Practicing one thing at a time: the secret to reward-based learning?

Binary reward feedback on movement success is sufficient for learning in some simple reaching tasks, but not in some more complex ones. It is unclear what the critical conditions for learning are. Here, we ask how reward-based sensorimotor learning depends on the number of factors that are task-relevant. In a task that involves two factors, we test whether learning improves by giving feedback on each factor in a separate phase of the learning. Participants learned to perform a 3D trajectory matching task on the basis of binary reward-feedback in three phases. In the first and second phase, the reward could be based on the produced slant, the produced length or the combination of the two. In the third phase, the feedback was always based on the combination of the two factors. The results showed that reward-based learning did not depend on the number of factors that were task-relevant. Consistently, providing feedback on a single factor in the first two phases did not improve motor learning in the third phase.