The goal of training is to produce learning for a range of activities that are typically more general than the training task itself. Despite a century of research, predicting the scope of learning from the content of training has proven extremely difficult, with the same task producing narrowly focused learning strategies in some cases and broadly scoped learning strategies in others. Here we test the hypothesis that human subjects will prefer a decision strategy that maximizes performance and reduces uncertainty given the demands of the training task and that the strategy chosen will then predict the extent to which learning is transferable. To test this hypothesis, we trained subjects on a moving dot extrapolation task that makes distinct predictions for two types of learning strategy: a narrow model-free strategy that learns an input-output mapping for training stimuli, and a general model-based strategy that utilizes humans' default predictive model for a class of trajectories. When the number of distinct training trajectories is low, we predict better performance for the mapping strategy, but as the number increases, a predictive model is increasingly favored. Consonant with predictions, subject extrapolations for test trajectories were consistent with using a mapping strategy when trained on a small number of training trajectories and a predictive model when trained on a larger number. The general framework developed here can thus be useful both in interpreting previous patterns of task-specific versus task-general learning, as well as in building future training paradigms with certain desired outcomes.