# Acquisition and consolidation processes following motor imagery practice

Célia Ruffino, Charlène Truong, William Dupont, Fatma Bouguila, Carine

Michel, Florent Lebon, Charalambos Papaxanthis

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Célia Ruffino, Charlène Truong, William Dupont, Fatma Bouguila, Carine Michel, et al. Acquisition and consolidation processes following motor imagery practice. Scientific Reports, 2021, 11, 10.1038/s41598-021-81994-y. http://hal-04185322

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# Acquisition and consolidation processes following motor imagery practice

Célia Ruffino✉, Charlène Truong, William Dupont, Fatma Bouguila, Carine Michel, Florent Lebon & Charalambos Papaxanthis

It well-known that mental training improves skill performance. Here, we evaluated skill acquisition and consolidation after physical or motor imagery practice, by means of an arm pointing task requiring speed-accuracy trade-off. In the main experiment, we showed a significant enhancement of skill after both practices (72 training trials), with a better acquisition after physical practice. Interestingly, we found a positive impact of the passage of time (+6 h post training) on skill consolidation for the motor imagery training only, without any effect of sleep (+24 h post training) for none of the interventions. In a control experiment, we matched the gain in skill learning after physical training (new group) with that obtained after motor imagery training (main experiment) to evaluate skill consolidation after the same amount of learning. Skill performance in this control group deteriorated with the passage of time and sleep. In another control experiment, we increased the number of imagined trials (n = 100, new group) to compare the acquisition and consolidation processes of this group with that observed in the motor imagery group of the main experiment. We did not find significant differences between the two groups. These findings suggest that physical and motor imagery practice drive skill learning through different acquisition and consolidation processes.

Motor learning is crucial for the accomplishment of numerous daily activities in a challenging environment. Through repetition, movements are executed faster, more accurately, and more effortlessly¹. With extensive practice a new initially fragile motor memory is transformed into a robust and stable motor memory²,³. In the initial stage, significant improvement in skilled motor learning is observed during a single or a few training sessions (fast learning process). Its consolidation, however, is achieved gradually (slow learning process) through multiple training sessions. Interestingly, consolidation of skill motor behavior can be observed between practice sessions, that is without additional practice, commonly referred as off-line learning²,⁴⁻⁸. Several investigations have highlighted the fundamental role of sleep and the passage of time on off-line motor learning⁴,⁹⁻¹¹. It appears that distinct mechanisms are engaged to support motor memory consolidation over wakefulness and sleep. Specifically, movement execution (i.e., speed and accuracy) is processed over wakefulness but not over sleep, whereas the spatial goal of a motor skill is processed over sleep but not wakefulness¹⁰⁻¹².

Although physical practice is undeniably fundamental for the acquisition and consolidation of new skills, complementary methods have also been proposed¹³,¹⁴. Among them, motor imagery, a form of mental practice, is widespread¹⁵⁻¹⁸. Motor imagery is defined as the mental simulation of an action without any corresponding motor output. Several investigations have provided robust evidences that executed and imagined movements trigger similar motor representations. This is attested by several similitudes in their temporal organization¹⁹⁻²³ and neural foundation, such as the activation of the parietal and prefrontal cortices, the supplementary motor area, the premotor and primary motor cortices, the basal ganglia, and the cerebellum²⁴. In addition, it is now well-accepted that motor imagery training improves muscle strength²⁵⁻²⁹, flexibility³⁰, speed and accuracy¹⁶,³¹⁻³⁴ and provides a high promising method for motor rehabilitation³⁵⁻³⁸. The positive impact of motor imagery training on motor learning is associated with neural adaptations at several levels within the central nervous system¹⁸,³⁹. Indeed, during the mental simulation of a movement, the generation of a subliminal motor command that triggers both cortical and spinal circuits is unmasked⁴⁰,⁴¹. This activation induces plastic modulations, leading to gains in motor performance²⁸,³⁹,⁴².

Surprisingly, despite accumulated evidence about the consolidation processes during the daytime or after a night of sleep following physical practice, only few experimental data exist regarding skill consolidation after motor imagery training⁴³⁻⁴⁵. For instance, Debarnot and colleagues⁴⁰,⁴¹ have eleganlty showed that sleep

Cognition, Action et Plasticité Sensorimotrice (CAPS), INSERM UMR1093, UFR STAPS, Université de Bougogne
Franche-Comté, 21000 Dijon, France. ✉email: celia.ruffino@u-bourgogne.fr
contributes to the consolidation of motor sequence learning acquired through motor imagery practice. Whether this finding is extended to different types of motor tasks, such as skilled motor learning, remained to be verified. In addition, the effects of the passage of time in motor skill consolidation has not been yet investigated.

Here, we used an arm pointing task emphasizing skill learning, namely a training-related change in the speed-accuracy tradeoff function. We assessed the immediate effects of physical or motor imagery training on skill acquisition (Post0h), as well as the effects of the passage of time (Post6h) and the effects of sleep (Post24h) on skill consolidation. During physical training, the advantage is that forward model predictions are compared with sensory feedback from the periphery, providing thus a better estimate of the state of the arm via a “self-supervised process” that minimizes prediction errors, namely the errors between the output from the forward model and the sensory outcome of the motor command<sup>46,47</sup>. Thus, after physical training, compared to mental training, we hypothesized a greater acquisition in skill learning. In addition, according to the literature showing that movement execution (i.e., speed and accuracy) is processed over wakefulness but not over sleep, whereas the spatial goal of a motor skill is processed over sleep but not wakefulness<sup>10–12</sup>, we also expected an effect of the passage of time but not sleep after both interventions.

## Main experiment

**Methods.** *Participants.* Forty-two right-handed adults, without neurological, mental or physical disorders and with normal or corrected-to-normal vision, were randomly assigned into three groups: (i) the physical training group (PT, n = 14, 6 females, mean age: 22 ± 3 years old), the motor imagery training group (MIT, n = 14, 7 females, mean age: 24 ± 3 years old), and the control group (CT, n = 14, 5 females, mean age: 23 ± 2 years old).

Motor imagery ability of the MIT group was assessed by the French version of the Movement Imagery Questionnaire “MIQr”<sup>48</sup>. The MIQr is an 8-item self-report questionnaire, in which the participants rate the vividness of their mental images using two 7-point scales, one associated to visual and the other to kinesthetic imagery. The score ‘7’ indicates really easy to feel/visualize, whereas the score ‘1’ corresponds to really difficult to feel/visualize. The average score obtained here (46.12 ± 5.02) indicate good imagery ability (maximum score: 56; minimum score: 8).

All participants were synchronized with a normal diurnal activity (7 a.m. to 12 a.m.) alternating with a night sleep. None of the participants had a daily nap or a physical activity during this period. The chronotype of the participants, evaluated by the Morningness-Eveningness Questionnaire<sup>49</sup>, was moderate morning type (n = 4), moderate evening (n = 5) or neither type (n = 33). There were no significant differences between groups regarding their chronotype (PT: 47.29 ± 10.24; MIT: 51.43. ± 8.32; CT: 48.93 ± 12.75; one factor ANOVA, F<sub>2,39</sub> = 0.50, *p* = 0.60).

The quality of participants’ sleep was evaluated by the Pittsburgh sleep quality index<sup>50</sup>. The general score in this questionnaire ranges from 0 (no particular difficulties to sleep) to 21 (major difficulties to sleep). All participants showed good sleep quality; individuals scores ranged from 1 to 9. Group averages were almost similar (PT: 4.50 ± 1.59; MIT: 4.79 ± 2.40; CT: 4.79 ± 1.93) and statistically not different (one factor ANOVA: F<sub>2,39</sub> = 0.09, *p* = 0.92).

The experimental design was approved by the regional ethic committee (Comité de Protection des Personnes—Région EST) and was conformed to the standards set by the Declaration of Helsinki. All participants provided written informed consent after being informed on the experimental procedures.

**Experimental device.** The participants were comfortably seated on a chair in front of a graphic tablet (Intuos4 XL, Wacom, Krefeld, Germany). Seven square targets were designed on it (see Fig. 1A). The target Ts (starting target, 1 × 1 cm) was aligned with the participants’ sternum at a distance of 10 cm. The other six targets had varying sizes and were placed at different distances from the Ts to vary the difficulty of the task within a trial (T1: 0.5 × 0.5 cm, 31 cm; T2: 1 × 1 cm, 38.5 cm, T3: 0.5 × 0.5 cm, 34 cm; T4: 1 × 1 cm, 42 cm; T5: 0.5 × 0.5, 31 cm; T6: 1 × 1 cm, 38.5 cm). One trial included 12 successive visually guided point-to-point arm movements between the targets in the following order: Ts-T1-Ts-T2-Ts-T3-Ts-T4-Ts-T5-Ts-T6-Ts. The participants, holding a pencil with their right hand, were requested to point between the targets as accurately and as fast as possible. Thus, the success of the task required a compromise between speed and accuracy. We notified the participants that all targets had to be reached in the specified order. No further instructions concerning motor performance (i.e., movement velocity or spatial accuracy) was provided to the participants during the whole protocol.

**Experimental procedure.** The experimental protocol included five sessions (see Fig. 1B). The pretest session was scheduled in the morning between 8:30 a.m. and 9:00 a.m. All the participants accomplished 4 trials as accurately and as fast as possible. To warm up and familiarize themselves with the task, participants performed two trials before the pretest trials (one at a natural speed and another one as fast as possible). The training session started 3 min after the pretest session. The participants of the PT group carried out 72 actual trials as accurately and as fast as possible. Those of the MIT group imagined 72 trials as accurately and as fast as possible, combining kinesthetic and visual (first-person perspective) modalities<sup>51</sup>. To ensure that all participants of the MIT group would correctly complete the training phase, we provided the following instructions: ‘try to imagine yourself performing the motor task, by seeing and feeling your arm moving as if you were actually moving it’. In addition, we asked the participants to report the quality of each imagined trial on a 7-point-scale (1 = very poor; 7 = excellent). The grand average score (5.18 ± 0.98) indicates a successful motor imagery training session. The participants of the CT group were trained to only move their eyes between the targets. As the motor task required eye-hand coordination, an enhancement in motor performance after MIT could be attributed either to eye movement training or to a cognitive-attentional effect. By including the CT group, we controlled for such a bias in the improvement of motor skill<sup>16</sup>. The experimenter counted the number of eye movements between the
<table><caption>**Table 1.** Average values (+SD) of Error Rate (%), Duration (s) and Skill (a.u.) at Pretest, Posttest 0 h, Posttest 6 h and Posttest 24 h for all groups for the main experiment (PT: Physical Training Group, MIT: Motor Imagery Training group, CT: Control Training group).</caption><thead><tr><th>Groups</th><th></th><th>Pretest</th><th>Posttest 0 h</th><th>Posttest 6 h</th><th>Posttest 24 h</th></tr></thead><tbody><tr><td colspan="6">Error Rate (%)</td></tr><tr><td rowspan="2">PT</td><td>Mean</td><td>8,63</td><td>11,66</td><td>10,27</td><td>9,67</td></tr><tr><td>SD</td><td>0,08</td><td>0,07</td><td>0,08</td><td>0,09</td></tr><tr><td rowspan="2">MIT</td><td>Mean</td><td>11,01</td><td>11,11</td><td>9,52</td><td>10,66</td></tr><tr><td>SD</td><td>0,05</td><td>0,07</td><td>0,06</td><td>0,06</td></tr><tr><td rowspan="2">CT</td><td>Mean</td><td>8,04</td><td>9,52</td><td>9,67</td><td>9,08</td></tr><tr><td>SD</td><td>0,04</td><td>0,06</td><td>0,07</td><td>0,05</td></tr><tr><td colspan="6">Duration (s)</td></tr><tr><td rowspan="2">PT</td><td>Mean</td><td>7,95</td><td>6,94</td><td>7,10</td><td>7,11</td></tr><tr><td>SD</td><td>1,26</td><td>1,38</td><td>1,15</td><td>1,24</td></tr><tr><td rowspan="2">MIT</td><td>Mean</td><td>7,76</td><td>7,36</td><td>7,08</td><td>7,07</td></tr><tr><td>SD</td><td>1,57</td><td>1,26</td><td>1,06</td><td>1,18</td></tr><tr><td rowspan="2">CT</td><td>Mean</td><td>8,01</td><td>7,92</td><td>7,94</td><td>7,87</td></tr><tr><td>SD</td><td>0,93</td><td>0,97</td><td>0,81</td><td>0,77</td></tr><tr><td colspan="6">Skill (a.u)</td></tr><tr><td rowspan="2">PT</td><td>Mean</td><td>0,117</td><td>0,131</td><td>0,129</td><td>0,130</td></tr><tr><td>SD</td><td>0,015</td><td>0,021</td><td>0,016</td><td>0,18</td></tr><tr><td rowspan="2">MIT</td><td>Mean</td><td>0,118</td><td>0,123</td><td>0,130</td><td>0,129</td></tr><tr><td>SD</td><td>0,019</td><td>0,017</td><td>0,015</td><td>0,018</td></tr><tr><td rowspan="2">CT</td><td>Mean</td><td>0,116</td><td>0,115</td><td>0,115</td><td>0,116</td></tr><tr><td>SD</td><td>0,010</td><td>0,010</td><td>0,012</td><td>0,010</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Average values (+SE) of gains (%) in error rate (A), movement duration (B), and motor skill (C) between sessions for the three groups. Stars indicate significant differences from the value zero (0). Arrows indicate significant differences between groups. PT, Physical Training; MIT, Motor Imagery Training; C, control group.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Average values (+SE) of gains (%) in skill performance between the pretest and the posttest sessions. Star indicates significant differences between the control group and the two other groups (PT and MIT).</figcaption></figure>

session and each of the three posttest sessions (Post0h, Post6h, Post24h) for both PT and MIT groups ($p < 0.02$, for all comparisons).

Figure 2B illustrates the average gains (+ SE) in movement duration. Gains comparison (t-tests) with the reference value zero (0) showed significant differences for the Pre_Pos0h gain of the PT group ($t = 5.86; p < 0.001$), as well as for the Pre_Pos0h gain ($t = 2.96; p = 0.01$) and the Post0h_Pos6h gain ($t = 2.30; p = 0.03$) of the MIT group. All the other comparisons did not reach the significant level ($t < 1.45$ and $p > 0.17$). This finding denotes that while movement speed was not further improved after the Post0h for the PT group, it was improved for the MIT group (Post6h). Thus, a positive effect of the passage of time could be inferred only for the MIT group. Note, that the Pre_Pos0h gain was greater in the PT than in the MIT group ($t = 3.14; p = 0.004$), indicating the superiority of physical practice for movement speed improvement immediately after the training session. This immediate advantage, however, reversed at Post6h, because the Post0h_Pos6h gain of the MIT group was almost significantly greater compared to that of the PT group ($t = -1.97; p = 0.058$). For both PT and MIT groups, gains in movement duration were consolidated at Post24h, without any off-line learning due to sleep (i.e., no further improvement in movement speed between Post6h and Post24h).

**Motor skill.** The lower row of Table 1 shows the average values of motor skill. rmANOVA revealed a significant interaction effect ($F_{6,117} = 3.738; p = 0.002; \eta^2 = 0.16$). Briefly, there were no significant effects between groups in the pretest session (in all, $p > 0.8$) nor between sessions for the CT group (in all, $p > 0.7$). Skill significantly improved between the pretest and each of the three posttest sessions (Post0h, Post6h, Post24h) for both PT and MIT groups ($p < 0.01$, for all comparisons). Figure 2C illustrates the average gains (+SE) in motor skill. Gain comparisons (t tests) with the reference value zero (0) revealed significant differences for the Pre_Pos0h gain of the PT group ($t = 5.07; p < 0.001$), as well as for the Pre_Pos0h gain ($t = 2.18; p = 0.04$) and the Post0h_Pos6h gain ($t = 2.85; p = 0.02$) of the MIT group. Pre_Pos0h gain was greater in the PT than in the MIT group ($t = 2.15; p = 0.04$). On the contrary, Post0h_Pos6h gain in the MIT group was significantly greater compared to PT group ($t = -2.11; p = 0.04$). For both PT and MIT groups, gains in movement speed were consolidated at Post24h, without any off-line learning due to sleep (i.e., no further improvement in movement speed between Post6h and Post24h).

Figure 3 shows the average gains (+ SE) in skill with respect to pretest. It can be observed that the initial worse skill acquisition after motor imagery training compared to physical training (see Pre-Post0h; difference between PT and MIT groups = 7.11%) is compensated by the passage of time (see Pre_Pos6h, difference between PT and MIT groups = -0.52%). The skill of the CT group did not show any improvement. rmANOVA (two-way: 3 groups × 3 gains) revealed a significant effect of group ($F_{2,39} = 8.05; p < 0.001; \eta^2 = 0.31$). Post-hoc comparisons showed that the skill of PT and MIT significantly differed from that of the CT group (for both, $p < 0.001$).

**Electromyographic analysis.** Participants did not activate their muscles during motor imagery training. The grand average of EMG activity during imagined trials was $0.003 \text{ mV} \pm 0.002$ and comparable to that recorded at rest ($0.003 \text{ mV} \pm 0.001$). Statistical comparison (Wilcoxon tests) of EMG activity between each imagined trial and the rest revealed no significant difference neither for the BB muscle (for all $Z < 1; p > 0.50$) nor for the TB muscle (for all, $Z < 1; p > 0.50$). Note that a more detailed analysis, using a moving window of 1 s for each imagined trial, did not show significant differences in EMG activity between imagined and rest trials (in all, $p > 0.1$).

## Control experiments

In the main experiment, we found that motor skill progressively enhanced between Post0h and Post6h for the MIT group, but not the PT group. This finding may suggest a different consolidation process between the two groups. Before considering such a premise, however, one must look for differences in the acquisition processes that could influence the subsequent consolidation process. Indeed, although both groups received the same amount of practice (i.e., 72 trials), motor performance of the PT group was significantly greater compared to
<figure><img src="image_5.png" /><figcaption>**Figure 5.** Control Experiment 2. Average values (+ SE) of gains (%) in motor skill between sessions. Stars indicate significant differences from the value zero (0).</figcaption></figure>

Discussion

In the current study, we examined motor skill acquisition and consolidation after motor imagery or physical training in a speed/accuracy trade-off task. We observed that although skill significantly improved immediately after both practices, acquisition was higher after physical practice. Interestingly, we found a positive impact of the passage of time on skill consolidation (off-line improvement) only after motor imagery training. Sleep did not promote skill consolidation for any of the interventions. Overall, our findings suggest that physical and motor imagery practices drive motor skill learning through different acquisition and consolidation processes.

**Skill acquisition.** In our study motor performance is limited by the trade-off between speed and accuracy. Therefore, skill development required either changes at both variables in the expected direction (i.e., more accurate and faster) or changes of at least in one variable in the expected direction (i.e., more accurate or faster) with the other variable remaining stable (same speed or same accuracy, respectively).

Like the participants of the PT group, those of the MIT group significantly enhanced their skill performance immediately after the training session. This finding corroborates and expands those of previous studies, which reported significant improvement in motor performance after motor imagery practice<sup>16,31-34,54,55</sup>. The lack of skill improvement for the CT group further supports the specific benefits of motor imagery training on motor behavior. Here, gains in skill performance for both training groups were obtained by mainly increasing movement speed. This finding qualitatively indicates a common acquisition process for the two practice methods. Note that similar results have also been observed on an arm pointing task. Precisely, Dearnot et al. (2009), using a computer mouse, instructed the participants to point from a central target toward peripheral targets. The results revealed that participants performed the sequence more rapidly, without improving the accuracy of their movements.

Not surprisingly, the PT group showed higher gains in skill than MIT group immediately after practice. This immediate superiority in motor acquisition of physical practice compared to mental practice is a constant finding in the literature<sup>16,25,32-34,56</sup>. Theoretically, the concept of internal forward models can explain this result<sup>16,33,56</sup>. Evidence support the hypothesis that motor prediction is generated by internal forward models, which are neural networks that mimic the causal flow of the physical process by predicting the future sensorimotor state (e.g., position, velocity) given the efferent copy of the motor command and the current state<sup>37</sup>. A recent study has elegantly showed that a forward model is triggered to predict the sensory consequence of the imagined movement<sup>58</sup>. During motor imagery practice, the initial state of the arm and a copy of the motor command (fferent copy) generated by the controller are both available to the forward model, which makes predictions for the future states of the arm. These internal predictions, which are the inputs of the forward model to the controller, could improve the output of the controller in the absence of movement-related sensory feedback, and therefore enhance motor performance in a subsequent motor task. However, although forward internal models are engaged during motor imagery, sensorimotor prediction is more variable, since it is not updated by sensory feedbacks, which may explain the difference between PT and MIT groups<sup>59</sup>. Recent findings have demonstrated that motor imagery-based practice drives motor learning of a kinematically complex multi-articular motor skill in the absence of sensory feedback<sup>60</sup>. During physical training, the advantage is that forward model predictions are compared with sensory feedback from the periphery, providing thus a better estimate of the state of the arm via a "self-supervised process" that minimizes prediction errors, namely the errors between the output from the forward model and the sensory outcome of the motor command<sup>46,47</sup>. Thus, under physical training, compare to mental training, one can hypothesize a better functional linkage between the forward model and the controller, leading to better skill learning. The fact that increasing the number of imagined trials (control experiment 2) within a single training session did not induce better acquisition further indicates that differences in acquisition between physical and motor imagery practice are not due to the quantity of training, but rather to functional process associating internal predictions with sensory feedback. However, it must be noted that multiple-sessions
training with motor imagery could induce further improvements in skill performance to that obtained after
physical practice.

When considering skill acquisition process, it is important to examine both temporal and spatial components of the task. In our study, we observed significant differences between PT and MIT groups for movement speed (i.e., temporal component) but not error rate (i.e., spatial component). More interestingly, while the PT group showed a small, although not significant, deterioration of error rate after training (approximatively -3%), the MIT group showed stabilization. This may reveal that skill learning does not equally involve spatial and temporal components in the two practices. Several research groups explored the difference between spatial (i.e., error rate) and temporal (movement duration) components<sup>61-64</sup>. They described that MI engenders learning that is more spatial than temporal in nature. Future studies, emphasizing more the spatial component of the motor task, could bring further information on the nature of skill performance improvement between physical and motor imagery practices.

**Skill consolidation.** Previous studies have shown that physical practice lead to off-line gains in motor performance after a day time interval and/or a period of sleep<sup>2,4,5,10,12,65,66</sup>. For instance, on a visuomotor adaptation paradigm, several authors observed a positive effect of a retention interval on offline learning processes, with a strong resistance to interference<sup>67,68</sup>. Here, we showed that a single session of motor imagery training, like physical training, lead to a long-term motor memory formation. Indeed, motor skill was significantly improved one-day after the motor imagery training session (Post24h versus Pretest). This result corroborates and expands findings from previous studies, which showed permanent changes in motor behavior after mental training<sup>16,28,34,43,69</sup>.

Skill improvement after physical or mental practice is, however, processed off-line over different brain states. Precisely, the passage of time enhanced motor skill after motor imagery training only (off-line learning at post6h), while sleep did not induce any offline learning (Post24h) whatever the practice method. Previous studies have shown that motor consolidation is related to the nature of the task, and more specifically to the explicit or implicit components composing it. Indeed, the spatial goal of a movement (explicit component) is processed over sleep but not during wakefulness, whereas the skilled movements are processed over wakefulness but not over sleep<sup>10,12</sup>. Our arm pointing task required skilled movements without any explicit learning; the goal of the task was to reach all the targets in the same predetermined order. Therefore, based on the above described previous findings, we expected an effect of the passage of time on skill. This hypothesis was verified for motor imagery training, but not for physical training. Note that like the acquisition process, the consolidation process of temporal and spatial components of the task differed between physical and motor imagery practices. Indeed, we observed a decrease in movement duration between the Pretest and the Post24h for both groups, though this decrease was significantly greater for the PT compared to the MIT group, while the error rate slightly decreased after mental practice and slightly increased after physical practice.

The different levels of skill performance achieved after acquisition could not explain the different consolidation processes between the two practices. Indeed, the first control experiment revealed that for almost similar acquisition gains, consolidation still differed between the two training methods. In addition, differences in acquisition and consolidation processes between physical and motor imagery practice cannot be explained by indirect factors, such as sleep quality or fatigue, because these parameters did not differ between groups. Note also that before each test session all groups carried out two warm-up trials. Although we cannot ignore a possible warm-up effect at the beginning of the training phase for the physical group, we consider that learning largely overrides such an effect and definitely contribute to the different acquisition and consolidation processes between groups. Lastly, increasing the number of imagined trials (experiment 2) did not qualitatively change the consolidation process.

Our findings suggest that through consolidation a new initially fragile memory after motor imagery training is transformed into a stable memory. At the neurophysiological level, several studies have identified the cerebellum as the neural site of rapid learning and the primary motor cortex as the neural site of consolidation<sup>70-72</sup>. At the functional level, the availability of internal predictions only to drive the controller, may lead to a slow motor learning process that necessitates the passage of time to be consolidated. On the contrary, self-supervised learning during physical training, that is the use both internal predictions and sensory feedback to improve the controller, may lead to a rapid acquisition process with complete consolidation. In addition, it is possible that there was a saturation during physical training, meaning that potential benefits of consolidation have already been achieved through physical practice, leaving little opportunity to enhance skill further during consolidation.

Differences in motor learning processes between mental and physical practice has been reported in the literature in a long-term scale. For example, a previous study showed that five days of motor imagery practice could lead to the same amount of learning as after five-days of physical practice only if participants of the motor imagery practice group could physically practice for 2 h at the end of the motor imagery practice<sup>73</sup>. In another study, despite similar amount of training (5 days), motor imagery training did not drive robust changes on motor performance and brain activation as physical training<sup>74</sup>. How the passage of time consolidates skill learning after mental practice is an important question and needs further investigation. Is, for example, the passage of time the crucial parameter of such a consolidation, or is physical practice on the same task necessary to consolidate what has been learned during motor imagery training?

In the literature, motor imagery is often described as a cognitive process that engages neural structures, such as the parietal cortex and the prefrontal cortex<sup>24,75</sup>, encoding the goal of the movement<sup>12,76</sup>. Therefore, a positive effect of sleep on off-line improvement after motor imagery practice could be expected (see above, Robertson 2009; Cohen et al. 2005). Indeed, Debarnot and colleagues reported the positive influence of a nap<sup>45</sup> or a night's sleep<sup>43,44</sup> on motor performance improvement after a motor imagery training session. We propose that what it matters for consolidation after motor imagery practice is not the motor imagery process per se, but its content.
When mental training implies larger goal-based components<sup>43-45</sup>, off-line gains could be observed after a period of sleep. On the other hand, motor imagery training in task with large motor skill components may consolidate through the passage of time.

Overall, the findings of the current study revealed different acquisition and consolidation processes after physical or motor imagery training. The key finding of this study is that after a consolidation period at least 6 h, motor imagery practice induces similar gains in motor skill than physical practice. In this way, imagery training may have high potential for motor rehabilitation.

Received: 29 March 2020; Accepted: 23 December 2020

Published online: 27 January 2021

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## Author contribution

C.R. and C.P. participated in the elaboration of the experimental protocol, made the statistical analysis, and wrote the paper. C.T., W.D., F.B. made all the acquisitions and participated in the elaboration of the experimental protocol. C.M. and F.L. wrote the paper.

## Funding

This work was supported by the French-German ANR program in human and social sciences (contract ANR-17-FRAL-0012-01) and the French "Investissements d'Avenir" program, project ISITE-BFC (contract ANR-15-IDEX-0003).

## Competing interests

The authors declare no competing interests.

## Additional information

Correspondence and requests for materials should be addressed to C.R.

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