Balance maintenance as an acquired motor skill: Delayed gains and robust retention after a single session of training in a virtual environment.
In our everyday life, a wide range of motor, perceptual, and cognitive abilities are gradually and implicitly acquired through our continuous interaction with the environment, a process referred to as skill learning. Converging data indicate that skill learning is a multiple step process that cannot be reduced to the acquisition episode only. In the initial step, while the subject is practicing the task, the performance asymptotically improves with continued practice. This corresponds to a process coined as fast learning by Karni and coworkers (Karni and Sagi 1993; Karni et al. 1995). Remarkably, however, the initially formed memory trace apparently continues to be reprocessed after the training has ended. Consequently, when tested at a later date, up to several days to weeks later, the performance to the task is markedly improved even without any intervening training sessions. This so-called slow component of learning has been observed in humans for both perceptual and motor skill learning (Karni and Sagi 1993; Karni et al. 1995), and seems to depend critically on sleep rather than simply on time or initial practice (Maquet 2001; Peigneux et al. 2001). In this issue, Walker and colleagues investigate the interplay between the fast and slow components of learning. In the domain of motor skill learning, the finger tapping task (FTT), or its variant the finger opposition task, has been a useful model to characterize the fast and slow components of the learning process as well as the respective effects of time, practice, and sleep on the latter. In this task, the subjects are asked to repeat a five-element sequence of finger movements with the nondominant hand, as fast and as accurately as possible (Fig. 1C). The performance measure consists of the number of correctly repeated sequences in a given time (usually 30 sec). Whereas a moderate, albeit significant, increase in performance is reported during the training session and between training episodes on the same day, a much larger gain in performance is systematically observed overnight (Fischer et al. 2002; Walker et al. 2002; Walker et al. 2003; Fig.1A,B). This suggests that sleep is a major factor underlying the overnight gain in performance. Accordingly, it was shown that slow learning is significantly enhanced when participants are allowed to sleep between the training and the retest sessions (Fischer et al. 2002). The large improvement in performance is observed both after night time and day time sleep, ruling out a potential circadian influence on slow learning (Fischer et al. 2002). The exact influence of sleep on the slow component of skill learning is still unclear. For some tasks, (FTT: Fischer et al. 2002; pursuit task: Maquet et al. 2003), a small gain in performance persists despite a total sleep deprivation during the first posttraining night. For other tasks (visual texture discrimination task; Stickgold et al. 2000a), sleep seems a prerequisite for any significant performance improvement. Likewise, the respective role of the various sleep stages in skill learning is still unclear. Early results suggested that perceptual (visual) learning was sensitive to rapid eye movement (REM) sleep deprivation (Karni et al. 1994). Subsequent work emphasized that a maximal improvement in performance was observed after the succession of a large amount of SWS early in the night and REM sleep late in the night, raising the possibility of a double-step process in memory consolidation (Gais et al. 2000; Stickgold et al. 2000a,b). For motor skill learning, data are still too fragmentary to reach any definite conclusion. The improvement in performance has been reported to be related linearly to the time spent in REM sleep (Fischer et al. 2002) or in stage-2 non-REM sleep late in the night (Walker et al. 2002). Future research should sort out this issue. In any case, the gain in performance after motor-skill learning is robust and long lasting. It is still there after two nights of sleep (Fischer et al. 2002) and as shown byWalker and colleagues in this issue, might still slightly increase after the third post training night. The combined effect of daily motor practice and regular sleep might eventually lead to dramatic improvement in performance that plateaus after 2–3 wk (Karni et al. 1995; Fig. 1C). In this issue, Walker and colleagues now focus on the relationships between the fast (within session) and the slow (between session, overnight) components of learning (Fig. 1A,B). Do these components evolve independently or not? No correlation between the within-session improvement and the overnight gain in performance was observed, suggesting statistical independence. This might indicate that fast and slow-learning phases rely on independent cellular mechanisms. Furthermore, they observe a nearly identical overnight improvement, irrespective of the amount of practice prior to sleep. Although subjects extensively trained reached a higher level of performance during practice than subjects with a limited amount of practice, the overnight gain was nearly identical (Fig.1B). This suggests that sleep-dependent slow learning is independent of the evolution of the fast component during prior practice. Still, this does not diminish the importance of the fast component of learning in order to initiate sleep-dependent slow learning processes, as suggested by Hauptmann and Karni (2002) in the case of a priming task. In this latter task, the fast (within-session) learning component has to be exhausted before any slow (between-day) learning can reliably be detected. As Walker and colleagues (this issue) used a pretty intensive training (4 12 30-sec blocks in group 1; 2 12 30-sec blocks in group 3), the fast learning component might have been exhausted on the first experimental day. Therefore, no significant within-session improvement occurs during subsequent sessions (on day 2), and the gain in performance is mainly accounted for by the slow sleep-dependent overnight component. Accordingly, at the systems level, it is known that there is a 1Corresponding author. E-MAIL Maquet@pet.crc.ulg.ac.be; FAX 32-4366-2946. Article and publication are at http://www.learnmem.org/cgi/doi/10.1101/ lm.64303.