In the human-machine collaboration context, understanding the reason behind each human decision is critical for interpreting the performance of the human-machine team. Via an experimental study of a system with varied levels of accuracy, we describe how human trust interplays with system performance, human perception and decisions. It is revealed that humans are able to perceive the performance of automatic systems and themselves, and adjust their trust levels according to the accuracy of systems. The 70% system accuracy suggests to be a threshold between increasing and decreasing human trust and system usage. We have also shown that trust can be derived from a series of users' decisions rather than from a single one, and relates to the perceptions of users. A general framework depicting how trust and perception affect human decision making is proposed, which can be used as future guidelines for human-machine collaboration design.