OpenMATB is an open-source variant of the Multi-Attribute Task Battery (MATB) and is available under a free software license. MATB consists of a set of tasks representative of those performed in aircraft piloting. It is used, in particular, to study the effect of automation on decision-making, mental workload, and vigilance. Since the publication of MATB 20 years ago, the subject of automation has grown considerably in importance. After introducing the task battery, this article highlights three main requirements for an up-to-date implementation of MATB. First, there is a need for task customization, to make it possible to change the values, appearance or integrated components (such as rating scales) of the tasks. Second, researchers need software extensibility to enable them to integrate specific features, such as synchronization with psychophysiological devices. Third, to achieve experiment replicability, it is necessary that the source code and the scenario files are easily available and auditable. In the present paper, we explain how these aspects are implemented in OpenMATB by presenting the software architecture and features, while placing special emphasis on the crucial role of the plugin system and the simplicity of the format used in the script files. Finally, we present a number of general trends for the future study of automation in human factors research and ergonomics.

Objective: We tested Hancock and Szalma’s mental workload model, which has never been experimentally validated at a global level with the measure of the pre-ejection period (PEP), an index of beta-adrenergic sympathetic impact. Background: Operators adapt to mental workload. When mental workload level increases, behavioral and physiological adaptability intensifies to reduce the decline in performance. However, if the mental workload exceeds an intermediate level, behavioral and physiological adaptability will decrease to protect individuals from excessive perturbations. This decrease is associated with a change in behavioral strategies and disengagement. Method: The experimental task was a modified Fitts’ task used in Hancock and Caird. Five levels of task difficulty were computed. Behavioral and physiological adaptability was indexed by the performance with speed–accuracy trade-off and PEP reactivity. Results: A curvilinear effect of task difficulty on PEP reactivity was significant, with high reactivity at the intermediate level but low reactivity at other levels. We observed a linear effect of task difficulty on error rate and a curvilinear effect on movement time. A decline in performance was noted up to the intermediate level, with a speed–accuracy trade-off above this level showing a faster movement time. Conclusion: We observed for the first time behavioral and physiological adaptability as a function of mental workload. Application: The results have important implications for the modeling of mental workload, particularly in the context of the performance-sensitive domain (car driving and air traffic control). They can help guide the design of human–computer interaction to maximize adaptive behavior, that is, the “comfort zone.”

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The message scheduling is an essential mechanism in the context of Networked Control Systems (NCS). It strongly influences the stability of the closed loop distributed control applications. The goal of this paper is to present a study of different priority schemes which can be used for the message scheduling of a process control application implemented through a network (the network CAN will be considered). Two great schemes will be presented: static priority, hybrid priority including a dynamic part and a static part. The dynamic part is based on the dynamic performance of the controlled application. Considering the hybrid priority scheme, several types will be considered for the dynamic part: dynamic part alone, dynamic part and a static time strategy, dynamically controlled time strategy. The comparison of these schemes is done with respect to the stability of a process control application.