Reactivation-induced motor skill learning

Learning motor skills commonly requires repeated execution to achieve gains in performance. Motivated by memory reactivation frameworks predominantly originating from fear-conditioning studies in rodents, which have extended to humans, we asked the following: Could motor skill learning be achieved by brief memory reactivations? To address this question, we had participants encode a motor sequence task in an initial test session, followed by brief task reactivations of only 30 s each, conducted on separate days. Learning was evaluated in a final retest session. The results showed that these brief reactivations induced significant motor skill learning gains. Nevertheless, the efficacy of reactivations was not consistent but determined by the number of consecutive correct sequences tapped during memory reactivations. Highly continuous reactivations resulted in higher learning gains, similar to those induced by full extensive practice, while lower continuity reactivations resulted in minimal learning gains. These results were replicated in a new independent sample of subjects, suggesting that the quality of memory reactivation, reflected by its continuity, regulates the magnitude of learning gains. In addition, the change in noninvasive brain stimulation measurements of corticospinal excitability evoked by transcranial magnetic stimulation over primary motor cortex between pre- and postlearning correlated with retest and transfer performance. These results demonstrate a unique form of rapid motor skill learning and may have far-reaching implications, for example, in accelerating motor rehabilitation following neurological injuries.

Motor skill learning, in healthy or clinical populations, usually requires extensive execution of a motor task to achieve gains in performance. These gains are accumulated in two different time windows: during skill execution (online learning, see refs. 1–3) and between sessions, possibly through offline consolidation processes (offline learning, see refs. 4–6). Interestingly, frameworks stemming from synaptic-level studies (7–9), and further supported by evidence in rodents (10–13) and humans (14–20), suggest that even fully consolidated memories, presumably stable, can be strengthened, updated, or degraded following their reactivation. Could such brief memory reactivations enhance motor skill performance without extensive practice over multiple sessions? Motivated by a proof-of-principle study in a different domain, visual perceptual learning (15), here, we tested whether brief reactivations of an encoded motor skill can induce learning gains. We additionally tested whether such form of rapid learning generalizes to the untrained hand. The possibility of achieving skill improvements with a minimal amount of task execution could strongly impact skill learning research and have promising potential for the development of strategies to improve practice efficiency in daily life and following neurological impairments.To test the ability of memory reactivations to induce motor skill learning, participants practiced a motor sequence task (21) in which they were asked to type a five-digit sequence as fast and as accurate as they could (see Materials and Methods). The motor skill was first encoded in an initial test session, with a retest session conducted following 1 wk. Participants in the “Reactivations” group performed brief reactivations on two separate days between the test and retest. Each of these reactivation sessions lasted only 30 s, in which participants reactivated the skill memory by briefly performing a single trial of the task (Fig. 1A). The “Control” group performed only test and retest sessions without memory reactivations. Participants in the “Full Practice” group performed two full training sessions (12 trials each) between the test and retest. Learning gains were quantified as the difference in performance between the last trial of the test session and the first retest trial (5, 22–24), with performance quantified as the number of correct sequences tapped, a highly common measure combining both speed and accuracy (17, 23, 25, 26).Open in a separate windowFig. 1.Reactivation-induced learning gains. (A) Experimental design. Subjects first encoded the motor skill memory in a test session including 12 trials of the task and performed a retest session following 1 wk, followed by an intermanual transfer test. Participants in the Reactivations group (composed of High Continuity and Low Continuity) performed brief reactivations between test and retest in which they reactivated their skill memory by performing only a single 30 s trial of the task. Participants in the Full Practice group performed full 12 trials training sessions between test and retest. The Control group performed only test and retest sessions without reactivations. (B) An illustrated explanation of the CS calculation. In both examples, the number of correct sequences, errors, and total key presses are identical, but the CS is different. (C) Single-trial performance for all groups (High Continuity Reactivations marked in light blue, Low Continuity Reactivations in light red, Control in gray, and Full Practice in purple. The combined Reactivations groups are illustrated in dashed black). (D) Test versus retest single-subject performance presented in a scatterplot along a unit slope line (y = x) where each point reflects a participant (5, 46). Data accumulating above the unit line reflect subjects who improved from test to retest, expressing learning gains, while data points below the line indicate degraded retest performance. (E) Dashed black bars (corresponding to the right y-axis) reflect the percentage of participants on each side of the unit slope line in D, and the colored bars reflect the mean performance in test and retest sessions (corresponding to the left y-axis). (F) Mean transfer test performance compared to test performance. *P < 0.05, **P < 0.001. Error bars represent SEM.Because of the variable efficacy of reactivations, we reasoned that the quality of reactivations may determine their efficacy in inducing learning gains. Unintentional errors during reactivation might reactivate a different version of the memory and could strengthen erroneous memories instead of the original memory trace. This could possibly cause a decrease in learning gains or even result in deteriorated performance of the original memory. This is consistent with the concept of interruptions, previously suggested to affect task performance (27, 28), possibly by preventing encoding of coherent representations of memories (27, 29, 30). Accordingly, we reasoned that continuity, reflecting minimal interruptions, might play a role in defining the efficacy of reactivations. To that effect, “High” and “Low Continuity Reactivations” were separately analyzed (see Materials and Methods and Results). In addition, a replication experiment was conducted to confirm the role of continuity in reactivation efficacy.