Learning flight procedures is part of any pilot training. The conventional learning method consists in learning and practicing the procedure written on a sheet of paper along with printed images of the cockpit. The purpose of the present paper was to test the efficiency of a tactile interactive multimedia training tool designed to foster the self-regulated learning of flight procedures, especially through enacting relevant gestures and providing feedback. Results showed that learning with this tool did not lead to significant shorter learning times than with the conventional learning. However, on a delayed retention test in a real A320 cockpit simulator, learners of the experimental group performed the procedure more rapidly than those of the control group. Results suggested that a training tool that incites learners to perform similar gestures than those in the real environment and that provides feedback, helped learners to transform declarative into procedural knowledge.

The domain of human-robot Joint Action is a growing field where roboticists, psychologists and philosophers start to collaborate in order to devise robot abilities that are as efficient and convenient for the human partner as possible. Besides studying Joint Action and developing algorithms and schemes to control the robot and manage the interaction, one of the current challenges is to come up with a method to properly evaluate the progresses made by the community. Several questionnaires have already been proposed to the community that deal with the evaluation of humanrobot interaction. However, these studies mainly concern either specific basic behaviors during Joint Action or human-robot interactions without effective physical Joint Action. When it comes to high level decisions during physical human-robot Joint Action, there are fewer contributions to the topic, and also, the methods to evaluate them are even rarer. The aim of this paper is to propose a reusable questionnaire PeRDITA (Pertinence of Robot Decisions In joinT Action) allowing us to evaluate the pertinence of high level decision abilities of a robot during physical Joint Action with a human.