A Rapid Form of Offline Consolidation in Skill Learning

Abstract

The brain strengthens memories through consolidation, defined as resistance to interference (stabilization) or performance improvements between the end of a practice session and the beginning of the next (offline gains) [1]. Typically, consolidation has been measured hours or days after the completion of training [2], but the same concept may apply to periods of rest that occur interspersed in a series of practice bouts within the same session. Here, we took an unprecedented close look at the within-seconds time course of early human procedural learning over alternating short periods of practice and rest that constitute a typical online training session. We found that performance did not markedly change over short periods of practice. On the other hand, performance improvements in between practice periods, when subjects were at rest, were significant and accounted for early procedural learning. These offline improvements were more prominent in early training trials when the learning curve was steep and no performance decrements during preceding practice periods were present. At the neural level, simultaneous magnetoencephalographic recordings showed an anatomically defined signature of this phenomenon. Beta-band brain oscillatory activity in a predominantly contralateral frontoparietal network predicted rest-period performance improvements. Consistent with its role in sensorimotor engagement [3], modulation of beta activity may reflect replay of task processes during rest periods. We report a rapid form of offline consolidation that substantially contributes to early skill learning and may extend the concept of consolidation to a time scale in the order of seconds, rather than the hours or days traditionally accepted.

Keywords: beta activity; consolidation; human motor learning; magnetoencephalography; offline learning; procedural memory; reactivation; replay; skill learning.

Figure 1. Motor skill task and performance curve.

A, Task: Participants learned the motor skill task [5, 6] over 36 trials (inset shows a single trial) consisting of alternating practice and rest periods of 10s duration for a total of 12 min. In each practice period, participants were asked to repetitively tap the sequence indicated on the screen as fast and accurately as possible using their left, non-dominant hand. The next day, performance was tested over 9 trials. Brain oscillatory activity was recorded with magnetoencephalography (MEG) for 5 min before (resting-state baseline) and during the task on Day 1. B, Skill was measured as the average inter-tap interval within correct sequences (tapping speed measured in keypresses/s) [10]. The average number of currect sequences per trial is given as green dots. The performance curve of Day 1 (mean + s.e.m.) and the modelled group average performance (overlaid) showed that 95% of learning occurred within the first 11 trials (vertical line, early learning) before reaching maximal performance. See also Figure S1 for supplemental behavioral data and Figure S2 for individual data.

Figure 2. Early online learning was evidenced during short rest periods.

A, Microscale early learning reveals performance increments over rest periods. Micro-online changes were calculated as the difference in tapping speed (keypresses/s) of the first and last correct sequence within a practice period (blue in inset) and micro-offline changes as the difference between the last correct sequence within a practice period compared to the first of the next practice period (red in inset). B, Trial-wise early learning. Each line depicts performance changes (micro-offline in red, micro-online in blue, total in black) per trial (mean + s.e.m.). Total learning is closely accounted for by micro-offline gains (black and red lines) whereas micro-online performance changes fluctuate around 0. Note the presence of large micro-offline gains and total early learning in the initial trials in the absence of micro-online performance decrements. Subsequently, within-practice performance decrements manifested gradually as learning slowed down. C, Data points in the violin plot depict the sum of changes in performance over early learning trials in each participant. Note that total early learning is accounted for by performance improvements during rest periods, but not during practice periods (two-tailed one-sample t test for each learning partition, ***P < 0.001, FDR-corrected for multiple comparisons). See also Figure S1.

Figure 3. Micro-offline learning occurs in a state of low beta power.

A, Brain oscillatory activity during rest periods predictive of micro-offline learning. The horizontal plane depicts the relative power during rest periods compared to resting-state baseline across spectra (x-axis, 1–90 Hz) and cortex (y-axis, 548 locations clustered at frontal (Fro), parietal (Par), temporal (Temp), occipital (Occ) and cerebellar (Post) lobes). Warm yellow colors depict significant power increases during rest periods compared to resting-state baseline, cold blue colors significant power decreases (two-tailed one-sample t tests, n = 27). The z-axis depicts the strength of the inverse relationship between oscillatory power and micro-offline learning (linear-mixed effects (LME) model coefficient, n = 10 trials × 27 participants) at the significant frequencies and locations (magenta). All P<0.05im, FDR-corrected for multiple comparisons. Note that only beta oscillatory activity at 16–22 Hz in frontoparietal areas was predictive of micro-offline learning. B, Inverse relationship between frontoparietal beta oscillatory activity during rest periods and micro-offline learning (n = 10 trials x 27 participants). See also Figure S3 for predictive oscillatory activity for micro-scale learning. See also Table S1.

Figure 4. Topography and timecourse of predictive beta oscillatory activity for micro-offline learning

A, Topography of the predominantly contralateral beta oscillatory activity during rest periods predictive of micro-offline learning indicated by the LME model coefficient (Table S1). B, Frontoparietal beta activity predicted micro-offline gains throughout the duration of early learning rest periods (averaged in each of 5 consecutive 2s segments, LME model coefficient ± s.e.m., n = 10 trials × 27 participants).