A Double Dissociation Between Savings And Long-term Memory in Motor Learning Part 2

Temporally persistent adaptation washes out more slowly than overall adaptation

The data from the long, 800-trial washout period allowed us to carefully examine the time course of unlearning for both the overall adaptation and the temporally persistent component of it. 

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Analysis of the washout curves revealed that overall adaptation displayed rapid unlearning; however, persistent adaptation (circles in Fig 2A) was unlearned much more slowly. We found that by trials 16 to 25, labeled as "early washout" in Fig 2A, overall adaptation had already dropped below 10% of the pre-washout asymptotic adaptation level, whereas about 40% of pre-washout persistent learning remained. By trials 51 to 150, labeled as "mid washout," overall adaptation had dropped below 3%, whereas about 20% of persistent adaptation remained (Fig 2A, see inset). 

Correspondingly, we found the retention of persistent learning to be significantly greater than overall learning in both early washouts (t(23) = 4.8, p = 6.9 × 10−5 and mid-washout periods (t(39) = 8.5, p = 1.9 × 10−10). 

To quantify the rate of unlearning during washout for both overall and persistent adaptation, we fit single exponential decay functions to the washout data (see Materials and methods). 

This revealed the time constants for unlearning to be 6-fold slower for temporally persistent adaptation than for overall adaptation (median time constant estimated using bootstrap: 106.0 trials, interquartile range (IQR) [92.6 to 121.9] versus 17.4 trials, IQR [15.1 to 20.1], p < 10−4, Fig 2A), in line with the higher retention we observed in the early- and mid-washout data.

As a minor point, we also noticed that unlearning curves for temporally persistent adaptation display somewhat greater retention during the early and mid-washout in Experiment 1 compared to Experiment 2 (Fig 2A). 

This might reflect the difference in the amount of training between the 2 conditions, as 2 training blocks (160 trials in total) preceded this washout period in Experiment 1, whereas only a single training block (80 trials) preceded this washout in Experiment 2, due to the condition balancing (see Fig 1). In summary, we found that temporally persistent adaptation is unlearned at a considerably slower rate than overall adaptation.

Residual adaptation before the onset of retraining

One consequence of the slower unlearning of persistent compared to overall adaptation is that, while the washout of overall adaptation can appear complete after a short 40 to 100 trial per direction washout period [15,16,18,24], substantial temporally persistent adaptation can nevertheless remain. 

This suggests that longer washout periods may be required to examine savings independent of the effect of residual temporally persistent adaptation and that measuring this residual adaptation before relearning may facilitate a better understanding of relearning behavior.

When we measured the residual overall and persistent adaptation before the onset of retraining, we found significant levels of both overall and temporally persistent adaptation for the 40-trial washout but no significant residuals of the previous adaptation following the 800-trial washout period. 

In particular, we found small but significant residuals for overall adaptation at the end of the 40-trial washout periods, around 5% of pre-washout levels (1.34 ± 0.37˚, t(39) = 3.6, p = 0.00087 for Experiments 1 and 2 combined, with positive values indicating adaptation in the direction of the previously imposed VMR, Fig 2B). 

Note that these residuals were related to previous adaptation rather than a movement direction bias because both Experiments 1 and 2 were balanced, with 10 participants trained clockwise, and 10 with counter-clockwise VMRs for each experiment. The residuals were even larger for temporally-persistent adaptation, in line with the substantially slower unlearning of temporally-persistent adaptation compared to the overall adaptation we observed. 

In particular, we found that the residual persistent adaptation before the end of the 40-trial washout was around 25% of pre-washout persistent adaptation (4.91 ± 0.49˚ for Experiments 1 and 2 combined, t (39) = 10.0, p = 2.7 × 10−12). In contrast, the 800-trial washout period was sufficient to bring both overall adaptation and temporally persistent adaptation back to baseline, with measured residuals of only 0% to 2% of pre-washout levels on average. 

These residuals were not consistently in the direction of the pre-washout adaptation and were not statistically significant. Overall adaptation at the end of the 800-trial washout was −0.00 ± 0.18˚ (t(39) = −0.01, p = 0.9955), whereas temporally-persistent adaptation was 0.13 ± 0.32˚ (t(39) = 0.44, p = 0.6772), as shown in Fig 2B.

These results show that a prolonged washout period is required to eliminate residual temporally persistent adaptation. As washout periods in previous experimental work on savings [15,16,18,24,64] are typically much shorter than the 800-trial washout period we examined, the savings observed in these studies likely, at least in part, driven by interactions between different components of adaptation that were not fully washed out before retraining -apparent savings-as suggested in Smith and colleagues [10]. 

To examine faster relearning that is not contaminated by such interactions-that is, examine true savings-one should ideally eliminate residual levels of overall, temporally persistent, and temporally-volatile adaptation, or, at least, take these residual levels into account.

Savings are present even when the previous adaptation is completely washed out

To investigate savings for overall and temporally persistent adaptation, we compared the learning curves for retraining and initial training, shown in Fig 3A and 3B (gray: initial training; green: retraining after 40 washout trials; blue: retraining after 800 washout trials). 

We found that the adaptation levels achieved in the early adaptation period (trials 8 to 12 after perturbation onset, excluding trial 10 which was after a time delay), when learning was most rapid, were noticeably higher for relearning (24.6 ± 0.7˚ overall; 24.9 ± 0.6˚ and 24.4 ± 1.0˚ after the 40-trial and 800-trial washout periods, green and blue lines, respectively, with data combined across Experiments 1 and 2 in all cases) compared to initial training (18.8 ± 1.2˚, gray; p < 10−5 compared to relearning after either washout period or both periods combined). 

Because pretraining adaptation levels were not identical across conditions as shown in Fig 2B (1.34 ± 0.37˚, −0.00 ± 0.18˚, and −0.04 ± 0.24˚, after short washout, long washout, and before initial adaptation), we normalized data to quantitatively compare learning and relearning curves independent of the effect of this residual pretraining adaptation. 

Specifically, we subtracted the pretraining adaptation separately for overall and temporally persistent adaptation and normalized each baseline-subtracted learning curve by the distance between the baseline and the ideal adaptation level (Eq 2, see Materials and methods). These normalized data, plotted in Fig 3C and 3D express adaptation levels as a percentage of that required for full adaptation. 

In particular, normalized early adaptation (trial 10 after perturbation onset) was faster compared to initial adaptation (initial adaptation: 62.8 ± 4.1% versus relearning: 81.7 ± 2.5%; 40-trial and 800-trial washout data separately: 82.1 ± 2.1% and 81.4 ± 3.2%, respectively). We defined savings simply as the difference between these normalized adaptation data for the retraining versus the initial learning conditions. 

The top panel in Fig 3E and 3F shows an estimate of this savings measure in the overall adaptation. We find statistically significant savings for early adaptation (trial 10; savings of 18.9 ± 3.3% of the ideal adaptation, t(39) = 5.8, p = 5.4 × 10−7 [40-trial washout data: 19.3 ± 3.3%, t(39) = 5.8, p = 4.4 × 10−7; 800-trial washout data: 18.6 ± 3.6%, t(39) = 5.1, p = 4.1 × 10−6 ). 

Inspection of the overall adaptation data in the top panels of Fig 3C and 3D reveals that both the readaptation and initial adaptation curves asymptote near the ideal adaptation level, meaning that the room for improvement, and thus the capacity for savings, is reduced as training proceeds. 

In line with this observation, the savings we observed for mid (trial 40) and late (trial 70) overall adaptation were smaller than the savings observed for early (trial 10) adaptation (<10% of the ideal adaptation in all cases). The overall savings we observed at trials 40 and 70 were, however, statistically significant (t(39) = 2.6, p = 0.0063 for trial 40 and t(39) = 3.3, p = 0.0011 for trial 70), as shown in Fig 3E and 3F.

Savings arise from the faster acquisition of temporally volatile memories

Remarkably, we found that the savings observed in overall adaptation were due to temporally volatile learning. We calculated savings for temporally volatile adaptation (bottom row in Fig 3E and 3F) based on normalized volatile adaptation (bottom row of Fig 3C and 3D), which was computed as the difference between the normalized overall and normalized persistent adaptation (top row of Fig 3C and 3D). 

We found that volatile adaptation during early training (trial 10) was 2- to 3-fold faster for retraining than for initial training after both the short and long washout periods in both experiments (as shown in the bottom row of Fig 3C and 3D). 

Specifically, we found that trial 10 volatile readaptation was 52.2 ± 3.8% of the ideal adaptation to the 30˚ VMR with data after both types of washout combined versus 22.0 ± 4.2% for initial adaptation, t(38) = 6.2, p = 1.4 × 10−7 (readaptation for 40-trial washout data: 51.3 ± 4.5%, t(37) = 5.7, p = 9.0 × 10−7 for savings; readaptation for 800-trial washout data: 52.1 ± 4.1%, t(38) = 5.6, p = 9.9 × 10−7 for savings). This indicates substantial, statistically significant savings in temporally-volatile adaptation as illustrated in the bottom row of Fig 3E and 3F.

Savings do not arise from the rapid reemergence of temporally persistent memories

memories Intriguingly, the clear pattern of savings we found in the learning curves for overall and temporally volatile adaptation was absent for temporally persistent adaptation. 

In only 1 of the 4 conditions in Experiments 1 and 2 (readaptation after a 40-trial washout in Experiment 1) was the unnormalized temporally-persistent adaptation even nominally higher during relearning the initial adaptation, and in that condition, the readaptation built upon a substantially higher pretraining level than the corresponding initial training condition (Fig 3A). 

When pretraining levels of persistent adaptation were taken into account by normalizing the learning curves, we found that relearning for temporally persistent adaptation was nominally slower, rather than faster, than initial learning in all 4 conditions as shown in Fig 3C and 3D. Specifically, early (trial 10) savings were, on average −10.0 ± 4.3% of the ideal persistent adaptation, t(38) = −2.3, p = 0.99 for savings (40-trial washout data: −7.3 ± 4.4%, t(37) = −1.7, p = 0.95; 800-trial washout data: −10.3 ± 4.5%, t(38) = −2.3, p = 0.99) as shown in Fig 3E and 3F. 

The temporally persistent adaptation measured 40 and 70 trials into the training period for the combined 40-trial and 800-trial washout data displays results similar to trial 10 adaptation, with a tendency towards anti-savings (slower readaptation) (t(39) = −3.4, p = 1.00 for trial 40 and t(38) = −2.2, p = 0.98 for trial 70). 

The absence of savings in temporally persistent adaptation stands in stark contrast to the high levels of savings observed in temporally volatile adaptation, suggesting that overall savings arise from the former, but not the latter. Thus, our result indicates that savings arises from the faster relearning of volatile memories, rather than the re-manifestation of persistent memories.

Temporally-persistent memories display anti-savings

Based on recent work that reported anti-savings for implicit motor adaptation [45], we asked, in a post-hoc analysis, whether temporally-persistent adaptation consistently displayed the slowed relearning that would constitute anti-savings. 

This analysis revealed that relearning for persistent adaptation was, in fact, significantly slower than initial learning (t(38) = −2.3, p = 0.0247, 2-tailed paired t-test), based on trial 10 the data from both washout periods combined. This anti-savings was most clear in the long 800-trial washout data, which allowed us to examine savings without any effects of residual temporally-persistent adaptation (t(38) = −2.3, p = 0.0276, 2-tailed paired t-test). 

Savings at trial 10 after the short incomplete washout were also nominally negative but, in this case, not significantly so (t(37) = −1.7, p = 0.1073, 2-tailed paired t-test). 

Analysis of temporally-persistent adaptation at trials 40 and 70 provides consistent results, with statistically significant anti-savings observed for the combined data from the 40 and 800-trial washout periods (t(39) = −3.4, p = 0.0017 at trial 40; t(38) = −2.2, p = 0.0359 at trial 70, 2-tailed paired t-tests) and also for the 800-trial washout data analyzed in isolation (t(39) = −3.3, p = 0.0022 at trial 40; t(38) = −3.1, p = 0.0035 at trial 70, 2-tailed paired t-tests). Accordingly, the 40-trial washout data analyzed in isolation showed mixed results at these individual time points (t(39) = −2.1, p = 0.0417 at trial 40; t(38) = −0.7, p = 0.5053 at trial 70, 2-tailed paired t-tests). 

However, when the data combined across all time points are used, we find individually significant anti-savings for both washout periods (t(39) = −5.0, p = 0.000012, for the 800-trial data; t(39) = −2.6, p = 0.0131 for the 40-trial data). 

In sum, the negative savings results we observe in the 800-trial and 40-trial washout data are similar, but it appears that the 800-trial result is somewhat clearer, possibly because the 40-trial washout data suffer from incomplete washout of the initial adaptation before relearning. 

Overall, our data show a conspicuous absence of savings in the relearning of temporally persistent adaptation in all conditions we examined, instead showing anti-savings despite robust savings in the relearning of temporally volatile adaptation.

Although a small effect, we found it interesting that anti-savings were somewhat more consistently observed following the 800-trial washout condition than the 40-trial condition, suggesting that the prolonged repeated execution of the same no-rotation trials that constitute the washout period might make anti-savings more consistent. 

Indeed, the strengthening of action following repeated execution, often termed use-dependent learning (UDL) [69,70], can manifest in reaching in the form of a directional bias toward its direction [70–72]. Interestingly, the expected bias toward the baseline no-rotation movement direction following washout would oppose the movement direction changes associated with VMR relearning, and thus act in the direction of anti-savings to reduce relearning.

However, it is critical to note that (1) because we are examining savings, the slowed relearning that constitutes anti-savings refers to slower than initial learning; and (2) the initial learning period in our experiments was also preceded by a prolonged period of the execution of repeated no-rotation trials, which would likewise elicit a UDL effect. 

The key question would not, therefore, be whether a UDL effect might slow relearning following the 800-trial washout period, but whether such an effect would show a meaningful size increase between the 220-trial duration of the no-rotation baseline period that precedes initial learning and the 800-trial no-rotation washout period that precedes the 800-trial relearning condition? However, the available literature on how UDL effects increase with the number of repeated trials suggests that this is unlikely. 

Studies examining UDL effects in reaching movements showed effects after only 1 to 15 trials ([71], Exp 3; [70]), and the one study that looked at the time course for UDL effects beyond 15 trials, found effects that asymptotic between 50 and 150 trials ([71], Fig 4), which is smaller than the duration of the 220-trial baseline that preceded initial learning in our experiment. 

This suggests that UDL effects if they indeed affect VMR training in our study, would do so equally for both initial learnings, which was preceded by 220 no-rotation trials, and relearning following long-washout, which was preceded by 800 no-rotation trials. 

Consequently, UDL effects should have little effect on the difference between these learning curves and thus on the savings we measure after 800 washout trials, and are, therefore, unlikely to explain the temporally persistent anti-savings we observed.

However, the one paper that looked at the effect of repeat duration [71] did not study UDL training periods as long as 800 trials (540 trials was their maximum) and investigated UDL outside the context of VMR adaptation. 

We thus performed an additional experiment (Experiment S1) to determine whether the differences between the number of no-rotation reaches before initial learning (220 trials) versus before the long-washout relearning (800 trials) might explain the anti-savings we observed. Experiment S1 examined initial learning after a baseline of 800 rather than 220 trials to match the duration of the action selection history of the 800-trial washout before relearning.

If the reduction in temporally-persistent relearning concerning initial learning we observed were indeed due to the longer 800-trial movement/action selection history, we would expect the initial temporally-persistent learning in this new dataset to match the slowed temporally persistent relearning from the 800-trial washout condition rather than the initial temporally-persistent learning that we previously observed after 220 baseline trials. 

However, the results from Experiment S1 instead show that the initial temporally-persistent learning following 800 baseline trials in this new dataset was a closer match to the initial temporally-persistent learning that was previously observed after 220 baseline trials, as this dataset did not show the slower initial temporally-persistent learning that would be predicted by increased UDL following 800 rather than 220 trials (trial 10 learning: 47.1 ± 9.6% for initial learning in the new data versus 40.1% ± 4.2% for initial learning in the previous data, t(49) = −0.8, p = 0.45, see S1 Fig). 

Instead, as shown in S1 Fig, the data are in line with the Verstynen and Sabes data [71] whereby UDL affects asymptote before 220 trials. In line with this prediction, the persistent relearning following 800-trial washout trials observed in the previous data was also significantly slowed compared to this new 800-trial baseline data with significant anti-savings observed when the data from trials 10, 40, and 70 were averaged together (t(50) = −3.2, p = 0.0014) and also when the data from these time points were analyzed separately (t(50) = −2.2, p = 0.017 at trial 10; t(50) = −2.5, p = 0.0086 at trial 40; t(50) = −2.2, p = 0.0162 at trial 70). 

These findings suggest the UDL effects cannot explain the 800-trial temporally-persistent anti-savings we observe.

As a side note, if UDL effects were, on the other hand, somewhat lower after 40 trials (the short washout period duration) compared to 220 trials (the baseline period duration) [71], it would lead to a reduction in the amount of UDL-induced slowing for relearning after 40 washout trials compared to the initial learning. This would suggest that, if anything, we underestimated temporally persistent anti-savings in the 40-trial washout data rather than overestimating it in the 800-trial washout data, perhaps contributing to the less consistent results when this condition was considered in isolation.

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