domain. A first experiment provided encouraging results: the cognitive task load of participants mostly reacted to the model as intended. Recommendations for improving the model are provided, such as adding more context information.

show abstract

Modeling the Cognitive Task Load and Performance of Naval Operators

Cited by 11 publications

References 5 publications

Modelling environmental and cognitive factors to predict performance in a stressful training scenario on a naval ship simulator

Modelling environmental and cognitive factors to predict performance in a stressful training scenario on a naval ship simulator

Real time modeling of the cognitive load of an Urban Search And Rescue robot operator

Dynamic Task Allocation for Human-robot Teams

Contact Info

Product

Resources

About