A key factor influencing how much people explore is their aspiration level. The aspiration level is often thought to be determined by state affordances as well as by a person’s trait tendency to satisfice or maximize. The present study aims to shed light on how state and trait aspiration levels shape exploratory behavior. Participants completed a multi-armed bandit task in which their state aspiration level was manipulated: in every round, they received the same instruction about how many points they needed to achieve a good result, while the environment was either scarce, rich, or medium. This manipulation resulted in an easy task (low state aspiration level), a hard task (high state aspiration level), and a medium task. The tendency to maximize or satisfice was assessed via self-report. Participants switched significantly more when their state aspiration level was high, and performed best when their state aspiration level was medium. Computational modeling using reinforcement learning revealed that these changes in switching behavior were due to changes in exploration strategy. These findings align with the predictions of risk sensitivity theory as well as with the notion of aspiration levels as stopping rules. However, contrary to our expectations, the trait tendency to maximize or satisfice was not related to switching behavior, nor to changes in switching between conditions. Overall, the results highlight the importance of state affordances over trait tendencies.