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.

A seminal work by Sheridan and Verplank depicted 10 levels of automation, ranging from no automation to an automation that acts completely autonomously without human support. These levels of automation were later complemented with a four-stage model of human information processing. Next, human-machine cooperation centred models and associated cooperation modes were introduced. The objective of the experiment was to test which human-machine theorie describe automation use better. The participants were asked to choose repeatedly between four automation types (i.e. no automation, warning, co-action, function delegation) to complete three multi-attribute task battery tasks. The results showed that the participants favour the selection of automation types offering the best human-machine interactions quality rather that the most effective automation type. Contrary to human-machine cooperation models, technology centred models could not predict accurately automation selection. The most advanced automation was not the most selected. Practitioner Summary: The experiment dealt with how people select different automation types to complete the multi-attribute task battery that emulates recreational aircraft pilot tasks. Automation performance was not the main criteria that explain automation use, as people tend to select an automation type based on the quality of the human-machine cooperation.

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