## 3 Materials and Methods
### Experimental Setup  
A makeshift workstation was developed for the experiment. Figure 1 shows the workstation structure and components. The workstation consisted of a table with assembly jigs, commands (button array and virtual button) for HRI, an emergency stop, some boxes for storing and removing assembly components, an LCD screen for displaying instructions and other information about the status of the robot systems (graphical user interface), a vision system for human-robot interaction and safety purposes, and a screwdriver. Participants were asked to work with a collaborative robot, namely, the Universal Robot UR3 model, to build a simplified version of a pneumatic cylinder. The robot was equipped with a collaborative gripper from Robotiq. The application was programmed through the Polyscope interface using Polyscript, which is a proprietary language of Universal Robot.  
The participants' point of view of the workstation is shown in Figure 2. Further details about the system components and integration are described in [50].  
To begin, a training session (without the robotic system) was conducted at a separate, designated workstation to minimise the impact of limited knowledge of the process and the learning effect of different trials. Second, a repeated measure design was implemented across three distinct and sequential scenarios (scenario 1, scenario 2 and scenario 3). Table 1 illustrates the features of the workstation and robot for each of the three scenarios. The guidelines were gradually implemented across the scenarios, with increasing levels of robot speed, autonomy, trajectories, user commands, notifications to the users and safety training measures.  
Figure 1: Workstation structure and components.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Scenario 1** & **Scenario 2** & **Scenario 3** \\ \hline _Robot’s speed_ & Lower than nominal values & Higher than nominal values & Set by participants (nominal, slower, higher) \\ _Robot’s autonomy_ & Low & High & Intermediate \\ _Robot’s trajectories_ & Point-to-point trapezoidal & Point-to-point trapezoidal & Minimum-jerk trajectories \\  & velocity profile trajectories & velocity profile trajectories & Set by participants (touch or gesture \\ _Type of commands_ & Touch button & Gesture recognition system & recognition) \\ _Notifications_ & Only instructions & Instructions and robot’s status & Instructions and robot’s status and speed \\ _Training on safety_ & No info to participants & Basic training & Full training (including commands and GUI) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Workstation and robot features in each scenario.  
Figure 2: Participant’s point of view of the workstation.  
The assignment of the scenarios was non-randomised, as the idea was to gradually improve the interaction conditions by changing different elements of the context. In each trial, some features of the collaborative workstation and the robot were manipulated according to the guidelines presented in [24].  
A questionnaire was administered to participants to assess the effect of feature manipulation on the selected cognitive variables. Participants were asked to complete the survey before the experiment began and between each scenario. In addition, gaze behaviour was measured throughout the whole duration of the experiment using a Pupil Labs eye tracker [51] to assess the participants' cognitive workload.