Abstract

There is robust evidence that sleep facilitates procedural memory consolidation. The exact mechanisms underlying this process are still unclear. We tested whether an active replay of prior experience can underlie sleep effects on procedural memory. Participants learned a finger-tapping task in which key presses were associated with tones during practice. Later, during a consolidation interval spent either sleeping or awake, we presented auditory cues to reactivate part of the learned sequence. We show that reactivation strengthens procedural memory formation during sleep, but not during wakefulness. The improvement was restricted to those finger transitions that were cued. Thus, reactivation is a very specific process underpinning procedural memory consolidation. When comparing periods of sleep with and without reactivation, we find that it is not the time spent in a specific stage of sleep per se, but rather the occurrence of reactivation that mediates the effect of sleep on memory consolidation. Our data show that longer sleep time as well as additional reactivation by cueing during sleep can enhance later memory performance.

INTRODUCTION

After memories have been encoded, they undergo a phase of consolidation. It has been shown that sleep facilitates this process (Diekelmann & Born, 2010). The exact mechanism by which this happens, however, is still under debate. A process of active systems consolidation has been proposed for declarative, hippocampus-dependent memory (Diekelmann & Born, 2010; Dudai, 2004). In that framework, off-line reactivation and replay of prior waking experience during sleep strengthen hippocampal memory traces and integrate information into neocortical long-term memory networks. Studies in rodents have shown that neuronal firing patterns observed at encoding are reactivated during postlearning sleep in both the hippocampus and the neocortex (Peyrache, Khamassi, Benchenane, Wiener, & Battaglia, 2009; Euston, Tatsuno, & McNaughton, 2007; Ji & Wilson, 2007; Ribeiro et al., 2004; Louie & Wilson, 2001; Nadasdy, Hirase, Czurko, Csicsvari, & Buzsaki, 1999; Wilson & McNaughton, 1994). Hippocampal activity precedes replay in neocortical sites and may thus hold a crucial role in the orchestration of memory trace reactivation and integration (Peyrache et al., 2009; Ji & Wilson, 2007).

Apart from this evidence for a replay of neuronal activity during sleep in rodents, studies in humans have shown that declarative memory consolidation benefits from reactivation. Navigation-related hippocampal activity after maze learning is reexpressed during postlearning sleep, and the amount of this reactivation predicts later memory performance (Peigneux et al., 2004). It is also possible to enhance later memory recall of declarative information by presenting cues of the learning task during sleep (Rudoy, Voss, Westerberg, & Paller, 2009; Rasch, Buchel, Gais, & Born, 2007). For example, if an odor context is associated with learning a spatial card location task and the odor cue is again presented during sleep, cueing during sleep led to superior memory than sleep without additional reactivation of the learning context. Interestingly, presentation of the odor during wakefulness was not effective (Rasch et al., 2007). These results indicate that, for declarative memory formation, sleep is a critical period during which reactivation of learned information reinforces consolidation (for a review, see Diekelmann & Born, 2010).

Procedural memory consolidation can also benefit from sleep (Korman et al., 2007; Fischer, Hallschmid, Elsner, & Born, 2002; Walker, Brakefield, Morgan, Hobson, & Stickgold, 2002). However, which aspects of procedural memory benefit from sleep is still not entirely clear. It has been shown that consolidation of procedural memory can also happen efficiently over periods of wakefulness (Song, Howard, & Howard, 2007; Press, Casement, Pascual-Leone, & Robertson, 2005). Awareness can modulate whether a task is influenced by sleep or not (Robertson, Pascual-Leone, & Press, 2004). And movement-related benefits on finger-tapping tasks do not seem to require sleep for consolidation, whereas consolidation of goal-related sequence information improves over sleep (Albouy, Fogel, et al., 2013; Cohen, Pascual-Leone, Press, & Robertson, 2005). Experimental evidence for memory reactivation of procedural content in sleep is still scarce. PET studies in humans have shown that brain regions that engage in learning a motor task are also more active during postlearning sleep (Destrebecqz et al., 2003; Peigneux et al., 2003; Maquet et al., 2000). In a recent study, Antony, Gobel, O'Hare, Reber, and Paller (2012) showed that reactivation also holds a functional role in procedural memory consolidation. In their study, participants learned to tap two differently pitched melodies on four keys of a keyboard. One of these melodies was later replayed during sleep, leading to a performance increase for the cued sequence. These findings show the possibility to bias memory processing during sleep.

The aim of this study was to test the hypothesis that reactivation enhances off-line memory consolidation for procedural memory, to investigate whether reactivation was specific to sleep, and to describe the relation between external reactivation by cue presentation and endogenous reactivation by sleep. We tested participants on a finger sequence task for which each finger was associated with a corresponding piano tone, so that the participants effectively played a short, repetitive tune. In the first experiment, half of the tone sequence was replayed to the participants during a 3-hr period of sleep or wakefulness. We hypothesized that later reproduction of the replayed half of the sequence would be better than the nonreplayed half, but only when replay took place during sleep. In two additional experiments, we addressed the question of how much sleep is required for procedural improvement to occur. It has been shown for the declarative memory domain that presenting external cues of a previous learning task condenses the time course of consolidation (Diekelmann, Biggel, Rasch, & Born, 2012). Therefore, in additional experiments, participants slept after learning either for 3 hr as in the first experiment or for a whole 8-hr night. Controls stayed awake during intervals of corresponding length. Because we suppose that reactivation occurs normally during sleep and longer periods of sleep should provide more reactivation, our hypothesis was that the beneficial effect of a short period of sleep together with external reactivation would be similar to the beneficial effect of a longer period of sleep without reactivation.

METHODS

Participants and General Procedure

One hundred sixty-two healthy, paid volunteers participated in three experiments. They were 18–30 years old (mean age = 22.3 ± 2.4 years), nonsmokers, and right-handed. They did not take any medication or ingest caffeine on the day of the experiment. Participants were not professional musicians, nor did they practice any musical instrument regularly in the last 4 years. All participants had a regular circadian rhythm and were not extreme morning or evening types, as assessed by the Munich Chronotype Questionnaire (Roenneberg et al., 2007). They had no long-distance flights within 6 weeks before the experiment. Potential participants not conforming to these requirements were not allowed to participate in the experiments.

In the first experiment, 64 participants (32 men) participated in one experimental session, which took place between 22.30 hr and 03.30 hr (Figure 1). First, participants learned a procedural finger sequence task, during which tones were played. Performance was tested immediately after learning and again after a 4-hr interval. They were randomly assigned to one of two experimental groups: 30 min after finishing the task, half of the participants went to bed to sleep for 3 hr whereas the other half stayed awake during the same time. Wake participants were playing board games and having conversations when at risk of falling asleep, but they also had periods of quiet wakefulness, in particular at the beginning of the night when the experimenter was still occupied with other tasks related to the experiments. Thus, the wake control included phases of both quiet and active wakefulness. During the first 2 hr of sleep or wakefulness, the sequence of tones from the learning task was played to the participants. Half an hour before the delayed test, participants in the sleep group were awoken. In the second experiment, 34 participants (16 men) followed the same experimental procedures and times, but without presentation of the tones during the intervening interval of sleep or wakefulness. Again, half of the participants slept, whereas the other half stayed awake during the consolidation interval. Another 64 participants (32 men) followed the same experimental procedure in the third experiment. Again, no tones were presented between learning and retest. In this study, the intervening period was prolonged to include a full night of sleep or a full day of wakefulness. Half of the participants learned the motor task 1 hr after getting up in the morning. They were tested in the evening, 12 hr later. In-between, participants followed their normal daily routine, but they were not allowed to sleep during that time. The other half learned the task in the evening, 13 hr after getting up. They were tested in the next morning 12 hr after learning, which was 1 hr after getting up, and, thus, included a whole night of sleep. Participants' activity was monitored using actimetry in both conditions.

Figure 1. 

(A) In the first experiment, participants practiced a finger-tapping task with tones associated to each key press and then either slept or stayed awake for 3 hr before being retested on the same task. During the first 2 hr of this time, half of the practiced sequence was replayed to them. (B) In two further experiments, we tested the effect of sleep on the learned task without additional external reactivation. The second experiment followed the exact same procedure as the first one, except that no tones were replayed to the participants during the consolidation interval. (C) In the third experiment, participants slept for a whole night or stayed awake during the day. The sleep group practiced the task in the evening and was retested in the morning, whereas the wake group practiced the task in the morning and was retested in the evening.

Figure 1. 

(A) In the first experiment, participants practiced a finger-tapping task with tones associated to each key press and then either slept or stayed awake for 3 hr before being retested on the same task. During the first 2 hr of this time, half of the practiced sequence was replayed to them. (B) In two further experiments, we tested the effect of sleep on the learned task without additional external reactivation. The second experiment followed the exact same procedure as the first one, except that no tones were replayed to the participants during the consolidation interval. (C) In the third experiment, participants slept for a whole night or stayed awake during the day. The sleep group practiced the task in the evening and was retested in the morning, whereas the wake group practiced the task in the morning and was retested in the evening.

Task and Stimulation

Participants were instructed that they had to learn a short finger tapping sequence. Four gray empty circles were presented on a black computer screen background along the horizontal middle axis. The circles were filled sequentially following a 12-element sequence. Participants had to react as fast and as accurately as possible to the filled circle target location by pressing a key on a computer keyboard with the corresponding finger of their nondominant left hand. With each correct reaction, a piano tone was played. Tones were assigned in a rising fashion to the keys and locations. Pitches were chosen according to a pentatonic scale so that any combination of keys would result in a harmonic, nonaversive melody. Tones were played at a sound pressure level of 57 dB. This level was chosen because it was clearly audible, but not intrusive or aversive, and it did not disturb participants during the retention interval. Furthermore, as previous work on external auditory stimulation during sleep has used comparable sound pressure levels, we expected this level to be sufficient to drive reactivation (Rudoy et al., 2009). If an error was made and the wrong key was pressed, no sound was played. Immediately after participants reacted to a given location, the next target location was indicated by a filled circle on the screen according to a fixed sequence. Sounds were presented over speakers during learning and over in-ear headphones fixated with medical tape during the night. The maximal sound pressure level was measured before each experiment with a digital sound level meter) at the location of the ear or with the headphones directly plugged into the sound level meter.

During the learning session, participants practiced one of two 12-element sequences: 423121432413 or 412431421323, balanced across groups. The sequences were chosen based on previous literature on motor sequence learning (Destrebecqz et al., 2003) and are balanced for first-order dependencies. Therefore, each possible transition between the four finger locations occurred only once in the 12-item string. We additionally chose the sequences in a way that the seventh item corresponded to the first item of each sequence. Thus, both the first and second half of the sequence could be replayed in an iterative loop, generating only transitions that were practiced in this order during learning. Participants completed 15 experimental blocks each containing eight repetitions of the sequence. The last three blocks were used to determine initial performance. During retest, participants completed another three blocks of eight repetitions of the previously learned sequence.

In the first experiment, during the first two hr of the 3-hr sleep period or the matching wake interval, tones corresponding to one half of the previously learned sequence were replayed to the participants. Reactivation started immediately after lights off and at corresponding times in the wake interval. Tones for reactivation were presented at a sound pressure level of 57 dB, the level at which tones were presented during learning, which is clearly audible but not too intrusive. The tones were played continuously over 2 hr, paced in a 1-sec rhythm. This tempo was chosen to allow the tone transitions to be easily processed, while still being pleasant to listen to and unobtrusive during sleep. Balanced over groups and sequences, either the first or second half of the sequence was cued. In the additional experiments, no tones were presented during the consolidation interval.

Statistical Analysis

Statistical testing for the first experiment relied on two-factorial repeated-measures ANOVA with one within-subject factor (Cued/Uncued) and one group factor (Sleep/Wake). Initial performance, as measured during the last three blocks of training, was entered as a covariate to account for individually different levels of performance. Subsequently, sleep and wake groups were analyzed separately with paired t tests to compare cued and uncued parts of the sequence. For the additional experiments, univariate ANOVA was used with one group factor (Sleep/Wake) and again the initial performance, as measured during the last three blocks of training, as a covariate. All analyses used typing accuracy during the retesting blocks as dependent measure. All tests were two-tailed with an α-level of 0.05. Participants had to type a fixed number of sequences in each block. Therefore, the total number of errors made when typing the whole sequence or half sequence during the last three blocks of training and during the three retesting blocks was used as a measure of accuracy. The mean time needed per key press was taken as a measure of speed.

Nine of the 162 participants had to be excluded based on unusually slow RTs or large numbers of errors. This left n = 29 participants in the wake group and n = 28 participants in the sleep group in the first experiment, all n = 18 (wake) and n = 16 (sleep) participants in the second experiment, and n = 31 (wake) and n = 31 (sleep) participants in the third experiment.

Sleep and EEG Recordings

Sleep was recorded polysomnographically in the sleep lab in the 3-hr sleep group. EEG was recorded from two scalp electrodes (C3, C4 according to the International 10–20 System) against a nose reference. Bipolar EOG and EMG were recorded as well. Recordings were scored off-line by two independent raters according to standard criteria (Rechtschaffen & Kales, 1968). Discrepant scorings were decided with the aid of a third rater. Sleep spindles were determined and counted by means of a previously published algorithm (Gais, Molle, Helms, & Born, 2002). Participants in the whole night of sleep condition in the third experiment slept at home. Here, sleep duration was assessed via wrist actigraphy (ActiSleep Monitor, ActiGraph, Pensacola, FL) and questionnaire data. Activity was recorded on all three spatial axes and integrated with a resolution of 1 min. Data were plotted and scored manually with an accuracy of 10 min. Activity data were compared manually with questionnaire data. Any discrepancies were either solved together with the participant or led to the exclusion of the participant from analysis. Actigraphy was used in the same way to confirm that participants in the wake group did not sleep during the daytime interval.

Subjective state was obtained using visual analogue scales with the endpoints “alert–not alert,” “motivated–not motivated,” “interested–not interested.” In addition, a 10-min version of the psychomotor vigilance task was used to assess potential differences in fatigue and vigilance between groups (Dinges & Powell, 1985).

RESULTS

In the first experiment, we compared performance on the half of the sequence that was replayed during the consolidation interval with the other half, which was not replayed. We also compared participants who slept during the interval with a wake control group. In the sleep group, participants made significantly fewer errors in the cued part of the sequence than in the uncued part (1.2 ± 0.4 vs. 2.1 ± 0.3 errors [mean ± SEM], F(1, 27) = 6.1, p = .02; see Figure 2). Reactivation during wakefulness had no such effect (2.3 ± 0.3 vs. 1.9 ± 0.3, F(1, 28) = 0.6, p = .44). A significant interaction confirmed that replaying the sequence of sounds to the participants improved performance only during sleep, F(1, 53) = 6.6, p = .01 for Sleep/Wake × Cued/Uncued. We could not show a significant effect of sleep on the uncued part (sleep: 2.1 ± 0.3, wake: 1.9 ± 0.3 errors, t(55) = 0.1, p = .88) and no significant main effect of condition Sleep/Wake, F(1, 53) = 1.2, p = .27. We found no differences in tapping speed between conditions (Sleep/Wake × Cued/Uncued: F(1, 53) = 0.1, p = .77; sleep group: 455 ± 10 msec in the cued vs. 452 ± 11 msec in the uncued half; wake group: 443 ± 10 msec in the cued vs. 442 ± 11 msec in the uncued half). Table 1 gives an overview over performance measured as number of errors for all experiments, groups, and conditions, averaged over the last three blocks of training and the three retesting blocks. Corresponding information for RTs can be found in Table 2.

Figure 2. 

(A) Participants practiced one of two 12-item tapping sequences. Each key was associated with a tone of a pentatonic scale. The sequences could be divided into halves without changing the transition sequence. During the 3-hr consolidation interval of the first experiment, either the first or the second half of the sequence was replayed to the participants. (B) Absolute number of typing errors made after periods of sleep or wakefulness. The significant interaction shows that a 3-hr sleep period enhanced consolidation of the finger tapping sequence significantly only for the part of the sequence that was cued. (C) Comparing sleep periods with the matching wake control conditions shows that a 3-hr period of sleep without external reactivation was not sufficient to improve consolidation of this task significantly. A longer sleep period of 8 hr, however, improves consolidation, similar to the shorter sleep period with additional external reactivation. Errors are given with respect to half sequences for the first experiment (left vertical axis) and to full sequences for the second and third experiment (right vertical axis). Values display differences between the sleep and wake groups' performance during retesting. Bars show SEM. * signifies an α-level of p < .05; ** signifies an α-level of p < .01.

Figure 2. 

(A) Participants practiced one of two 12-item tapping sequences. Each key was associated with a tone of a pentatonic scale. The sequences could be divided into halves without changing the transition sequence. During the 3-hr consolidation interval of the first experiment, either the first or the second half of the sequence was replayed to the participants. (B) Absolute number of typing errors made after periods of sleep or wakefulness. The significant interaction shows that a 3-hr sleep period enhanced consolidation of the finger tapping sequence significantly only for the part of the sequence that was cued. (C) Comparing sleep periods with the matching wake control conditions shows that a 3-hr period of sleep without external reactivation was not sufficient to improve consolidation of this task significantly. A longer sleep period of 8 hr, however, improves consolidation, similar to the shorter sleep period with additional external reactivation. Errors are given with respect to half sequences for the first experiment (left vertical axis) and to full sequences for the second and third experiment (right vertical axis). Values display differences between the sleep and wake groups' performance during retesting. Bars show SEM. * signifies an α-level of p < .05; ** signifies an α-level of p < .01.

Table 1. 

Performance Measured as Errors during the Last Three Block of Training and the Three Retesting Blocks for All Experiments, Groups, and Conditions


3 hr Sleep, with Reactivation (per Half Sequence)
3 hr Sleep, No Reactivation (per Full Sequence)
8 hr Sleep, No Reactivation (per Full Sequence)
Cued
Uncued
Sleep 
Pre 3.8 ± 0.8 3.5 ± 0.4 9.6 ± 1.7 9.0 ± 1.0 
Post 1.2 ± 0.3 2.0 ± 0.3 4.2 ± 1.2 3.1 ± 0.5 
 
Wake 
Pre 4.2 ± 0.6 4.4 ± 0.7 9.7 ± 1.2 8.6 ± 0.9 
Post 2.3 ± 0.5 2.0 ± 0.4 5.2 ± 1.2 4.4 ± 0.8 

3 hr Sleep, with Reactivation (per Half Sequence)
3 hr Sleep, No Reactivation (per Full Sequence)
8 hr Sleep, No Reactivation (per Full Sequence)
Cued
Uncued
Sleep 
Pre 3.8 ± 0.8 3.5 ± 0.4 9.6 ± 1.7 9.0 ± 1.0 
Post 1.2 ± 0.3 2.0 ± 0.3 4.2 ± 1.2 3.1 ± 0.5 
 
Wake 
Pre 4.2 ± 0.6 4.4 ± 0.7 9.7 ± 1.2 8.6 ± 0.9 
Post 2.3 ± 0.5 2.0 ± 0.4 5.2 ± 1.2 4.4 ± 0.8 
Table 2. 

Average RTs in Microseconds during the Last Three Blocks of Training and the Three Retesting Blocks for All Experiments, Groups, and Conditions


3 hr Sleep, with Reactivation
3 hr Sleep, No Reactivation
8 hr Sleep, No Reactivation
Cued
Uncued
Sleep 
Pre 509 ± 19 506 ± 20 461 ± 21 465 ± 12 
Post 463 ± 19 461 ± 22 414 ± 19 392 ± 10 
 
Wake 
Pre 493 ± 17 485 ± 15 445 ± 14 520 ± 12 
Post 436 ± 17 434 ± 14 405 ± 14 423 ± 8 

3 hr Sleep, with Reactivation
3 hr Sleep, No Reactivation
8 hr Sleep, No Reactivation
Cued
Uncued
Sleep 
Pre 509 ± 19 506 ± 20 461 ± 21 465 ± 12 
Post 463 ± 19 461 ± 22 414 ± 19 392 ± 10 
 
Wake 
Pre 493 ± 17 485 ± 15 445 ± 14 520 ± 12 
Post 436 ± 17 434 ± 14 405 ± 14 423 ± 8 

Over the five groups of three blocks of the training session, tapping speed improved in parallel for all conditions, F(4, 52) = 20.6, p < .001 (see Figure 3A), whereas, at the same time, the number of errors increased significantly, F(4, 52) = 4.7, p = .003 (see Figure 3B). After the consolidation interval, we observe further improvement in speed and, concurrently, a steep decline in the number of errors. Although the improvement in speed did not differ between conditions, the improvement in accuracy was most pronounced in the cued half of the sequence for the sleep group, F(1, 55) = 4.6, p = .04 for Sleep/Wake × Cued/Uncued (Figure 3; again the value represents an average over three blocks).

Figure 3. 

Tapping speed and errors across the six groups of three blocks of training and retesting in experiment one. (A) Tapping speed improves in parallel for all groups and parts of the sequences during training (1–5) and over the consolidation interval as measured during retesting (R). (B) Errors increase with faster RTs and fatigue during training (1–5) but show a steep decline over the consolidation interval as measured during retesting (R). One can clearly observe the significant benefit achieved by additional external reactivation in the sleep group, which is spaced apart from all other conditions and lies even below the lowest error rate observed during training. Errors are given with respect to half sequences. Bars show SEM. * signifies an α-level of <0.05.

Figure 3. 

Tapping speed and errors across the six groups of three blocks of training and retesting in experiment one. (A) Tapping speed improves in parallel for all groups and parts of the sequences during training (1–5) and over the consolidation interval as measured during retesting (R). (B) Errors increase with faster RTs and fatigue during training (1–5) but show a steep decline over the consolidation interval as measured during retesting (R). One can clearly observe the significant benefit achieved by additional external reactivation in the sleep group, which is spaced apart from all other conditions and lies even below the lowest error rate observed during training. Errors are given with respect to half sequences. Bars show SEM. * signifies an α-level of <0.05.

In two additional experiments, participants slept for either 3 or 8 hr between learning and retesting, without external replay of the sound cues. Control groups stayed awake for the same duration. Compared with wakefulness, the 3-hr sleep period did not suffice to affect memory consolidation. There was no significant difference in the number of errors between 3-hr sleep and 3-hr wakefulness conditions (4.2 ± 1.1 vs. 5.2 ± 1.0, F(1, 31) = 0.4, p = .51; note that these numbers refer to full sequences, not half-sequences as above). We again found no differences in tapping speed between groups (sleep: 407 ± 9 msec, wake: 410 ± 7 msec, F(1, 31) = 0.1, p = .81). In contrast, participants who slept for a full night were significantly better than participants who stayed awake during the day. After the longer sleep interval participants made significantly fewer errors at later retesting than after the wake interval (3.0 ± 0.5 vs. 4.5 ± 0.5, F(1, 59) = 4.1, p = .047). Again, tapping speed did not differ across groups (sleep: 407 ± 7 msec, wake: 408 ± 7 msec, F(1, 59) < 0.1, p = .95).

Wake conditions of all three experiments show a comparable number of errors, irrespective of reactivation or duration, F(2, 75) = 0.2, p = .81 (see Figure 2). Pairwise comparisons between all of these conditions did not reveal any significant difference (all p > .55). Conversely, benefits from a long sleep interval are similar to benefits from external reactivation. No difference was found between 3-hr sleep with reactivation and a full night sleep condition, F(1, 57) = 1.0, p = .32. Sample size independent estimates of effect sizes for the sleep–wake comparisons show that, in the 3-hr condition without additional reactivation, sleep exhibits only a small, nonsignificant effect on performance of the 12-element finger-tapping task (Cohen's d = 0.21). The significant effect of sleep in the full-night condition without reactivation is d = 0.37. We observe the largest effect of sleep in the 3-hr condition with additional reactivation, which is d = 0.52 for the cued part of the sequence. Thus, additional external reactivation seems to bolster the inherent reactivation processes active during sleep and to substitute for a longer sleep period.

The distribution of sleep stages for the 3-hr sleep periods with and without replay of sound stimuli can be found in Table 3. There was no significant difference in total sleep duration between 3-hr groups with and without reactivation. However, participants who received external reactivation cues spent less time in SWS and spent more time in Stage 2 sleep. No differences were seen in time spent in REM sleep or Stage 1 sleep and spindle activity. Regarding alertness, motivation, and interest in the experiment self-report scales showed no difference between the sleep and wake condition; all p > .25. Number of lapses, as measured by the PVT, was comparable between sleep and wake groups in all experiments (all p > .61).

Table 3. 

Sleep Duration and Sleep Stage Information (Mean ± SEM)


3-hr Sleep, with Reactivation
3-hr Sleep, No Reactivation
t
p
8-hr Sleep, No Reactivation
Time asleep 2 hr 45 min ± 3 min 2 hr 49 min ± 3 min 0.8 .45 7 hr 39 min ± 10 min 
Wake 10 ± 3% 5 ± 2% 1.4 .17  
S1 8 ± 1% 9 ± 1% 0.5 .63  
S2 39 ± 2% 26 ± 2% 3.9 <.01  
SWS 34 ± 3% 52 ± 4% 3.8 <.01  
REMS 9 ± 1% 8 ± 2% 0.8 .44  
Spindle density 2.4 ± 0.2 2.3 ± 0.1 0.4 .70  

3-hr Sleep, with Reactivation
3-hr Sleep, No Reactivation
t
p
8-hr Sleep, No Reactivation
Time asleep 2 hr 45 min ± 3 min 2 hr 49 min ± 3 min 0.8 .45 7 hr 39 min ± 10 min 
Wake 10 ± 3% 5 ± 2% 1.4 .17  
S1 8 ± 1% 9 ± 1% 0.5 .63  
S2 39 ± 2% 26 ± 2% 3.9 <.01  
SWS 34 ± 3% 52 ± 4% 3.8 <.01  
REMS 9 ± 1% 8 ± 2% 0.8 .44  
Spindle density 2.4 ± 0.2 2.3 ± 0.1 0.4 .70  

To determine whether sleep-related improvements could be caused by sleep stage-specific processes, we further analyzed correlations between sleep parameters and postsleep task performance. We found no correlation between the amount of time spent in individual sleep stages and performance. Neither the number of errors after sleep nor the improvement between the last three blocks of training and the three retesting blocks, nor the benefit by reactivation, that is, the difference in improvement between the cued and the uncued part, were related to sleep parameters. The number of sleep spindles per 30-sec epoch of sleep (spindle density) for S2 and SWS was also unrelated to later task performance (see Table 4).

Table 4. 

Correlations between Sleep Parameters and Memory Performance


3-hr Sleep, with Reactivationa
3-hr Sleep, No Reactivation
r
p
r
p
Number of Errors after Sleep 
S2 −.11 .57 .25 .36 
SWS .17 .38 −.15 .58 
REMS .17 .38 .31 .24 
Spindle density .15 .46 −.07 .80 
 
Improvement over Sleep 
S2 .12 .54 −.13 .62 
SWS .05 .80 .12 .67 
REMS .25 .21 −.38 .14 
Spindle density −.01 .96 −.27 .32 
 
Benefit by Reactivation 
S2 .02 .92   
SWS .09 .64   
REMS .30 .12   
Spindle density .04 .85   

3-hr Sleep, with Reactivationa
3-hr Sleep, No Reactivation
r
p
r
p
Number of Errors after Sleep 
S2 −.11 .57 .25 .36 
SWS .17 .38 −.15 .58 
REMS .17 .38 .31 .24 
Spindle density .15 .46 −.07 .80 
 
Improvement over Sleep 
S2 .12 .54 −.13 .62 
SWS .05 .80 .12 .67 
REMS .25 .21 −.38 .14 
Spindle density −.01 .96 −.27 .32 
 
Benefit by Reactivation 
S2 .02 .92   
SWS .09 .64   
REMS .30 .12   
Spindle density .04 .85   

aThe values are given for the cued part of the sequence only.

DISCUSSION

Presenting sound cues during sleep that had been related to procedural sequence tapping beforehand improved later performance of this task. This indicates that reactivation of procedural memory traces can indeed be a mechanism that supports memory consolidation. Notably, the beneficial effect of external reactivation occurred only during sleep. Presenting the same cues during wakefulness had no influence on the consolidation process at the behavioral level. Additionally, external reactivation showed a similar effect as extending sleep: A short 3-hr period of sleep with reactivation cues was as beneficial for consolidation as a whole night of sleep without. Thus, we suggest that presenting cues of a previous learning task drives an internal and naturally occurring mechanism of memory reactivation, resulting in an accelerated consolidation process. Astonishingly, we did not find any significant relationship between sleep parameters and memory performance after sleep. Actually, participants who received sound cues during sleep demonstrated enhanced consolidation although they had less SWS than participants who were not presented with the cues. This unpredicted finding indicates that it might not be the stage of sleep per se which determines how well we consolidate procedural memory, but perhaps rather the availability of sufficient reactivation during sleep.

This study shows that effects of reactivation are highly specific for the material being cued. Instead of cueing the entire context of a finger sequence using differently pitched tunes, as has been done previously (Antony et al., 2012), we explicitly designed the task in a way that it was possible to cue only a portion of a sequence. We used sequences of tones that only differed in the order of elements. The rationale behind this approach was that this method allows cued and uncued conditions to be entirely symmetrical. Comparing nights with and without cueing could obviously result in unspecific changes in sleep structure, which might influence performance. Using two melodies made from different tones could result in a general reactivation of the context. In our tapping sequence of 12 unique finger transitions, only those transitions belonging to the replayed part of the sequence showed a cueing-related benefit. Transitions in the uncued part of the sequence, although composed of the same tones as the cued part, did not benefit from the procedure. Thus, cueing can target highly specific content, like individual aspects of a memory. Because of this material specificity, our findings cannot be explained in terms of attention, fatigue, or other postintervention effects, as those would affect both the cued and uncued half of the sequence. They rather indicate a direct influence of cueing on memory consolidation.

Some previous studies suggest that rest and recovery from fatigue mediate performance gains in procedural learning after sleep (Mednick, Makovski, Cai, & Jiang, 2009; Rickard, Cai, Rieth, Jones, & Ard, 2008). Our data show that such recovery occurs in both the sleep and wake groups. However, we also show another process, which is related to reactivation and which leads to stronger improvement in the sleep group than in the wake group. Thus, our study identifies an active, sleep-specific mechanism that goes beyond recovery (Korman et al., 2007; Korman, Raz, Flash, & Karni, 2003; Walker, Brakefield, Seidman, et al., 2003). Reactivation might also underlie changes in the neuronal representation of procedural memory on the systems level observed after sleep (Korman et al., 2007; Fischer, Nitschke, Melchert, Erdmann, & Born, 2005; Walker, Stickgold, Alsop, Gaab, & Schlaug, 2005), similar to what has been suggested for declarative memory (Gais et al., 2007; Takashima et al., 2006).

It has recently been demonstrated in rats that presenting acoustic cues for a spatial learning task during sleep biases the content of replay, but not the number of reactivations (Bendor & Wilson, 2012). After presentation of acoustic cues, hippocampal reactivation events showed more frequent replay of those spatial memory episodes that were associated with the cue than those that were not. Similarly, in our experiments, reactivation enhanced consolidation only for those finger transitions associated with the cue beforehand. This enhancement might even have come at the expense of the uncued transitions, which do not show any benefit of sleep at all. Because of this specificity, the observed off-line gains in performance cannot be explained in terms of global processes that affect the brain on a wider scale, but might rather be related to cueing biasing the content of neuronal replay. Moreover, longer sleep periods enhanced consolidation more strongly than shorter ones. We suggest therefore that sufficient reactivation of the specific memory trace is required for the observed sleep-related benefits on memory consolidation, and this depends on the availability of external or internal cueing and the duration of the sleep period. Whether this influence is dose dependent cannot be concluded from the present data. However, regarding sleep times and interventions, it is tempting to speculate that more reactivation, either by externally driving the inherent process or by longer sleep times, leads to stronger memory consolidation during sleep.

In the framework of systems consolidation in the declarative domain, hippocampal networks are thought to be reactivated during sleep, leading to stabilization of the neural trace and integration of information into neocortical networks (Diekelmann & Born, 2010). Studies on declarative memory have also shown that external reactivation enhances (Diekelmann, Buchel, Born, & Rasch, 2011; Rasch et al., 2007) and accelerates (Diekelmann et al., 2012) the consolidation of memories. Rodent studies demonstrate that the sequences of hippocampal place cell activation observed during learning are replayed during postlearning sleep together with a reactivation in neocortical areas (Peyrache et al., 2009; Ji & Wilson, 2007; Nadasdy et al., 1999; Wilson & McNaughton, 1994). Although the standard model of memory ascribes only declarative memory to the hippocampus, a number of fMRI studies find activity in the hippocampus during motor tasks using explicit and implicit sequences (Walker et al., 2005; Schendan, Searl, Melrose, & Stern, 2003). Additionally, studies indicate that hippocampal activation during sleep can also lead reactivation in subcortical structures like the striatum, which is a central area for procedural learning (Lansink, Goltstein, Lankelma, McNaughton, & Pennartz, 2009; Pennartz et al., 2004). Furthermore, it has been shown that the interaction between the hippocampal and striatal systems during training predicts the subsequent consolidation of a finger-tapping task (Albouy, Sterpenich, et al., 2013) and that caudate activity increases when task performance becomes more consistent (Albouy et al., 2012). Although activation of the hippocampus and striatum are competitive during learning, they become cooperative during the night, and this interaction optimizes later behavior (Albouy et al., 2008). We have not measured explicit sequence recall; however, it is very likely that the melody allowed the participants, at least partially, to memorize the sequence explicitly. This finding is therefore compatible with findings that mainly explicit sequence learning benefits from sleep (Robertson et al., 2004). The idea of a hippocampal–striatal interaction would suggest that automatization of the task would lead to a more implicit representation of the task after sleep, which depends more on the striatum. To sum up, we propose that hippocampal–striatal interactions during sleep contribute to consolidation in the present procedural memory task. However, this remains to be tested in further studies.

Our data show no relation between sleep macro- and microstructure and the observed effects of sleep on procedural memory consolidation. Previous animal studies investigating reactivation in the hippocampus and neocortex, and human studies on memory reactivation suggest that reactivation occurs primarily in SWS (Antony et al., 2012; Rudoy et al., 2009; Ji & Wilson, 2007). However, that finding might be biased because most of these studies focused their investigations on SWS. Beneficial effects of reactivation in other sleep stages may thus have been overlooked. In contrast, we presented reactivation cues not only during SWS but for 2 hr during all sleep stages. Whereas previous studies, which cued only during SWS, find a correlation between the amount of SWS and task performance (Antony et al., 2012), we did not find such a correlation. In fact, it can be argued that our data even speak somewhat against the idea that SWS is central to consolidation of this task, because by introducing the sound cues, we actually decreased the amount of SWS while improving performance. Thus, our findings, although not contradicting possible effects of reactivation during SWS, also provide no indication that reactivation is restricted to SWS.

Cueing in the present experiments was not restricted to SWS. We therefore cannot distinguish whether cue presentation was effective during light or deep sleep. To exclude that hearing the cues before falling asleep was sufficient to improve task performance, we compared reactivation during sleep with reactivation during wakefulness. Both the sleep and wake condition comprised periods of quiet wakefulness during which participants listened to the sound cues. An interaction analysis shows that the effect of reactivation differs significantly between sleep and wake groups. Furthermore, Antony et al. (2012) presented cues solely during sleep, and they still observed an effect of a reactivated as compared with a nonreactivated melody, which hints further at a unique role of sleep in memory consolidation. The effect of cueing seems therefore to be nonconscious and sleep-specific.

Our choice of stimulating during the whole sleep period and rely on a wake control group was based on a number of considerations. First, because the present task is procedural in nature and at the time of data acquisition no other data were available, we could not be certain that cueing during SWS would show the expected effect. Additionally, restricting cue presentation to SWS does not prove that cueing effects are SWS related. Only by providing an interaction between sleep and wake conditions, it can be ascertained that effects are limited to sleep. Finally, if cue presentation starts only after sleep onset, there is a higher chance of the participant awakening. Embedding the cues into white noise, on the other hand, might have impaired stimulus discernibility. Potential influences of habituation are assumed to be negligible because repeated stimulation has been found to elicit reliable responses even during sleep (Atienza, Cantero, & Escera, 2001), and, if present, they should affect both sleep and wake conditions equally. Habituation should also lead to less severe disturbance of sleep, because no unexpected sounds occur during sleep. In fact, this experimental design did not disturb participants' sleep, as indicated by similar amounts of time in wake and Stage 1 sleep as in nights without cue presentation.

Comparing effect sizes for the different experimental groups also leads to the conclusion that the magnitude of the effect primarily depends on sleep duration and external cueing, rather than on specific sleep stages. A shorter 3-hr period of sleep with a high proportion of SWS only induces a small, nonsignificant effect. In contrast, a longer 8-hr interval, which contains chiefly more Stage 2 and REM sleep than the shorter interval, significantly enhances performance compared with a wake interval. We thus suggest that it is internal and external reactivation in sleep, which mediates the observed effects on memory consolidation.

Our findings complement a number of previous studies, which see effects of reactivation on consolidation of different material. However, Rasch et al. (2007) also tested cueing of a procedural finger-tapping task and failed to find an enhancing effect. We believe that this was because of the type of cueing used in that study. A slow stimulus like the odor used in that study is more suited to cue the temporal and spatial context of a task, which relates to its declarative components, than individual finger movements. Similar effects of stimulus specificity can also be observed in conditioning, where odor cues can provoke slow responses like taste aversions, but are unable to elicit fast reactions like startle responses.

For procedural learning, sleep-related effects are found for measures of speed and accuracy (Witt, Margraf, Bieber, Born, & Deuschl, 2010; Brown & Robertson, 2007; Walker, Brakefield, Hobson, & Stickgold, 2003; Fischer et al., 2002). Figure 3 shows that speed and accuracy both provide sensitive measures of performance in the present task. Speed increases with training and even more over the retention interval, which speaks for a recovery function of these periods. Accuracy, on the other hand, deteriorates with training, probably because of fatigue. After the retention period, improvement in speed is accompanied by a restoration of accuracy in the 3-hr conditions without reactivation and an improved accuracy with reactivation as well as in the 8-hr sleep condition. Thus, similar to the study of Antony et al. (2012), our results show a significant effect of sleep on accuracy only. We suggest that the lack of a sleep effect on speed, both in the cued and the uncued condition, is because of tone presentation during learning, which has resulted in a tendency to type the sequence more rhythmically and with a consistent speed.

Together, we show that reactivation during sleep can constitute an efficient way to selectively strengthen learned skills. Reactivation during wakefulness did not show the same effect, suggesting that sleep has certain memory-supporting properties, which are not present in the awake state. An explanation based solely on homeostatic recovery from fatigue cannot account for our observation that reactivation only enhanced the replayed parts of the motor sequence task. A surprising finding was that the effect of reactivation was independent of the time spent in specific sleep stages. Although cueing reduced SWS, memory consolidation was enhanced. Our results therefore indicate that it might be rather the amount of internal and external reactivation and not the time spent in a specific sleep stage that determines the strength of memory consolidation.

Acknowledgments

This study was supported by the Deutsche Forschungsgemeinschaft grant GA730/3-1. We thank Alessa Hörmann, Anne Lorenz, Julia Neitzel, Victoria Schröder, and Anna Vogel for assistance with data acquisition and analysis.

Reprint requests should be sent to Steffen Gais, General and Experimental Psychology, Department of Psychology, Ludwig-Maximilians-Universität München, Leopoldstr. 13, 80802 Munich, Germany, or via e-mail: gais@psy.lmu.de.

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