The team demonstrated that mice are able to solve 1-step- as well as 4-step-lockboxes, and their performance in solving the lockboxes improved over time. This was due to an increased capability of successfully manipulating the mechanisms and partially also due to the acquisition of a smart solution strategy. Lockboxes may hence be a promising approach to study both abstract sequential decision making and low-level motor learning in a single task that can be rapidly learned by mice. Mice that had access to lockboxes over a prolonged period of time showed less trait anxiety-related behavior and improved sequential problem-solving (i.e., solving novel lockboxes) when compared to mice that were not trained on lockboxes. However, the relevant phenotype of the mice was not affected by the lockboxes, i.e. no changes in state anxiety-related behavior and physiological variables (i.e., bodyweight, resting metabolic rate, stress hormone metabolite concentrations, adrenal gland weights) were found. Lockboxes did not negatively influence the relative variability of the variables analyzed. There was no evidence of toxicopathological effects associated with the material from which the lockboxes were made. The team also found that such mouse experiments and the large video dataset created can be automatically analyzed through modern machine learning methods. A newly developed pipeline consisting of tracking keypoints and subsequent detection of interactions with the mechanism proved successful and eliminated a huge amount of manual labor, but not all.