 Abstract we develop a method for selecting meaningful learning strategies based solely on the behavioral data of a single individual in a learning experiment. We use simple activity credit assignment algorithms to model the different strategies and couple them with a novel holdout statistical selection method. Application on right behavioral data in a continuous teammate's task reveals a particular learning strategy that consists in chunking the paths used by the animal. Neuronal data collected in a dorsomedial striatum confirm this strategy. This article was offered by Ashwin James, Patricia Rhino-Bouret, Julia Mazzadri, and others.