Using cognitive models to test interventions against mind-wandering during driving

In this study, we contrasted six different models to show the effects of different interventions by adaptive systems designed to prevent mind-wandering while driving. Although cognitive load associated with secondary tasks tends to affect driving negatively (e.g., Unni et al., 2017; Salvucci & Macuga, 2002; Ito et al., 2001), sometimes a simple secondary task can improve driving performance when the situation is mundane (e.g., Engström et al., 2017; Nijboer et al., 2016). Nijboer and colleagues (2016) have hypothesized that if the driving task is simple, people might start mind wandering, which interferes with driving (Yanko & Spalek, 2013, 2014; Martens & Brouwer, 2013). A simple secondary task, which imposes less workload than mind-wandering, could prevent this from happening. Automation system that adapt to the cognitive state of the driver could leverage this effect by inducing mild cognitive load during mundane driving scenarios with the goal to improve driving performance. To test suitable interventions, we combined an existing driver model (Salvucci, 2006) with an existing model of mind wandering in the cognitive architecture ACT-R (van Vugt et al., 2015) and tested different interventions that impose cognitive workload in different amounts during specific times of the simulation. Using these different models we, firstly, show how mind-wandering harms driving performance, secondly, show that mild cognitive load can mitigate this effect and, lastly, show that adapting to the cognitive state of the model incurs a significant processing cost that adaptive automation systems have to account for.