Automated vehicles (AVs) can bring about numerous benefits for society but they are unprepared to enter our roads yet, in large part because of the difficulties in interacting with humans around them. Addressing this issue requires not only developing novel interaction planning approaches for AVs, but also understanding human behavior in interactions with them. Existing investigations of such behavior have predominantly focused on situations in which AV a priori needs to take action because the human has the right of way, e.g., pedestrian crossing interactions. However, future AVs would likely need to proactively manage interactions even if they have the right of way over humans. Yet it remains unclear how AVs could behave in such interactions and how humans would react to them. In this paper, we investigate human responses to subtle nudge maneuvers of an oncoming AV when making an unprotected left turn. We found that participants (N=19) of a driving simulator experiment were sensitive to the deceleration nudge but not acceleration nudge (for the studied magnitude and duration of the nudge). Most importantly, we compare the obtained data to predictions of a computational cognitive model based on the concept of evidence accumulation. We demonstrate that the model captures the key qualitative patterns observed in participants' decisions and response times. Finally, we illustrate how the cognitive model, even if fitted to a limited set of experimental conditions, can flexibly generate predictions of human responses to arbitrary longitudinal AV maneuvers. These predictions are quantitative and testable, making the model a potentially useful tool for both informing future studies of human behavior and incorporating insights from such studies into computational frameworks potentially suitable for AV interaction planning.