Mechanical problem solving in mice

Abstract Behavioral learning is a complex phenomenon that involves cognitive, perceptual, motor and emotional contributions. Yet, most animal studies focus on reductionist tasks that aim to isolate one of these aspects [1, 2, 3, 4]. Here, we use a lockbox — a complex, multi-step mechanical puzzle — to study learning dynamics in freely behaving mice. The mice engaged spontaneously with the task and learned to solve it within just a few trials. To dissect different contributions to this rapid form of learning, we combined deep learning-based behavioral tracking in a multi-camera setup with probabilistic inference and computational modeling. We find that the learning progress of the mice was initially dominated by the acquisition of motor skills, i.e., the increased ability to manipulate the individual mechanisms, while a cognitive strategy for the task sequence emerged only later. The lockbox paradigm may hence offer a promising framework for studying the interaction between low-level motor learning and high-level decision-making strategies in a single, ethologically relevant task. Competing Interest Statement The authors have declared no competing interest. Footnotes This version of the manuscript provides a major update on many of the results. While the entire manuscript is revised, we mainly expanded on our behavioral analyses (mainly Figs 3 and 4). We now describe how a combination of motor learning and the acquisition of a higher-level cognitive strategy explains the mouse behavior, which was absent in the previous version.