In order to preserve election integrity, it is crucial for voters to check their ballots for errors and other potential anomalies. Previous research has explored voters’ ability to detect anomalous changes to their ballots and the factors affecting this capability. However, the specific strategies voters use when checking their ballots remain unexplored. This study aims to fill this gap by utilizing eye-tracking methods and computational modeling to examine voters’ checking behavior. To this end, an experiment was designed where participants took part in a fictitious election by interacting with a paper ballot layout displayed on a computer screen. The ballot interface was programmed to alter the voters’ selections. The findings reveal that voters’ ability to detect these anomalous selections varies and is influenced by their visual and checking strategies, along with the availability of a slate or voter guide. More specifically, these elements collectively impact voters’ performance, resulting in different anomaly detection rates depending on the combination of strategies used by a voter. Based on these data, ACT-R models were constructed that replicated the observed strategies. These models were validated by matching their anomaly detection performance to that of the voters and were then used to make predictions about voters’ ability to detect anomalies and their own errors. Key suggestions from this study are that voter guides should always be provided in voting booths, voters should avoid using a random search strategy, and voters should be reminded to check for both undervotes and wrong candidate selections.