We present a novel approach using curriculum learning to identify principles by which a neural circuit learns to perform different tasks. Previous work has focused on how curricula can be designed to improve learning of a model on particular tasks. We consider the inverse problem:what can a curriculum tell us about how a learning system acquired a task? Using recurrent neural networks (RNNs) and commonly used tasks in experimental neuroscience, we demonstrate that curricula can be used to differentiate learning principles, with target-based and a representation-based loss functions as use cases. In particular, we compare the performance of RNNs using learning rules based on matching target functions versus those based on modifying neural representations directly on three different curricula in the context of two tasks. We show that the learned state-space trajectories of RNNs trained by these two learning rules under all curricula tested are indistinguishable.However, by comparing learning times during different curricula, we can disambiguate the learning rules. We therefore challenge traditional approaches of interrogating learning systems by showing that individual learning rules respond to–and benefit from–different curricula differently. Although animals in neuroscience lab settings are trained by curriculum based procedures called shaping,very little behavioral or neural data are collected on the relative successes or training times under different curricula. Our results motivate the systematic collection and curation of data during shaping by demonstrating curriculum learning in RNNs as a tool to probe and differentiate learning principles used by neural circuits in the brain, over conventional statistical analyses of learned state spaces.