Methods

Experiment I: Observational Memory.

Participants.

To investigate whether performance of a novel task depends on previous observation of these same movements, we recruited 64 healthy, young right-handed subjects [32 M, 32 F, 25.7 ± 5.5 years of age (mean ± standard deviation), range 19–43 years] through local advertisement, in accordance with medical ethical committee guidelines; all respondents were screened for handedness, normal sleeping patterns, drug and medication use and psychiatric and neurological health. All subjects had completed primary school and were pursuing or had completed a university or a specialized degree. We excluded skilled musicians, such as piano players. Each subject participated twice, randomly allocated into different experimental subgroups resulting in a total of 128 datasets (Fig. 1). During the nights before and during the testing procedure, subjects were instructed to maintain normal habits and sleeping hours.

Procedure.

Participants watched one of three different videos of a task-naïve right-handed person performing six 23-s epochs of a computerized finger-tapping task, as adapted by Walker et al. (9) from Karni et al. (25), with his left hand, spaced by 20-s waiting periods (Fig. 2A). The subject performing the task on the video showed a positive learning curve and made several errors throughout the task, making the observational learning of the task as naturalistic as possible. The three videos did not differ in terms of the skill acquisition speed and learning curve; they differed only in the sequence of fingers used (41324, 23142, or 32413). Subjects were simply instructed to watch the hand closely, without specific instruction for remembering the sequence. The digit sequence used for the finger tapping was shown on the screen throughout the tapping epochs. The hand in the video tapped when the background screen was green and stopped when it turned red. We prevented overt motor practice of the task during observation by requiring the subjects to press the ‘alt’ and ‘a’ keys on the computer keyboard with the index and middle finger of their left hand throughout the viewing procedure (Fig. 2B); if for any reason they let go of the keys, the video was interrupted and resumed only when the keys were pressed.

On the subsequent testing session, the subjects were asked to perform one of the three fingertapping tasks on a computer using Eprime software (Psychological Software Tools, Inc.). Subjects performed 12 trials of the fingertapping task, with the same duration of epochs as used in the video observed in the previous session; they tapped during the 23-s epochs when the screen was green and stopped when it turned red for 20 s. Before the start of each of the fingertapping epochs, a warning signal occurred allowing the subject to place his/her fingers on the one to four keys on the keyboard. To investigate the effect of immediate sleep on the enhancement of initial performance by prior observation of a congruent tapping sequence, video observation took place in the evening in half of the cases (immediate sleep, S) and in the morning in the other half of the cases (delayed sleep, W). Within both the immediate sleep and delayed sleep subgroups, half of the participants were assessed after a 12-h interval between observation and motor performance and the other half after a 24-h interval, thus balancing possible differences in performance due to time-of-day (morning versus evening) or interval duration (12 h versus 24 h). At each of these testing sessions half of the subjects tapped the sequence congruent with the sequence they previously observed, while the other half served as a reference control group tapping a sequence incongruent with the sequence previously observed (Fig. 1).

The three videos, the congruent-incongruent condition, the allocation of the S and W subgroups, and the order of participation for the two times each subject participated were randomized across the 64 subjects and orthogonalized to each other.

Experiment II: Verifying Absence of Motor Activity During Observation.

Overt finger movements during observation in Experiment I were prohibited by requiring subjects to press two keyboard keys throughout viewing; This would still leave the possibility of muscle activations subliminal for eliciting an overt movement. Similar to Experiment I, we prevented overt motor practice of the task during observation in Experiment II, by requiring the subjects (3 F, 4 M, 22–36 y—same exclusion criteria as in Experiment I) to press the ‘alt’ and ‘a’ keys on the computer keyboard with the index and middle finger of their left hand throughout the viewing procedure; when they let go of the keys, the video was interrupted and resumed only when the keys were pressed. A bipolar arrangement of disposable electrodes (Medicotest, Ag/AgCl-electrodes, square 5 × 5 mm pick-up area) was attached with a center-to-center distance of 3 cm after cleansing and abrasion of the skin (Fig. 2B). The electrodes were positioned over the center of the muscle belly of the left flexor digitorum superficialis on the line from origin to insertion, as determined by palpation. EMG signals were sampled at 1,000 Hz (Porti5–16/ASD; 22 bits ADC recorder, TMS International) after band-pass filtering (0.5–450 Hz), and stored on a PC. Similar to experiment I, the observation condition (A) consisted of viewing the six 23-s epochs of one of the three fingertapping videos. The tapping condition (B) required the subjects to tap along to the hand shown on the screen for another six epochs. Any subliminal muscle activation during observation would result in epochs with spectral characteristics that resemble actual tapping (11). We discarded the first two epochs to obtain a stable performance suitable for spectral analysis, and to avoid movement start-up artifacts the first second of each of the remaining 4 epochs was also omitted. EMG records were band-pass (10–400 Hz) filtered using a bi-directional second-order Butterworth filter to reduce artifacts and, after whitening to remove autoregression components, full-wave rectified. A narrowband spectrogram was obtained using non-overlapping Gaussian window of 3,000 samples, resulting in a time-resolved frequency analysis of seven essentially independent time-frequency bins of 3 s × 0.33 Hz each, resulting in a total of 28 samples per subject per condition. A total of 150 frequency bands were examined (excluding frequencies below 1.0 Hz—that is, components containing DC offset and slow drift in amplitudes—and above 50.667 Hz—that is, components that did not designate tapping-related muscle activity). For each of the bins the dominant frequency (F_max) was determined and the distribution of F_max across the seven subjects × 28 bins is shown in Fig. 3. The mean of F_max was calculated for each subject, and a paired t test was used for statistical comparison of these subject means across conditions.

Experiment III: Sleep-Dependent Consolidation of Sequence or Rhythm.

To investigate whether familiarity with the fingertapping digit sequence or learning the sequence of alternation of locations would result in sleep-dependent performance improvements, a second group of 64 healthy right-handed task-naive subjects [32 F, 32 M, 24.0 ± 4.4 years of age (mean ± standard deviation), range 18–43 years] were recruited using the same exclusion criteria as in Experiment I. This group of subjects observed, in the evening, either a digit sequence or a spatiotemporal rhythm video of one of the three sequences used in experiment I, that is, 41324, 23142, or 32413. The next morning or evening, they performed the fingertapping task as in Experiment I, being either the same sequence (congruent) or one of the other two sequences (incongruent). Subjects participated twice and performed a congruent fingertapping sequence on one and an incongruent sequence on the other occasion, in balanced order, again resulting in 128 data sets. In this way, every subject was exposed to each of the three sequences only once. We chose to implement only the Immediate sleep subgroup (S, consisting of the sleep and the sleep-wake sub-subgroups), as this was the condition that showed a significant sleep-dependent improvement in Experiment I. By using a partial within- and between subject paradigm with 64 subjects tested twice, we were sure to have the same statistical power for detecting possible sleep-dependent performance differences between the congruent and incongruent sessions, i.e., 16 subjects per testing sub-subgroup, see Fig. 1 (Note: the timing of the S subgroup was used for both the sequence and rhythm learning tasks).

The videos were identical to the videos used in Experiment I with respect to duration and number of blocks; the sequence videos showed the digit sequence (i.e., 41324, 23142, or 32413; Fig. 2C) on the screen for 23 s, interspersed with 20-s intervals in which the screen color changed to red and the digit sequence disappeared. The subjects were instructed to simply view the screen and to keep two fingers on two keys of the keyboard throughout viewing. The spatiotemporal rhythm videos showed four black square boxes on a horizontal row in the middle of a computer screen that would light up in the order of each of the sequences used (Fig. 2D). The speed of the sequential highlighting of the squares was identical to the speed of the fingers in the fingertapping videos used in Experiment I, and increased across the six 23-s viewing blocks in accord with the improvement of the hand in the videos.

Experiment IV: Performance Immediately After Viewing.

We recruited 32 additional subjects [16 F, 16 M, 25.1 ± 5.0 years of age (mean ± standard deviation), range 19–43 years] to study performance on the fingertapping task immediately (5 min) after observation. Every subject participated twice, allocated to a different condition such that every subject performed an incongruent sequence and a congruent sequence once. In this way, every subject saw two different videos and performed two different versions of the task. We balanced morning vs. evening, video version and order of incongruent vs. congruent conditions across the subjects and orthogonalized them to each other. The videos and instructions to the subjects were identical to those used in Experiment I.

Statistical Analyses.

We calculated initial performance level at the second testing session (i.e., post-observation) as the average speed (number of correct sequences tapped) and the average error rate (number of erroneous relative to total number of sequences) for the first six epochs of fingertapping of a total of 12 epochs, to avoid the effects of practice on performance but rather reflecting the immediately present sleep-related enhancement. In the analyses of the performance data, we made optimal use of the mixed design (i.e., all subjects tested twice in different experimental conditions) by using mixed effect model regression analyses (MLWIN 2.0 software, Centre for Multilevel Modeling, University of Bristol, Bristol, U.K.) with subject and testing session as hierarchical levels. The regression analyses estimated the performance gain by prior observation of a congruent sequence versus prior observation of an incongruent sequence either in the immediate sleep condition and the delayed sleep condition (Experiment I), or in the sequence and rhythm conditions (Experiment III), which were fully balanced with respect to the time of day of performance and the interval between observation and performance. Wald tests were applied to obtain the significance of estimated effects on speed and on error rate, with P < 0.05 regarded significant.