Stress and Motivation on Reliance Decisions with Automation

Abstract

The decision to rely on automation is crucial in high-stress environments where there is an element of uncertainty. It is equally vital in human-automation partnership that the human’s expectations of automation reliability are appropriately calibrated. Therefore, it is important to better understand reliance decisions with varying automation reliability. The current study examined the effects of stress and motivation on the decision to rely on autonomous partners. Participants were randomly assigned to a stress and motivation condition, using the Trier Social Stress Test (TSST) for stress induction, and monetary incentive for motivation. The main task was an iterative pattern learning task where one of two AI partners, one with high reliability and one with low reliability, gave advice at every iteration; the AI partner alternated every ten iterations. While motivation had a stronger effect than stress, both motivation and stress affected reliance decisions with the high reliability AI. The low reliability AI was affected to a lesser degree if at all. Overall, the decision to not rely on the AI partner, especially with the higher in reliability was slower than the decision to rely on the AI partner, with the slowest decision times occurring in the high stress condition with motivated participants, suggesting more deliberate processing was utilized when deciding against the advice of the AI higher in reliability.