Learning to use a novel human-in-the-loop control system is often a slow and frustrating process due to the need to understand new interaction paradigms. Unsurprisingly, research has commonly focused on identifying methods to accelerate such learning. In this paper we consider an alternative approach of motivating learners to persist with their learning. If learners are motivated to continue investing time in using a novel control system they will transition to proficiency, albeit at different timescales. Participants controlled the movements of a virtual robot in real-time by adjusting their movements whilst seated on a pressure sensing mat. In two experiments, participants played a game where their task was to move a virtual robot to collect targets as quickly as possible. Targets were only presented for a fixed duration such that participants received binary reward feedback dependent upon whether they collected a given target in time or not. This feedback was used to calculate each participant's success frequency which was used as a proxy for their skill level and thus learning. Experiment 1 showed that participants could learn the control system but that their motivation to play the game decreased as the experiment continued. Experiment 2 investigated whether adapting task difficulty as a function of the participants' current skill level (indexed by success frequencies) would increase the time participants chose to invest playing the game. Participants did not choose to play the game for longer when playing the game under this adaptive difficulty condition compared with fixed difficulty conditions. We conclude that most participants improved at using the pressure sensing mat for remote control but adapting the difficulty of the task to participant's skill level did not increase the time they were willing to invest in playing it.
Crowe et al. (Tue,) studied this question.