The evolution of mental models in relation to initial information while driving automated

Abstract Objective Mental models guide drivers’ expectations about the functioning of a conditionally automated vehicle. We induced different mental models using preliminary system descriptions to explore mental models with an objective, online measurement during conditionally automated driving. Background Human-machine interaction, based mainly on mental models, has been examined mostly by employing subjective measurements. However, an objective measurement method could improve the comparability of studies and provide a broader understanding of mental models in automated driving, thus leading to road safety in times of mixed traffic. Methods and results In two experiments (total N = 148), we manipulated the participants’ mental models by providing correct and incorrect system descriptions. In Experiment 1, contrary to our expectations, the results showed faster reaction times in the condition with an incorrect mental model. In Experiment 2, we replicated this finding and showed that this effect can be traced back to the very first experience of the mismatch between the mental model and actual system behavior. Conclusion Overall, our results showed an impact of the preliminary system description on mental models. Moreover, the importance of a complete and correct manipulation of materials in conditionally automated driving research is emphasized. Application Potential applications include the online assessment of mental models during automated driving (e.g. dead man’s switch).