A Double Dissociation Between Savings And Long-term Memory in Motor Learning Part 3

Temporally-volatile savings arise from implicit adaptation

Previous research associated savings in visuomotor adaptation with the rapid recall of explicit strategies, rather than faster implicit adaptation [18,43,44]. 

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This led us to investigate the contributions of implicit and explicit processes in the temporally-volatile savings we observed in our paradigm. 

We thus ran Experiment 3 (N = 40), which consisted of two 80-trial learning episodes separated by 800 washout trials. 

We dissected savings into implicit and explicit components using special instruction trials that prompted participants to disengage any explicit strategy by aiming their hand directly at the target [44,73–78]. 

These instructions were presented immediately before and after the first (trial 10) 60-s time delay following the onset of the VMR in both initial learning and relearning and allowed us to dissect adaptation into 4 subcomponents: implicit-persistent, implicit-volatile, explicit-persistent, and explicit-volatile (Fig 4B, see Materials and methods for details).

In line with our findings in Experiments 1 and 2, we found savings for overall and volatile adaptation (14.3 ± 3.6%, t(39) = 4.0, p = 0.00014 and 11.2 ± 4.8%, t(37) = 2.4, p = 0.0119, correspondingly) but not persistent adaptation (4.0 ± 4.9%, t(38) = 0.8, p = 0.21). 

Dissection of savings into explicit and implicit components revealed savings for both overall implicit and implicit-volatile adaptation (14.1 ± 5.7%, t(38) = 2.5, p = 0.0088 and 13.3 ± 5.5%, t(37) = 2.4, p = 0.0104, correspondingly) but not explicit-volatile adaptation (−2.1 ± 6.2%, t(37) = −0.3, p = 0.63) or any of the persistent subcomponents (implicit-persistent: 3.8 ± 4.7%, t(38) = 0.8, p = 0.21; explicit-persistent: 0.2 ± 4.4%, t(38) = 0.1, p = 0.48). 

This finding suggests that overall savings were driven by the implicit and temporally-volatile component of adaptation, in turn suggesting that the temporally-volatile savings we observed in Experiments 1 and 2 predominantly reflect an implicit process rather than an explicit strategy. 

That the volatile component observed in Experiments 1 and 2 is primarily implicit is not surprising: First, it is unclear why an explicit strategy could be temporally volatile to the point of being largely or completely forgotten after a short 1-minute delay. Our recent work indicates that explicit adaptation displays essentially no temporal volatility, with over 95% stability across 1-minute delays [79]. 

Second, our paradigm elicited scant explicit adaptation (likely due to elements of our experiment design aimed at inducing implicit learning such as the use of point-to-point (rather than shooting) movements, the lack of aiming instructions, the lack of markers that could aid off target aiming, and the presence of low-latency online feedback [76,80–83]) and without substantial explicit adaptation we lacked power for measuring explicit savings.

Dissecting long-term memory in visuomotor adaptation

We next investigated whether the ability to dissect motor learning into temporally persistent and temporally volatile components could shed light on the mechanisms for the formation of long-term memories. 

To accomplish this, we examined the relationship between the levels of temporally persistent and temporally volatile learning observed after initial training and the amount of retention observed 24 hours later (Experiment 4). After a baseline period, we trained 25 participants on a 30˚ VMR for 120 trials. After this initial training, they were tested for temporally persistent adaptation as present after a rest break (average break duration: 125 ± 8 s, which would let >99% of temporally volatile adaptation decay based on a time constant of approximately 20 s). 

The above measurements were then repeated, with participants retrained for 60 trials and retested for temporally-persistent adaptation (the average of these 2 measurement sessions was used to quantify temporally-persistent adaptation for each individual). Participants then returned the following day to be tested for retention (Fig 5A, see Materials and methods).

We found that the overall adaptation measured late in training (the last 20 trials) in Experiment 4 was similar to that observed in Experiments 1 and 2 (27.4 ± 0.3˚ for Experiment 4 versus 26.4 ± 0.6˚ and 27.7 ± 0.5˚ for Experiments 1 and 2, see Fig 5B). 

Similarly, the persistent component of adaptation was also similar across the 3 experiments (16.7 ± 1.0˚ for Experiment 4 versus 18.1 ± 0.7˚ and 20.4 ± 1.0˚ for Experiments 1 and 2, see Fig 5B), suggesting that the somewhat longer training duration in Experiment 4 had little effect on either overall or temporally-persistent adaptation. 

When examining long-term memory, retained 24 h after training, we found that participants retained 8.9 ± 1.1˚ of the trained 30˚ rotation (orange bar in Fig 5B). This corresponded to 32.4 ± 4.2% of the overall learning and 52.7 ± 5.2% of the temporally-persistent learning from day 1.

Dissociable effects of temporally-volatile and temporally-persistent adaptation on the formation of long-term memory

To examine whether the dissection of day 1 learning into temporally-persistent and temporally-volatile components could shed light on the mechanism for long-term motor memory formation, we compared the levels of temporally-volatile, temporally-persistent, and overall learning to the amount of 24-h retention for each participant. 

Looking for positive contributions of each component to 24-h retention (using linear regression with regression coefficients restricted to be positive), we found no significant relationship between overall learning on day 1 and 24-h retention on day 2 (r = +0.14, F(23,1) = 0.4, p = 0.51). However, we found a highly significant positive relationship between persistent learning on day 1 and 24-hour retention (slope = 0.80, r = +0.71, F(23,1) = 22.9, p = 0.00008). 

In contrast, we found no positive relationship between volatile learning on day 1 and 24-h retention; in fact, the best-fit slope was zero (r = 0.0, F(23,1) = 0, p = 1), as the best-fit slope without restricting regression coefficients to positive values would have been negative. 

This indicates that temporally-volatile learning does not lead to 24-hour retention, consistent with the fact that volatile learning, by definition, will decay over 1 min. We thus find that, whereas neither overall adaptation nor the temporally volatile component can predict it, the temporally persistent component of adaptation, measured only 1 min after training, can accurately predict retention 24 h after training.

We next performed a stepwise bivariate regression analysis of how 24-hour retention was associated with temporally volatile and temporally persistent learning from day 1, as illustrated in Figures 6A and 6B. 

This analysis was particularly important here because temporally-volatile and temporally-persistent learning were not independent across individuals but instead displayed a strong negative relationship such that participants with higher day 1 temporally-volatile learning displayed smaller day 1 temporally-persistent learning and vice versa. 

This bivariate regression revealed that adding temporally-volatile learning as a second regressor after temporally-persistent learning resulted in no significant improvement in the ability to explain 24-h retention (R2 increased from 49.8% to 51.7% corresponding to a partial R2 of only 3.8%, F (22,1) = 0.9, p = 0.36). In contrast, adding temporally persistent learning as a second regressor after temporally volatile learning resulted in a large improvement in the ability to explain 24-h retention (R2 increased from 0.0% to 51.7%, corresponding to a partial R2 of 51.7%, F(22,1) = 23.6, p = 0.00007). 

The results of this analysis are shown in Fig 6A and 6B where we illustrate the partial R2 analysis by comparing each component of day 1 learning with the portion of 24-h retention not explained by the other (see Materials and methods for details). 

When we repeated this analysis using estimates of temporally persistent and temporally volatile adaptation based on either the first or the second measurement session alone rather than the averaged data, we found similar results (Session 1 only: partial R2 of 43.1%, p = 0.0005 for temporally persistent adaptation versus partial R2 of 1.0%, p = 0.64 for temporally-volatile adaptation; Session 2 only: partial R2 of 53.0%, p = 0.00005 versus partial R2 of 8.6%, p = 0.16, correspondingly). 

This indicates that the measurements of temporally persistent learning from both day 1 sessions independently predict subsequent 24-hour retention on day 2, albeit with a nominally stronger association for the second session which was adjacent to the 24-hour retention measurement.

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