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Human operators who are tasked with monitoring automation systems may experience a high visual demand to process the information streams from these systems. The visual sampling behavior of human operators can be described using mathematical models. These models can help designers improve environments where multiple signals are present for human operators to monitor, to a configuration that can be processed properly. This study consisted of two parts. The first part investigated how peripheral vision plays a role in visual sampling behavior and task performance, specifically in the experimental eye-tracking setup presented in Eisma et al. (2018). In this setup, participants were instructed to monitor a bank of six dials, of which each dial pointer had a threshold indicator, and press a response key whenever a dial pointer crossed the threshold indicator. In the second part, the sampling models as presented by Senders (1983) are implemented to predict sampling trajectories. The sampling characteristics that resulted from the predictions were then evaluated. The results of the experiments show that peripheral vision plays a role in visual sampling and task performance. More specifically, sampling behavior is more evenly distributed among dials, and task performance is lower when peripheral vision is absent. The main attractor in the peripheral vision is shown to be the pointer speed. Moreover, the learning effect presented in Eisma et al. (2018) is not apparent when peripheral vision is absent. The results of the predictions showcase the sampling behavior characteristics, some of which show similarities with the results from the experimental data., Mechanical Engineering