Humans tend to repeat past actions, with habits as extreme case. With each execution of an action this repetition bias increases the probability to repeat this action. However, there is a lack of experiments investigating repetition bias.To test for the presence of a repetition bias, we developed a new sequential decision-making task (see Figure). For this task, participants had to collect points with four moves and meet a trial-specific goal as closely as possible. To ensure frequent repetition of at least one sequence of actions, we informed participants about a default action sequence that resulted in the highest expected reward in about half of the trials.The proposed sequence was the most frequently used one by all participants, but, using summary statistics, evidence for a repetition bias did not show a clear pattern. To perform a more refined analysis, we used a computational model for action selection with a repetition bias to individually quantify the strength of the repetition bias. We used an adaptation of the prior-based control model (Schwöbel et al., 2021, Journal of Mathematical Psychology, 100, 102472), which balances the influence of expected rewards and repetition behavior on action selection.This computational model best fit participants’ behavior when compared to alternative models that did not consider a repetition bias. In addition, the repetition bias significantly correlated negatively with the collected reward. The proposed model may more generally serve as a blueprint for establishing evidence for a human repetition bias in a wide range of other tasks.