Accelerating locomotor savings in learning: compressing four training days to one

Acquiring new movements requires the capacity of the nervous system to remember previously experienced motor patterns. The phenomenon of faster relearning after initial learning is termed "savings." Here we studied how savings of a novel walking pattern develops over several days of practice and how this process can be accelerated. We introduced participants to a split-belt treadmill adaptation paradigm for 30 min for 5 consecutive days. By training day 5, participants were able to produce near-perfect performance when switching between split and tied-belt environments. We found that this was due to their ability to shift specific elements of their stepping pattern to account for the split treadmill speeds from day to day. We also applied a state-space model to further characterize multiday locomotor savings. We then explored methods of achieving comparable savings with less total training time. We studied people training only on day 1, with either one extended split-belt exposure or alternating four times between split-belt and tied-belt conditions rapidly in succession. Both of these single-day training groups were tested again on day 5. Experiencing four abbreviated exposures on day 1 improved the performance on day 5 compared with one extended exposure on day 1. Moreover, this abbreviated group performed similarly to the group that trained for 4 consecutive days before testing on day 5, despite only having one-quarter of the total training time. These results demonstrate that we can leverage training structure to achieve a high degree of performance while minimizing training sessions. NEW & NOTEWORTHY Learning a new movement requires repetition. Here, we demonstrate how to more efficiently train an adapted walking pattern. By compressing split-belt treadmill training delivered over 4 days to four abbreviated bouts of training delivered on the first day of training, we were able to induce equivalent savings over a 5-day span. These results suggest that we can manipulate the delivery of training to most efficiently drive multiday learning of a novel walking pattern.