Social robot teammates have entered the workforce in a variety of sectors including healthcare and the service industry. Understanding how trust in robot partners differ from human partners is imperative in ensuring effective and cohesive work environments. The calibration of trust can help to prevent the misuse (overreliance) or disuse (underutilization) of autonomous systems. To gain deeper insight into trust dynamics further research on objective physiological indicators, like eye tracking, is essential. This research aims to explore novel measures of trust in human-robot interaction, aligning perceptions with actual capabilities. Specifically, we will examine differences in trust when participants are paired with either a human or robot teammate, with varying capabilities, while completing a visual search task. We will use eye tracking to analyze gaze behavior and teammate monitoring, found to be a good physiological indicator of automation trust, and mouse clicks as behavioral indicators of trust. We hypothesize that monitoring (fixation points on the partner screen) and mouse clicks (assisting their partner) will increase for both human and robot teammates when their capabilities are low versus high, demonstrating that our objective measures are capturing varying levels of trust. Additionally, we hypothesize that robot partners will be monitored less than human partners because of an overreliance on mechanistic agents when completing analytical tasks. This study has the potential to identify a new objective measure of trust in human-robot interactions.