08:30 - 10:00
Talk Session VI
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08:30 - 10:00
Wed-HS1-Talk VI-
Wed-Talk VI-
Room: HS1
Chair/s:
Moritz Held, Jochem Rieger
Both psychological experimentation and (cognitive) models are established approaches to evaluate the safety, ergonomics, and usability of Human-machine-interactions in real-life scenarios. However, they often excel at different stages in the scientific process. While psychological experiments are, for example, often used to critically assess the influence of cognitive processes in real-life environments, (cognitive) models are best used to inform how said processes influence or interact with the task environment. In real-life scenarios, the interplay between models and experimentation can be especially helpful due to the challenges that arise when evaluating these models, for example, the individual differences between humans. In this symposium, we bring together research from both experimental psychologists as well as (cognitive) modelers to foster an integrated evaluation of applied research environments that combines these methods. In the first talk, Biebl & Bengler will present their work on modeling intersection-related collision due to impaired visual ability. The second talk by Russwinkel will discuss anticipatory models for real-life decisions. The third talk summarizes an evidence accumulation model of a driving task. The fourth talk by Baumann et al. showcases several examples in modeling cooperation in traffic while highlighting the potential difficulties that can arise in the process. The last talk by Held et al. presents an ACT-R model, which attempts to explain an often-observed behavior of decreased driving performance in mundane driving environments and why this effect can be reversed by a low-effort mental task. The symposium will end with a moderated discussion between the speakers and the audience.
A cognitive model testing different interventions to prevent harmful mind-wandering during driving
Wed-HS1-Talk VI-05
Presented by: Moritz Held
Moritz Held 1, 2, Andreea Minculescu 2, Jochem Rieger 1, Jelmer Borst 2
1 University of Oldenburg, 2 University of Groningen
In this study, we contrasted six different models to show the effects of different interventions by
adaptive automation 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-wanderng, 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.
Keywords: Cognitive modeling, driving, workload, ACT-R, adaptive automation