People routinely learn how to manipulate new tools or make new movements. This learning requires the transformation of sensed movement error into updates of predictive neural control. Here, we demonstrate that the richness of motor training determines not only what we learn but how we learn. Human subjects made reaching movements while holding a robotic arm whose perturbing forces changed directions at the same rate, twice as fast, or four times as fast as the direction of movement, therefore exposing subjects to environments of increasing complexity across movement space. Subjects learned all three environments and learned the low- and medium-complexity environments equally well. We found that subjects lessened their movement-by-movement adaptation and narrowed the spatial extent of generalization to match the environmental complexity. This result demonstrated that people can rapidly reshape the transformation of sense into motor prediction to best learn a new movement task. We then modeled this adaptation using a neural network and found that, to mimic human behavior, the modeled neuronal tuning of movement space needed to narrow and reduce gain with increased environmental complexity. Prominent theories of neural computation have hypothesized that neuronal tuning of space, which determines generalization, should remained fixed during learning so that a combination of neuronal outputs can underlie adaptation simply and flexibly. Here, we challenge those theories with evidence that the neuronal tuning of movement space changed within minutes of training.