Modeling Responses to Alarm Systems: A Drift Diffusion Model Approach

Abstract

In numerous applications, alarm systems play an important role, supporting human decision-making. So far, however, little research dealt with the cognitive mechanisms that are at play in alarm-supported decision-making. In the present study, we aim to disentangle underlying cognitive mechanisms by using drift diffusion modeling. The results showed that going beyond standard approaches of analyzing alarm-system supported binary decision tasks can reveal results unlikely to be captured otherwise. That is, the analyses revealed that the alarm system’s output biased the decision-making process, requiring less evidence to be sampled for agreeing with the system than for disagreeing with the system. Moreover, evidence was accumulated faster on correct than on incorrect alarm system recommendations. Thus, the present results point to promising directions for gaining a more fine-grained picture of automation supported decision making.