The increasing use of automation to supplant human intervention in controlling complex systems changes the operators' role from that of an active controller (directly involved with the system) to that of a supervisory controller (managing the use of different degrees of automatic and manual control). This research examined the role of supervisory controllers by investigating the relationship between trust in the automatic controllers, self-confidence in manual control abilities, and the use of automatic controllers. In particular, four experiments examined changes in operators' ratings of trust and selfconfidence, as well as the operators' allocation of function, during an interaction with a simulated semiautomatic pasteurization plant. The results of the first experiment indicated that the operators' trust changed dynamically, with the effects of transient and continuous faults spread over several trials. The second experiment showed that trust, combined with self confidence, corresponded to the operators' allocation strategy. Specifically, an ARMAV time series model of the dynamic interaction of trust and self confidence, combined with individual biases, accounted for a between 60.9% and 86.5% of the variance in the use of the three automatic controllers. The third experiment replicated the second, showing that the time series model, based on the operators' trust and self confidence, described the operators’ use of automation, even under different of fault conditions. In addition, it showed that the individual biases, identified in the time series analysis, corresponded to operators' general attitudes towards automation. The fourth experiment examined the information operators used to control the system, showing that the operators monitored untrusted automation more frequently. In addition, a first order transition probability analysis of the monitoring behavior showed that certain patterns were indicative of poor performance. Together, these four experiments provide a starting point for a more complete understanding of the factors mediating operators' adaptation to automation in the context of supervisory control.